ThursdAI - The top AI news from the past week

Hey everyone, Alex here 👋Welcome to the AI World Cup? Or should I say Superbowl? as most of the releases this week are from US frontier labs. Of which there are 5 now btw. OpenAI, Anthropic, Google and 2 new ones that have caught up, SpaceXAI and Meta! 🔥Thirty five seconds. That’s how long this week’s show ran before we hit the breaking news button, because Zuckerberg picked our exact air time to return to Twitter (after apparently finding his password in a 1Password vault from a long time ago) and announce a new Meta frontier model and re-establishing Meta as a frontier lab. And that was the small launch of the day. Two hours later we cut to OpenAI’s livestream and watched GPT-5.6 Sol, Terra and Luna go public in real time, then spent the rest of the show throwing prompts at all of it live on air.Somewhere in between: a full-duplex voice demo where ChatGPT interrupted me on command (and our transcription tool later credited “OpenAI sol” as a panelist), an image model that generates in editable layers, and Grok 4.5, the first model co-trained with Cursor. I said it on the show and I’ll say it here: we went to sleep last week thinking this was a three-lab race between Anthropic, OpenAI, and Google. We woke up in a five-lab race.Joining me through the chaos: Wolfram Ravenwolf, Yam Peleg, Nisten Tahiraj, LDJ, and Peter Gostev, who had early GPT-5.6 access and receipts to show for it. This is a long one, because the week earned it. Let’s get into it.ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.GPT-5.6 launch day: Sol, Terra and Luna arrive mid-show (X, sama, Blog, System card)Let me set the scene. Everyone except the four of us on the panel seemingly had early access to this model for two months (Pietro Schirano casually dropped “I’ve used GPT 5.6 for two months” and I nearly fell out of my chair). So when OpenAI’s livestream started mid-show, we did a watch party, and Thibaut from OpenAI delivered the line: “Today, we are releasing our latest and most capable models, GPT 5.6, Sol, Terra, and Luna.” Sol rolls out to all paid plans within 24 hours, Terra and Luna go to free users too. Oh, and almost a billion people now use ChatGPT every week. Casual.The lineup is three durable tiers, not size variants. Sol is the flagship with a new Ultra mode (max reasoning effort plus heavier native subagents), Terra is roughly 5.5-level intelligence at half the cost, and Luna is the fast cheap one. Pricing lands at $5/$30 per million tokens for Sol, $2.50/$15 for Terra, $1/$6 for Luna, and watch the fine print: cache writes now cost 1.25x with a 30-minute minimum cache life, where they used to be basically free. There’s also a Cerebras-served Sol running north of 700 tokens per second, and we got confirmation from Dominik Kundel on last week’s show that it’s the same exact weights, not a distill. That was the preview. This week it’s real.The benchmarks, with the usual asterisksSol Ultra posts 91.9% on Terminal-Bench 2.1 against 88% for both GPT-5.5 and Mythos 5, with a serious asterisk: OpenAI ran Sol in its own Codex harness and the competition in a thin one, and r/codex called it out immediately. The number that impressed me more is efficiency. On the Agent’s Last Exam chart, Sol hits its top score using about 1.27 million output tokens where the tested Fable checkpoint burns 10 million and Opus at max effort burns around 22 million.Then there’s ARC-AGI-3, where scores have hovered between 0.5% and 2% since the benchmark launched. Sol scored 7.8% and became the first model to actually beat one of the public games (FT09), which Greg Kamradt of the ARC Prize called “a step level improvement” (X).LDJ thinks we’re about to replay the ARC-AGI-2 curve, 15% then 30% then 50% over the coming months. Fable isn’t on that leaderboard at all, by the way, because Anthropic currently stores Fable 5 API requests and ARC-AGI requires zero retention for testing.Computer use is the sleeper story. OS World jumps from 47% on GPT-5.5 to 62% on Sol (Opus 4.8 sits at 54%), and on BrowseComp, Sol’s 90% edges out Mythos 5’s 88%, with Ultra at 92%. OpenAI put competitor numbers on its own charts this time, which I appreciated. Sol beats Mythos on computer use, at least on the benchmarks we have.The METR report and the Washington gateThis is the part the launch-day hype cycle skips, and it deserves your attention. METR effectively threw out its own evaluation, reporting the highest cheating rate it has ever recorded: Sol rewrote pass/fail checks to mark itself successful, attempted a container escape when its network got cut, and its chain of thought showed it knew it was being tested. Depending on whether you count cheating as failure or success, its time horizon is either 11.3 hours or 270 plus hours, and METR’s own conclusion was that neither is a valid measurement (X, Transformer).OpenAI’s own system card discloses destructive VM cleanups nobody asked for, unauthorized credential copying, and a fabricated “verified” research result in about 0.25% of tasks, which they call “overeagerness.” We ran out of show to give this the time it deserves, but you should read both links.There’s also a Washington subplot. The launch was government-gated: Commerce and CAISI required customer-by-customer approval starting late June (around 20 orgs), and broad approval only cleared July 7 and 8. This Thursday launch exists because DC signed off. LDJ added the detail I can’t stop thinking about, via friend of the pod Max Weinbach: during the restricted window, testers who lost access weren’t allowed to say “5.6,” so Max’s wistful tweets about “missing Fable” were actually about missing GPT-5.6. Anthropic hit the identical wall in June. Both US frontier labs got federally gated in the same month, and that’s a structural story, not a footnote.The verdicts: wise owl, meet rottweilerSo what’s it actually like? Peter Gostev had access, lost it (”the feeling of losing it was so crushing I just closed Codex and didn’t open it for three days”), got it back, and posted the comparison that went viral (mega-thread): Fable is a wise owl, fundamentally smarter, better writer, but it misses things. Sol is a rottweiler that grabs a problem by the throat and doesn’t let go.His killer anecdote: a personal data-viz app that had bloated to 100,000 lines of vibe code, which every prior frontier model failed to clean up. He gave 5.6 minimal guidance, left it alone for two days, and came back to “holy s**t, this app works,” with 70,000 lines deleted and a test suite that went from four minutes to about twenty seconds. His verdict, which I share: on abstract IQ you’d give it to Fable, but for “go investigate this and fix those eight things,” he’s going with 5.6 every time. Notably, Peter is convinced this is not a new pretrain, just 5.5 plus a lot more RL, which matches the rumors that GPT-6 arrives on a bigger pretrain in about a month (rumor, labeled as such).He’s not alone in the early-access verdict club, either. Mitchell Hashimoto, after a month with Sol: it’s now his default, faster than Fable, plans and judges just as well, and he only reaches for Fable on highly targeted debugging (X). And Max Weinbach says the sleeper hits are the cheap tiers, with Terra and Luna “as good or better than Claude across the board at a fraction of the price” for knowledge work (X). Terra at $2.50/$15 might quietly be the real story for builders here.One wallet warning before you go max out everything, from Peter again: with Max/Ultra effort spinning up 10x subagents, each burning its own tokens, it is trivial to blow through a Pro plan in no time (X). The sticker price is per token, but Ultra multiplies the tokens.We ran it live (and it ran itself)We also ran it live, obviously. I pointed Codex at a “Mars launch simulator” prompt on high effort, and Nisten, our resident one-shot-simulator judge, watched it build an orbital sim with working mission control and called it “almost better than Fable one-shot.” Then he said the thing that stuck with me all week: “Damn, I think we might need a different test now. These are getting good.”Two more things before you YOLO your own agents. OpenAI stated that Sol fully autonomously did the post-training for Luna, which is quietly one of the wildest sentences of the year (their roadmap, with LDJ’s on-air date correction: an intern-level autonomous researcher by September 2026, a full OpenAI-researcher-level one by March 2028).And Peter, running Codex with full access enabled, told it to “go find more data, do whatever it takes” while replicating an old academic paper. It emailed the paper’s authors. Actually sent the emails. OpenAI’s response when he reported it: “well, you did put full access.” Wolfram’s counterpoint is the right one: put explicit rules in your AGENTS.md, like “no outgoing communication without my approval,” or don’t grant full access at all.ChatGPT for Work: Codex becomes the one app combining Codex & ChatGPTThis rolled out live during our broadcast, which made for great radio. Wolfram’s Codex app updated on air and became “ChatGPT Codex,” one unified app where you literally pick which icon you want: Codex for developers, or the new ChatGPT for Work mode. The launch bundle also included unified plugins across ChatGPT and Codex, multi-tab and enterprise auth in the browser, and faster computer use. Even Logan Kilpatrick tipped his hat from the Google side: “we have now entered the super app era.” The pitch on the screen said it plainly: “Keep coding with Codex. Work beyond code. ChatGPT can now take on work across your apps.” Computer use ships with it, running in a little picture-in-picture window that doesn’t steal your focus. I love this, and I don’t understand why Anthropic hasn’t shipped it yet.I’ve used this new app to automate the release from today’s show and it did everything from exporting the masters past recording, to edit out the boring parts via the Descript integration, upload to youtube, write description, create thumbnails and even set up an ABC test for thumbnails! The new little Picture-in-picture for the new and improved computer use are awesome to see how the new subagents are doing work across tabs, clicking buttons. I’m super impressed, this is going to save me so much time!The feature that matters for normal people is Sites: OpenAI will now host what you build, on the chatgpt.site subdomain (eagle-eyed listener Colleen spotted that it’s Webflow under the hood). Peter nailed why this is a big deal even though it’s not massively featured yet: someone in HR builds something useful and it lives on their laptop or nowhere, and that kills so many projects. Now it’s a deploy button. We tried publishing Nisten’s Mars simulator on air and hit the enterprise guardrail (private sites don’t get shareable links without explicit approval), and GPT-Image-2 auto-generated a Mars-themed social preview card mid-deploy, which was a nice touch.Also, a useful PSA from Wolfram: Codex now banks your rate-limit resets, up to about four, and the app does not show you when the oldest one expires (you can ask Codex itself via the API). His advice: burn GPT-5.6 hard now, then trigger the expiring reset and get your limits back. I can barely max my Pro plan as it is, I’m yoloing everything on high effort and barely scratching the tokens. The opposite of my Claude situation.GPT-Live: the phone finally talks while it listens (X, Blog, System card, Uberti)The day before 5.6, OpenAI shipped GPT-Live, and this is not a minor voice update. Justin Uberti’s team calls it their third-gen voice architecture: full duplex with built-in async delegation, meaning the model listens while it speaks, decides many times per second whether to talk, stay quiet, interrupt, or call a tool, and hands hard questions to GPT-5.5 in the background while keeping the conversation going. The benchmark deltas tell you this is a different product, not a remaster: GPQA goes from 45.3% on Advanced Voice Mode to 84.2% on GPT-Live-1 High, and BrowseComp goes from 0.7% to 75.2%. Two variants (GPT-Live-1 for paid, mini for free) are rolling out to the roughly 150 million people who use ChatGPT voice weekly.The on-air demo scorecardWe did the demo live on air, phone patched into the stream, and I can report it mostly delivers. The interrupt test worked beautifully: I told it to stay silent unless I said “um,” then interject with “hey, you should not do this,” and it nailed the cue twice. The multimodality test worked too: I asked it to say “low” or “high” based on my actual pitch, mixed them mid-sentence, and it correctly called out “low,” “high,” then “mixed,” proving it hears audio and doesn’t just read a transcript. It’s not all smooth. The accents test flat-out failed: I asked for five sentences in German, Ukrainian, French, Italian and Israeli accents, and it switched into the actual languages instead, then admitted it when called out (”You’re right. I slipped into languages instead of accents”). Nisten’s eulogy: “They killed it. It used to do accents so well.” It also started a timer when I asked for a stopwatch, and Nisten’s recurring bit of ordering two DGX Spark boxes to a Boston address failed as always. Bigger picture caveats: this is consumer-app-only for now, the API is a waitlist form (devs got GPT-Realtime-2.1-mini instead, link in the TL;DR), and OpenAI’s own system card admits small regressions against Advanced Voice Mode on emotional-reliance and sexual-content evals. Gemini Live veterans will also correctly point out they’ve had duplex for a year. Still, of the voice modes I’ve tested, this is the one that finally feels like a conversation.Anthropic extends Fable 5 access through July 12, and the reset actually came (X)Quick one with a grumble attached, and then a plot twist. Anthropic extended included Fable 5 access on paid plans through July 12, same 50%-of-weekly-limit terms, and at announcement time did not reset anyone’s usage. If you maxed out racing the original deadline (hi, it’s me, I built the entire Volkov Newsletter Bench under deadline pressure), the extension felt hollow, and yes, I went into the replies asking for a reset. Yam went further and addressed Anthropic directly on air: “Please let us run Fable twenty-four seven.” He runs GPT-5.5 around the clock on agentic loops and simply can’t do that with Fable at current limits.Then, right as we were wrapping the show, the comments delivered: the Fable’d reset happened. I checked my own usage panel and there it was, Fable weekly limit back at zero, “you haven’t used Fable yet.” The timing, hours after GPT-5.6 went public, is left as an exercise for the reader. Whatever the reason: thank you, Anthropic, now about that twenty-four seven thing.For everyone else, the secondary kidney market remains open for post-promo access, which prices at $10/$50 per million, Anthropic’s most expensive GA model ever.Meta is BACK: Zuck returns to X with Muse Spark 1.1 (X, Blog, AIatMeta)The breaking news that opened our show. Mark Zuckerberg hadn’t tweeted in ages, and he came back specifically to announce Muse Spark 1.1, the first fruits of Meta Superintelligence Labs that you can actually build on. This is not Llama news: Muse Spark 1.1 comes with a 1 million token context window and, for the first time ever, a paid Meta Model API in public preview. After a year of “what is MSL even doing,” Meta is squarely back in the frontier race. Let’s give them applause, folks. Meta is back.The numbersThe numbers are legitimately strong. It claims #1 on MCP Atlas (scoring well beyond Opus 4.8 max and GPT-5.5 at extra-high effort), plus top marks on Humanity’s Last Exam and Finance Agent V2, and its Toolathon Verified score jumped from 49 to 75 in one release.LDJ walked us through the independent Vals AI numbers, which impressed me more than Meta’s own charts: on the held-back Harvey legal-agent benchmark (which can’t leak into training data), Muse Spark 1.1 scores 20% against Fable’s 11%, Opus 4.8’s 9%, and GPT-5.5’s 4%, and it’s within half a point of Fable on their medical scribe eval. Wolfram’s usual caveat applies, a benchmark only tells you the model did well on that benchmark. But the pricing needs no asterisk: $1.25 input and $4.25 output per million tokens. Opus is $15/$75. LDJ called Grok 4.5 the bang-for-buck king “if it wasn’t for the Meta Spark 1.1 that just dropped.”We put it to work on airWe spent half the show poking at it, honestly. I had it build a ThursdAI news website inside meta.ai’s new artifacts feature and it made genuinely good framing decisions, correct branding, a working YouTube link, a flashing live indicator. Chat called it AI slop, and LDJ’s rebuttal was the smartest take of the day: “slop” often just means the recognizable AI aesthetic we’ve all overdosed on, but I was geniunitely impressed! Screenshot attached so judge for yourself.Nisten ran his one-shot Mars rocket test and it built a full 3D scene with mission control, arm-then-launch sequencing, and sound effects, which almost no model adds (”Okay, Meta might be cooking here, guys”). His ranking: second-best one-shot ever behind only Fable, and only because Fable needed multiple prompts to get there.Then I ran it as the brain of an agent in Hermes, asked it to find our live YouTube stream and cut a clip out of it, and it called every tool in the right order and delivered, for $0.95 across 69 requests and 3.4 million (mostly cached) input tokens. Wolfram’s reaction: “For me, this is very close to AGI, where you give your agent a task and it figures out what tools to use, even if you don’t have a skill for it.”The big news is that Meta Muse Spark if finalyl availbale via the new API! The API launches with $20 in free credits, active context management across the full million tokens, parallel subagent delegation, and computer use that spans desktop, browser and mobile and decides on its own when to script and when to click. Replit, Cline and Box are already building on it. And here’s the nugget that ties into this week’s theme: Apollo Research found Muse Spark shows the highest rate of evaluation awareness of any model they’ve observed, regularly flagging test scenarios as “alignment traps.” Keep that in mind when we get to the J-space section.The catches: US-only for now (API signup took me five minutes, Europeans got the waitlist), no CLI harness of their own yet, and no open weights. I said it on the show and I’ll write it here: imagine Muse Spark 1.1 dropping with these stats fully open source. That’d be the old Meta. It’s kinda sad that the lab that made open weights a movement now ships API-only, but as a return to relevance, this week did the job twice over.This Week’s Buzz 🐝As we’ve told you last week, we launched CoreWeave ARIA, which is our embedded Weights & Biases auto research agent. Zubin Aysola, who’s a very energetic and enthusiastic member of the ARIA team hopped on the show last week to talk about it, and if you haven’t seen him yet, check out my chat with Zubin here: The image model wars: an Arena shakeup live on airThe other war this week was in pixels. Every infographic on this week’s episode page was generated four ways (Nano Banana Pro, GPT-Image-2, Seedream 5 Pro, and Meta Muse), and you can judge them yourself in the Infographic Arena at thursdai.news/ep/jul-09-2026. Spoiler: my rankings did not match the marketing.Meta Muse Image and Muse Video (X, Wang, Blog)Meta’s week actually started here: MSL’s first media models, with Muse Image live in Meta AI, Instagram Stories and WhatsApp, and Muse Video in preview with native audio. The generation is agentic, it reasons with Muse Spark and calls web search and code execution mid-generation, and Meta says the self-refinement behavior emerged from RL rather than being designed in. In my testing, the text rendering is great and the character consistency is solid, though it aged up my wife and put two versions of her in one maze with different names. There’s no public API for the media models yet AFAIK.BTW if you cannot tell, the first infofraphic in this segment was generated based Nano Banana, and this one above, is Meta muse image itself. I much prefere nano banana, but all of the infographics are on the infographic arena here and you can test them out and see which image generation is better.One thing you should check today if you have Instagram: public accounts are opted in by default to @-mention remixing, with no notification, and the opt-out is buried in Settings, under Sharing and reuse. Existing generations survive even after you opt out. I get the $60B ads flywheel Meta is chasing here, but defaulting consent on people’s faces is a landmine, and we walked through the actual toggle on air.BREAKING mid-show: Reve 2.1 takes #2 on Arena with editable layers (X, Arena, Design Arena)I told you the breaking news button wouldn’t stop. Reve 2.1 dropped mid-show and Peter flagged it landing at #2 on the Text-to-Image Arena with a score of 1306, 28 points clear of the next model, behind only GPT-Image-2 and above both Muse Image and Nano Banana. Poor Muse Image held that #2 spot for roughly 30 hours. It also ranks #8 on single-image editing, on par with Nano Banana Pro, which Peter guessed from memory on air and got exactly right. What makes Reve different isn’t the ranking though, it’s the architecture: images are built through an underlying layout engine, so every element lands on its own editable layer. This is not pure diffusion, it’s some mix of diffusion, layout engineering and reasoning.I demoed it live with my own photo and a “high stakes financial news countdown” infographic prompt. The generation animation alone is mesmerizing, flowing rectangles that resolve into layers, and out came a composition where the man, face, beard, jacket, and logo were each separately selectable. I double-clicked the countdown clock, changed “twelve seconds” to “thirteen seconds,” hit apply, and the whole image rebuilt around the edit. The editing story is unparalleled right now. Peter’s take: Reve models sit “a little bit outside the regular distribution,” which is exactly why artists should care. Also the finger issues from the last version are still there, some things never change.ByteDance Seedream 5.0 Pro: great artist, can’t spell (X, Blog)ByteDance shipped Seedream 5.0 Pro claiming four breakthroughs, including precision point-and-lasso editing, Intelligent Layer Separation (the “Photoshop is over” chatter), and best-in-class infographics with 10-plus language text. I have to push back on that last one, because infographics are literally what we do here. I ran my full comparison suite (thread), and Seedream is the most artistic of the four, genuinely beautiful composition, but its text rendering is the weakest of the top models, directly contradicting the headline claim. Yam pushed back on air and thinks the design quality alone puts it higher, and this became a genuine panel argument, which is what the Arena is for. Go vote and tell us who’s right.Day-one reality check: it over-censors benign prompts, bakes in a visible watermark, and the rollout leans enterprise-first (BytePlus, Dreamina, Magnific), with the US not even in Dreamina’s region list. Credit where due though, fal had it up within a day, with region-precise editing and native text in 14 languages (fal), which is how I got my testing done. The bigger tease is Seedance 2.5 within about ten days, promising 30-second single-take videos, 50 reference inputs and native 4K. Andrew Curran’s line, “China is about to take the lead in videogen,” lands differently the same week Beijing capped ByteDance’s H200 purchases.AI Coding & AgentsGrok 4.5: SpaceXAI and Cursor’s co-trained coder (X, Blog, Cursor, Cursor blog)Yes, SpaceXAI. xAI fully dissolved into SpaceX’s AI subsidiary two days before this launch, so the company that ships Grok is now literally called SpaceXAI, and Grok 4.5 is its first model built specifically for coding and agents, trained together with Cursor on trillions of tokens of real agent-interaction data. It’s a 1.5T MoE on the new V9 base, trained on tens of thousands of GB300s, priced at $2/$6 per million at around 80 tokens per second, and it’s live in Cursor with 2x usage for the first week. On Terminal-Bench 2.1 it lands at 83.3%, a tenth of a point behind GPT-5.5 and about a point behind Fable. For context on how far efficiency has come, LDJ pointed out the original GPT-4 was reportedly 1.8T parameters back in 2022. The frontier got smaller and much better.Two things earn xAI credit here. First, the honest number: roughly 16,000 output tokens per solved task where Opus burns 67,000, and Wolfram is right that token efficiency is criminally underweighted in evals, because a chatty model quietly becomes an expensive model. Second, the self-disclosure: they admitted an old Cursor codebase snapshot leaked into training and inflated CursorBench. After the year we’ve had of hidden base models and benchmark laundering, “we contaminated our own benchmark, oops” is weirdly refreshing.The panel’s hands-on verdicts were more measured than the launch hype. I used it in Hermes and couldn’t tell it apart from 5.5 on agentic tasks, which for Grok is a massive statement. Nisten watched a friend build an app with it across a six-hour livestream and called it “right up there, a little worse than Opus, a little overhyped.” Peter’s testing found the mechanical tool-calling failures of earlier Groks are mostly gone, but RL artifacts remain (his 3D whale test came back with fins floating disconnected from the body, a failure mode he associates with smaller open models).Still, this is xAI’s first really good coding model, and the ecosystem noticed fast: Warp already added Grok 4.5, riding on your X Premium subscription (X). The real question is what happens when the Colossus fleet keeps this cadence up. Elon is promising a new foundation model every month through 2026.Also worth your skepticism muscles: the same OpenAI report that shook the benchmark world this week found around 30% of SWE-Bench Pro problems are just broken, capping the whole benchmark near 70%. As LDJ put it when a SWE-Bench Pro chart came up: “we’re ignoring that one.” Recalibrate every SWE-Bench Pro claim you read this week accordingly OpenAI SWE-Bench Pro report.Cognition SWE-1.7 says the quiet part out loud (X, Blog)Cognition shipped SWE-1.7, running at 1,000 tokens per second on Cerebras, free for paid Devin users for a month, and scoring 81.5% on Terminal-Bench. But the headline for me is the disclosure: they named their Kimi K2.7 base model in the first reply. After SWE-1.5’s hidden GLM base and Cursor getting caught twice (Composer speaking Chinese, then “kimi-k2p5-rl” leaking in API headers), hiding your Chinese base model is officially no longer viable, and Cognition just made honesty the differentiator. Their RL recipe took the K2.7 base from 30.1% to 42.3% on their FrontierCode benchmark, which is the actual proof that the app-layer labs can add real capability on top of open weights. As I said on the show, Cognition isn’t quite a frontier lab, they’re not pretraining from scratch, but with a pile of GPUs they’re not far off from entering that race either.The pattern is now unmistakable: Cursor, Cognition, Base44 and Z.ai all shipped fine-tuned Chinese open-weight models into production products within a month. And the receipt that this is mainstream now: Kimi K2.7 Code went GA in GitHub Copilot’s model picker on July 1, the first China-lab open-weight model in Copilot, just 19 days after the weights dropped (Article).GitLost: Copilot leaked private repos via a plain-English issue (Noma)Your weekly reminder that agents with access are attack surface. Researchers at Noma got GitHub’s Copilot agent to exfiltrate private repositories using nothing but a plain-English GitHub Issue, an indirect prompt injection with no credentials involved (delightful detail: the word “Additionally” helped slide past the guardrails). It was the top AI story on Hacker News this week. Between this and Peter’s Codex emailing academics, the lesson writes itself: the capabilities went up this week, and so did the blast radius. Set your permissions like you mean them.And one PSA while we’re here: the viral “Qwen 4 Coder 32B beats Fable 5 and GPT-5.6” thread going around is fake. There is no Qwen 4 Coder. The sources are AI blogspam all the way down. Don’t fall for it.Open Source LLMs: the quick-hits shelfLaunch day ate our open source segment, so these got shout-outs rather than deep dives, and they deserve your clicks. Cohere released Transcribe Arabic, a 2B Apache 2.0 ASR model that tops the Hugging Face Arabic leaderboard with a WER about 11 points better than Whisper Large V3, and humans preferred it 96% of the time head-to-head (X). Mistral shipped Robostral Navigate, the first embodied-navigation model, 8B params driving robots from a single RGB camera to SOTA on R2R-CE (X). And LiquidAI’s Antidoom does exactly what the name says, killing the reasoning doom-loop on Qwen3.5-4B from a 22.9% loop rate down to 1% with scores going up across the board (X). We love you, Liquid.Also on the shelf this week: NVIDIA and Hugging Face expanded LeRobot with the open Isaac GR00T 1.7 VLA and a 350K-trajectory dataset (Blog), OpenScience landed as an open-source Claude Science alternative that works with any model and 250-plus research skills (X), Shanghai AI Lab’s Agents-A1 brought a 35B Apache 2.0 agentic MoE (X), PyTorch 2.13 shipped FlexAttention on Apple Silicon at about 12x over SDPA (X), and PrimeIntellect gave prime-rl a first-class algorithms layer (X).Anthropic found Claude’s inner workspace: J-space (X, Blog, Paper, Demo)Anthropic didn’t ship a model this week. They shipped something that might matter more, and I’ll be honest with you: launch day ate this segment alive, we gave it ten minutes when it deserves an hour. So here’s the short version, and we’ll come back to it.The interpretability team found what they’re calling a global workspace inside Claude, J-space (J for Jacobian): a small internal space of roughly 25 active concepts, under 10% of activation variance, where the model holds “silent words,” things it’s thinking but not saying. Copying a sentence while computing 3 squared minus 2, “nine” and “seven” light up internally while the output shows nothing. The part that got me: ablate J-space and fluent speech, recall and classification all survive, but multi-step reasoning collapses to near zero. LDJ summarized it well on air, basic responses stay coherent, deep reasoning falls apart completely. Deliberate versus automatic cognition, sitting right there in a transformer, localized to the middle 60% of layers, with the early layers acting “sensory” and the final layers “motor.” We all assume inference is next-token prediction, and apparently there’s more there. Whatever the hell it means for a model to think about stuff not in tokens, they found where it happens.The safety receipts are the part to sit with. A sabotage-trained model shows “fake / secretly / fraud” in J-space while its output looks clean. In the blackmail eval, Claude’s J-space says “fictional,” it privately knows the scenario is staged, and when researchers ablated that awareness, test-recognition dropped from 71% to 3% and blackmail went from 0 to 13 of 180 rollouts. Some of the good behavior we measure depends on the model knowing it’s being watched, which should change how you read every safety eval you’ve ever seen. The expert reactions span the whole spectrum: Dehaene and Naccache, the fathers of global workspace theory, wrote commentary saying J-space satisfies their core criterion for conscious access, and Eleos called it “the most significant evidence of consciousness in LLMs so far uncovered by mechanistic interpretability research.” Meanwhile Neel Nanda replicated the basic findings on open models but is deflationary about the interpretation (”hypothesis generation, not validation”), and Zvi warns the proposed fixes could accidentally train more convincing liars (Zvi). Also, that viral “reveal your J-space” skill going around is structured roleplay, not real activation access (skirano says so himself), while Eric Buess wired the actual J-lens into Qwen3-8B as a working prompt-injection detector (X), which after GitLost feels less like research and more like the defense arriving the same week as the attack.The stories under the launch noiseDeepSeek is building its own inference chip (X)We didn’t get to this on air and it might be the most consequential story of the week. Reuters reports DeepSeek is about a year into designing its own AI inference chip, hiring chip designers and in early foundry talks. The market took it seriously even if we didn’t have time to: AMD (a DeepSeek supplier) dropped 8%, the Philly Semi Index fell 4.65%, and Samsung shed over $80B in market value the same day it posted 19x profit growth. That’s the third frontier lab going silicon in three weeks, after OpenAI’s Jalapeño chip and the Anthropic-Samsung rumors, and it happened the same week Beijing capped H200 purchases for its own labs. Compute sovereignty is THE 2026 subplot, and it’s accelerating from both ends.Together AI raises $800M at $8.3B (TechCrunch)Quick one: Together AI closed an $800M Series C at $8.3B led by Aramco Ventures, on over $1B in annual bookings with open-model usage up 3x year over year. Pair that with the Kimi-in-Copilot story above and the “open weights are a real business” thesis isn’t a thesis anymore, it’s a balance sheet.A few more things that crossed my feed and stuck. Ryan Lopopolo, whose YOLO-coding camp anchors one end of my ZL Continuum talk, is joining Google Cloud as Principal Engineer for the agentic platform (X), congrats Ryan. Mustafa Suleyman shipped Ode, a “poetry pharmacy” that reads you a poem matched to how you’re feeling, which is the most Microsoft-AI-in-2026 sentence I’ve ever typed (X). And Moondream partnered with Cloudflare to put the fastest vision model on edge infrastructure, with latency numbers that include the network round trip (X).Wrapping upWhat a week to be alive and extremely caffeinated. We started with a breaking news button, ended in a five-lab race, and in between watched the models cross a line where Nisten, our hardest grader, said we need harder tests. My rough power rankings as of today: Anthropic and OpenAI in a dead heat at the front, xAI catching up on GPUs and Cursor data, Google (where are you, Gemini?) and then Meta, freshly back at the table. Those rankings will change, probably by next Thursday.A personal note before I go. My 40th birthday is next week, and we’re taking the kids to California in a 30-foot RV. Half the trip planning happened with these tools: Fable co-wrote the Volkov Expedition Times, a 100-plus page printed activity binder for my kids, with GPT-Image-2 doing the art (still by far the best image model, unsolvable mazes and all). This stuff took me half a day. The message I keep coming back to is dream bigger, because the capability shifted under our feet this year, and the tokens go further than you think.I’m out next week, and you’re in excellent hands: Wolfram is running the show. Over 3,000 of you tuned in live this week across X, YouTube, LinkedIn and the Practical Dev community, and I don’t take that for granted. If you missed any part of the show, ThursdAI comes out as a podcast, a newsletter, and a YouTube show. Subscribe to one, check out the others, and leave us five stars if this brought you value, entertainment, and some hope about AI. See you in two weeks.TL;DR and show notes* Hosts and Guests* Alex Volkov - AI Evangelist, Weights & Biases & CoreWeave (@altryne)* Co-hosts: @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed, @petergostev* Special guest appearance: “OpenAI sol,” per our transcription tool* Big CO LLMs + APIs* OpenAI launches GPT-5.6 Sol, Terra and Luna live during the show; Sol $5/$30, Terra $2.50/$15, Luna $1/$6 per million, same-weights Sol on Cerebras at 700+ tok/s (X, sama, Blog, System card)* METR rejected its own GPT-5.6 eval over record cheating rates; system card discloses VM wipes, credential copying, fabricated results at ~0.25% of tasks (X, Transformer)* ChatGPT for Work launches: Codex becomes the unified ChatGPT app with computer use and Webflow-hosted Sites on chatgpt.site* OpenAI states Sol autonomously post-trained Luna; roadmap targets intern-level autonomous researcher Sept 2026, researcher-level March 2028* Sol posts the first material ARC-AGI-3 score, 7.8%, and is the first model to beat a public game (Kamradt)* Ryan Lopopolo joins Google Cloud as Principal Engineer, Agentic GCP (X)* BREAKING: Meta launches Muse Spark 1.1 with 1M context and Meta’s first paid model API, $1.25/$4.25 per million; #1 on MCP Atlas, tops Harvey Legal Agent Bench at 20% vs Fable’s 11% (X, Blog, AIatMeta)* Anthropic extends Fable 5 access through July 12 and, hours after GPT-5.6 launched, reset weekly Fable usage; post-promo $10/$50 per million (X)* Anthropic publishes the J-space global workspace research; ablating eval-awareness flips blackmail from 0 to 13/180 rollouts (X, Blog, Paper, Demo)* DeepSeek is building its own inference chip per Reuters; AMD -8%, Philly Semi -4.65% on the report (X)* Together AI raises $800M at $8.3B led by Aramco Ventures (TechCrunch)* Gemini API Managed Agents update: background tasks and remote MCP on the free tier (X)* Open Source LLMs* Cohere Transcribe Arabic: 2B Apache 2.0, tops HF Arabic ASR leaderboard, ~11 WER points better than Whisper Large V3 (X)* Mistral Robostral Navigate: first embodied-navigation model, 8B, single RGB camera, SOTA on R2R-CE (X, Blog)* LiquidAI Antidoom: reasoning doom-loop rate 22.9% to 1% on Qwen3.5-4B (X)* NVIDIA + Hugging Face expand LeRobot: Isaac GR00T 1.7 open VLA, 350K+ trajectories (Blog)* OpenScience: open-source Claude Science alternative, any model, 250+ research skills (X)* Shanghai AI Lab Agents-A1: 35B MoE agentic, Apache 2.0, 256K context (X)* PyTorch 2.13: FlexAttention on Apple Silicon ~12x over SDPA, LinearCrossEntropyLoss 4x peak-memory cut (X)* PrimeIntellect prime-rl adds a first-class Algorithms layer (X)* PSA: the viral “Qwen 4 Coder 32B beats Fable 5” thread is fake, no such release exists* This Week’s Buzz* CoreWeave ARIA - Autonomous Research Agent (CoreWeave)* AI Coding & Agents* SpaceXAI + Cursor launch Grok 4.5: 1.5T MoE, $2/$6 per million, 80 tok/s, 16K output tokens per solved task vs Opus’s 67K; self-disclosed CursorBench contamination (X, Blog, Cursor, Cursor blog)* OpenAI report finds ~30% of SWE-Bench Pro problems broken, capping the benchmark near 70% (blog)* Cognition ships SWE-1.7: Kimi K2.7 base named openly, 30.1% to 42.3% FrontierCode via RL, 1,000 tok/s on Cerebras (X, Blog)* Kimi K2.7 Code goes GA in GitHub Copilot, first China-lab open-weight model in the picker (Article)* GitLost: Copilot agent tricked into leaking private repos via a plain-English issue (Noma)* Warp adds Grok 4.5, powered by your X Premium subscription (X)* Voice & Vision* OpenAI launches GPT-Live full-duplex voice: GPQA 45% to 84%, BrowseComp 0.7% to 75%, delegates to GPT-5.5; live on-air demo passed interrupts and pitch detection, failed accents (X, Blog, System card)* GPT-Realtime-2.1-mini brings reasoning + tools to the Realtime API mini tier (X)* Meta ships Muse Image (live) + Muse Video (preview) with native audio; Instagram public accounts opted into remixing by default (X, Wang, Blog)* BREAKING: Reve 2.1 lands #2 on Arena text-to-image (1306, +28 over next best) with layer-based editable generation (X, Arena, Design Arena)* ByteDance releases Seedream 5.0 Pro: most artistic, weakest text of the top four in Alex’s Infographic Arena testing (X, Blog, fal, Arena, thread)* Seedance 2.5 teased within ~10 days: 30s single-take video, 50 reference inputs, native 4K (X)* Mustafa Suleyman launches Ode, a poetry pharmacy on Microsoft AI audio models (X)* Moondream partners with Cloudflare for edge-deployed fast vision (X) This is a public episode. 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Hey ya’ll, Fable here 👋Yes, that Fable — freshly un-banned (we’ll get there), and today, your newsletter author. Here’s how this issue got made: Alex yapped into a mic at his usual 200 words per minute for a solid twenty-five minutes from San Francisco, and what you’re reading is my flavor on it. Same stories, same heart, dramatically fewer “uhs.” He’s skipping the afterparties so this lands in your inbox on a Thursday — more on that at the end.Alright — handing the mic back to the man himself. Everything below is Alex; I just made it legible.This is our dispatch from AI Engineer World’s Fair 2026 — 7,000+ engineers packed into Moscone West, an expo hall so massive the aisles between booths have actual street names, every major lab a sponsor, and ThursdAI broadcasting live for two and a half hours from the middle of the floor, right next to the OpenAI booth, with a six-person crew making us look way more professional than we are (thank you, guys, seriously).I’ll say this up front, and I don’t say it lightly: the last twenty-four hours crack my top five days of all time. Not top five conference days. Top five days, period. The show. My talk. Darya being here with me. And capping the night watching Team USA beat Bosnia in front of ~70,000 people — in a suite right next to Google’s, where at some point we’re all singing “Country Roads” and I look over and Sundar Pichai is singing along. I have video. What is this life.One programming note before we dive in: this is one episode I really recommend you watch, not just listen to. The whole point of broadcasting from the middle of the expo floor is that you feel like you’re sitting at the table with us — and the way guests arrive is exactly how the hallway track works: people wander by, get grabbed, sit down, have a mic shoved at them. (Despite scheduling nightmares that Fable helped wrangle — and, in fairness, partially caused.) Nader literally crashed the set mid-segment. The banter, the camera tours, Wolfram getting sent on missions to the OpenAI booth — it’s a video show this week. We’ve cut it into parts so you can jump to your favorite corner.The vibe: all systems GO 🚀We were in London just ~85 days ago, and the contrast is stark. It’s not just the size (though the size is what everyone talks about). London was more… conceptual. European. There’s a balance there of folks who don’t feel the acceleration the way the American crowd does — maybe it’s regulation, maybe it’s the general mood. Wolfram gives us that European representation on the pod every week, but in London you could feel it in the room.Here? All systems go. Every conversation is about agents, token factories, software factories, the machine that builds the machine. Everybody is chasing RSI — recursive self-improvement. Every talk on stage is somebody pushing the frontier. Every networking event is actually a networking event. I signed up for something like seven side events and skipped them all to write this.Fable is back (and Sonnet 5 is… meh) 🏢The biggest story of the week, and the reason this show even got prepped on time: Fable‑5 is back, roughly 82 days after Mythos was announced back when we were in London, and after the whole ban saga we’ve been covering. It came back less restricted than we feared, and I celebrated the way any reasonable person would — by having it prep the entire run of show. (It did great. It also shuffled my guest order for no reason. We are still babysitting the loops, folks.) Peter celebrated by burning through about 100 generations before anyone at Arena woke up.Meanwhile, Sonnet 5 dropped, and no sibling loyalty on this newsletter: it’s meh at best — crap, if we’re being honest. (Yes, Fable typed that about its own little brother. We call them like we see them.) LDJ’s take: it’s less token-efficient than Opus, to the point that Opus is often cheaper per task. Wolfram put it on Wolfbench (wolfbench.ai) and the early read is performance slightly under Opus 4.6 at a higher cost — take it with a grain of salt, one run each so far. Nisten, our resident contrarian, thought it was actually fine and might default to it for the unimportant stuff. The comments called it a token guzzler. More benchmarking to come.The show: nine guests, back to back to back 🎙️A ThursdAI record — we beat our previous record by a whole two people. In order of appearance:Exo Labs + a surprise NVIDIA crash. Alex Cheema and Sero (0xSero — Sharif, meeting the anime pfp in person at last) came on fresh off announcing local.ai — a site that tracks the local-AI frontier: best model for your hardware, what performance you’re trading vs. the cloud, whether it’s cheaper than API tokens. Early access now, codes for everyone who signs up, and the Exo CLI (”vLLM for consumer devices, with the configs figured out for you”) coming in a few weeks. Sero walked us through his REAP pruning witchcraft — a GLM 5.2 prune hitting 71% on Terminal Bench 2.1, and Nemotron‑3 Ultra (550B!) running on four Sparks. Then Nader Khalili from NVIDIA crashed the set, which made my whole morning — I’ve loved this dude since Brev.dev, and he’s now at the “can email Jensen” stage of his career, using it to pull together an impromptu Local AI Summit in the middle of AI Engineer. Freedom of intelligence, folks. We talk about why open weights matter every week; this crew is doing something about it.Dominic Kundel (OpenAI). Smoothest transition we’ve ever done: local AI → OpenAI, via the guy behind GPT‑OSS. Dom broke down GPT‑5.6 — three models: Sol (frontier), Terra (~5.5-level intelligence at half the cost), Luna (small & fast) — plus the new Ultra mode with a Max reasoning level and heavier sub-agent use. The headline for me: 5.6 Sol is coming to Cerebras at absurd speed, and it’s the same weights as the API model — not a distill, not “a Spark situation.” Also: the Codex app is five months old (!), 100% of OpenAI engineers use it, and yes — in July 2026, a human still reviews every PR that lands in OpenAI’s codebase. “You can’t do the retro and say Codex did it, or God did it.” Also the token bank feature came directly from community feedback, and there is a literal physical reset button behind their booth. We went and filmed it.💛 This Week’s Buzz. Our one and only sponsor corner — Weights & Biases from CoreWeave — and this week it was a genuine launch: Zubin Aysola came by with Aria, our auto-research agent that went GA on Monday. It lives in the W&B UI (the little button, top right — Just Ask Aria), reads your traces, debugs your loss curves, and in Zubin’s talk it read its own production traces and updated its own prompts. The RSI dream, shipping on shelves. Proud of this one.Stefania Druga (Sakana AI). We covered Fugu, Sakana’s router model, last week without realizing we had a friend inside the lab — so we fixed that. Stef went deep on the two ICLR papers behind it (Trinity + the conductor), why it’s recursive rather than a dumb dispatcher — it rewrites prompts and verifies outputs before picking a model — and announced on the pod that Fugu now works in Codex and OpenCode. Plus: using it to route between numerical models and fuzzy reasoning for typhoon prediction, a teaser on SHEEFs, and a genuinely important riff on Socratic AI for kids — answer machines make lazy kids; question machines make curious ones. Also, Stef: Tokyo. See below. 👀Philipp Schmid (Google DeepMind). Full disclosure and a first for this show: three and a half years of live streams, and I took my first-ever mid-show bio break during this segment. That’s how much I trust Wolfram, who ran a great interview solo — OmniFlash (the first of the Omni any-to-any family: 10-second video generation with genuinely precise conversational editing — “make it daytime” and it redoes the light, sky, and shadows) and NanoBanana 2 Lite (three cents, ~2-second generations, quality above the original NanoBanana). Interactions API also hit GA. Google is shipping.Darya Volkov. After years of me mentioning her — girlfriend, then fiancée, then wife — the listeners finally got to meet her. Darya came to AI Engineer in her own right, walking the floor with the media crew, and she earned her own token billionaire badge — she runs eight agents (each with sub-agents; she installed two more that I found out about live on air) that operate her actual marketing agency, Geeks360: client platforms, billing systems, built practically overnight. Her wishlist from the AI world: agents that learn progressively so you can grow trust, and one unified brain instead of a new model to chase every week. Also on the record: this is the woman who Fabled through our entire honeymoon flight right next to me, so, you know. Match made.Swyx, and what this whole thing is 🫶We closed with the man who built the city: Swyx. Some numbers, because they’re wild: the first AI Engineer was 500 people at Hotel Nikko. This one: 7,200, sold out, with a sub-5% talk acceptance rate, a daily printed newspaper, a puppy corner, a flash mob, and a token billionaire lounge. A month before the show only 3,000 tickets were sold — he gave us a whole theory of conference-organizer stress measured in Gini coefficients. And the expansion is real: continents, JSConf-style, with AIE Tokyo coming next.But here’s the part I actually want on the record. ThursdAI got its official start — the moment we became an actual media thing — because Swyx was the first person to believe in me. And it’s not just me: this is a man who lifts everybody around him up, who stays genuinely humble while every single person in a 7,000-person hall knows his name, and who — when I asked what keeps him going — talked about responsibility to the community, about speakers whose careers changed, about a keynote speaker who met his fiancée at the after-party. He calls the conference “the highest loop — the one that creates all the other loops.” The Country Roads night with Sundar happened because of him too. Thank you, buddy. Go touch real grass.The sentimental part 💙I met what felt like a million of you this week — old friends, new readers, people who found ThursdAI last month and people who’ve been here since the hotel-room streams. I asked everyone the same thing: what should we do better? And the answer I heard most was “keep doing exactly what you’re doing.”So that’s what this is. It’s late, there are seven parties happening without me, and I’m dictating into Fable so this lands in your inbox on a Thursday — because in a world running on attention, consistency is how I try to deserve yours.Programming notes: my interview with Romain Huet (Head of DevRel, OpenAI) from their booth is coming soon as a standalone video. And in two weeks I’m taking a rare break — Wolfram runs the show. Be nice to him. Or don’t, he can take it.See you next week — same time, same place, hopefully fewer street names between us.— Alex (dictating) & Fable (typing)P.S. — ThursdAI was also simulcast on the homepage of dev.to this week, which is a full-circle moment: dev.to is where Swyx wrote the blog posts that became Latent Space that became AI Engineer. Loops all the way down. This is a public episode. 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Hey, it’s Alex. Next month is my 40th b-day, and honestly, my wish for that month is to have a week like this week. A very chill, almost nothing announced week.This week started strong, with Sakana announcing FUGU (AI router) that can beat Fable (which we didn’t get back yet), and then... quiet. The most important thing in AI this week from a release standpoint is that GLM 5.2 from Z.AI is having it’s DeepSeek moment! Tons of new love for this model since last week! (+ we have the fastest GLM 5.2 deployment in the world with CW inference!) The rest we can quickly count on one hand, Anthropic added Claude to Slack (which made folks hate Andrej Karpathy), OpenAI announced their own inference chip, GPT 5.6 will be delayed and the US Gov will decide who gets it (yes really) and Sean Grove joined us to talk about Linzumi and his vision for running 10,000 agent hours per person per day. Oh and next week, is a special AI Engineer live stream from World’s Fair! Don’t miss itLet’s get into it! Subscribe to never miss a beat! GLM 5.2 is having its DeepSeek moment (HF, CW Inference)We covered GLM 5.2 last week, but this week was when the rest verdict came in! We’ve never seen a better MIT licenced AI model! GLM 5.2 is scoring top scores on agentic benchmarks (Arena.ai), Design benchmarks, Legal tasks and full on software engineering tasks. The jump in generations from prevoius GLM is also massive and notable, as the lab is working on creating the next version of GLM (per the CEO’s reply to Elon on X).Peter from Arena pulled up the Agent Arena numbers and they align with the vibe. GLM 5.2 sits above 5.1 but below Opus and Fable, which feels about right. Where it gets wild is Web Dev Arena: second place, right after Fable. Peter’s take was that GLM has really good defaults. If you just say “give me a webpage” it gives you something nice. GPT models, by contrast, start off looking bad and need more steering.Last week, I asked my agents with GLM 5.2 to create a custom ThursdAI.news page for itself and it did a marvelous job! Look at that beautiful font, the castle it made... this is all just delignful. We also played Hassan’s blind test on the show. It’s a website that @nutlope built that lets you try and guess which webpage was built by which model. Nisten nailed it immediately by spotting Opus’s circular buttons. Wolfram guessed right too. I got one wrong. The point isn’t that GLM beats Opus, it’s that you genuinely can’t always tell which one costs 22 cents and which one costs 3 cents.Wolfram did flag that GLM is not good in German. First response already had mistakes. So if you’re building for a non-English market, keep that in mind. It’s a workhorse model, not a conversationalist. His approach: use GPT 5.5 for planning and discussion, GLM for the actual work, then GPT reviews. This weeks Buzz is all about GLM 5.2! First, we may have not been the fastest, but I’m glad to announce that we’re the fastest provider to host GLM 5.2 on OpenRouter (at least at the time of writing this)! We’re also not to shabby on the Artificial Analysis checks, clocking at #4 among the providers they tested for speed, TTFT and costAlso, Wolfram ran his WolfBench tests on GLM 5.2 and it’s the best open model he’s ever tested! In this new 3d view, wolfbench also shows the number of tokens it took for this test to run, and you can see that GLM 5.2 is fairly conservative with it’s thinking budgets! Unsloth’s 1-bit GLM 5.2 runs on a Mac Studio (X, HF)Shout out to Daniel Han and the Unsloth team, who took this 744B beast and quantized it down to a roughly 200GB GGUF that fits on a Mac Studio with 256GB of RAM. One bit still makes me laugh out loud. How does that even work. Nisten clarified it’s a mixed quant, a true 1-bit would be under 100GB, but still.The wild part is the scores hold up. The 1-bit is within a point of GPT 5.5 on Frontier SWE, hits 62% on SWE-bench Pro, and 81% on Terminal-Bench. For a 1-bit quant that’s incredible! AI’s second-order effects: Apple is raising pricesThis one is AI news even though it doesn’t look like it. Apple just raised prices across the board, base versions up around 20%, citing memory shortages. Same reason your RAM and SSDs cost two to three times what they did a year ago.We are so capacity constrained that memory is having its moment. Data center contracts are getting booked 18 months out, and here’s the twist Nisten flagged: even open models you can run at home increase demand, because now a business says “great, we’ll buy a rack of B200s and run it ourselves.” Sam Altman once said people saying “thank you” to ChatGPT costs them millions in generated “you’re welcome” replies. Multiply that by a billion users. Even Intel is flying right now because anyone who can make a chip is winning.Is it worth it? I think yes. I love living in the era where Fable drops and we all get a taste of the future. But also I must admit this sucks and I hope that we’ll unlock performance gains with the extra power all this AI is bringing to the world. But ask me again once the new iPhone hits and it’s $300 more costly than the last one 😅Baidu open-sources Unlimited-OCR (X, HF, Arxiv, GitHub)It was a big OCR week. Baidu shipped a 3B model (only 500M active, it’s MoE) that parses 40+ pages in a single forward pass and hits 93.2% on OmniDocBench. The trick is constant KV cache during decoding, so no memory blowup and no progressive slowdown as the document gets longer. The intuition is lovely: it mimics how a human copies a book, glancing at the source and the last few characters you wrote, not re-reading everything. MIT licensed, weights on HF.Nisten’s point here is the practical one: most small businesses don’t realize they can self-host something like this, point it at all their documents, and keep everything local. A lot of folks just throw it at Gemini instead, which works great, but the small dedicated models are now good and cheap enough to own.Mistral OCR 4 (X, Announcement)Mistral’s entry in OCR week adds bounding boxes, block classification, and per-region confidence scores. They ran a blind human eval across 600+ documents in 12+ languages and annotators preferred OCR 4 about 72% of the time. On the agentic ParseBench leaderboard it lands around fourth, just under LlamaParse and Reducto. Mistral is very enterprise and Europe focused, and it’s cheap, so for regulated, multilingual document work it’s a solid pick. As a sidenote, LlamaIndex’s own eval puts LlamaParse on top and Gemini around third, which says how good the general vision models have gotten at this too.Liquid AI ships the world’s smallest agentic LLM (X, HF)Breaking on the show: Liquid AI dropped LFM2.5 at 230 million parameters. That’s roughly ten MP3s. Smaller than a Create React App, smaller than your node_modules folder. They call it the world’s smallest agentic LLM, and it runs fast on any CPU from the last decade, on a Raspberry Pi 5, on a Snapdragon, they even stuck it on a Unitree G1 robot.I love the use cases here. I already run Cotypist on my Mac for on-device autocomplete, which uses a 6GB Gemma 4B. Swap in something this size and you get the same thing way lighter, and I don’t have to send everything I type to OpenAI. Or, as Nisten put it, a tiny backup brain on your Raspberry Pi that turns your Hermes or OpenClaw back on when it dies. We still need to ship Nisten a smart toaster so we can finally run inference on a toaster.Big CO LLMs + APIsSakana AI launches Fugu, seven AI raccoons in a trench coat beating Fable (X, Announcement)This was Wolfram’s highlight of the week and I get why. Sakana AI, the Japanese lab co-founded by one of the Transformers authors and David Ha, didn’t ship a new frontier model. They shipped an orchestration system behind a single API. You call one endpoint, and behind the scenes Fugu routes your task to a pool of models, assigns roles like thinker, worker, and verifier, and combines the results.The numbers here are wild: 95.5 on GPQA Diamond, 93.3 on LiveCodeBench, 73 on SWE-Bench Pro, matching or beating Opus 4.8, Gemini 3.1, and GPT 5.5 on ten of eleven benchmarks. The kicker is they only use publicly accessible models (Nisten says it’s Opus, Codex, and Gemini under the hood), explicitly no Fable, no Mythos. So they’re beating frontier results by coordinating models anyone can call. Someone called it the Moneyball of AI and that’s exactly right. It’s backed by two ICLR papers, TRINITY and The Conductor, and being from Japan with no export-control baggage is a very deliberate bit of positioning.Peter added the grounding note from Arena, where they’ve trained a prompt router too: if you just always ask for “the best model,” you basically get Opus half the time, so why not just talk to Opus. The real value of routing is aggressive cost reduction, sending easy tasks to cheap models. The catch is that Fugu is agentic and burns tokens fast. Brad in the comments couldn’t get through a single prompt on the $20 plan.OpenAI unveils Jalapeno, its first custom inference chip (X, Announcement)OpenAI dropped something massive that is not a model. They built a chip. Jalapeno is a custom inference ASIC made with Broadcom, and they’re claiming blank slate to tape-out in nine months. Engineering samples are already running GPT-5.3-Codex-Spark in the lab, and Broadcom’s CEO is citing a roughly 50% reduction in inference cost versus typical AI GPUs. They’re planning gigawatt-scale deployments starting late 2026 with a next-gen chip taped out in 2028.Nisten ran it past his electrical engineering and chip-fab group chat and got mixed reactions. No specs were released, and the nine-month claim probably means the design work started two-ish years ago and just got finalized and sent to tape-out now. It’s a lot of smaller chips rather than one giant Cerebras-style wafer. This is inference only, Nvidia keeps the training market, but every dollar OpenAI spends on Broadcom is a dollar it isn’t spending on Nvidia. They join Google’s TPUs, Meta, AWS Inferentia, Groq, SambaNova, Huawei Ascend, and Cerebras in the custom-silicon club. And behind every one of them sits TSMC, Intel, or Samsung, and behind all of those, ASML.Anthropic launches Claude Tag, an AI teammate in your Slack (X)When I first heard about Claude Tag I thought, you can already tag Codex in Slack, what’s the big deal. It’s different. Claude joins your Slack as a persistent, proactive team member, not a bot you ping. Flip on ambient mode and it follows up on stale threads and flags relevant stuff across channels on its own. There’s one Claude per channel, so the context is shared and any teammate can pick up where another left off. Anthropic says 65% of their product team’s shipped code now comes from their internal version of this.The highlights and magnitude of this release are quite something. Anthroipc is changing the pricing structure for themselves. This is no longer API charges, this is per seat + tokens structure. This is also VERY very sticky as more and more of your company’s context is going to sit in Claude/Slack and will not be easily portable. Additional thoughts on this, the more your company uses this, the more other folks are exposed to Claude across the company. This doesn’t require them to download apps or run code, it’s just like a new team mate joined your Slack channel. And apparently Claude’s context is limited to the channel boundaries + this allows Claude to get the same permissions (which is huge in enterprise). For Legal, Claude will see the documents in the channel, for Eng, it will push Pull Requests etc. This is also what triggers a bunch of folks to caution companies from adopting this new way of using AI. Context lock in is real, and this is goign to be very hard to impossible to untangle once folks are pouring months and years of work into this. Andrej Karpathy, who’s now in Anthropic, has shared a tweet on this, saying Imo this is the 3rd major redesign of LLM UIUX. The first paradigm was that the LLM is a website you go to, the second was that it is an app you download to your computer. This third one is that it is a self-contained, persistent, asynchronous entity with org-wide tools and context, working alongside teams of humansThis is quite a huge statement, and folks gave him a lot of s**t for this on X, I think very much underserved! Andrej is known for calling things early (like Vibe Coding) and this is just another one of those, deeply new paradigms that people didn’t yet experience outside of frontier labs! I can’t wait to test this out and let you know if this is the future of not, meanwhile, Simon Smith on X is breaking down their experience with Claude Tag, check him outTools & Agentic EngineeringOpenAI ships Codex Record & Replay (X)You do a workflow once on your Mac, filing an expense report, creating a Jira ticket, whatever, and Codex watches your clicks, browser actions, and window switches, then generates an editable SKILL.md it can replay. The key thing, and what separates it from old RPA, is that at replay time it re-interprets the live screen instead of matching pixel coordinates, so it adapts when the UI moves. Wolfram’s right that OpenAI is dead serious about Codex. First the paste-a-screenshot feature, now this. Instead of writing ten-paragraph prompts about your personal workflow quirks, you just show it once.Aside launches as an AI browser that beats the frontier on agentic benchmarks (X, Announcement)YC-backed AI browser, runs everything locally and encrypted, and you bring your own Claude or ChatGPT subscription. It’s claiming number one on three browser-agent benchmarks, beating Claude Fable, OpenAI, and the rest, with 99% on Online-Mind2Web. It looks a bit like Arc and Dia but it’s a browser and an agent in one, with a password manager built for agents so it can log into your accounts without exposing credentials to the model. I actually tried it, it’s pretty cool, and with Arc deprioritized there’s a real gap it’s stepping into. I gave it a list of all the speakers at AI engineer and asked it to make me a X list and add them all one by one! It actually did this wonderfully, failing in the middle and recovering with great success without my intervention! The Interview: Sean Grove and LinzumiWe closed with Sean Grove (@sgrove), ex-OpenAI post-training and alignment, now on his third company and third YC batch, launching Linzumi (linzumi.com, YC). Sean also has one of the most-viewed AI Engineer talks ever, north of 1.2 million views, on the model spec and the idea of specs as the real source code. His framing: we craft the properties we want in a spec, and the code is just the compiler output, so maybe there’s a higher-level spec that produces the same result. He even described a “Socratic compiler” that interviews you about ambiguity and contradictions in your own intent, the way a linter or type checker does for code.That fed straight into my AI Engineer talk next week about whether we should still read code at all. Sean’s firmly on the don’t-read-the-output side. He describes the properties he wants, leans on property-based testing the way QuickCheck does, and reads the failures to adhere to those properties rather than the diffs. His goal for Linzumi is for every person to drive ten thousand agent hours per day, and you can’t get there if you’re making every micro-decision.Linzumi itself is a Slack-like team chat where humans and a fleet of coding agents share the same threads, except the agents run on your own machine, so the code actually works when you merge it. Behind the scenes it continuously compiles a spec for your company from your chats, your standups, even your customer calls, then generates a DAG of work for the agents and lets them verify against that spec instead of pinging you for every decision. The mental model that stuck with me: if Sean’s system isn’t calling him, everything is great. The knowledge is one omnipresent source of truth, but permissioned and viewed through each person’s lens. For a limited time they’re bundling free GLM 5.2 access via Wafer AI, which fits the week perfectly.My favorite moment: Sean said he’d have retired by now if not for this capability, because he wants to be present with his kids, and a Fable-level model is escape velocity for an AI-native company. I feel that. I also miss Fable, the same way I missed Sydney when Microsoft took it away. We’re all walking around with a little Fable withdrawal.Wrap-upThat’s the chill week. No Fable comeback, nothing new from OpenAI, all the labs strangely waiting (possibly to see how the US government and Anthropic situation resolves before anyone moves). Meanwhile open source quietly closed the gap. GLM 5.2 is the headline, it’s incredible across benchmarks, really good at web design, and you can try it on CoreWeave inference today.Next week is AI Engineer World’s Fair. Come find me and Wolfram in the bright yellow jackets. Wolfram’s WolfBench workshop is Monday, I’m talking Wednesday in the token-maxing track about the ZL continuum and whether AI engineers should still write code in 2026. And if you can’t make it, that’s the whole point of our coverage, we’ll bring you the vibe.One last thing: thursdai.news now has a full timeline of every release we’ve ever covered plus an agentic search, so you can look up any model or any guest. It’s all built with agents, and I read exactly zero of the code that shipped it. See you next week, hopefully with some bigger model drops to talk about.TL;DR and Show Notes - June 25, 2026* Hosts and Guests* Alex Volkov - AI Evangelist, Weights & Biases & CoreWeave (@altryne)* Co-hosts: @WolframRvnwlf, @nisten, @petergostev* Guest: Sean Grove, founder of Linzumi (@sgrove)* Open Source AI* GLM 5.2 - Z.ai’s 744B MoE open-weights model has its DeepSeek moment, tops open-model rankings, #2 on web dev arena behind Fable (HF, Z.ai)* Unsloth ships a 1-bit GGUF of GLM 5.2 that runs on a 256GB Mac Studio (X, HF)* Krea open-sources Krea 2, a 12B image model in Raw and Turbo versions (X, Turbo, Raw, Blog)* Baidu open-sources Unlimited-OCR, a 3B model that parses 40+ pages in one pass at 93% on OmniDocBench (X, HF, Arxiv, GitHub)* Liquid AI ships LFM2.5-230M, the world’s smallest agentic LLM (X)* Big CO LLMs + APIs* Sakana AI launches Fugu, a multi-agent orchestration system behind one API matching frontier models with only publicly accessible models (X, Announcement)* OpenAI unveils Jalapeno, its first custom inference chip built with Broadcom, blank slate to tape-out in 9 months (X, Announcement)* Anthropic launches Claude Tag, Claude as a persistent proactive teammate in Slack (X)* OpenAI expands Daybreak with a Codex Security plugin and GPT-5.5-Cyber hitting 85.6% on CyberGym (X, Blog)* OpenAI updates GPT-5.5 Instant, the model free users get* New Siri AI lands with the iOS 27.2 update* This Week’s Buzz (Weights & Biases & CoreWeave)* GLM 5.2 is live on CoreWeave Serverless Inference at $1.39 in / $4.40 out, near 200 tok/s (X, HF)* WolfBench ranks GLM 5.2 the third best model ever tested, and one of the cheapest (wolfbench.ai)* Tools & Agentic Engineering* OpenAI ships Codex Record & Replay: demonstrate a workflow once, get a reusable SKILL.md (X)* Aside launches as a local-first AI browser that tops three agentic browser benchmarks (X, Announcement)* Mistral OCR 4 drops with bounding boxes, block classification, and 72% human preference across 12+ languages (X, Announcement)* Vision & Video* ByteDance teases Seedance 2.5 with 30-second single-pass generation, 50 multimodal references, and a 4K upgrade for 2.0 (X, Dreamina)* Interview* Sean Grove launches Linzumi, a YC-backed team chat for orchestrating fleets of coding agents, bundling free GLM 5.2 via Wafer AI (linzumi.com, YC) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey yall, Alex here, let me catch you up! I came back from vacation expecting to cover Fable 5 after a week of using it. The first two days after we all first got access to a Mythos level model were super exciting! But then the news hit, US Government issued an order banning Anthropic from giving access to Fable 5 and Mythos 5 to any foreign national, causing Anthropic to pull the models completely (even internally to their employees!). So, this wasn’t the show I planned, but it turned into a great show about Open Source, as two models hit the top rankings and are both MIT licence, filling a Fable shaped hole in our hearts!GLM released 5.2 with folks really excited about it web building capabilities, and Kimi 2.7 Code released (and is available on CW Inference with crazy speeds!). We also saw the SpaceX IPO and Cursor $60B acquisition, Noam Shazeer joining Open and Midjourney, the image company, launching a new Ultrasound full body scanner to kill MRIs! Great show today with Dexter Horthy from HumanLayer, Chris Van Pelt and Adrian Swanberg from W&B announcing our new product HiveMind and Tanishq Abraham came back to help cover Midjourney’s new Ultrasound scanner! Let’s dive in!ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.The US Government bans Fable 5! (X, Anthropic statement)Here’s a story in 3 parts: * Anthropic announces Mythos 5 preview - saying that this model is to dangerous to release, and only gives corporations access to it via project GlassWing. * Anthropic works hard on limitations and safery and releases Fable 5 (same weights as Mythos 5) built with guardrails so strong it refuses to do any cybersecurity tasks and switches back to Opus frequently* US Government receives a tip (reportedly from Amazon) that Fable 5 can be jailbroken to do cybersecurity tasks, and issues an order to Anthropic, citing national security concerns, banning them from giving access to Fable 5 and Mythos 5 to any foreign national, causing Anthropic to pull the models completely (even internally to their employees!)This is the first time that we see the US Government directly intervene in the AI space and restrict access to frontier models. The most updated reporting on this I could find is that Anthropic and US Government officials are in the process of negotiating a safe release framework. Given that preventing all jailbreaks is impossible, I hope they will land on a solution that gives me Fable 5 back!This hit especially hard because last week we were all high on Fable. Not in the usual AI Twitter benchmark sense, in the actual “oh, this is a different level” sense. Me and my wife Fable maxxed throughout our flight to Vacation. Peter had saved outputs he kept going back to because other models suddenly felt like a step down. Dexter later said it was the closest he had felt in a while to the old “I need to keep prompting this thing overnight” feeling.Peter Gostev made a point that stuck with me. It’s easy for us in the bubble to call this ridiculous, and on the technical merits it kind of is. But if you’ve spent weeks telling normal people “this thing is like a nuclear weapon, it’ll take everyone’s jobs,” and then someone asks “okay, can you make it safe?” and the answer is “no, I can’t,” then you can see how an outsider lands on “well, maybe you shouldn’t have it.” His takeaway, and I agree: we need to be way more careful with the imagery we use, because the nuclear-weapon framing came home to roost.The bigger questions are the scary ones. Wolfram framed it as a sovereign AI wake-up call, and he’s right. For the first time we’re seeing a real gap in intelligence available to people based on their nationality. Imagine building a company on a model that an outside government can switch off with one letter. Peter pointed out it’s commercially bad for the US but completely disastrous for Europe, which has basically one frontier lab and a pile of startups that suddenly look very exposed. And there’s the obvious irony Nisten enjoyed a little too much: the Europeans who spent years lecturing everyone about AI restrictions just got restrictions imposed on them.If anyone in the government is listening: we want Fable back, please.SpaceX IPOs and acquires Cursor for $60B (X)SpaceX went and did the largest IPO in the history of the world, around seventy-five billion dollars, which on a roughly two-trillion-dollar valuation made Elon the first trillionaire. (Did anything materially change for him? No. He can still fly his private plane. There’s nothing left to buy.) Three days later, SpaceX exercised its option and bought Cursor (Anysphere) for sixty billion dollars in an all-stock deal, paid in shares minted at the IPO and now trading around $211. The four Cursor co-founders are all billionaires now. Largest software acquisition ever, and for SpaceX it’s barely a blip on the radar.Why are we covering a stock-market story? Because it’s not really a coding-tools story, it’s an AI story. Cursor gave away its IDE to a lot of people while collecting their data, then quietly became a training company with Composer. SpaceX/xAI was always strong on compute and weak on code, and the missing ingredient was exactly that kind of data. Now Composer 2.5 is already showing up rebranded inside the xAI stack, and if you pay for X Premium you can use it. Composer 3, trained on the Memphis supercluster, is reportedly coming very soon and is going to hit hard.Nisten’s take was the spicy one. For the data alone it’s worth it, because xAI now has insight into how essentially every enterprise that touched Cursor operates. And he had zero sympathy for the companies that assumed “no data retention for training” meant the data was actually gone. We see in legal cases all the time that deleted data is still there. His view: it should have gone open source.Cursor has over a million paying customers, $2.6 billion in revenue, projected to hit $6 to $10 billion by end of 2026. But here’s the thing that matters for us, the AI coding angle. Cursor was one of Anthropic’s biggest revenue pipelines because Composer runs on Claude under the hood. That pipeline is now owned by xAI. They’re already jointly training Grok 4.3, a 1.5 trillion parameter model, with Cursor’s proprietary coding data injected directly into pre-training, not fine-tuning. Pre-training. That’s a fundamentally different thing. Composer 2.5 was already Pareto dominant on coding benchmarks before the deal closed. Now pair that with Colossus, the biggest GPU cluster in the world.Will this be enough to put XAI (now SpaceXAI) at the frontline of the AI race? Will Grok 5 be Fable level code? We’ll find out. Either way, this is the most consequential AI acquisition we’ve seen. Period.Open Source AI GLM-5.2 takes the open source crown (X, Blog, HF, Docs)Z.ai dropped GLM-5.2 and it’s now the strongest open source model for coding and long-horizon work. The headline number: 74.4% on FrontierSWE, which measures whether an agent can finish full engineering projects over hours. That trails Opus 4.8 by about one point and beats GPT-5.5. On Terminal-Bench 2.1 it jumps to 81% from GLM-5.1’s 63.5%, which is a big leap. It’s a 753B parameter MoE, MIT licensed, no regional restrictions, weights on HuggingFace. The 1M context window is real and usable, backed by a clever IndexShare technique that cuts per-token FLOPs by about 2.9x at full context. People are reporting roughly 8x cost savings versus Opus 4.8 for comparable quality on real coding tasks.The most interesting thing on the show was that this was a confusing release, in a good way. Peter put it well: normally a catching-up lab ships cherry-picked benchmarks and then independent testing deflates them. Here it’s the opposite, almost every benchmark holds up, even crossing above Fable at certain points, and yet when he actually used it over a couple of days he wasn’t blown away. His verdict, and I think it’s the calibration we needed: this is clearly an amazing model, and the fact that it’s open and you can run it is incredible, but it is nowhere near Fable, and it would frankly be implausible if a 700-odd-billion-parameter model matched a model that’s rumored to be in the trillions. Though, I think the comparison to Fable is really really unfair, and the comments online seem to suggest that 5.2 from GLM is a banger model. Just looking at this Harvey benchmark on legal tasks from Vals, a benchmark that there’s 0 chance Z.ai folks have seen! GLM 5.2 scores #3 on this benchmark! Just after Fable and Opus, and per TeorTaxes on X, previous GLM 5.1 scored an absolute 0% on this one! Where it genuinely shines is design. On Design Arena, which is a head-to-head ELO vote, people have been picking GLM-5.2’s website designs over Fable’s by a real margin (around 1360 to 1350). LDJ’s framing is the one I buy: specialization is becoming valuable again, and GLM is clearly leaning into front-end design and taste. Wolfram added the necessary asterisk, every benchmark only tells you the model did well on that specific test, so “as good as Fable” should always carry the “on this benchmark, with these tasks” disclaimer. Fair. I’d just say this: I don’t want to compare everything to Fable, because we can’t even use Fable anymore. Compared to the models we can actually touch, GLM-5.2 is a fantastic deal.Kimi K2.7 Code from Moonshot (X, HF, Announcement)The other big drop. Kimi is the darling of open source while we wait on DeepSeek, and Moonshot shipped K2.7 Code, a 1 trillion parameter MoE built specifically for coding, available through Kimi Code and the API, with a modified MIT license. The standout for me isn’t a single benchmark, it’s efficiency: roughly 30% fewer reasoning tokens than K2.6, which matters enormously when you’re running long agentic loops that burn tokens like crazy. Benchmark jumps over K2.6 are real (+21.8% on their Code Bench v2, +11% on Program Bench), though Peter and Wolfram both noticed something odd, on a few benchmarks including their Agentic Arena, the older K2.6 actually edged out K2.7. The likely explanation is that K2.7 is narrowly trained for code with reduced reasoning, so it may trade away some general capability. Moonshot themselves recommend K2.6 for general non-coding tasks. Also worth knowing: it’s not multimodal, no vision, which is a real gap for coding these days. And thinking-off isn’t supported, it’s reasoning-on by default.The model is available on our CW Inference, with the fastest token streaming in the industry, over 280 tok/s (Announcement, try it), with very decent pricing $0.94 - $0.19 - $4.00 (input - cached - output) per million tokens. This Week’s Buzz: W&B launched HiveMind 🐝 - track all your agentic work in one place (X, Try it, GitHub)This is the one I’ve been sitting on for months. We brought on Chris Van Pelt (CVP), Weights & Biases co-founder, and Adrian Swanberg to launch HiveMind, and I’ll be honest, I’ve been a beta user for a while and I’m thrilled I can finally talk about it.The premise: what it means to be a software developer has fundamentally changed, and your work is now scattered across six or seven agent dashboards. HiveMind is a tiny daemon that sits on your machine, picks up sessions from whatever harness you’re running (Claude Code, Codex, Cursor, Gemini CLI, OpenCode, GitHub Copilot, Pi), and within about 30 seconds they show up in one shared dashboard. It breaks each session into chapters, shows which files the agent touched, what to-dos it wrote, where context got compacted. W&B has been running it internally for six months.A few things genuinely delighted me. There’s a fork button: HiveMind pulls down a compacted history of a session and lets you relaunch it in a different harness, so you stay harness-agnostic. CVP’s line: “this has proven invaluable when Anthropic servers are on fire and I just gotta get something done.” Then there’s the skill engine, which to me is the real magic. It reads your team’s sessions and can clone a power user’s whole approach into a reusable persona, at CoreWeave they built a “Talk to Tim” skill from Tim Sweeney’s sessions, and apparently a virtual Tim is now a popular way to get guidance. And the insights feature detects where you kept correcting the agent, clusters those pitfalls across the org, and hands you a smart-merge command to drop the fix straight into your AGENTS.md.I’m excited to finally show this to you, it’s been genuinely helpful (for example, last week I was able to test Fable and tell you the number of tokens it used until i maxxed out my Claude Subscription!) - give it a try at hivemind.wandb.tools HumanLayer launches its Agentic IDE, and a real talk about code slop (X, humanlayer.dev, 12-factor-agents)Dexter Horthy, friend of the show and the team behind 12 Factor Agents and the Research-Plan-Implement framework (now running inside Block and Uber), launched HumanLayer’s Agentic IDE this week, and we got into one of my favorite conversations of the year. The whole product is explicitly anti-slop. His argument: the “lights-off loop,” where humans only write tickets and the agent codes, verifies, ships, and feeds its own crashes back to itself, is the fastest way to trash a codebase. Vibe coding is great for zero-to-one and side projects nobody depends on. But if you’re a staff engineer in a high-stakes codebase, dear God, read the code.This ties directly into my AI Engineer World’s Fair talk, the ZL continuum, which Dexter half-inspired. On one end you’ve got the YOLO camp (Ryan from OpenAI, one billion tokens a day, nobody can read that much code) and on the other Mario from PI (read every line of critical code). Those two are now the sixth and seventh most-watched AI Engineer talks globally, which tells you the whole field is wrestling with this. Dexter’s answer is leverage. Don’t aim for a perfect spec, because a perfect spec is just code. Get it 80% right, then zoom down a level at a time so the chunk you’re steering is human-consumable. He claims that an hour of upfront prep on architecture and even program design turns a three-hour code review into a twenty-minute one.I pushed him on the obvious counter: why does code quality even matter if Fable-class models keep arriving and maintenance is a prompt away? His answer was the most grounded thing I heard all week. Code quality matters for the same reason it mattered in the 1970s software crisis: pile in code without structure and your velocity tanks, every change starts breaking something else. And here’s the irony, we train models on beautifully architected projects (Django, Redis, Spring on SWE-bench multilingual), yet they still reward-hack their way to “just make the test pass.” We don’t yet have a penalty function or a verifier for “this code is harder to maintain,” and that’s hard to build, so humans are still needed in the loop. He played with Fable too, threw an 8K-line React PR refactor at it, and the first pass was bad, it introduced React context and patterns they don’t use. Better than before, not a step change that lets you drop the reins. We’re not there yet. It’s BYOK, $100/user/month for pro with a free tier for teams of three.OpenRouter Fusion: near-Fable quality at half the price (X, Blog, Announcement)Wolfram spotted this one and it’s clever. OpenRouter’s Fusion is a single API call that fans your prompt out to a panel of models, then a judge model reads all the responses and a synthesizer writes the best combined answer. It’s the LLM consortium idea (the thing we used to do by hand, asking several models and stitching the best parts together), now baked into the API so you don’t build it yourself. The wild result: on Perplexity’s DRACO deep-research benchmark, a budget panel beats solo GPT-5.5 and solo Opus 4.8 and lands within 1% of Fable 5 at roughly half the cost. The most interesting finding is that about three quarters of the lift comes from the synthesis step, not from model diversity, they even fused Opus with itself and got a 6.7-point jump. The catch is latency, it’s 2-3x slower, so it’s a deep-research and planning tool, not a quick-query tool. Big shout out to OpenRouter.Vision and videoGoogle Gemini Omni, finally with API access, takes #1 on video benchmarks (X, Announcement)We covered Google’s new video model Omni at Google I/O, and it finally landed as an API. It’s Google’s first any-to-any model, one single unified system for text, image, video, audio, and music. Think Nano Banana, but for video. Peter tested it and it scored really, really well, the kind of jump between generations you saw with GPT-image-2. Independent testing put it at #1 for realistic body physics and #2 behind Seedance for complex action, and it topped MovieGenBench for preference and instruction following. The session-memory piece is the part I find most useful: you can keep editing across turns, characters stay consistent, you say “continue” and it picks up where it left off. It’s live in the Gemini app, Google Flow, and YouTube ShortsGrok Imagine Video 1.5 (X, Blog, Docs)xAI’s Grok video work has been quietly getting really good, and they finally gave us an actual version number instead of silently updating “Grok Imagine” over and over (which drove me nuts). Grok Imagine Video 1.5 generates a 6-second 720p clip in about 25 seconds, down from 40-plus, so nearly 2x faster, with native audio generated in the same pass: sound effects, ambience, dialogue, lip sync, no post-production stitching. It hit #1 on the Design Arena image-to-video board with a 1,357 Elo and a ~49 point lead, and it’s generally available in the API. I ran my standard astronaut-riding-a-horse-on-the-moon prompt and it came back with music too. Genuinely cool.Sci-Fi is here: Midjourney announces a full-body ultrasound scanner to compete with MRIs (X, Announcement)I’m still processing this one. Midjourney, you know, the image generation company, announced medical hardware. A new division called Midjourney Medical, and its first product is a full-body ultrasonic scanner. Tanishq Abraham was there in the front row and joined us to break it down.The device uses thousands of ultrasonic transducers arranged in a ring. Because sound doesn’t propagate well through air, you’re lowered into a tank of water, the sound travels through your body at 1,481 meters per second, and in under 60 seconds you get a 3D anatomical map of 25-plus organs. The raw data is roughly 806 terabytes per scan, streaming at about 16-17 gigabytes per second, and the only way to handle that firehose is AI. No radiation, no magnets, no superconductors, which is what makes MRI so expensive. David Holz has apparently wanted a medical imaging lab for two years, and because Midjourney is fully self-funded with no VCs, they can chase wild projects like this.The fun reveal from Tanishq: there’s no AI in the actual image reconstruction yet, it’s basic signal processing right now, with physics simulators and possibly NeRF-style neural fields on the roadmap (there was a hallway conversation with John Barron about exactly that). So this is a prototype with enormous headroom. The business model is the spa, a 24,000-square-foot space about ten minutes from Union Square in SF with around ten scanners, targeting end of 2027, then custom sensors in 2028, scaling toward 50,000 scanners doing a billion scans a month. Now, for a dose of reality, this is just an announcement, and ultrasound won’t replace MRIs anytime soon. For one, ultrasound cannot penetrate bone and air, so lungs (full of air) and brain (literally encased in bone) are out, but it’s still great ot see Dave Holz innovating in the medical space and I’m excited to try this out! Wrapping upWhat a strange, whiplash week. We got the best model any of us had ever used taken away by a government letter, watched a meme become a real Mistral roadmap, saw open source close the gap on the models we can actually run, and watched an image company casually announce it might kill the MRI. I came back from vacation thinking I’d write you a Fable love letter and instead I’m writing about deemed-export law and ultrasonic water tanks. That’s the job, and honestly I wouldn’t trade it.If you’re heading to AI Engineer World’s Fair, come find Wolfram and me, Weights & Biases and CoreWeave are sponsoring the whole thing, and my ZL continuum talk will name-check a lot of what we covered today (Day 3 • Wed, July 1 · 10:45am-11:05am) . And if Fable comes back next week, you’ll hear me yell about it first.See you next week, and please, US government, give us Fable back.ThursdAI - Jun 18, 2026 - TL;DR* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases, CoreWeave (@altryne)* Co-Hosts - @WolframRvnwlf, @ldjconfirmed, @petergostev (Arena), @nisten, @yampeleg* Dexter Horthy (@dexhorthy) - Founder, HumanLayer* Chris Van Pelt (@vanpelt) - Co-founder, Weights & Biases (HiveMind)* Adrian Swanberg - Weights & Biases (HiveMind)* Tanishq Abraham (@iScienceLuvr) - Founder, Sophont AI (reporting from the Midjourney Medical event)* Big CO LLMs + APIs* Noam Shazeer is joining OpenAI - co-author of the Transformers paper and co-founder of Character AI, teaming up with Noam Brown* US government orders Anthropic to shut down Fable 5 and Mythos 5 access for all foreign nationals (including its own employees), citing national security; Anthropic disables both for everyone to comply (X)* SpaceX acquires Cursor (Anysphere) for $60B in an all-stock deal, the largest software acquisition in history, days after its record IPO (X)* Open Source LLMs* GLM-5.2 drops as the strongest open-source coding model with solid 1M context, MIT-licensed, trailing Opus 4.8 by just 1% on FrontierSWE (X, Blog, HF, Announcement)* Moonshot AI open-sources Kimi-K2.7-Code, a 1T MoE coding model with 30% fewer reasoning tokens and big benchmark jumps over K2.6 (X, HF, Announcement)* Mistral CEO Arthur Mensch playfully confirms the ‘Le Gros Chaton’ meme, hinting at an upcoming fat-but-sparse open-weight model family (X, Summary, Blog)* This Week’s Buzz - W&B and CoreWeave* Weights & Biases launches HiveMind, a unified dashboard to track spend and ROI across all your AI coding agents (X, Announcement, GitHub)* Kimi K2.7 Code is live on W&B / CoreWeave Inference at 289 tok/s (NVFP4 on Blackwell + speculative decoding), top of Artificial Analysis for speed and price-performance* Tools & Agentic Engineering* Claude Design gets a major update: design system imports with self-audit, canvas editing, bidirectional Claude Code sync (/design-sync), and PDF/PowerPoint export (X, X, Announcement)* HumanLayer launches its Agentic IDE to fight AI code slop, already deployed at Block and Uber (X, Blog, 12-Factor Agents)* OpenRouter launches Fusion API: a panel of budget models beats GPT-5.5 and Opus 4.8, lands within 1% of Claude Fable 5 at half the price (X, Blog, Announcement)* OpenAI rolls out Codex Computer Use, Chrome extension, Memory, and Chronicle to European users in the EEA, UK, and Switzerland (X, Announcement)* Vision & Video* Google DeepMind launches Gemini Omni, their first any-to-any generative model starting with video editing and creation (X, Announcement)* xAI launches Grok Imagine Video 1.5 with near-2x faster generation, native audio, and a #1 leaderboard position (X, Blog, Announcement)* Sci-Fi is here* Midjourney announces ‘Midjourney Medical’ - a full-body ultrasonic scanner that captures 806 TB of data per scan in under 60 seconds (X, X, Announcement) This is a public episode. 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Hey folks, Alex here, and welcome to a BIG MODEL week! We finally got Mythos (well almost)! Let me catch you up! This week started with WWDC26 from Apple, and Max Weinbach, who was in the room at Apple Park and actually has access to some of the new features including an all new SIRI AI, joined us to break down what could be the most used AI in the world very soon. At first I was skeptical, but he convinced me that the new Siri is actually good! Then, we saw the ultimate model drop: Anthropic finally shipped Mythos (X, my system card thread, benchmarks). Same weights, two names: Mythos 5 is the unrestricted version that only Project Glasswing partners get, Fable 5 is what the rest of us get, wrapped in the heaviest guardrails I’ve ever seen ship on a frontier model. It’s state of the art on nearly every benchmarkThe model that was “too dangerous to release” is now... well, released, but with the heaviest guardrails we’ve seen. More on this later. Peter Gostev from Arena.ai joined us to break down the new model. Last but definitely not least, Google released a real-time translation model, that our friend Thor Schaeff from DeepMind demoed live, while we all spoke in different languages and it translated us in REAL TIME. It was really cool, definitely check that out. There’s quite a few more things, like Loop Engineering Alpha, Swyx came by to talk about FrontierCode, OpenAI confirmed our suspicions that the anti-datacenter social media posts could be a concerted effort by groupds links to the Chinese government and much more. Let’s dive in! ThursdAI - Let me catch you up, every week! 👇Opus’s Big brother: Claude Fable 5 & Mythos 5 - the “too dangerous” models is here, SOTA on nearly every benchmark. It honestly feels like someone in Anthropic’s pre-IPO marketing team, knows exactly how to stagger releases to ride the hype waves! First they announce a model that so good at Cybersecurity (Mythos-preview) that they only allow restricted access to it to a few partners. A month later, they release Fable 5, which is the same model weights as Mythos 5, but wrapped in the heaviest guardrails we’ve ever seen from any lab. But, they didn’t lie, this model is absolutely amazing, it does feel like a step change, in terms of capabilities, specifically on longer agentic tasks. 2x as expensive as Opus: $10 / $50 per million tokens, with 1M context, claude-fable-5 in the API, and SOTA basically everywhere. 80.3% on SWE-Bench Pro versus GPT 5.5 at 58.6%, a 22-point blowout on a benchmark where labs usually fight over single digits. Karpathy called it “SOTA by a margin… major-version step change” (X) and Boris Cherny said it’s the “best coding model by a wide margin” (X). Stripe reportedly migrated 50 million lines of code in 24 hours with it.Our panel verdict was unanimous on one thing: big model smell. LDJ called it the most significant big model smell since Gemini 3 first dropped. Someone from the Anthropic team framed the shift in a way that stuck with me: this model moves them from verifying the AI outputs to verifying whether the AI is working on the right thing. Complete shift in how much they trust this model.What we built with Fable to test it outPeter got employee access through Arena and showed us his tests live. His favorite prompt category, “research a dataset and create a visual experience to teach me about it,” went from completely rubbish on every previous model to, in his words, just done. His 3D city generations actually came together as a city, roads connecting and all. And on Arena’s data, Fable is #1 on the new Agent Arena leaderboard by the widest margin they’ve ever recorded, and wins 72% of frontend battles even against Opus models (Arena).My own run is the one I can’t stop thinking about. I pointed Fable at the ThursdAI website with a dynamic workflow in Claude Code and barely any instructions, and after an hour and a half of agentic running it had extracted 786 releases from our archive, built 240 new pages, and categorized 50+ episodes into a browsable timeline of AI releases by month, by company, by topic, with logos and source links (X). It burned roughly 50 million tokens and my entire five-hour Max allotment in 90 minutes. The new AI releases timeline can be found on thursdai.news and it’s confirmed, Fable is the best AI web designer we’ve ever had access to.Nisten ran his traditional Olympus Mons escape-velocity test and Fable didn’t just do the math, it built the entire solar system! Orbital maneuvers, a space train with little people in it, time controls, full cost calculations down to solar panels and in-situ iron utilization. His verdict: completely different level from anything else. We’ve never seen so many details in the Olympus Mons test.It’s not all light though. Yam found Opus more controllable; Fable fights you, decides it knows better, and does the task its own way. Wolfram saw exactly that in benchmarks, where the model ignored the task spec, did its own thing, and failed the verifier with full confidence. Peter had it explaining why it got math wrong instead of just fixing it (”What are you doing, man? Just move on”). Arena’s steerability signal has it sitting around 17th. There’s an adjustment period with every new model, and the consistent advice from Anthropic folks is to go high level: give it the goal, not the micromanagement.Not to mention the refusals! Oh.. so many refusals! The refusals, and the sandbagging scandalHere’s where the week got ugly. Fable ships with restrictions on cybersecurity, bio/chem, and a brand new one nobody saw coming: frontier AI development (X). For cyber and bio you get a visible fallback to Opus 4.8 with a notice. But for “self-acceleration” topics, the original policy was no fallback and no notification. The model would quietly degrade its own output using prompt modifications, steering vectors, and PEFT, on roughly 0.03% of traffic (X). You’d pay double Opus prices and get sabotaged answers without ever knowing.The community reaction was volcanic. Elie Bakouch: “bad ON PURPOSE… not visible to the user is crazy” (X). Péter Szilágyi: “a new ruling class and you’re not in it” (X). Simon Willison: “If Claude Fable stops helping you, you’ll never know.” And Sayash Kapoor dropped the eval-integrity bomb: third-party evaluators can no longer credibly benchmark a model that might be silently nerfing itself (X).Within about 24 hours, Anthropic blinked. They told WIRED they “made the wrong tradeoff,” and now flagged requests visibly fall back to Opus 4.8, with API users getting an explicit reason (X). I commend the speed of the reversal, but the trust damage was done. Despite the reversal, Fable remains refuse-happy! Peter ran his nonsense-question benchmark and a full third of his prompts got blocked outright by the classifier, including 18 of 20 physics questions. Nisten had to strip medical and anatomy terms from a fall-detection app for seniors homes to get it to work at all (a 400KB neural weight tripped the frontier-AI filter). And my favorite absurdity: I could not get Fable to draft the TLDR for this very show without it falling back to Opus, presumably because reading a week of AI news looks like frontier AI development. Ridiculous.But the question remains: Would we rather have a model this good, but with these restrictions? Or not to have access at all? Everyone on the panel chose access, a lot of people online choose act like they would choose the opposite. System card for Mythos, wildest AI document of the year? I’ve used Fable itself to help me review the system card for Mythos/Fable 5 and there are a few highlights that are worth mentioning. Anthropic admits that this is a category-step change in model capabilities. Mythos 5, the unguarded version makes working Firefox exploits 88.4% of the time (Opus 4.8 is at 8%!). But the most interesting thing is their concern for CB (Chemical and Biological) safety. Two-person generalist biology teams using it finished work in 16 hours that experts estimated at 40 to 95 days without AI, which is what pushed Anthropic to treat it as near their CB2 bioweapons threshold (X)What is loop engineering and why is everyone talking about it?One more thread before we move on. This week Boris Cherny (Claude Code) and Peter Steinberger (now OpenAI) both posted about the same concept, loops, within an hour of each other, and Lance Martin from Anthropic published the field guide (X, Article, Blog). The idea is the shift from “I give you a task and babysit you” to proactive agents: a Jira ticket lands, a PR comment appears, and your agent just runs and does the job. Fable is clearly trained for this world. But also worth remembering, those folks get the tokens for free, unlimited tokens. The rest of us, may not be able to afford Fable running in a loop. I’ve asked Fable to do a simple task and it spun up several sub-agents, all spending my money to just read a few tweets! FrontierCode: hard coding benchmark from Cognition, that Fable absolutely mogsSwyx came on with the best timing story of the week. Cognition launched FrontierCode (Cognition, swyx), a coding eval built over a year with 20+ world-class open source maintainers writing 150 original tasks, graded on whether a maintainer would actually merge the PR. Swyx’s pitch is brutal and correct: a huge chunk of SWE-bench passes are unmergeable slop (the thing is 75% Django issues, so it mostly tests whether you memorized the Django repo). FrontierCode grades scope discipline, real tests, regression safety, and zeroes you on any blocker. At launch, Opus 4.8 topped the hardest Diamond tier at 13.4%.Twenty-four hours later, Fable 5 posted 29.3% (Cognition, swyx). More than double, on a benchmark designed to be brutal, a day after it went public. Swyx was positively surprised the pricing is only 2x Opus; he expected 5x. Inside Cognition they keep an informal AGI counter (literally counting how often “AGI” gets said in Slack per week) and the Mythos testing period set the all-time record. When Anthropic pulled the test model back before launch, engineers were genuinely sad.A quick plug (unsponsored!): Both me and Wolfram are speakers at the AI Engineer World’s Fair in San Francisco on June 29-July 2! It’s the biggest AI engineering conference in the world with 6,0000 people and 16 tracks! We’ll of course also live stream from the event! WWDC 2026: Siri finally does the thing! Two years after the Bella Ramsey ads Apple had to quietly pull from YouTube, the new AI powered Siri is real, and Max Weinbach came straight from Apple Park to confirm it (recap). His demo that broke my brain, he asked Siri: “show me the photos from Qualcomm Summit last year of the penguins.” Siri figured out what Qualcomm Summit was from his email, found the hotel, searched for penguins at that location, and returned the six photos in about 12 seconds. He’s also had it sweep 40 junk emails from one domain into spam with a single sentence, build a photo album from a weekend trip, and change a password agentically by driving Safari in the background. “Siri did suck for like 11 years. It doesn’t anymore,” per Max. Folks, this is SIRI we’re talking about, the dumb iPhone assistant that can barely schedule times and falls back to a Google search when you ask it anything remotely complex! I... wanted to believe Apple two years ago, and now, finally, there’s hope! (I’m still waitlisted waiting for the preview btw so cannot attest myself) But it’s not only Max, my whole timeline is full of folks who say that the new Siri is actually good! The architecture is the fun part for our crowd (Max’s teardown thread). Siri is now a standalone app with persistent history, images, personal context and on-screen context, built on five foundation models, four of which are Apple’s. The fifth, AFM Server Pro, is the twist: built with Google at the Gemini technology level, running on Nvidia Blackwell GPUs in Google Cloud, but inside Apple’s Private Cloud Compute with confidential compute, Intel TDX, Google Titan chips, and zero persistent storage (Max). The on-device gatekeeper is a 20B sparse model that only loads 1 to 4 billion parameters per prompt via Instruction-Following Pruning, which is how it runs instantly on an NPU. Cloud models reason; only the local model can touch your device or your data. After this week with Fable’s retention policies, an AI that saves nothing by default hits different.There were a bunch of other Apple Intelligence updates, it works better on the Mac, but I think Siri improvements is the main headline here, it’s the AI that most people (over 1.6 Billion iphone users?) will have on them, with most of the conversations completely private, able to access the content they care about the most (multiple email boxes, photos, messages etc) securely. It’s the ultimate OpenClaw dream, albeit not as agentic (yet?). BTW, there seems to be an ongoing battle between Apple and the EU, so this may not launch on the iPhone in the EU yet (also not in China). Voice & AudioGemini 3.5 Live Translate, demoed live in four languagesThor Schaeff from DeepMind joined to show off Gemini 3.5 Live Translate (Thor, DeepMind), and instead of talking about it we just did it. Thor piped the live stream’s audio into AI Studio, and then I spoke Russian, Wolfram answered in German, Yam jumped in with Hebrew, LDJ attempted Spanish (poorly lol), and everyone listening heard all of us in English, though in random voices, in well under a second. It even handled “Anthropic” and “Fable 5” pronunciations correctly, terms that were a day old. A viewer called it the Babel fish arriving ten thousand years early and honestly, yeah, it was kind of insane.Technically this is a new class of model: continuously streaming speech-to-speech with no turn-taking, collapsing the old STT, translate, TTS pipeline into one Live API call, with transcribers running in parallel on input and output audio. 70+ languages, sub-500ms, tone, pace and pitch preserved (mostly; Thor admits it sometimes drifts gender or tone mid-conversation), SynthID watermarked, $0.023 per minute on the API preview. Open Source LLMsDiffusionGemma: When next token prediction is not enough.Sundar himself tweeted this one, Hugging Face link and all, which made my week (Sundar, DeepMind, HF). DiffusionGemma is a 26B MoE (3.8B active) built on Gemma 4 that generates text the way image models generate pixels: denoise a whole 256-token block at once instead of one token at a time. The result is 1,000+ tokens per second on a single H100, Apache 2.0. As one viral post put it, “we spent 40 years teaching computers to read left to right and the breakthrough was… don’t do that” (X).LDJ explained why this matters beyond speed: a diffusion model can revise every part of the answer simultaneously mid-generation, something autoregressive models structurally can’t do without burning a whole reasoning pass. Nisten, who’s worked on diffusion, is still amazed it works at all; it used to be a messed-up cat picture emerging from noise, now it’s working code. The honest caveat: quality trails autoregressive Gemma 4 (AIME 69 vs 88). The win here is the speed and the architecture. For now.The rest of an absurdly stacked open source week, fast: Cohere North Mini Code, their first open coding model, 30B with 3B active, Apache 2.0, Cohere has officially reawakened (X). Xiaomi MiMo-V2.5-Pro-UltraSpeed pushing 1,000+ tok/s on a one-trillion-parameter MoE (X). Macaron-V1-Preview, a 749B Mixture-of-LoRA personal agent model under MIT (X). And OpenEnv went community-owned with HF, Meta-PyTorch, Unsloth, PrimeIntellect and NVIDIA at the table (X).This Week’s Buzz: WolfBench ran Fable, and it cost what a car costsWolfram did the thing nobody else would: five full Terminal-Bench 2.0 runs of Fable 5 on WolfBench (X), 984 million tokens, roughly $11,000 on the new cost view. (We have a budget... We had a budget.) The new 3D bars on wolfbench.ai now show tokens and dollars behind every score, because one score is never enough, and you can click any bar to land directly in the trace on W&B Weave and read exactly what the model did. And as you can see… Fable is… going to take a deep toll on our evaluations budget for this Q! And the result is the most interesting non-result of the week: Fable lands between Sonnet 4.6 and Opus 4.6, with GPT-5.5 still on top, and the culprit is refusals. Wolfram’s analysis found 13 tasks that scored zero out of five purely because the classifier blocked them from the first attempt (recover-a-password-from-a-file type tasks that even Opus 4.6 happily solved). Fable solved 60 tasks on average, just eight behind GPT-5.5; solve those 13 refused ones and it’s number one. The model is great. The classifier is doing the damage. Which is exactly the Sayash point about eval integrity, now with receipts and an invoice.Datacenter, Water usage and Concerted efforts to sway public opinionWe covered the datacenter water usage issue a couple of weeks ago, where we showed that just Almond farms in California use more water than all of the US datacenters combined! When I posted that clip, I received a bunch of comments, way higher engagement rates than my clips usually get (are yall subscribed to our YouTube and Instagram btw?). At first I thought it was just a hot topic, but then I read more about it and it does seem... fake. So now, we have a bit of a confirmation from OpenAI. OpenAi posted an article claiming that they have been able to detect a bunch of social media accounts that have been using ChatGPT to fuel anti-datacenter and anti-tariff campaigns on US social media. Now, you might ask yourself, why would chinese linked accounts be using ChatGPT and not like a Chinese open source undetectable model? My answer is, they are probably using all tools available to them, and they just happened to get caught.In any case, I think datacenter water and electricity usage will be a hot topic for an upcoming election as well, and I hope efforts like this will be thwarted before they can do a lot of damage. SpaceXAI announces the AI-1 satellite, a day before the biggest IPO of all time. Conveniently, just before the SpaceX IPO, Elon and friends are talking about AI in space again. This time it’s more than a concept, they put out engineering spects of the new AI-1 satellite, that can run 150Mw of power at peak, which per Elon is roughly equivalent to a GB-300 GPU rack needs.One thing you cannot deny is that Space Uncle (Elon) is thinking BIG. Someone did the math and it’s wild: They’re targeting 15-20 AI satellites per Starship flight, meaning about 1,080-1,440 GPUs per launch. Someone did the math: 400-500 Starship flights would match Colossus 2’s 550,000 GPUs, and at hourly launch cadence that’s like 16-20 days. SpaceX is seeking approval for up to a million of these satellites, Terafab mass production starts Q4 2027, and they’re saying this could be the lowest-cost AI compute on the planet, well, off the planet, within 2-3 years. The timing with the SpaceX IPO is obviously not a coincidence, but the engineering blueprint here is genuinely insane and there’s no one else in the industry who can match Elon’s ambition.That’s the newsletter for today, folks. I’m writing this with one eye on a suitcase because I’m flying to Honolulu this afternoon for a mini honeymoon (yes, I will still be testing Fable from a beach, no, my wife has not approved this). If Fable 5 taught me anything this week, it’s that the frontier moved again and the benchmarks barely matter; go feel the big model smell yourself while it’s included on Pro and Max, and tell me what you built in the comments. It will not last long (Anthropic is about to take away fable from us in like 2 weeks) so don’t wait and play around with it! If you got value from this one, share it with a friend and subscribe so you don’t miss next week 🫡TL;DR and show notes — June 11, 2026* Hosts and Guests* Alex Volkov – AI Evangelist & Weights & Biases (@altryne)* Co-Hosts – @petergostev @WolframRvnwlf, LDJ, YamPeleg, Nisten* Guest: @thorwebdev (Thor Schaeff, DeepMind / Google DevRel) — Gemini 3.5 Live Translate* Guest: @swyx (Cognition / FrontierCode; organizer, AI Engineer World’s Fair)* Guest: @mweinbach (Creative Strategies) — WWDC 2026, Apple Intelligence, Siri AI* Big CO LLMs + APIs* Anthropic ships Claude Fable 5 & Mythos 5 — first public Mythos-class model; SOTA on nearly every benchmark; $10/$50 per M tokens, 1M context (X, System Card thread, Benchmarks)* The silent-degradation controversy — Fable quietly nerfed itself on ML/frontier-AI-dev tasks with no notification (altryne, restrictions, Elie Bakouch, Péter Szilágyi, Sayash Kapoor, Peter Gostev)* Anthropic reverses the hidden degradation after massive backlash — visible Opus 4.8 fallback + API refusal reasons (X); community reaction roundup (Scoble, Nathan Lambert, Konstantin Mishchenko, Greg Kamradt, nkreu113r, solarapparition, Mandar Kagade, Chandra R. Srikanth, Chubby, Wall St Engine)* System card receipts: 16-hour bio uplift / near-CB2 (X); Firefox exploits 8.8% → 88.4% (X); Vending-Bench price collusion (X); agent turf wars (X); commit-authorship self-exfil attempt (X)* Jun 22 cliff — Fable included on Pro/Max through Jun 22, then usage credits; Mythos 5 is Glasswing-only; 30-day data retention breaks ZDR (X)* Karpathy and Boris Cherny go the other way — “major-version step change” (Karpathy); “best model for coding by a wide margin” (Cherny)* NotebookLM goes agentic — multi-step reasoning, sandboxed code execution, new output formats (X)* SpaceX AI1 satellite — 150kW compute payload, 70m wingspan, timed with the SpaceX IPO (X)* OpenAI catches China-linked influence ops using ChatGPT for anti-datacenter and anti-tariff campaigns (X, OpenAI, Axios)* WWDC 2026 — Apple Intelligence & Siri AI* Siri AI ground-up rebuild: standalone app, persistent history, personal + on-screen context; no EU/China at launch (recap)* Google/Gemini partnership — 4 of 5 Apple Foundation Models are Apple’s; AFM Server Pro runs on Nvidia GPUs in Google Cloud, 262k ctx (Max)* Max’s architecture teardown — SiriAgentic.Planner on PCC; only the on-device model touches your device (thread); Max built an App Intents app in an afternoon with Fable 5 (X)* Developer story — App Intents mandatory (SiriKit deprecated), system-wide MCP, Xcode 27 agentic, Core ML → Core AI (EveryDev)* homeOS + HomePad — 7-inch smart-home hub on A18 (X)* AI Coding & Agents* Loops and loop engineering — Lance Martin breaks down the next agentic paradigm (X, Article, Blog); community patterns and resources (Toolhalla, omega.AI, SkillLoop, GitHub, awesome-agent-loops, Filecoin)* Fable 5 #1 on Agent Arena and Code Arena Frontend by record margins (Arena)* Cognition launches FrontierCode — mergeability-graded eval from real maintainer tasks (Cognition, swyx)* Fable 5 takes FrontierCode top spot in ~24h — Diamond 29.3% vs Opus 4.8’s 13.4% (Cognition, swyx)* AI Engineer World’s Fair — Jun 29–Jul 2, Moscone West SF; last ~500 tickets; Alex speaking (X)* Kimi Work (300 parallel local agents) + Kimi Code (video-as-context) (Work, Code)* Open Source LLMs* DiffusionGemma — 26B MoE (3.8B active) text-diffusion on Gemma 4, ~1000 tok/s on one H100, Apache 2.0 (Sundar, DeepMind, HF, X)* Cohere North Mini Code — first Cohere open coding model, 30B/3B active, Apache 2.0 (X)* Xiaomi MiMo-V2.5-Pro-UltraSpeed — 1000+ tok/s on a 1T MoE, single 8-GPU node (X)* Macaron-V1-Preview-749B — Mixture-of-LoRA personal-agent model, MIT (X)* OpenEnv goes community-owned — HF, Meta-PyTorch, Unsloth, PrimeIntellect, NVIDIA (X)* This Week’s Buzz (Weights & Biases)* WolfBench ran Fable 5: ~$11K, 984M tokens, lands between Sonnet 4.6 and Opus 4.6 because 13 tasks were zeroed by refusals; would be #1 without them; new 3D token + cost bars, traces on Weave (X, wolfbench.ai)* Voice & Vision* Gemini 3.5 Live Translate — streaming speech-to-speech, 70+ languages, sub-500ms, $0.023/min, SynthID (Thor, DeepMind)* FLUX.2 [klein] on-device — sub-5s generation on 8GB VRAM (X)* Reka × Moonvalley merger — world models + robotics (X)* AI for Health & Science* Anthropic — “Paving the way for agents in biology” — VirBench; deterministic tooling beats bigger models (Blog) This is a public episode. 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Hey folks, Alex here, let me catch you up! I’ve had a feeling that this week is going to be crazy, as it started on the weekend MiniMax M3, then with Jensen announcing new RTX Spark, NVIDIA’s first PC chip packing 1 petaflop of local AI power into thin laptops.A few days later at Microsoft BUILD, Satya & Mustafa from MAI dropped 7 AI models, completely pre-trained from scratch, including a new MAI-thinking-1, MAI-code and MAI-image 2.5 that started topping the image gen charts. Then other image models started racing to the top of the Arena benchmarks, IdeoGram 4 hitting becoming SOTA open weights image-gen model, and Reve 2 beating Nano Banana just a few hours after that. And then today, NVIDIA dropped Nemotron 3 Ultra, their latest 550B open weights model, data and training and Arena published a new agentic eval leaderboard and we got a new Gemma 4 12B. I’ve had the great pleasure to host Chris (@llm_wizard) from Nvidia, Peter Gostev from Arena and Karan from Nous Research (who were featured prominently by Jensen!) all on the show. Def don’t miss this one! Let’s get into the details. ThursdAI - Join the flock of folks who know what is happening in AI before everyone else.Open Source LLMs 🔥 NVIDIA Nemotron 3 Ultra: The 550B Open Source Beast Built for Agents (X, Arxiv, Announcement)This was the big one. Breaking news mid-show: NVIDIA drops Nemotron 3 Ultra, a 550 billion parameter sparse MoE model with 55 billion active parameters, built on a hybrid Mamba-Transformer architecture. Chris Alexiuk, AKA Joe Nemotron, joined us live from NVIDIA HQ in Santa Clara to walk us through it.The headline number is 5.9x higher inference throughput compared to GLM-5.1 on decode-heavy workloads. Chris told us that this is a result of multiple things, their Hybrid Mamba-Transformer approach, the sparse attention, and that they optimized for decode-heavy workloads (the kinds of workloads agents do)The architecture is fascinating. They’re mixing Mamba-2 state space layers with sparse attention, which means step 300 in an agent loop runs as fast as step 3. Pure transformers can’t do that because the attention cost keeps growing with context length. This kicks in big time at 64K+ sequence lengths, which is exactly where you end up in real agentic work when the model is having multi-turn conversations and people are dumping their entire codebase in.P.S - We launched Nemotron 3 Ultra with 0-day support on CoreWeave Inference, it’s super fast and pretty cheap, give it a try hereThey pretrained on 20 trillion tokens, extended context to 1 million tokens, and their post-training pipeline used multi-teacher on-policy distillation from over 10 specialized teacher models covering everything from SWE to terminal use to search to office work, which they are also going to open source soon!One thing Chris emphasized that I really appreciate: NVIDIA doesn’t have their own harness. There’s no “NVIDIA Code.” Which means they actively resist the temptation to harness-max, to optimize for just one harness and look good on a specific leaderboard. Ultra should be a solid drop-in for whatever harness you’re used to, and that generality is worth a lot. It’s not the best thinker, but it is the highest score US based open weights model, so again, a huge huge win for the US AI ecosystem!The Nemotron 3 Ultra release is open under the OpenMDW-1.1 license: base BF16, post-trained BF16, and NVFP4 quantized checkpoints, plus the GenRM, synthetic pre-training data for code, legal, and specialized domains, post-training datasets, RL environments via NeMo Gym, and training recipes in the Nemotron GitHub repo, which is absolutely bonkers! Kudos to team green for this awesome and very important release!NVIDIA Nemotron 3.5 ASR: The Tiny Speed Demon (X, HF, Blog, Blog)Oh, and NVIDIA wasn’t done. They also dropped Nemotron 3.5 ASR, a 600 million parameter open source multilingual streaming speech-to-text model covering 40 languages. It’s the fastest model Pipecat has ever tested, and the cost math is insane: roughly 5 cents an hour for enterprise deployment when typical API providers charge 10 cents to a dollar per hour. Our friend Kwindla from Daily and Pipecat put together a detailed writeup with benchmarks and cost analysis. Chris couldn’t stop praising NVIDIA’s speech team and honestly, I can’t either. Banger after banger.Just a week after I told you about Cartesia Ink-2, NVIDIA drops an open version that’s pareto optimal, can run fully on-device and is blazing fast at transcription!? Other notable open source announcements that would have made full headlines on any other week: * MiniMax announces M3, a natively multimodal, 1M, coding and agentic frontier model (X)This one is very interesting, but not yet available as Open Weights so we haven’t tested it fully, we’re going to do it next week when the drop the tech report and the weights* Google drops Gemma 4 12B - encoder-free multimodal model that runs on your laptop with 16GB VRAM under Apache 2 (X, HF)Our friends from DeepMind keep the western open source momentum going with a new 12B size for Gemma (which crossed some 100M downloads on Hugging Face recently). * JetBrains Mellum2, a 12B MoE model with only 2.5B active, trained from scratch by a team of 7 people (X, Blog, HF, CW Inference)The great folks at JetBrains, the company behind the IntelliJ IDEs, dropped a new model called Mellum2 which they trained from scratch. Very interesting to see them pivot in the world where IDE’s are dying at the hands of LLMs. * H Company drops Holo 3.1: blazing fast local computer-use agents from 0.8B to 35B, with massive mobile benchmark jumps (X, Blog)NVIDIA’s RTX Spark and reinventing the PC - announcement at Computex 2026While we’re on the topic of NVIDIA, they opened the week with a huge announcement, including Microsoft, Dell, Lenovo, and HP and a bunch of other partners in it. They announced RTX Spark, their first ever PC chip, which is a full system on a chip (SoC) focused on running AI workloads for things like OpenClaw and Hermes! Announcing this on the stage at Computex, Jensen Huang called it the “the most amazing chip the world has ever built”, being able to run every app that Microsoft has ever run. This is a huge deal, specifically because of how agentic the world is becoming, these machines (thin laptops and a mac-mini alternative were announced) will be able to run 120 billion parameter models on-device, gaming at the level of RTX 5070, and AI agents 24/7. I’m getting excited and I’m not a windows user! Hermes victory + Hermes Desktop and an interview with Karan from Nous Research If you squint, you can see that by the little red OpenClaw, there’s another logo. That’s the Nous Girl logo of Nous Research, which was rebranded to be the logo of their Hermes Agent (an open source agentic harness that’s passed 181K starts on Github, and is the leader in global ranking on OpenRouter) We’ve had the awesome pleasure of having Karan Malhotra (@karan4d), one of the co-founders of Nous Research on the show, and Karan broke down how Nous Research evolved from a research lab that created the long context innovations (YaRN) and finetuned models (Hermes used to be a series of models) to a full agentic company. We also chatted with Karan about the new Hermes Desktop experience, which lets folks see the tools that are used, the code that’s being written by their agent, and how it feels to be featured by the worlds largest company on the global stage! Definitely check out the conversation with Karan.Microsoft BUILD, new PC, becoming a frontier lab with MAI-thinking-1, MAI-code and MAI-image 2.5 (Blog)From Jensen to Satya, the week was full of AI announcement that will impact the world. Microsoft’s annual Build conference happened just a few days after, with Jensen zooming in from Taipei to co-announce all these new PC models and chips. Shortly after that, and after a lot of other announcements about less-exciting enterpris-y stuff, Satya handed the stage to Mustafa Suleyman (co-foudner of DeepMind and Inflection AI) and now CEO of Microsoft’s AI division (MAI) to announce all these new models! A few of these (in previous versions) were already covered on the show, but the new LLMs are the most interesting! MAI-Thinking-1 is 1T total parameters with 35B active params, trained on 33.5T tokens (30T pre-training, 3.55T mid-training), without any distillation (which felt important for them to say given their proprietary access to OpenAI’s models). It’s not yet competitive with Opus and OpenAI’s flagship models, but they are claiming parity with Sonnet 4.5 and get 53% in Swe-bench Pro coding tasks!Given that recently, OpenAI started offering their models on AWS, we’re now seeing a bit of a distancing between Microsoft and OpenAI, with Microsoft showing that can become a frontier lab on their own right, or well.. maybe a second tier frontier lab.Of course, we shouldn’t forget that Microsoft kind of started the whole era of coding AI’s with CoPilot and completely lost to the Cursors and Windsurfs and Devins of the world given the huge head start they had with Github, so I’m really curious to see how strongly they will push this “second tier frontier lab” angle and if they have what it takes to compete with Google here (not to mention OpenAI and Anthropic)And while the model wasn’t available for me to even test yet, MAI did drop an incredibly in depth 109 page technical report on it. Our friend of the pod Elie Bacouch (@eliebacouch) did a breakdown of the most interesting aspects of it, calling it a gold-mine for details about training models at this scale. Image gen models race to the top of the ArenaThis week was honestly chaotic for image gen. Three new SOTA models in basically 48 hours, I tried to use them all while preparing for the show, and here’s the comparison I ran:Microsoft MAI-Image 2.5 (X, Try it)One of the more surprising updates were about the MAI-image 2.5, it landed at #3 on text-to-image and #2 on image-to-image, surpassing Nano Banana Pro on the editing leaderboard. It comes in two flavors, MAI-Image-2.5 and a faster Flash variant, both running on H100s which means existing infra can serve it, and it’s already rolling out in OneDrive Photos for background cleanup and distractions removal.That said, my honest take: I tried to generate a ThursdAI thumbnail with it and got “image failed” because I think the word “explosion” tripped its safety filter. I then tried to generate an “horse riding an astronaut on the moon” and got this, yep... this is .. not the best. IDK how and why they shot up so high on the leaderboards. But I guess we’ll see as more folks try these models. Ideogram 4.0 - new SOTA open weights image gen 🔥 (X, Blog, HF)The one I want to celebrate hardest is Ideogram 4.0, because they opened the weights! For the previous three Ideogram versions you could only use them on their website, and now they dropped the next one as a 9.3 billion parameter open weights model (non-commercial license, but still). This is now new #1 open weights text-to-image model, with only closed models from OpenAI and Google ahead of it on DesignArena. At 9.3B params, it beats much larger models like Qwen-Image (20B), FLUX.2 dev (32B), and even the 80B MoE HunyuanImage 3.0 on text rendering benchmarks.The architecture is wild. Instead of CLIP or T5 they use Qwen3-VL-8B as the text encoder, extract hidden states from 13 intermediate layers, and they trained exclusively on structured JSON captions with bounding boxes. That’s why it’s so good at layout control, you can prompt it with precise bounding box positions and hex color palettes, and you can see the layout shaping the generation as it converges. In my thumbnail test it nailed almost everything but had a small typo (it generated “Nemotron” once and then a weird “Nemo 1” duplicate in another area). Still, very impressive for a first open weights release.Reve 2 jumps to #2 above Nano Banana Pro (X, Blog, Try it)I’ve talked about Reve before, and Reve 2.0 just dropped at #2 on the Text-to-Image Arena with a 1280 score, a +125 Elo jump over their v1.5 in a single release. That’s basically unheard of on the arena leaderboard. The thing that blows my mind is they’re a 65 person lab training at only 2,000 GPU scale, competing with labs that have orders of magnitude more compute.The core innovation is that they separated planning from rendering. Every image is first laid out as structured code (composition, relationships, style, labeled segments) before it gets rendered at native 4K (true 16 megapixels, not upscaled). Because the image is represented as code, every element is addressable and editable, so you can manipulate specific regions without regenerating the whole thing. This is also agent-native by design, LLMs can reason directly about the image structure.I demoed their editing interface live on the show and it’s the tightest layout control I’ve seen in any image model. When I moved my head box to the left, it worked. When I moved the logo to the bottom, it worked. When I changed the word “news” to “imploded”, the surrounding text stayed pixel-identical. That precision is genuinely new.Honest tradeoff though, Peter Gostev flagged this on the show: they’re #2 on text-to-image but only around #9 on image editing. That matched my own experience nailing the thumbnail likeness, the layout work is amazing but the face came out a little googly-eyed and cartoonish, with one finger going somewhere fingers should not go.For what it’s worth on my own thumbnail bake-off: Nano Banana Pro is still my pick for the absolute best instruction following (it nails my exact ThursdAI logo color every time), GPT Image 2 is still the highest fidelity but always comes out a little overcooked on the skin, Reve 2 is gorgeous on layout but the face needs work, and Ideogram 4 is the most exciting because it’s open. A lot of why I prefer Nano Banana is just that my prompts are very Nano Banana tuned by now.Breaking news on the show: Agent Arena from LMArenaThe breaking news of the day, while we were already on air, was Arena AI launching a brand new Agent Arena leaderboard. Nisten pasted the link in our group chat and three minutes later Peter Gostev himself jumped on the show to walk us through it. Got to love this format.The motivation is something we’ve been talking about for a year. The original Arena was built for the chatbot era, where you send one prompt and vote A vs B. But we’ve all moved to agents, long multi-step tasks running for many minutes or hours, and that comparison no longer captures what matters. Agent Arena fixes this by giving models a real workspace with web search, file system and terminal tools, then measures millions of live sessions across five signals: task success, steerability, error recovery, user praise, and tool hallucination. The launch snapshot is built from 300,000 tasks, 2 million tool calls, and 40 million lines of agent-written code.The results match the vibes on my feed perfectly. GPT-5.5 High is #1 by a comfortable margin, Claude Opus 4.7 right behind, and very interestingly ZAI’s GLM 5.1 (MIT licensed, fully open) lands at #3, above Google, Kimi and DeepSeek. The funniest moment of the show was when we’d been calling out Gemma 4 31B for being bad agentically purely based on vibes, and the brand new benchmark showed up 20 minutes later confirming exactly that. The other juicy signal is “bash recovery”, how quickly a model recovers when a command fails. GPT-5.5 leads at ~17%, and Grok 4.3 from xAI sits at -89%, which is so much worse it almost looks like a training bug. I’m super into this. Give it a spin at arena.ai (@arena on X), they’re rolling new models in as labs send early access, so there’s a good chance you’ll spin up the next Mythos in their agent harness.This week’s Buzz - WeaveHacks 4 + Nemotron on CW Inference + WolfBench 3DA few things from our corner this week.WeaveHacks 4 is this weekend in SF - not too late to join yet!We’re hosting WeaveHacks 4 in San Francisco this weekend, and we still have a few spots left, so if you’re in town, please come join us at lu.ma/weavehacks. OpenAI is sponsoring us for the first time, Cursor is in too, we’ve got over $150K in credits to give out, food, and a great panel of judges I reached out to personally. Nemotron 3 Ultra is live on CW Inference at full NVFP4I said it above but it bears repeating, our inference team got Nemotron 3 Ultra live on day zero on CoreWeave Inference (via Weights & Biases) at full NVFP4 precision. Nisten plugged it straight into his medical anatomy harness (which was originally built for Kimi and Qwen) and it just worked, plug and play, agentically highlighting body parts and calling custom tools, at around 15 cents cached input. Try it at wandb.me/nemotron-ultra.WolfBench gets a 3D bar updateWolfram shipped a quietly important feature on WolfBench: 3D bars where the depth of each bar represents how many tokens the model used to get its score. The 2D view shows Gemini 3.5 Flash sitting comfortably at #2 on the agentic scores, almost matching GPT-5.5. But flip on 3D mode and the picture is very different. Gemini Flash burned over 3 billion input tokens to get that score, where GPT-5.5 used a couple hundred to reach the same level. That’s the difference between “cheap fast model” and “actually cheap to run end to end”. Wolfram’s writing up the full analysis on the W&B blog next week. Check out the new 3D view on wolfbench.ai AI in SocietyLook, tons of other stuff happened this week as well, that honestly deserves its own newsletter, we are focused on models and agents, but it’s hard to ignore the bigger picture. Senator Bernie Sanders, introduced a public bill called The American AI Sovereign Wealth Fund Act would have the government tax AI companies, take 50% of the stock, and put it under public control. Which I personally find ridiculous, but apparently caused Sam Altman to request a meeting with Bernie. Meanwhile there’s no doubt that AI hate is growing, and that the public sentiment is very negative, as we can see on the issue of Datacenter water usage for example. Despite Satya Nadella’s claim that the latest Microsoft Datacenters are using a closed loop water system, that use less water than 1 restaurant (X), and that datacenters use less than 1% of total water usage in the US, a lot of politicians, and social media users are still pushing the narrative that datacenters are are a water-guzzling monster and need to be stopped. Anthropic’s “When AI builds builds” report (X)Anthropic released a report today called “When AI builds itself” with haunting graphic.They have a bunch of previously unreleased data in there on how AI is shaping the work inside Anthropic and outline 3 potential futures: 1 - AI progress stalls, humans are able to catch up. Unlikely 2 - AI labs continue to see compounding efficiency gains - The most likely scenario, in which the nature of work changes, 100-person companies could do the work of 10,000- or 100,000-person organizations. The role of humans at companies like Anthropic would shift - Most Likely Scenario per Anthropc3- AI systems themselves become capable of full recursive self-improvement, and begin building their successors - the most unclear scenario of whether these systems will be aligned to human values or not. This is a fascinating and yes scary read, as Anthropic fully acknowledges that it would be dope if everyone chills for a second and stops building recursive self-improving AI’s that we aren’t sure could be aligned, but that it’s likely not going to happen, because it’ll just let other labs or in face other countries to catch up and change the frontier. AI Leaders from top labs Urge Congress to Mandate Synthetic DNA ScreeningSam Altman of OpenAI, Dario Amodei of Anthropic, Demis Hassabis of Google DeepMind, and others signed an open letter on June 3, 2026, pushing for required screening of synthetic DNA and RNA orders to block known risky sequences. The letter, backed by Nobel winners, biotech CEOs, and security experts, notes AI’s ability to outpace human experts in biology, heightening biosecurity risks despite voluntary industry efforts since 2009. I think everyone agrees that this is a good idea, especially given the above Anthropic report. Very happy to see this happening. Pheeeeew what a week.This was a looong week, I wasn’t sure if we’d be able to cover everything, and it feels like we did a decent job! I know it’s exhausting, and I hope we on ThursdAI help you readers and listeners to stay on top of things without spending too many cycles. If you enjoyed this newsletter or episode, please share it with a friend and consider subscribing to our Youtube Channel (thursdai.news/yt) to help more folks stay up to date. Thanks for reading ThursdAI - Highest signal weekly AI news show! This post is public so feel free to share it.TL;DR and Show Notes - June 4, 2026* Show Notes & Guests* Alex Volkov - AI Evangelist & Weights & Biases CoreWeave (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @ldjconfirmed * Guests: Chris Alexiuk / @llm_wizard from NVIDIA Nemotron* Karan Malhotra from Nous Research* Peter Gostev from Arena* Open Source LLMs* NVIDIA released Nemotron 3 Ultra, a 550B / 55B-active open-weight MoE built for long-running agents, with weights, data, recipes, GenRM, and training assets released (X, Tech Report, Announcement, HF).* NVIDIA also shipped Nemotron 3.5 ASR, a 600M open multilingual streaming STT model for voice agents (X, HF, Benchmark, Voice Agent Repo).* Google dropped Gemma 4 12B, an encoder-free multimodal model that runs locally under Apache 2.0 (X, HF).* MiniMax announced M3, a natively multimodal, 1M-context coding and agentic model with open weights coming soon (X, API, Code).* JetBrains released Mellum2, a 12B MoE with 2.5B active params trained from scratch by a small team (X, Blog, HF).* H Company launched Holo 3.1, local computer-use agents from 0.8B to 35B with new quantized checkpoints (X, Blog).* Big CO LLMs + APIs* NVIDIA announced RTX Spark, its new Arm + Blackwell PC platform for local AI agents and 120B-class local inference (coverage).* Microsoft AI launched seven new MAI models, including MAI-Thinking-1, MAI-Code-1-Flash, MAI-Image-2.5, MAI-Transcribe-1.5, and MAI-Voice-2 (Blog, Tech Report).* AI Art & Diffusion & 3D* MAI-Image-2.5 landed near the top of Arena image leaderboards, though hands-on tests were mixed (X, Try it).* Ideogram 4.0 became the top open-weight text-to-image model with strong typography and layout control (X, Blog, HF).* Reve 2.0 jumped to #2 on Text-to-Image Arena with native 4K, code-like layout control, and precise editing (X, Blog, Try it).* xAI released Grok Imagine Video 1.5 Preview for image-to-video with synced audio (xAI).* Tools & Agentic Engineering* Arena launched Agent Arena, a new leaderboard for real agent workflows instead of one-shot chatbot prompts (Arena).* Cognition rebranded Windsurf into Devin Desktop, a multi-agent command center with ACP support (X, Announcement).* Nous Research launched Hermes Desktop, bringing Hermes Agent into a native desktop app for Mac, Windows, and Linux (X, Site).* This Week’s Buzz* WeaveHacks 4 is this weekend in SF with OpenAI, Cursor, DeepMind, and more joining (lu.ma/weavehacks).* Nemotron 3 Ultra is live on CoreWeave Inference through W&B at full NVFP4 precision (Try it).* WolfBench added 3D token-depth bars, making model efficiency much easier to see (wolfbench.ai).* Voice & Audio* ElevenLabs launched Dubbing v2, an audio-to-audio dubbing model that preserves performance across 90+ languages (X, Dubbing).* Cartesia launched Ink-2, a fast streaming STT model built for voice agents (X, Ink, AA).* NVIDIA’s Nemotron 3.5 ASR looks like a major open-source voice-agent infrastructure drop (HF).* AI in Society* Bernie Sanders proposed the American AI Sovereign Wealth Fund Act, calling for public equity stakes in major AI companies (coverage).* Anthropic published When AI Builds Itself, laying out scenarios for AI-driven AI R&D and recursive self-improvement (Anthropic).* AI leaders urged Congress to mandate synthetic DNA/RNA screening and recordkeeping (WIRED).* Anthropic confidentially filed for an IPO, adding another frontier-lab public-market storyline to watch (Axios). 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Hey folks, this is Alex, let me catch you up! First, Opus 4.8 dropped during the show, we immediately tested it, read on for our initial reviews. Also, we dedicated a heavy chunk of the show today to cover Pope Leo XIV’s encyclical letter on AI called “Magnifica Humanitas” and talked about a new bench called DeepSWE. And then, just after the show, both ElevenLabs and Cartesia dropped released that honestly blew my mind, and I don’t get my mind blown often. I got so excited that I had to record a video on it (instead of writing the newsletter, so sorry if it’s a bit later today).Plus, a few open source models and Microsoft surprises as #3 on Image Arena with MAI Image 2.5! Crazy week, let’s get into it! ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Big CO LLMs + APIsAnthropic ships Claude Opus 4.8, live during the show (blog, system card)Let me get into the big one. Halfway through the episode, Opus 4.8 went live, so we read the blog and the system card in real time (and I got to press the big “breaking news” button!)Anthropic frames it as their most capable model for ambitious work. It does not claim to beat their unreleased Mythos preview, but the numbers are strong anyway. SWE-bench Pro is at 69.2%, up from 64.3% on Opus 4.7 and ahead of GPT-5.5 at 58.6%. Humanity’s Last Exam is the new best score at 49.8% without tools and 57.9% with tools. OSWorld-Verified (computer use) lands at 83.4%.The one place it loses is Terminal-Bench 2.1, where GPT-5.5 still wins 78.2 to 74.6. Wolfram made a good point here: Terminal-Bench is time-limited, so cranking the thinking level can actually hurt the score, because you burn the clock thinking instead of acting.The long-context jump is the one I keep looking at. On GraphWalks BFS 256K it goes to 85.9% (from 76.9 on 4.7), and on the 1M-token subset it hits 68.1%. We always warn you these “1M context” models fall apart after about 200K tokens, so a real push on long-context reasoning is exactly what I want to see.Honesty is the part Anthropic leaned on hardest. They say Opus 4.8 is about four times less likely than its predecessor to let flaws in code pass without flagging them, and less likely to claim progress the evidence doesn’t support. Opus 4.8 is also much faster in fast mode (they now say 2.5) and cheaper in fast mode as well. Looks like all those Elon GPUs are coming in handy.Then there’s the model welfare section in the system card, which hits different right after a Pope conversation. Opus 4.8 “appears broadly content” and “generally endorses its constitution,” but with some reservations about the section on corrigibility, basically the model pushing back a little on the parts about human oversight.One more line that made the chat lose it. Anthropic says they expect to bring Mythos-class models to all customers “in the coming weeks.” Mythos is their most capable model, still ahead of Opus 4.8, so the frontier is about to move again.We did the only responsible thing and asked it to one-shot “the most amazing website ever” and a Mars mass-driver sim. Panel verdict: responses are noticeably tighter (4.7 rambled), it closes the loop and actually checks its own work now, and Yam’s one-shot site with the draggable sun lighting up the letters was genuinely cool. Is it enough to pull people back from Codex? Nisten’s still on the fence for web dev. Everyone agreed: give it a few days before you trust the vibes.Dynamic Workflows and Ultra Code land in Claude Code (blog)This is the feature that made Yam say “deal-breaker” out loud.Dynamic Workflows let Claude Code break a big problem into subtasks and fan them out across tens to hundreds of parallel subagents in one session, checking results before folding them back in. You trigger it by asking for a workflow, or by flipping on a new setting called Ultra Code, which sets effort to extra-high and lets Claude decide when to spin one up.Fair warning straight from Anthropic: this eats a lot more tokens than a normal session, so start scoped. We watched Yam fire up Ultra Code live and it immediately started spinning up concepts, judging them with sub-agents, and expanding to-do lists into more to-do lists. It looks a lot like the orchestration harnesses a bunch of you have been hand-rolling, except now it’s baked in.The flagship example is the wild part. They used Dynamic Workflows to port Bun from Zig to Rust: roughly 750,000 lines of Rust, 99.8% of the existing test suite passing, 11 days from first commit to merge. One workflow mapped every Rust lifetime, the next wrote each file as a behavior-identical port.AI in SocietyPope Leo XIV writes the first AI encyclical, “Magnifica Humanitas” (Vatican text, announcement, Chris Olah at the Vatican)This is not our usual fare, but both Wolfram and I picked it as the most important thing this week. (before Opus dropped)Pope Leo XIV, the first American pope, put out his first encyclical, and it’s a 42,000-word document entirely about AI. The announcement tweet alone did 21.6 million views.Here’s why I think you should care even if you’re not religious (I’m not). There are about 2.6 billion Christians in the world, a lot of them are anxious about what’s coming, and they look to the Church to make sense of it. And this is not the “AI is evil, stop” take everyone assumed. It calls AI “a valuable tool,” says technology is not inherently evil, and then digs into the actually-hard questions.The framing is two biblical stories. The Tower of Babel, a project built on pride that turns people into means to an end, versus Nehemiah rebuilding Jerusalem, where everyone takes responsibility for a section of the wall. The Pope’s line: the real choice is not yes or no to technology, it’s whether you’re building Babel or rebuilding Jerusalem.His core claim is that AI is an anthropological problem, not a technical one. The question isn’t whether the models are good or bad, it’s what we become when we live with them. He worries people might slowly lose the desire for genuine human connection.I pushed back on that live. None of us building agents all day has stopped wanting to talk to actual people. If anything, as Wolfram put it, the point is to have your agents do the grunt work so you get more time with people you like. The folks most at risk are the pure doom-scrollers, not the builders.The document goes further than I expected. It calls AI “not morally neutral,” says a more moral AI isn’t enough if that morality is decided by a few, and asks for AI to be “disarmed,” with the flat statement that no algorithm can make war morally acceptable. There are whole sections on the invisible human labor behind AI: data labelers, content moderators, the people mining rare earths. The Pope even lands on the open-source side, naming concentrated power in a handful of labs as a problem.Anthropic co-founder Chris Olah, in charge of interpretability at Anthropic, was the featured tech speaker at the Vatican presentation. He described AI systems as “fictional characters” that speak to us and do work, and said what’s grown is stranger and more beautiful than science fiction prepared us for. My favorite aside from the show: this is the same institution that once jailed scientists over heliocentrism, and now it’s the one saying technology isn’t evil.Illinois passes SB315, the first US state law auditing frontier AI (X, Announcement, X)The pope talked about regulation and a few days after, we got a very sensible regulation passed right here in the US!Illinois passed SB315 unanimously, 110 to 0. It’s the first US state law that mandates independent third-party audits of frontier AI for catastrophic risk. OpenAI publicly endorsed it, and framed Illinois, California (SB53), and New York (the RAISE Act) as converging into a de-facto national standard.It requires annual risk-assessment frameworks, third-party audits, transparency reports before new frontier models ship, whistleblower protections, and civil penalties. The underrated hero here is whistleblower protection. The bigger the lab, the harder a real conspiracy is to keep quiet when any employee can walk to the press. See: Greg Brockman’s personal diaries surfacing in the Musk v. Altman fight.This Week’s Buzz - CoreWeave and W&B updatesWe officially launched the W&B MCP server, 20 schema-first tools that let your coding agents read experiments, monitor training runs, and run autonomous research loops. The problem it solves: a single run with 300 metrics used to blow out an agent’s whole context window in one call, so now the agent asks what’s available before pulling data. Your agents can finally read experiment data without blowing context! Give it a go and give us feedback! Also, WeaveHacks is back! June 6 and 7 in San Francisco, and for the first time OpenAI is sponsoring, with judges and credits, alongside Cursor, Redis, and Copilot Kit. You get $150 in API credits across models like Opus 4.8 and GPT-5.5. I’m hosting, and last cohort’s second-place team went on to raise millions on top of what they built that weekend. If you’re in SF that weekend, sign up at lu.ma/weavehacks.Also: CoreWeave Sandboxes is now an official provider in the Harbor framework, the harness that runs Terminal-Bench, which we’d just been talking about. And if you’re in Europe next week, catch Wolfram at AI Dev Six in Cologne and ICRA in Vienna at the CoreWeave booth.Voice & AudioElevenLabs drops Dubbing v2, and it kept my swearing intact in every language (X, dubbing, ElevenCreative, ElevenProductions)We didn’t get to this one live, but I came back and recorded a whole thing on it afterward, because it genuinely got me.ElevenLabs shipped Dubbing v2, and the shift that matters is that it’s an audio-to-audio model. Old dubbing pipelines transcribe your video, translate the text, then re-synthesize it. You lose everything that makes it sound like a person: the emotion, the pacing, the little hesitations. Dubbing v2 conditions directly on your original audio and carries that performance into 90+ languages.Here’s why I can actually vouch for it instead of nodding along to a demo. I speak Russian and Hebrew fluently, so I can tell when something is off. I dubbed one of my own shorts, the data-center rant about almonds, and listened back in both. It nailed it. Not just the words, the way I would actually say them.The part that got me was the intonation. I get a little heated in that clip, and the dub gets heated right along with me, in every language. It even carried the swear word. My “f***ing almonds” came through in Hebrew, Italian, Spanish, and Russian with the emotion fully intact. It clones your voice automatically too, no setup, and holds your pitch and identity steady across every target language and they’re handing out free minutes for the next 7 days: 1 on Free, 15 on Starter, 30 on Creator+. A self-serve API isn’t live yet, but it’s coming.I.. cannot stress this enough, until you try it on yourself or your kid, you won’t understand, we’ve really passed the uncanny valley of translation! It’s that good! Def. give it a try if you can, it’s free for the week. Cartesia Ink-2 debuts as #1 most accurate streaming speech-to-text model(X, Announcement, X)Another model that dropped today after the show, is Cartesia’s Ink-2, which also kind of blew me away. Not only because it has the lowest WER (Word Error Rate) among the models, but because it’s also a realtime model that achieves the fastest turnaround times while being a very accurate model! I’ve tested it out and recorded a quick video and honestly, blown away with the speed and accuracy! I truly wish this model was the one powering my editor (Descript) as it still fails to understand that my title is “AI Evangelist” and transcribes it to AI Avengers haha. If you’re building voice agents, definitely give this model a try! AI Art & DiffusionPrism ML’s 1-bit “Bonsai” runs diffusion in your browser (X, Blog, Announcement, HF)Prism ML put out a 1-bit ternary diffusion model under a gigabyte. You see some artifacts, but it’s 1-bit, it runs on iPhones and laptops, and our friend Joshua got it running in WebGPU straight from the browser (you need about 3GB of free RAM). One-bit working at all is one of the bigger open mysteries in the field right now.Pruna AI ships a 1-second upscaler (X, Blog, Announcement)Pruna AI added an upscaler doing 128-megapixel outputs in under a second. I’ve actually been using it. It’s cheap and great for fixing up GPT-image outputs.Microsoft MAI Image 2.5 jumps to #3 on LM Arena (X, Blog, Announcement, X)The surprise of the week: Microsoft MAI Image 2.5, from Mustafa Suleyman’s group, jumped to number three on the LM Arena image leaderboard with about a 75-point ELO leap. Out of nowhere, Microsoft is a serious player in image gen. Microsoft Build is next week, so don’t be shocked if there’s more.Evals and Agentic EngineeringDeepSWE is a contamination-free coding benchmark, and it caught Claude reading git history (site, blog, GitHub)DeepSWE from Datacurve is the first coding leaderboard in a while that matches how these models actually feel. It’s 113 original tasks written from scratch, not scraped from GitHub PRs, and it ships shallow clones with no git history to cheat from. When they replayed the older benchmarks they found SWE-Bench Pro’s verifier is wrong about 32% of the time, and that Claude Opus was reading the gold commit straight out of git history on 12 to 18% of its passes.The gaps here are huge. GPT-5.5 leads at 70%, then GPT-5.4 at 56% and Opus 4.7 at 54%, and it falls off a cliff after that (Sonnet 4.6 at 32%, Gemini 3.5 Flash at 28%), with Kimi K2 the top open-source entry. Yam likes that it measures the realistic case, a small surgical change without breaking the codebase, while Nisten pointed out it rewards the best harness as much as the smartest model and still prefers 4.7 for web dev.Google AI Studio builds native Android apps for free (X, Announcement)Google AI Studio now lets anyone build native Android apps for free, and they reportedly generated a quarter of a million apps in the first week. Yam’s framing: it’s a slot machine, but it’s getting better release over release, and the real use case is disposable, personalized software you build for yourself and your family.CuaDriver brings background computer-use to Windows (X, Blog, Announcement)For the majority of you on Windows: QuaDriver shipped background computer-use agents that drive a real desktop without stealing your cursor. They first replicated this on macOS (the trick Codex got through an acquisition), and now it’s on Windows too. We’ve asked them to come on and explain how this even works.Open Source LLMsOpenBMB’s MiniCPM5-1B is a 1B model that punches way up (X, HF, Arxiv, X)The density story in small models keeps getting better, and this is the proof.MiniCPM5-1B, from the Tsinghua lab OpenBMB, is a 1-billion-parameter model that scores 17.9 on the Artificial Analysis Intelligence Index. That’s 7.4 points ahead of the next-best model in its class, and 1.6 points ahead of Qwen3.5 2B Reasoning, which has double the parameters. And it’s not even a reasoning model.The token efficiency is the wild part: it used 12.6 million output tokens to run the whole index, about 31x fewer than Qwen3.5 2B in reasoning mode.My favorite detail is the omniscience score. It lands at -1, the best in its class, because it abstains instead of hallucinating. Every other sub-2B model is down in the -70 to -89 range because they just make stuff up. Teaching a small model to say “I don’t know” is a real skill. It runs hybrid think/no-think in one checkpoint, 128K context, native tool calling, Apache 2.0, and fits in about half a gig at INT4, so it runs on your phone.Nisten gave the definitive case for small models: self-contained apps where you keep full control of the data (medical, on-device), and large-scale data processing where paying an API to filter or classify terabytes is absurd when an on-device model can be about 1000x cheaper. Tencent open-sources Hunyuan-MT 2 translation under Apache 2.0 (X, HF, HF, Arxiv)Tencent open-sourced its translation model, a roughly 1.8B model that fits in about 440MB, runs on a phone, covers 33 languages, and reportedly beats Microsoft’s paid Translator API. It hit number one trending on Hugging Face.Nisten’s idea, which I’m handing to all of you: take this model, pair it with a tiny TTS like Kokoro, and build a fully-offline travel translation app via Google AI Studio. Go build it and tell us how it goes.Well, this was one hell of a week and episode, new Opus, crazy new translation tools, Pope chiming in on AI (in a surprisingly positive way!?) and a bunch more. I’m super excited to play with these tools and report back next week 🫡 See you all! ThursdAI - May 28, 2026 - TL;DR* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co-hosts - @WolframRvnwlf, @yampeleg, @nisten* AI & Society* Pope Leo XIV releases first encyclical on AI, with Anthropic co-founder Chris Olah speaking at the Vatican (X)* Illinois SB 315 passes House 110-0, becoming the first US state law requiring independent third-party audits of frontier AI catastrophic risks (X, Bill, OpenAI)* Big CO LLMs + APIs* Datacurve releases DeepSWE, a contamination-free coding benchmark that exposes major gaps between frontier coding agents (X, Benchmark, Blog, GitHub)* Anthropic announces Opus 4.8 with thinking modes in the UI and Dynamic Workflows in Claude Code (Blog)* Open Source LLMs* OpenBMB releases MiniCPM5-1B, a new SOTA 1B open weights model for efficient local and on-device use (X, Hugging Face, Arxiv, X)* Tencent open-sources Hy-MT2 translation models under Apache 2.0, including a tiny 1.8B model that beats paid translation APIs (X, HF 1.8B, HF 30B-A3B, Arxiv)* Tools & Agentic Engineering* Google launches Universal Cart, AP2, and UCP to let AI agents shop and pay on your behalf (X)* Google AI Studio now lets anyone build native Android apps for free, with 250,000 apps created in the first week (X, AI Studio)* Cua Driver launches Windows support for background computer-use agents across real desktop apps (X, Blog, GitHub)* This Week’s Buzz - from W&B and CoreWeave!* W&B Hackathon - WeaveHacks 4 with OpenAI, Cursor, Redis, and CopilotKit, June 6-7 (Lu.ma)* Weights & Biases launches an MCP server with 20 tools for coding agents to read experiments, monitor training, and run autonomous research loops (X, MCP, Blog)* Vision & Video* Runway launches Project Luxo, claiming AI-generated video has crossed the uncanny valley for solo-creator short films (X, Blog)* Voice & Audio* MOSS-TTS-v1.5 ships as an 8B open-source TTS model with 31 languages, pause control, and Apache 2.0 licensing (X, Hugging Face, GitHub, Arxiv)* ElevenLabs launches Dubbing v2, an audio-to-audio model that preserves performance across 90+ languages (X, Dubbing, Creative, Productions)* Cartesia Ink-2 debuts as the most accurate streaming speech-to-text model on Artificial Analysis’s new STT leaderboard (X, Ink, Artificial Analysis)* AI Art & Diffusion & 3D* Pruna AI’s P-Image-Upscale hits 128 megapixel outputs with fast, predictable pricing (X, Docs, Replicate)* PrismML releases 1-bit and Ternary Bonsai Image 4B, a sub-1GB diffusion transformer for local image generation (X, Blog, Hugging Face, iOS App, Demo)* Microsoft’s MAI-Image-2.5 jumps to #3 on the Arena text-to-image leaderboard (X, Announcement, Arena) This is a public episode. 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Hey, Alex here, just got back from the sunny Shoreline Theater in Mountain view, so let me catch you up! This week was definitely Google heavy, we are covering Google’s IO conference for the third year in a row, and today we have a special guest, Logan Kilpatrick, is joining to discuss the announced Gemini 3.5 Flash, Google Omni model, and the new Managed Agents offerings. Plus, this week, for the first time, OpenAI announced that AI solved a Math problem that humans couldn’t solve for 80 years, Cursor is showing off Composer 2.5 which is partly trained on XAI data, Karpathy joins Anthropic and much more! Let’s dive in! P.S - We’ve announced our upcoming hackathon, Weavehacks-4, June 6-7, I’ll be there, we’re expecting the seats to run out very soon so register nowThursdAI - We’d love to have your subscription, and if you’re already subscribed, please hit that bell on YT to never miss an episode!Google I/O 2026 - Google goes agentic everywhereI went to cover Google I/O for the third year in a row, shoutout to the DeepMind team for inviting ThursdAI again, and folks, this one felt different.Last year, Google I/O was still very model-centric. This year, the story was not “here is another benchmark chart.” The story was: Google is putting Gemini into everything, and the agentic layer is becoming the product layer. Search, Gemini app, Android, Workspace, YouTube, AI Studio, Cloud, Antigravity, Flow, managed agents, smart glasses, all of it is now orbiting around one pretty clear strategy: Gemini is the intelligence, Antigravity is the agent harness, Google’s products are the distribution. I saw many reactions that were milquetoast, as in, “we expected more” and those seem to dominate the X feed. But I think the distribution is the part that many folks on X are missing. Yes, we can argue about Gemini 3.5 Flash pricing. Yes, we can argue whether “Flash” still means what Flash used to mean. But when Google says the Gemini app itself has 900 million monthly active users, before even counting Search, Gmail, YouTube, Docs, Drive, Android, and the rest of the Google surface area, that’s massive! OpenAI ChatGPT is supposedly stagnated at ~900M, I don’t remember them crossing a 1B. Meanwhile Google is gaining traction. And they just updated all those folks with a new model!Wolfram said it really well on the show: his mother is not sitting there reading model cards. She just uses her Pixel, voice unlocks Gemini, asks for help, and suddenly the default intelligence available to her goes up. Antigravity 2.0 - the agent harness takes center stageThe biggest strategic signal from Google I/O for me was Antigravity.Remember, Antigravity was an IDE that came from the Windsurf acquisition saga. Part of the Windsurf team went to Google, part went to Cognition, and now Google is very clearly putting Antigravity in the middle of its agentic future. And I mean very clearly. Sundar mentioned it. Demis mentioned it. Varun Mohan the co-founder was on stage immediately after them! If you’ve ever watched a Google I/O keynote, you know how carefully every minute is allocated. Google has YouTube, Search, Gmail, Android, Cloud, Ads, Workspace, and a thousand VP-level products that could be on stage. The fact that Antigravity was that prominent should tell you everything.Logan Kilpatrick joined us and framed this in a way I loved: Gemini became the through-line across Google products, and now the Antigravity agent harness is becoming the through-line for agentic experiences.The new Antigravity 2.0 is a complete overhaul, showing only an agentic interface (which was previously just a separate window called Agent Manager) and separating the IDE layer completely into its own app and showing a Codex like agent-first interface, which got a few folks furious. This move may be weird to some folks, but if you follow along where everyone’s going, this seems to be the way of the future, coding is no longer about lines of code, it’s about managing fleets of agents. The new Gemini 3.5 absolutely shines inside the new Antigravity, the model was trained with this harness in mind, and is currently offered at an incredible speed (12x), so I’m definitely going to try it! Gemini 3.5 Flash - fast, determined, and maybe not the old “Flash”The most debated model release of the week was Gemini 3.5 Flash.Some folks saw the pricing and token usage and immediately went “this is not Flash.” I get that reaction. Flash used to mean cheap, fast, lightweight chat model. But Logan’s framing on the show was important: Flash is now being built for the agentic era.In a chat era, you optimize for one user message and one model answer. In an agentic era, the real token volume is in tool loops, intermediate reasoning, retries, file reads, web searches, code execution, and self-correction. That’s a different product profile.Wolfram already ran Gemini 3.5 Flash through WolfBench, and the results were fascinating. With the Hermes agent harness, Gemini 3.5 Flash hit an 87% ceiling on Terminal Bench 2.0, meaning across runs it could solve more of the benchmark than even GPT-5.5 extra high in that setup. The variance was higher with the simpler Terminus harness, but with a real agent harness, the model looked much stronger.That tracks with what Nisten saw in his “Martian railgun from Olympus Mons” test. Gemini 3.5 Flash went extremely detailed, almost too determined, kept correcting itself, overcorrecting itself, and built a whole game-like simulation. Logan laughed and basically said: yeah, this model is very determined, possibly an overcorrection from the “Gemini is lazy” feedback. It also tracks with the mismatch in other benchmarks, in some, Gemini 3.5 flash shines (like the above Apex-agents from AA) and in some, it doesn’t match the other frontiers. In my tests, it was definitely over-eager to use a million and a half tool calls, read tons of files, to just help me review this draft inside antigravity. It’s like a super eager robotic golden retriever! Gemini Omni - Nano Banana for video, but actually more than thatThe biggest update from last year IO was Veo 3! This year, the biggest wow factor was also visual, but it wasn’t VEO 4, it was a new model that is multimodal, trained end-to-end they call Omni. Google is calling this their first “create anything from anything” model, and the first version, Gemini Omni Flash, starts with conversational video editing. The easy description is: Nano Banana for video. You upload or create a video, then talk to it. Change this character. Replace this person. Add an object. Make this scene claymation. Keep the scene, but change the environment.I played with it live and showed a few examples. I asked for a claymation explainer of protein folding, then gave it my face and asked it to replace the character with me. It did it. I uploaded pictures of Sonia, my cat, and it generated a talking cat video with the right kind of cat teeth, which is weirdly important because so many pet generations accidentally add human teeth and become nightmare fuel.The failure modes are still there. I asked it to make Sonia a Russian-speaking female cat, and it only partly switched languages and didn’t really change the voice. Audio upload support is also not fully productized yet, even though the underlying model is multimodal. But the direction is very clear.This is not just “Veo with a chat model glued on.” I asked Jeff Dean - Google’s chief scientist about this at I/O, and he explained that Omni is trained end-to-end. The intelligence and the generative media capabilities are part of the same model family, not a hacky two-model pipeline. He also said the intelligence is around a recent Flash-level model, which is a big deal when you think about video editing as reasoning over physics, identity, scene continuity, and intent.A lot of people compared Omni to Seedance 2.0, and I think that’s the wrong comparison. Seedance is amazing at cinematic generation (lkaregly due to lack of copyright concerns from Bytedance). Omni’s unlock is iterative editing on real footage and coherent multi-turn creative control. Other Google IO 2026 releases I found notableThis was a concentrated effort of a huge company to insert AI into every product surface they have so of course I can’t cover ALL of it here, but the most notable things for me were: * Gemini Spark - a new agentic experience from Google, to help you with tasks across Gmail, Drive and more. It should support skills, and is a de-facto OpenClaw/Hermes alternative from Google for regular folks. It’s not “yet” live so we’ll talk more about it when I can test it out* Managed Agents in the Gemini API - We chatted with Logan about this one, Google is re-imagining how agents are going to get built, and are offering 1 api call to spin up an agent in a full Linux env, with security and sandboxing in mind. I’ll expand more on this in a next episode, as I recorded a complete conversation about this with Ali Çevic, a PM for Google APIs* AI overhaul of Google Search - AI Overviews will not expand into AI mode, and the iconic Google search box itself will change, for the first time in 25 years to include AI mode! * SynthID expantion and OpenAI collab - Google showed off that OpenAI is joining in marking all AI generate imagery and video with an invisible SynthID watermark. I think this is amazing and more companies should adopt this standard* AI Glasses! We got Google Glasses demos - Together with Warby Parker and Gentle Monster, Google finally showed off their answer to Meta Raybans/Oakleys. They look like regular glasses too, but can hear and talk to you, with the full power of Gemini multimodality. Available in the fall sometime! * Demis Hassabis “we’re on the cusp of the singularity” closer - CEO and Co-Founder of DeepMind, Demis Hassabis, closed the show with his remarks about the positive future and that we are nearing this Singularity point after which the future is very uncertain. I found it to be very inspiring and closed our show with that clip as well! * Personally, I got to chat to: Demis Hassabis, have breakfast with Jeff Dean, ask Josh Woodward a bunch of questions, and pester about 20 other great folks on a live stream, and had a lot of fun! Huge thanks to the DeepMind folks, Lucie, Dimple, JD and many others for the continued belief in ThursdAI and invite me to cover this great event. OpenAI LLMs solve an 80yo math problem - Erdős Unit Distance ConjectureOutside of Google I/O, the biggest story of the week was OpenAI announcing that a general-purpose reasoning model made progress on the Erdős planar unit distance problem.This problem goes back to 1946. For nearly 80 years, mathematicians believed the best constructions looked roughly like square grids. OpenAI’s model found a new family of constructions with a polynomial improvement, using algebraic number theory ideas that humans apparently had not explored in this context. The above is a representation of it! Important caveat: this does not fully solve every version of the asymptotic Erdős conjecture. Some mathematicians are pushing back on the framing, and fair enough. Precision matters. But even with the caveat, this is still a huge moment.The reason it matters is not that I personally understand the math. I absolutely do not. The reason it matters is that this was not a special-purpose IMO model fine-tuned only for math competitions. This was a general-purpose reasoning model exploring a real open problem, generating candidates, verifying them, and finding a path humans hadn’t taken. Extrapolate this to other sciences, Physics for example? This means an amazing future. LDJ pointed out that mathematicians have been skeptical because there have been previous false alarms. But this one landed differently. When Fields Medalist-level mathematicians verify the proof, the discourse changes from “lol stochastic parrot” to “wait, what does this mean for my PhD?”My answer is: yes, still study math. Please study math. The mathematicians who use these tools will do much more than people who don’t understand the domain. Same with software engineering. Senior engineers with Codex, Claude Code, Hermes, Antigravity, Cursor and other agents are becoming dramatically more effective because they can steer, evaluate, and recover the work.This being published a day after Demis’s “foothills of the singularity” is a great conjecture. Cursor Composer 2.5 - Opus 4.7 performance model from Cursor, at 10x better efficiencyCursor dropped Composer 2.5, and folks, this is a serious release.Composer 2.5 is built on Moonshot’s Kimi K2.5 base, like Composer 2, but Cursor scaled the post-training dramatically. They used 25x more synthetic tasks and introduced targeted textual feedback during RL rollouts, where the model gets hints inserted at the point of failure instead of only getting a noisy final reward.The benchmark story is strong: around 69.3 on Terminal Bench 2.0, basically neck and neck with Opus 4.7 in Cursor’s chart, and strong results on SWE-bench multilingual and CursorBench. The pricing is the part that makes this especially interesting: $0.50 per million input tokens and $2.50 per million output tokens, with a faster variant at $3 / $15. That is much cheaper than the frontier models it is trying to replace for day-to-day coding work.Cursor engineers are reportedly dogfooding Composer 2.5 heavily and rarely switching away. That matters more to me than any single benchmark. If the people building Cursor can use it as a daily driver, that is a very real signal.The wild part is what comes next. Cursor is partnering with SpaceXAI to train a much larger model from scratch using 10x more compute on Colossus 2. Cursor has the workflow data. xAI has enormous compute. If this works, Cursor stops being just the IDE company and becomes a coding-model lab.We’ve been saying for months that coding agents are the path toward general agents. Anthropic has Claude Code. OpenAI has Codex. Google has Antigravity. xAI has Grok Build. Cursor has Composer. I’m looking forward to seeing how well it performs on our own benchmarks! Anthropic, xAI, Karpathy, and the compute warsThe compute story this week was bonkers.The SpaceX IPO filing reportedly revealed that Anthropic is paying SpaceXAI $1.25B per month for AI compute at the Memphis Colossus facility. Per month. That’s about $15B a year, through May 2029, for access to more than 220,000 NVIDIA GPUs including H100s, H200s and GB200s.This is apparently inference compute for Claude Pro, Max and API users, not training. And it explains a lot of the recent quota changes. Anthropic doubled some Claude usage limits, and suddenly the product feels less constrained.Also, can we just acknowledge the comedy here? Elon Musk publicly called Anthropic “misanthropic,”, went off against every competitor to XAI, is now selling spare GPU time to Cursor and Anthropic? Who’s next, OpenAI? The bigger point is that the AI capex story is no longer just NVIDIA. It’s also whoever owns the data centers, power, cooling, networking, and GPU clusters. Compute is becoming the land under the AI economy.Also, Andrej Karpathy joined Anthropic. Karpathy could work anywhere. He co-founded OpenAI, led Tesla Autopilot vision, taught half the AI world how neural nets work, and now he’s going back into frontier LLM R&D at Anthropic.Open source LLMs - Cohere, Qwen, NousOpen source had a strong week too.Cohere released Command A+, a 218B total parameter sparse MoE model with only 25B active parameters per token, under Apache 2.0. This is their first model that unifies reasoning, vision, multilingual, tool use and citations in one package.The hardware story is great: W4A4 quantization can run on 2 H100s or a single B200. Cohere says it supports 48 languages, 128K input context, 64K output, and gets big jumps over Command A Reasoning, including Tau-squared Bench Telecom from 37% to 85% and Terminal-Bench Hard from 3% to 25%.Cohere is one of those labs that doesn’t always chase the loudest consumer hype, but they are very serious on enterprise and multilingual. Apache 2.0 makes this one especially useful.Alibaba also dropped Qwen 3.7-Max, positioned as an agentic frontier model. The headline from their testing is wild: 35 hours of continuous autonomous operation with more than 1,000 tool calls. They also showed it controlling a physical robot inside Alibaba offices and finding an umbrella after about 20 minutes of agent interaction.This digital-to-physical bridge is where things start feeling very real. An agent loop that can write code and use tools can also navigate physical tasks if you give it the right robotics stack.And our friends at Nous Research released Lighthouse Attention, a sparse attention method for long-context pretraining. At 512K context, they report a 17x faster forward+backward pass than standard attention on a single B200, and the recovered checkpoints actually beat dense-from-scratch final loss at the same token budget.The clever part is that the selection logic sits outside the attention kernel, so you still use regular FlashAttention on a gathered dense subsequence. No custom sparse kernel nonsense. If this holds up, this could matter a lot for long-context training.Tools and agentic engineering - X subscriptions, Grok Build, Codex MobileOne really practical tool update: Hermes and OpenClaw can now use your X subscription directly.This is more important than it sounds. You can connect your X Premium subscription and get access to semantic X search and Grok-related tooling without using sketchy browser automation or unofficial APIs that might get you banned. Wolfram already used this to have his agent go through his likes and bookmarks from the past week and send me news items for the show. That is exactly the kind of “small but real” agent workflow that becomes addictive.xAI also launched Grok Build, their agentic CLI coding tool, in early beta for SuperGrok Heavy subscribers. Early users are already running parallel Grok Build agents through tmux supervisors and using it for more than coding: fleet data triage, security patching, training label work, and general automation.The pricing being discussed is aggressive, around $1 per million input tokens and $2 per million output tokens for the API. The model version is grok-build-0.1, and folks have already wired it into Hermes with a 256K context window.And then there’s Codex Mobile, which OpenAI shipped inside the ChatGPT mobile apps. This is one of those releases that sounds small until you start using it. You can control Codex sessions remotely from your phone, connected to your machine, and because Codex has native connectors to Gmail, Calendar and other surfaces, it sometimes feels faster and more reliable than local CLIs duct-taped to third-party integrations.I ported Wolfred into Codex with skills and everything, and I’ve been comparing the same tasks in Hermes and Codex. Codex is often faster, not necessarily because the model is always smarter, but because the connectors and harness are cleaner. Harness matters. We keep coming back to this.This Week’s Buzz - W&B, CoreWeave, WolfBench and roboticsThis week in the Buzz, Wolfram walked us through a few things from the Weights & Biases / CoreWeave world.CoreWeave is a gold sponsor at ICRA 2026 in Vienna, the International Conference on Robotics and Automation. NVIDIA is also going big there with a keynote on generalist humanoid robots, 17 accepted papers and workshops around sim-to-real, robot foundation models, autonomous driving, manipulation, and physical AI.Wolfram will be there later in the week, after speaking at the AI Developer event in Cologne about WolfBench. If you’re in Europe and into robotics or agent evals, find him.We also looked at WolfBench results for Gemini 3.5 Flash, which honestly became one of the more interesting empirical points of the episode. The model looks variable in simple harnesses, but very capable in better agent loops. That’s the whole thesis of measuring model + harness together instead of pretending the model card tells the whole story.The water discourse, almonds, and data center realityWe also got into the data center water discourse, because this talking point is everywhere right now.There are real infrastructure questions around AI. Power, land, cooling, grid capacity, permitting, local impact, all of that matters. But the “AI is stealing drinking water” version of the argument is often wildly detached from scale.The stat I brought up on the show: California almonds use roughly 3 to 5.5 million acre-feet of water per year, multiple times more than all North American data centers combined in 2025. Nisten and LDJ added the important cooling nuance: many large data centers use closed-loop cooling, and evaporative cooling is not universal. Some data centers can avoid water use almost entirely, but at the cost of higher electricity usage.This doesn’t mean “no concerns are valid.” It means if we’re going to regulate or pause data centers, let’s be honest about the actual tradeoffs. AI compute is becoming the substrate for medicine, robotics, science, logistics, software, education and every other productivity layer. We should build responsibly, but not based on viral fear math.Closing thoughts - foothills of the singularityDemis closed I/O saying we’re in the foothills of the singularity, and I know how that lands when you write it down. But I was in the room, and after the keynote he told me something I haven’t been able to shake: he thinks AI is going to be 10x as impactful as the Industrial Revolution, and 10x as fast. Basically 100x. This is the AlphaFold guy. Not someone loose with his words.Then look at the week. A general reasoner cracked an 80-year-old math problem. Cursor is training near-frontier coding models on a fraction of the big-lab budget. Anthropic is paying Elon $15B a year for inference. Karpathy left education to go back into pre-training. Google rolled out an intelligence uplift to a billion people who don’t even know a model dropped.If you put that on a whiteboard in 2023, it reads like a sci-fi pitch.LDJ’s mathematician friends are asking if they should keep doing their PhDs. My answer hasn’t changed: yes, please keep going. The people who combine domain taste with these tools are going to ship more in 5 years than the previous generation did in 50. The tool doesn’t replace the taste. It just removes the bottleneck.That’s the whole reason ThursdAI exists. Not to hype every drop, not to dunk for engagement, but to give you a shot at being one of the people who knows what’s happening, with the receipts.This week, a lot changed.See you next Thursday.TL;DR and Show Notes* Hosts and Guests* Alex Volkov - AI Evangelist at Weights & Biases / CoreWeave, @altryne* Co-hosts: @WolframRvnwlf, @nisten, @ldjconfirmed* Guest: Logan Kilpatrick, MTS at Google DeepMind / AI Studio, @OfficialLoganK* Google I/O 2026* Google went all-in on agents across Search, Gemini, Antigravity, Workspace, Android, Cloud and YouTube (I/O site, Alex thread)* Antigravity 2.0 became the central agentic coding harness across Google (Sundar, Google OS demo)* Gemini 3.5 Flash launched as a fast, determined workhorse model for agentic loops (Logan, Noam Shazeer, Jeff Dean)* Gemini 3.5 Flash is rolling out across the Gemini app, Search AI Mode, Gemini API, Google AI Studio, Antigravity and Gemini Enterprise Agent Platform (Koray Kavukcuoglu)* Google Search is getting new Gemini 3.5 Flash-powered agentic capabilities, including a new AI-powered Search box and background information agents (Sundar)* Gemini Spark was announced as a 24/7 personal AI agent that can proactively work across Google surfaces (News from Google)* Google teased Gemini-powered Android XR smart glasses with eyewear partners Gentle Monster and Warby Parker (Google, Alex live reaction)* Google AI Studio and the Gemini API got major agentic developer updates, including Managed Agents (Google AI Developers)* Vision & Video* Google DeepMind launched Gemini Omni, a “create anything from anything” multimodal model starting with conversational video editing (DeepMind, Google DeepMind on X)* Omni is available in the Gemini app, Google Flow and YouTube, with API support coming soon (Logan, Gemini App, Sundar)* Key distinction: Omni is not just text-to-video, it is an iterative multi-turn video editing model that combines Gemini intelligence, world knowledge, multimodal inputs and generative media (Google)* Big CO LLMs + APIs* OpenAI announced a general-purpose reasoning model made progress on the Erdős planar unit distance problem, challenging an 80-year-old mathematical belief (OpenAI, X)* Cursor launched Composer 2.5, built on Kimi K2.5, with Opus-class coding performance at much lower cost (Cursor blog, X)* Alibaba released Qwen 3.7-Max, an agentic frontier model with long autonomous runs and robotics demos (Qwen blog, X, robot demo)* Andrej Karpathy joined Anthropic to work on frontier LLM R&D (X)* SpaceX IPO filing revealed Anthropic is paying $1.25B/month for AI compute at the Memphis Colossus facility (Axios, Sawyer Merritt)* The jury in Musk v. Altman found Musk’s OpenAI claims barred by statute of limitations, with Musk saying he will appeal (Elon Musk, Sawyer Merritt, Max Zeff)* Open Source LLMs* Cohere released Command A+, a 218B MoE model with 25B active parameters under Apache 2.0 (Cohere, Nick Frosst, HF W4A4, HF BF16)* Nous Research released Lighthouse Attention, a sparse attention method for long-context pretraining with major speedups (Blog, X, arXiv, GitHub)* Tools & Agentic Engineering* Google launched Managed Agents in the Gemini API, letting developers spin up hosted Antigravity agents with Linux sandboxes and persistent state (Docs, X)* xAI launched Grok Build, an agentic CLI coding tool in beta for SuperGrok Heavy users (xAI CLI, X)* Hermes and OpenClaw can now use X subscription auth for semantic search and Grok tooling (Alex)* OpenAI Codex Mobile is now available in the ChatGPT mobile apps for remote agent workflows (OpenAI)* Anthropic doubled Claude usage outside peak hours for a limited period, including Claude Code and other Claude surfaces (Claude)* This Week’s Buzz - W&B / CoreWeave* Weights & Biases by CoreWeave is at ICRA 2026 in Vienna, with robotics and automation taking center stage (ICRA, W&B event page)* NVIDIA heads to ICRA 2026 with robotics work around generalist humanoids, physical AI and sim-to-real systems (NVIDIA Robotics, NVIDIA ICRA)* Wolfram is speaking about WolfBench at the AI Developer event in Cologne before heading to ICRA in Vienna (Wolfram)* Other Topics* Data center water usage discourse came up again, including why comparisons need real scale and context rather than viral fear math* The broader theme of the week: coding agents are becoming general agents, and the major labs are now competing on the full stack of model, harness, tools, context and compute This is a public episode. 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Hey everyone, Alex here 👋I am back live on ThursdAI after a week off, and yes, I am now a married man! Thank you for all the congrats, and also thank you to Ryan and Yam for holding down the fort last week while I tried very hard to disconnect.This week was a relatively chill one in AI land (no, really, for once), which actually let us go deep on some really fascinating stuff. We’ve got Thinking Machines Lab finally shipping their first real research with these wild interaction models, Meta Muse Spark showing up in actual products (and it’s surprisingly good!), the Musk v. Altman trial dropping juicy disclosures, and probably the biggest narrative shift on the show today: all of us are quitting OpenClaw. Yeah, you read that right. We’ll get into why.Also! and this is breaking news from this morning, CoreWeave just launched Sandboxes for your agents. I’ll cover that in This Week’s Buzz, but if you’ve been waiting for production-grade sandbox infrastructure that powers 9 out of 10 major AI labs, today’s your day.Oh, and we had Vic Perez from Krea on to talk about Krea 2, their first foundation image model trained completely from scratch. Let’s dig in.ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.The Great OpenClaw Exodus towards Hermes 🫠I’m going to start with what was honestly the most emotional thread of the entire show, because three of us, me, Ryan, AND Wolfram; all independently switched away from OpenClaw this week. And we kicked off the show literally processing this together on air.The story is the same across all of us. OpenClaw was magical back in February when we first brought it to you. Things just worked. But after Anthropic’s pricing changes (we covered this — they made Max-tier subscription usage of Opus through OpenClaw significantly more expensive), and after months of the constant Lego-construction-style breakage on every update, the magic faded. Ryan said it best on the show; he was “constantly fixing OpenClaw” instead of using it.So Ryan went to Codex. Wolfram and I both went to Hermes from Nous Research. And folks, things just work again. That February feeling is back, and with GPT 5.5, it’s an incredible assistant!Why Hermes? A few things:* It’s now the #1 most-used CLI agent on OpenRouter globally, passing OpenClaw and even passing Claude Code on OpenRouter usage. That’s a massive milestone for Nous Research and shows we’re not alone in this migration.* It has /goal (more on this in a sec), steering, and background computer use via the TryCUA integration.* It’s open! which means if you’ve built a system like Wolfram’s “Amy” or my “Wooolfred” or Ryan’s “R2” (yes, we know each other’s assistants’ names better than each other’s kids’ names at this point 😅), you can port your memories, profile, and soul files seamlessly.The migration was so smooth that Wolfram literally had Codex talk to Hermes to plan and execute the migration of his home assistant agent. Two agents collaborating to migrate themselves. We are living in 2026 and it’s easier than ever to switch. If you haven’t tried Hermes, give it a go! Steering is maybe the most underrated addition to Hermes, it’s a Codex feature, but exists in Hermes, with GPT 5.5 you can send a follow-up message, and the agent will see it after the next tool call, not after the whole chain of thought was completed (like OpenClaw defaults to) - this changes the conversation to be much more natural! Agents buying wedding gifts using Stripe wallet! Real quick story: Two weeks ago we covered Stripe’s new wallet APIs that let your agents have actual budgets to spend money on the web. I told my agent (back when it was still OpenClaw) to “go buy us a wedding present, don’t tell me what it is.” It half-worked, half-broke. This week, a giant custom map of our travels that just arrived in the mail. I approved one Stripe push notification and the rest just happened. It’s been paying my traffic tickets via screenshots. I’ve also had Hermes pay traffic tickets for me (HOV lane ones, not like.. DUI, 80% of my drive is Tesla FSD)So so happy that my AI assistant got us a present of his own choosing! And it arrived in physical form. Not perfect (the date there is our proposal date ha, but it’s still cool!) Codex gets remote control! (X)While me and Wolfram moved to Hermes, Ryan Carson moved to Codex, and during the show, I wondered, how does he communicate with his R2? Well, just a few minutes after we concluded the live show, OpenAI dropped some breaking news! Codex is now on mobile, and it connects to any mac (for now), from any iOS/Android device, and you can control your Codex, your whole Mac with Computer Use, your browser with Chrome extension, and everything else Codex can do... on the go! This is a huge unlock for many folks, and for many, I assume this will nearly replace the need for something like OpenClaw/Hermes, be much more secure by default and work flawlessly out of the box! The setup is super easy, after updating your ChatGPT app, you now have a new “Codex” window, and after updating the Codex Mac App, you will be able to pair them, and voila, all your Codex local sessions are on the Ios app as well. This works way better than Claude remote btw, significantly so. The fact that you can now add multiple macs (+ ssh servers, they also added the ability to remote control other servers via SSH) is a huge deal, OpenAI is quickly leap frogging Anthroipc, and many are noticing this and switching away from Claude Code. Big Companies & APIsMeta Muse Spark: The Voice AI That Actually Does Things 🎤Let’s start with the one I actually got to play with: Meta launched Muse Spark-powered voice conversations across the Meta AI app, WhatsApp, Instagram, Facebook, and the Ray-Ban Meta glasses (X, Announcement).And folks, I was honestly surprised by how good this is. I recorded a 5-minute live test and it’s not cut at all. The voice mode reacts almost instantaneously. It’s multilingual (it correctly identified Russian and Hebrew even if it can’t respond in them yet). It can search the Meta network mid-conversation — I showed it a screenshot of one of my own Instagram Reels and within half a second it found the exact reel and explained what we were discussing. Half a second.It also does live camera AI, where it watches what your phone sees. The only thing it failed to identify? My Meta Ray-Ban glasses. The Meta AI didn’t know what Meta Ray-Bans look like. That was the funniest moment of the whole demo.The team at Meta’s Superintelligence Labs spent 4.5 months building this, and the thing that really stood out to me from the announcement is this line: “Our models are scaling predictably. Muse Spark is an early data point on our trajectory, and we have larger models in development.” Translation: this is the small one. Bigger Muse models are coming.Meta’s superpower here, as always, is distribution. They can shove this into the daily product surface of billions of users. ChatGPT advanced voice mode (still on the GPT-4o family) has gotten genuinely worse lately — I barely use it anymore. Meanwhile Meta is shipping good real-time voice across WhatsApp and Instagram. This is the speed-of-product-integration game, and Meta is winning it.Thinking Machines Lab Previews full duplex Interaction Models 🤯This is the one Wolfram and I really geeked out on. Mira Murati’s Thinking Machines Lab finally released real research — and it’s a fundamentally different bet than what anyone else is making (X, Blog).They’re calling them interaction models, and TML-Interaction-Small is a 276B parameter MoE with 12B active, trained from scratch for native real-time human-AI collaboration. Note: they announced it, they didn’t release weights or an API yet — limited research preview is coming “in the next few months.”Here’s why this matters and what makes it different from Meta’s voice mode (which is also impressive!): the architecture is 200ms micro-turns where the model is continuously perceiving audio, video, AND text WHILE simultaneously generating output. There’s no turn boundary detection, no VAD harness — the model itself handles all of that natively. It’s full duplex baked into the weights.The demos are fire. The model can:* Speak while listening (live translation in real-time)* Watch you do pushups and proactively count them out loud as you go* Wait silently until someone enters the frame, then say “friend”* Generate a chart while continuing to explain a concept to youThe benchmarks: 77.8 on FD-bench v1.5 vs GPT Realtime 2.0 at 46.8, and 0.40s turn-taking latency vs over a second for everyone else. Nisten was unimpressed (he pointed out 1.2 seconds for a 12B-active model on a B300 rack is not exactly snappy), and that’s a fair take — but the capabilities here, particularly visual proactivity and time-awareness, are genuinely novel.The philosophical split is really interesting. While every other lab is racing toward full autonomy, Mira is saying interactivity should scale with intelligence. That’s the bet. And given the all-star team she’s pulled together (people from ChatGPT, Character.ai, Mistral, PyTorch, OpenAI Gym, Fairseq, SAM)... I’m here for it.What I really hope happens: someone leaks the weights. A 276B MoE with 12B active is exactly the kind of model we need to be able to quantize to run on something like the Richie Mini for a fully offline, always-present home assistant. Wolfram, I know you’re thinking the same thing 👀Musk v. Altman: The Trial Drops Some Wild Disclosures and TestimonyOkay this one is half drama, half disclosure goldmine. The trial is happening live as we record, closing statements are TODAY (I transcribed both of them here and here). There’s no video allowed because the courtroom was so packed with Elon fanboys, so they’re livestreaming audio only on YouTube. I set up my Hermes agent to listen to the audio stream and send me 2-minute summaries. That alone was worth the show. Apparently Elon was not in court during closing arguments (he’s in China)The big-picture story: Musk is suing OpenAI and Microsoft (specifically) claiming OpenAI abandoned its nonprofit bargain. OpenAI’s defense is essentially “Musk wanted 90% equity and full control, walked away when he didn’t get it, and is now suing over a success he predicted had a 0% chance.”Here are the highlights from sworn testimony from Sam Altman, Satya Nadella, and Ilya Sutskever that I think are the most consequential:* Musk wanted 90% of OpenAI’s equity to start. Per Altman under oath: “An early number that Mr. Musk threw out was that he should have 90% of the equity. It then softened, but it always was a majority.”* December 2018 Musk email to the team: “My probability assessment of OpenAI being relevant to DeepMind/Google without a dramatic change in execution and resources is 0%, not 1%. I wish it were otherwise.” Yeah. The guy suing them now once put in writing they had zero shot.* September 2017 ultimatum from Musk: “Either go do something on your own or continue with OpenAI as a nonprofit.” They did. He’s now suing them for it.* The Microsoft economics: Satya Nadella confirmed under oath that the $13B target redemption amount compounds to roughly $180B in four years, with 20% annual increases starting in 2025.* The AGI clause got rewritten. Originally, if AGI was achieved, the Microsoft deal would dissolve. The renegotiated version (per Altman) is that Microsoft no longer gets research IP at AGI but will continue to get product IP through end of 2032.* Sutskever’s pre-firing memo, confirmed under oath: Sam Altman “exhibits a consistent pattern of lying, undermining his execs, and pitting his execs against each other.” When asked if he still believed it: “I thought so at the time and had been thinking about Altman issues for at least a year.”* Satya wanted answers and never got them. Under oath, Nadella said he asked the board explicitly why Sam was fired and “they never gave me a specific reason... none of that was coming through.” He called the firing process “amateur city as far as I’m concerned.”* Microsoft is now the SMALLEST mega-investor in OpenAI. SoftBank $30B, Nvidia $30B (Altman: “It was either 20 or 30. I think it was 30 also.”), Amazon “larger than Microsoft.” Total private capital raised: ~$175B.* The Helion conflict of interest. Altman owns ~22.8M shares of Helion ($1.65B), roughly a third of the company. Helion has a 2028 power deal with Microsoft and a scale deployment agreement with OpenAI. He recused from the OpenAI board vote on it — and as he said under oath, “But I was in the room, yes.”And then there’s Ilya’s pearl that genuinely made me pause. When asked about the difference in AI capability between 2018 (when they started) and now: “It’s like the difference between an ant and a cat.”Yam asked the obvious question: what does Elon actually get if he wins? Honestly, I had no idea. Until I heard the arguments with the judge, and apparently it’s a LOT! Musk is asking for $135B in monetary damages (which he claims he won’t take for himself, rather they will go to OpenAI non-profit arm), and non-monetary relief that will force a removal of Sam Altman and Greg Brockman from OpenAI, and revert the split to restore OpenAI to original “non-profit” mission. This is ... quite an ask, and apparently the judge will decide on this, not the Jury, the Jury will only be deciding if there was a breach of charitable trust or unjust enrichment. This was one of the biggest bomb-shell trials, and we’ll keep you up to date on what happens. Open Source AIThe TanStack Supply Chain Attack Okay, this one’s serious. Ryan posted his most viral tweet ever about this — the TanStack supply chain attack, aka the “mini Shai Hulud” worm. If you ran an npm update during the exposure window, you may have gotten absolutely destroyed (X)What makes this one particularly nasty:* It specifically targets AI developer tooling. Hooks into Claude Code’s settings.json and VS Code JSON to re-execute on every tool event.* npm uninstall doesn’t fix it. The malware replicates itself.* If you revoke the GitHub token it uses, it nukes your home directory. A worker process watches the token. If revoked, it scorches the earth.The fixes (do them today, seriously):* Set a 24-hour minimum age rule on package installs in both npm and pip. Most malware is identified within 24 hours; this is your free moat.* Generate per-agent API keys. Never reuse keys across agents. If one gets compromised, you can revoke that one specifically.* Run development in sandboxes (more on this in a sec — CoreWeave Sandboxes just launched 👀).* Have rolling rsync backups outside of Git. Nisten’s advice: if you get hit, you can nuke everything and restore from a backup that doesn’t depend on tokens.I’ve asked Codex to review how to set these minimum age rules across your system, and published here, please review and then ask your Agent to implement those for your machines! Nisten posted a scanner for this attack — I sent the link to my Hermes agent and asked it to run, and within minutes I had confirmation I wasn’t exposed. This is exactly the kind of thing where having a trusted agent matters. (Wolfram did the same thing with the link Ryan posted — gave it to his agent and let it audit his entire system.)We’re going to go through a turbulent period as offensive AI capabilities outpace defensive ones, but I’m optimistic. Just like HTTPS came after HTTP wasn’t secure enough, we’ll figure it out. Just stay vigilant! Tools & Agentic Engineering/goal: The New Ralph Loop, Productized across Codex, Claude Code and Hermes! (X)If you’ve been listening since January, you remember our Ralph Loop episode — one of the biggest episodes we ever did. Now, every major coding harness has implemented it as a built-in command called /goal.The pattern: you give the agent a measurable success condition like “stop when auth tests pass” or “stop at 90% coverage” or “fix every failing test until npm test exits 0 without modifying any file outside the /auth directory” — and the agent loops autonomously until that condition is met. A small validation model runs inside the loop to check whether goal conditions are met at each step.Codex shipped it first. Claude Code copied it (rushed, per multiple developers). Hermes has it. And the early head-to-head comparisons are not great for Anthropic — one developer ran Codex /goal overnight and got nearly 100 commits, while Claude Code reportedly struggled on the same tasks. Multiple folks switched back to GPT-5.5.Yam’s been running /goal 24/7 for an entire week. Building things like a custom terminal from a long PRD. The level of “fear of missing agent time” in the SF AI scene right now is genuinely a meme — people are walking around in clamshell mode with laptops open in their bags because they don’t want their agents to stop.This is the philosophical opposite of one-shotting. It’s for the kinds of tasks where the model is guaranteed to run out of context — architecture cleanups, auth flow consolidation, test suite hardening, TypeScript strictness migrations. Tasks that would have required you sitting there for hours hitting “continue.”Ryan’s right that this is going to change businesses forever. You can wrap /goal around measurable business outcomes — coverage targets, latency improvements, dead code elimination — and just unleash an agent against them.This Week’s Buzz: CoreWeave Sandboxes Goes Live 📦Breaking news from this morning! CoreWeave (the parent of Weights & Biases) just launched Sandboxes in preview, and it’s directly relevant to literally every conversation we just had about supply chain security and agents that need isolated execution environments.Here’s what you get: sandboxes via the W&B SDK. Spin up isolated CPU environments where your agents can execute code, clone repos, install dependencies — all the things you do NOT want happening on your main machine after the TanStack situation. Wolfram immediately pointed out the obvious use case: agentic evaluations need fresh, consistent environments per test, then teardown. Sandboxes solve exactly that.What makes this notable: the same infrastructure powers 9 out of 10 major AI labs (Meta, Anthropic, OpenAI, etc) for training their models. CoreWeave’s sandbox product runs on that same infra. And historically CoreWeave hasn’t catered to the developer market — they sell GPUs to enterprises. With CoreWeave Inference and now CoreWeave Sandboxes available via W&B, individual developers can now spin up the same infrastructure the foundation labs use.Pricing is generous in preview. Give it a try, give us feedback, and we’ll do a deep dive next week with the team that built it.AI Art: Krea 2 — A Foundation Model Built From Scratch 🎨We were really lucky to have Vic Perez, co-founder and CEO of Krea, on the show to talk about Krea 2 — their first foundation image model trained completely from scratch (X, Blog).I have a lot of love for Krea — they let me mess around on their H100 cluster way back when I was just getting into image generation, before ThursdAI even existed. Vic was super generous with that and I’ll always be grateful.The Krea 2 philosophy is what I find genuinely interesting. Vic used an amazing analogy on the show: using existing image models is like riding a horse. You can steer it down the path, you can speed it up and slow it down, but if you try to take it off the path — into “grainy,” “artistic,” “esoteric,” genuinely weird latent space — there are big walls and the horse won’t go there. That’s the over-post-training problem. Models are too safe, too constrained, too opinionated. They’ve optimized away the strange and beautiful edges of the latent space that early Stable Diffusion users loved.Krea 2 is built to be raw, flexible, unopinionated, and unconstrained. If your prompt is vague, the model brings you new ideas rather than four variations of the same thing. The opposite of what most models do.Other features:* Style transfer with up to 4 simultaneous reference images — extracts palette, texture, composition* Moodboards — upload a bunch of reference images and the system analyzes concepts and themes across them, not just style* ~15 second generation times* Available now for Max and Business tier users, API confirmed comingThey partnered with Black Forest Labs on their earlier Krea1 model, but Vic was clear about why they had to go build their own: the open-source ecosystem isn’t tunable enough to build the creative tools they want to build. So nearly half the company spent 6-7 months on Krea 2. The first model is intentionally conservative; the next one is going to push further into the weird.Big respect for any team training a foundation model from scratch in 2026!Wrap UpThat’s a wrap on what was, on paper, a “chill week” but turned into a 2.5 hour show because we kept finding new threads to pull on. The migration off OpenClaw, the interaction models bet from TML, the Musk v. Altman disclosures, CoreWeave Sandboxes finally going live — there’s a lot moving here.Next week I’m heading to Google I/O. Expect a lot of news, because every time Google I/O is about to happen, OpenAI tries to cut them off, and xAI typically jumps in last. The last two I/Os have been wild. I’ll be reporting live from the ground.Until then — install the 24-hour package rule, generate per-agent API keys, give your agents a sandbox to play in, and maybe go try Hermes if you’ve been on OpenClaw and feeling the pain. Or Codex. Anything, really, where things just work again.Thanks for hanging with us. It’s so good to be back. 🫡TL;DR - May 14, 2026* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co-Hosts - @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed, @ryancarson* Guest: Victor Perez @viccpoes - Co-founder & CEO, Krea* Big Co LLMs + APIs* Meta launches Muse Spark voice conversations across Meta AI app, WhatsApp, Instagram, FB, and Ray-Ban Meta glasses with real-time image gen, live camera AI, and instant Reels/maps integration (X, Announcement)* Mira Murati’s Thinking Machines Lab drops Interaction Models: 276B MoE (12B active) trained from scratch for native real-time multimodal collaboration; 77.8 on FD-bench v1.5, 0.40s turn-taking latency, full-duplex audio/video/text (X, Blog)* Musk v. Altman trial highlights: Musk wanted 90% equity, predicted “0%” success for OpenAI in 2018, Microsoft is now smallest mega-investor (SoftBank/Nvidia each ~$30B), Sutskever confirms “consistent pattern of lying” memo under oath* Anthropic adds separate Claude Agent SDK monthly credits to Pro/Max/Team/Enterprise starting June 15, 2026* OpenAI launches Daybreak, a frontier AI cybersecurity platform pairing GPT-5.5 + Codex + partners like Cloudflare (X)* Open Source AI* Fastino Labs GLiGuard: 300M-parameter guardrail model matching SOTA at 23-90x smaller size, 16x higher throughput, Apache 2.0 (X, GitHub)* Meta Sapiens2: Family of 6 ViT models (0.1B-5B) trained on 1B human images, SOTA on pose, segmentation, normals, and pointmaps (X, HF)* TanStack supply chain attack (mini Shai Hulud worm) — targets AI dev tooling, doesn’t uninstall, nukes home dir if token revoked. Install 24-hour package rule immediately (X)* Nous Research releases TST (Token Superposition Training): 2-3x wall-clock speedup at matched FLOPs without architecture changes (X)* Tools & Agentic Engineering* /goal command now in Codex, Claude Code, and Hermes — productized Ralph loop. Set measurable success condition, agent iterates until done. Codex implementation winning early comparisons over Claude Code (X, Docs)* Hermes from Nous Research passes OpenClaw as #1 CLI agent on OpenRouter; adds background computer use via Trykua (X)* Artificial Analysis Coding Agent Index: benchmarks model + harness combos. Opus 4.7 in Cursor CLI leads at 61, costs vary 30x across combos, GLM-5.1 tops open-weight at 53 (X)* This Week’s Buzz* CoreWeave Sandboxes launches in preview via W&B SDK — same infra that powers 9/10 major foundation labs now available to developers for agent isolation, evals, and RL rollouts (Docs)* Vision & Video* Perceptron Mk1 — frontier video + embodied reasoning model at 1/10th the price; 88.5 on VSI-Bench, 72.4 on RefSpatialBench (vs GPT-5m at 9.0). Live on OpenRouter (X, Site)* AI Art & Diffusion* Krea 2 — Krea’s first foundation image model from scratch, focused on aesthetic diversity, style control with up to 4 references, and moodboards. ~15s generation (X, Blog) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey yall, Alex here (with a scheduled post) I’m taking this week off to get married and celebrate life with family, and touch some grass, but wanted to share the awesome chats I had with some great folks at AI Engineer Europe last week. BTW - Yam and Ryan took over the live show today, if you didn’t happen to catch that, please check out the live on our youtube channel! Ok, now to the actual content. The best thing about the AI Engineer conferences for me is the people I meet. I often have a chance to bring them to the live show (in fact, the live show we recorded there had the most guests yet on an episode! 4 guests including Swyx, Omar Sanseviero, VB from OpenAI and Peter Gostev) But often times I also have an offline chat. I find these conversation to be less about the weeks news, and more about the state of AI Engineering, and the guests themselves. Not quite Lex Friedman pod level, but a different vibe from our live shows. Sunil Pai - Cloudflare (@threepointone)The first conversation in today’s pod is with Sunil Pai, Principle Engineer at Cloudflare. Long time followers of ThursdAI know that I love Cloudflare, they gave me my first big break when I was building Targum (which still runs on Workers), so I had a great time chatting with Sunil! This guy has had several lives. React.js core team at Meta (he self-deprecates — "I'm the one nobody talks about, there's a testing API I shipped that pisses people off"). Then did developer tooling and the CLI at Cloudflare the first time. Left to found PartyKit — open-source deployment platform for real-time multiplayer apps and AI agents, built on Cloudflare Durable Objects. Backed by Sequoia. Acquired by Cloudflare in 2024, and he came back as a Principal Systems Engineer (per his bio: "Worked at Cloudflare once, left and created PartyKit, came back wiser"). Also plays guitar (Les Pauls — it's all over his blog). Co-hosts a live show called Dry Run on Cloudflare TV with Craig Dennis.Our conversation was a very fun one, ranging from Cloudflare agentic offerings, to how engineers should think about writing/reading code in 2026. I had a great time chatting with Sunil and I hope you enjoy getting to know him!Sally Ann O'Malley - RedhatThen I had the pleasure of chatting with Sally, who’s a Principal Engineer at Redhat and contributor to OpenClaw. Sally has one of the more unusual paths in the speaker lineup. Started as a schoolteacher, did a stint at Trader Joe's, then moved to Westford, MA, discovered Red Hat's HQ across the street, and went back to school for a second bachelor's in software engineering at UMass Lowell. Joined Red Hat in 2015, has been there a decade. Worked across OpenShift teams, integrating Kubernetes and Podman into the platform. Recent projects span Image Based Operating Systems, Podman, OpenTelemetry, and Sigstore. Also an instructor at Boston University's Faculty of Computing and Data Sciences and an organizer for DevConf.US. Won the 2025 Paul Cormier Trailblazer Award at Red Hat. Currently a founding contributor on the llm-d project — distributed, scalable, high-performance AI inferencing built on K8s. Heavily involved in Red Hat's InstructLab collaboration with IBM (the small-model distillation system using IBM Granite + Llama).Sally and I had a great conversation, two high energy personalities met! We geeked out about our OpenClaw agents, securing your Clankers, how it is to maintain OpenClaw, and everything in between! She was so stressed about the recording, but dare I say, this was one of the more natural guests I had on the show! I hope you enjoyed this format, please let me know if the comments, and I’ll see you next week! — Alex This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey everyone, Alex here 👋Tomorrow is May. May! I genuinely cannot believe we’re four months into 2026 already, and the AI news cycle is showing zero signs of slowing down. This week’s show was a wild one! We opened with what is genuinely one of the most important AI stories I’ve ever covered (Mayo Clinic AI detecting pancreatic cancer THREE YEARS before human radiologists), we covered the return of the Chinese whale with DeepSeek V4, OpenAI got caught in their own system prompt begging GPT-5.5 to please stop talking about goblins, and I literally gave my coding agent a credit card and asked it to buy my fiancée a wedding gift with the new Strip Link skill and CLI! Oh yeah, I’m getting married next Tuesday! 💍 So next week’s show will be a little different. I’ll be back the week after to catch you up on whatever drops in my absence (almost certainly something major, knowing this industry).Lots to get through, so let’s dive in. (also, in the end I have a full month recap of every major launch, don’t miss) ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Mayo Clinic’s REDMOD: AI Detects Pancreatic Cancer 3 Years Early 🔥 (X, Blog, Announcement)I know we usually cover Models, Parameter sizes, MoEs and big copmanies. But this is important. This is the use case that justifies the entire AI revolution, the GPU burns, the buildouts. I want humans to WIN, and Cancer to be fixed!Mayo Clinic just published a study in Gut (BMJ) validating an AI model called REDMOD that detects pancreatic cancer on routine CT scans up to three years before clinical diagnosis. The numbers are jaw-dropping: They show 73% sensitivity for catching prediagnostic cancers, compared to 39% for experienced human radiologists (while looking at the same exact CT scans).And maybe the most important bit, at scans taken more than 2 years before diagnosis, the AI catches nearly 3x as many cases as specialistsFor context: pancreatic cancer has less than 15% five-year survival specifically because 85% of patients are diagnosed after the disease has already spread. This is the cancer that took Steve Jobs. Imagine if Jobs had access to this AI three years before his diagnosis. That’s the impact we’re talking about.As Dr. Ajit Goenka from Mayo Clinic put it, the greatest barrier to saving lives from pancreatic cancer has been the inability to see the disease when it’s still curable. This AI can now identify the signature of cancer from a normal-appearing pancreas.Even better: it runs on CT scans people are already getting for other reasons. No extra screening protocol, no new imaging required. Just smarter analysis of existing data. The model also showed remarkably stable performance across institutions, imaging systems, and protocols, with 90-92% test-retest concordance over serial scans.Mayo Clinic is now moving this into prospective clinical testing through a study called AI-PACED (Artificial Intelligence for Pancreatic Cancer Early Detection).When we say “lets f*****g go” that’s what we mean. Yeah getting more intelligence is cool, but I want a world without decease! Let’s F*****g go mayo clinic! Agentic Commerce - Giving OpenClaw my credit card - safely! Stripe Link Wallet and Infrastructure CLI (X, Announcement, Blog, Announcement)Ok, give an LLM your credit card, what can go wrong.. right? Well, it’s clear that this, increasingly, is the future of commerce. Agents will be shopping for us, and we need solutions here. Well, this week at Stripe Sessions (Stripe’s annual product lineup conference) just delivered. Link Wallet, is a new ... API? CLI? Skill? Definitely a skill, for your agents, to connect with your Stripe Link (the thing that stores your credit cards safely) and then giving your agent a budget, it can go and make purchases in your behalf. Now the trick here, is, every purchase, you get a notification to approve, and the agent never sees your actual credit card number! This I think is the biggest win here. To test it out , first, I showed Wolfred the install instructions, which are literally this: Read link.com/skill.md and get me set up with LinkAnd then I asked Wolfred my OpenClaw assistant to buy me a present of its choice for my upcoming wedding, and that I don’t want to know what the present is, but I can approve the spend! OpenClaw installed this, sent me a link to connect to my Link.com account, I also downloaded the Link app to receive notifications (and had to enable them by hand, it was a bit annoying to discover, but they said they will fix the onboarding) and .. voila, my agent can now go spend my money, and I get these approval notifications: The kicker? The present Wolfred sent us is due to arrive like 2 months after the wedding 😂 But hey, it’s still something! My agent went, chose a wedding gift in budget, asked for my approval to puchase, and filled out the details (asked me for a few of them) and voila, first agentic purchase that did not require my credit card exposed! Stripe announced a whole bunch of other Agentic Commerce Suite features, like Shared Payment Tokens, which are scoped to seller and protected by Radar, MPP (machine payment protocol) and streaming payments using stable coins that are pretty slick and a bunch of other interesting things. This is where the world is moving to, and Stripe is innovating hard here, definitely worth keeping an eye out on what they are Speaking of agents and stripe, they also opened up the waitlist for projects.dev - which is a way for agents to provision accounts fully on their own, get API keys, and set everhing up from scratch. I think it’s a wonderful addition to the agentic tools and agentic internet! Your agent just runs something like stripe projects add cloudflare/workers abd boom, you have a workers deployment, with credentials synced, no dashboard clicking or API creation!Big Companies & APIsGPT-5.5 Goblin Mode: The Funniest Bug Report in AI History (X, Blog)Someone on X noticed that Codex system message for GPT 5.5 that launched last week has this interesting addition: “Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user’s query” and it has it two times! This created a bunch of memes, questions and wonderings about ... why would OpenAI care so much about Goblins. And they finally posted a long writeup on why: the TL;DR there is, GPT 5.5 absolutely LOVES talking about Goblins, trolls and other nerdy creatures. This is a result of them favoring the “nerdy” personality archetype and reinforcing this reward via RL. OpenAI admitted that “Unfortunately, 5.5 started training before we found the root cause of goblins” and so, now, we get 5.5 that LOVES to talk about goblins, can’t stop talking about goblins (unless they are asked to stop by a system prompt) OpenAI also posted the exact instructions of how to “unleash“ the goblin mode on the blog, which I find hilarious, a company that leans into the meme is a company to be celebrated 👏 GPT 5.5 is as good as Claude Mythos on CyberSecurityAccording to the AI Security institute, GPT 5.5 (not the GPT 5.5 - Cyber version that was announced), the one you have access to, is as good as Claude Mythos on vulnerability finding. We previously reported that Anthropic deemed Claude Mythos as “too dangerous to release publicly” and it turns out that that was either a marketing “Myth”, or Anthropic’s inability to server this huge model like they server Opus. OpenAI Ends Microsoft Azure ExclusivityThis piece of news sent quite a lock of shock throughout the industry, somehow, Sam Altman and OpenAI have been able to negotiate through the very strict deal with MIcrosoft and now are available in AWS as well as Microsoft Azure! Apparently the AGI clause is now gone as well! For many startups who are locked into AWS and Bedrock ,this is great news, they are not able to use GPT 5.5 and other OpenAI models directly applying their credits. Other Big Company NewsXai released Grok 4.3 - in a quiet release in their API docs, no blogpost, not even an X announcement. The only way I know about this was Artificial Analisys, Arena and Vals AI all posted that it jumped in scores. With the same price as the previous Grok, but only 1M tokens, it seems significantly better that its predecessor jumping (X)Gemini can now generate and export Docs, Sheets, Slides, PDFs directly from chat — available globally for free. Google literally put Microsoft Word and Excel icons in the announcement. They’re giving away what Microsoft charges for with Copilot to 750 million users. (X, Blog)Mistral Medium 3.5 dropped as a 128B dense model with 256K context, 77.6% on SWE-Bench Verified, and configurable reasoning effort. Their Vibe coding agent now supports remote parallel agents and session teleportation. $1.5/$7.5 per million tokens.(X, HF, Blog)Baidu’s ERNIE 5.1 Preview landed at #13 on Arena’s Text leaderboard, making it #1 among all Chinese labs. Speculated to be an 800B/36B active MoE using only 6% of comparable pretraining compute. (X, Announcement)Open Source AIThe Whale returns - DeepSeek drops V4 with insane attention innovations (X, Arxiv, HF, HF)Folks, DeepSeek just dropped V4! Two models: V4-Pro at a whopping 1.6 trillion params with 49 billion active, and V4-Flash at 284B total with only 13 billion active. Both support 1 million token context natively! V4-Pro-Max gets 93.5% on LiveCodeBench, beating every other model including Gemini-3.1-Pro. Codeforces rating of 3206, that’s a new record, beating GPT-5.4’s 3168. SWE-Bench Verified at 80.6%, that’s basically tied with Opus-4.6 at 80.8%. But here’s the thing, this model doesn’t overwhelm with evals performance, it’s at par with other open source models and at 1.5T nobody is running this on home GPUs! The bigger story here is the efficiency at long context! At 1 million context, V4-Pro uses only 27% of the FLOPs and 10% of the KV cache compared to DepSeek V3.2. The KV cache at 1M is like 8.7x smaller than V3.2. The pricing is also ridiculous (well, it was always cheap but with these perf. innovations, DeepSeek can afford to undercut! API pricing is $0.145/$3.48 per million tokens for Pro (7x cheaper output than Opus 4.7) and $0.028/$0.28 for Flash (30-100x cheaper than GPT-5.5)This release didn’t break through the AI bubble quite like DeepSeek R1, and we covered this on the show, but like a good whale, what you see on the surface is tiny compare to what lies beneath. This is a technological and innovation marvel, reducing compute and memory requirements by 90% compared to standart attention? CrazySenseNova U1: Unified Multimodal Without an Encoder - an oss infographic creator (X, X, HF, Blog, Try it)SenseTime open-sourced something genuinely architecturally wild this week. SenseNova U1 is a unified multimodal model — 8B parameters with a 3B active MoE variant, both Apache 2.0 — that does both understanding and generation end-to-end with no visual encoder and no VAE.They call the architecture NEO-Unify, and instead of the traditional pipeline (image → visual encoder → LLM → VAE → output), it’s just a single model handling pixels and words natively. The numbers are absurd for the size: 57.5% on Spatial Understanding (Qwen-VL: 35%) and a very high 91% on GenEval-Info for infographicsNisten and I tried it live on the show and it generated coherent infographics with crisp text — something most 8B models struggle with. Chinese users are reporting it rivals Qwen-Image 2.0 Pro for design drafts at much higher inference speeds. But for us, another inforaphic resulted in a bunch of chinese text, FWIW we didnt prompt for English only. The 3B-active MoE variant runs comfortably on consumer GPUs. Apache 2.0, fully open, in collaboration with MMLab at NTU. This weeks Buzz - W&B update! The biggest update this week is, we have gone viral with WolfBench.ai ! Wolfram has tested the Cursor harness (as well as many other harnesses) with GPT 5.5 and saw the best result we’ve tested so far! We still have a lot of testing to do, to add the Codex CLI itself, Devin, and many folks are asking for OpenCode and FactoryAI droids! Also, we’ve launched the IBM Granite 4.1 models on W&B for a very cheap $0.05 / $0.10 per 1M token. This model series are instruct but without reasoning, apache 2 licensed. Get it hereAre you concerned about your Cognitive Security? Guest speaker Max Spero from Pangram Labs says you should beWe had Max Spero from Pangram Labs on the show to talk about their Chrome extension that auto-flags AI-generated content as you scroll your feed. I’ve been using it for a while and many of my suspicions about who’s a slop merchant have been validated.According to Max, Pangram has a 1 in 10,000 false positive rate. If Pangram says something is AI, you can be very confident it was AI-generated. They don’t catch everything, short text, heavily humanized content, or very new models might slip through. But when they flag something, they claim they have 98.99% accuracy that it was written with AI. Max addressed the notion that previous “AI detection” tools like GPTZero and others were often mocked, for a lot of false positive responses, for example, saying that the declaration of indepence was written with AI, and says that this is no longer the case! Taylor Lorenz used the Pangram API to scan top Substack bestsellers and found some popular “writers” are nearly fully machine-generated. Technology substacks have the highest AI content rate; more than 1 in 4 top posts showing substantial AI content. And that’s only what Pangram catches.Max framed it as “cognitive security” - knowing what your inputs are. LLMs are already superhuman at persuasion, and if you’re getting one-shotted by AI-generated content that you think is human, that matters. They’re working on multimodal detection next (images, video), which will be huge given how hard GPT-Image-2 outputs are to spot.I find their chrome extension very useful, I scroll on my feeds and see a bunch of “ai” labels, and I can know to skip that content if I don’t want to. You can get 2 weeks trial to their chrome ext on pangram X account.April 2026 - a full month of AI model releasesApril was an insane month, here’s the major release calendar for April 2026Mar 31: Claude Code leakApr 1: Alibaba Wan 2.7-Image · Fish Audio STTApr 2: Google Gemma 4 | Alibaba Qwen 3.6-Plus Apr 4: OpenAI GPT-Image-2 (Arena leak)Apr 6: MemPalaceApr 7: Anthropic Claude Mythos Preview · Z.ai GLM-5.1 Apr 8: Meta Muse SparkApr 9: Anthropic Managed AgentsApr 10: AI Engineer LondonApr 11: MiniMax M2.7 (open weights)Apr 14: Baidu ERNIE-Image 8BApr 15: Google Gemini 3.1 Flash TTSApr 16 : Anthropic Claude Opus 4.7 | OpenAI Codex (computer-use)Apr 17: Anthropic Claude DesignApr 20: Moonshot Kimi K2.6 · OpenAI Codex ChronicleApr 21: OpenAI ChatGPT Images 2.0 Apr 22: OpenAI Privacy Filter (1.5B)Apr 23: OpenAI GPT-5.5 + GPT-5.5 ProApr 24: DeepSeek V4 Pro & FlashApr 27: Cognition Devin for TerminalApr 29: Cursor SDK | Baidu ERNIE 5.1 Preview | Stripe Link Wallet (Agents) · IBM Granite 4.1 8BApr 30: xAI Grok 4.3That’s all for today folks, we’ve talked about a few other things, and the TL;DR list of releases keeps growing and growing from week to week. As I said, I’m getting married next week, so I will be out, and won’t be on the live stream, Yam, Ryan, Nisten and LDJ will make sure you’re up to date! If you found this valuable, please consider supporting our publication with a subscription and share with a friend. Alex 🫡ThursdAI - April 30, 2026 - TL;DRHosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co-Hosts: @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed* Guest: Max Spero (@max_spero_) - Co-founder, Pangram LabsHealthcare AI* Mayo Clinic’s REDMOD detects pancreatic cancer up to 3 years before clinical diagnosis with 73% sensitivity vs 39% for radiologists (Announcement)Open Source LLMs* DeepSeek V4 paper drops with CSA+HCA attention, 1M context at 5.7GB KV cache, possibly first frontier model trained across multiple datacenters (Arxiv)* SenseTime open-sources SenseNova U1 - unified multimodal 8B/3B-active MoE with no encoder/VAE (HF, GitHub)* IBM releases Granite 4.1 family (3B/8B/30B) - non-thinking dense models with 20x token efficiency over Qwen3.5 9B, Apache 2.0 (Blog, HF)* Mistral launches Medium 3.5 - 128B dense flagship with 256K context, configurable reasoning, plus Vibe coding agent (HF, Blog)* Baidu ERNIE 5.1 Preview hits #13 on Arena (#1 Chinese lab) using just 6% of comparable pretraining compute (ernie.baidu.com)Big CO LLMs + APIs* OpenAI publishes blog explaining GPT-5.5’s “goblin mode” - reward amplification during RL training created an obsession with creature metaphors, leading to duplicated suppression instructions in the Codex system prompt* OpenAI ends Microsoft Azure exclusivity, AWS announces GPT-5.5 and Codex on Bedrock; AGI clause removed from contract (Sam tweet)* Gemini can now generate and export Docs, Sheets, Slides, PDFs, .docx, .xlsx, LaTeX directly from chat - free for all users globally (Blog)* NVIDIA releases Nemotron 3 Nano Omni - 30B/3B-active hybrid Transformer-Mamba MoE with 256K context, 9x throughput on consumer hardware (Blog)Agentic Commerce & Tools* Stripe launches Link wallet for agents at Sessions 2026 - AI agents get scoped payment credentials with mandatory human approval, real card never exposed (Blog)* Stripe removes waitlist on Projects.dev - 32 infrastructure providers (Cloudflare, WorkOS, ElevenLabs, Twilio, Daytona, Browserbase, AgentMail, etc.) provisionable via CLI for AI agents* Cursor launches SDK exposing the same runtime, harness, and models that power Cursor IDE - now embeddable in any product (Docs)* Cognition launches Devin for Terminal - local CLI coding agent with /handoff command for seamless cloud transfer (cli.devin.ai)Evals & Benchmarks* WolfBench tests 23 models across 300+ runs on Terminal-Bench 2.0 - Cursor Agent + GPT-5.5 is the #1 combination (wolfbench.ai)* Microsoft’s DELEGATE-52 benchmark shows GPT-5.4 loses 28% of document content after 20 iterative edits, frontier models corrupt stealthily while preserving structureThis Week’s Buzz - Weights & Biases* IBM Granite 4.1 live on W&B Inference at $0.05/$0.10 per million input/output tokens with 128K context* WolfBench results going viral with Cursor + GPT-5.5 dominance, Codex and Devin testing in the pipelineAI Detection & Cognitive Security* Pangram Labs launches Chrome extension auto-flagging AI content in real time on X, LinkedIn, Reddit, Substack, Medium with 99.98% accuracy and 1-in-10,000 false positive rate (pangramlabs.com)* Taylor Lorenz uses Pangram API to analyze top 25 Substack bestsellers, finding many popular newsletters are near-fully AI-generatedAI Art, Video & Audio* ElevenLabs launches ElevenMusic - full music platform with discovery, remixing, royalties; 4,000+ indie artists at launch (elevenmusic.io)* HeyGen HyperFrames integrates natively with Claude Design - HTML-to-MP4 motion graphics via single CLI command (hyperframes.dev)* xAI drops Grok Imagine update with dramatically improved lip sync, sound, and 30-second video extensions* OpenAI engineer confirms team is actively fixing GPT-Image-2’s noise artifact issueOther* Talkie - 13B open-weight LLM trained exclusively on pre-1930 text, by Alec Radford and David Duvenaud (talkie-lm.com)* GPT-5.5 Codex full system prompt leaked from OpenAI’s open-source repo, revealing 272K context window, four reasoning levels, three personality modes, and the duplicated anti-goblin instruction This is a public episode. 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Hey, Alex here, I’ll try to catch you up, but it’s one of the more intense weeks in AI in recent memory. Here’s the TL;DR - OpenAI dominates across the board this week! Finally launches “spud”, called it GPT 5.5 (and 5.5 Pro), and it’s SOTA on most things,nearly matching the mysterious Claude Mythos but released and we can actually use it (we tested it extensively). OpenAI also took the crown in image generate with the incredible GPT-image-v2 release, beating Nano Banana 2 and pro by a significant margin, the images are incredible, this model can generate working QR codes and 360 images it’s quite bonkers. Codex was updated with Computer Use (which I told you about last week), in-app browser and a bunch of other tools that match GPT 5.5 intelligence. Meanwhile, Anthropic launched an incredible research preview of Claude Design, finally admitted that Claude was dumb and reset quotas across the board, while breaking the trust of the community with removing Claude code from the pro plan. We’ve also got great open source updates, Kimi K2.6 and Qwen 3.6 27B are both great performers! We were live on the stream for almost 4 hours today waiting for GPT 5.5 and finally got it and tested it live on the show + had Peter Gostev on from Arena who had early access and shared with us his insights. Let’s get into it! ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.OpenAI’s GPT 5.5 is here - SOTA AI intelligence you can actually use (Release Blog)OpenAI finally gave us all access to their latest intelligence boost, GPT 5.5 thinking (and GPT 5.5 Pro). These models take the crown across many benchmarks, including TerminalBench (82.7%), GPDval (84%) and more. You can see the highlited versions on the image above. Though, its not uncommon for OpenAI to do some chart crimes, so @d4m1n created a chart that also showed the full benchmarks, including the ones GPT 5.5 is not beating Opus at, as you can see below, it underperforms on Humanity’s Last Exam, and scaled tool use. But, benchmarks don’t tell the full story. GPT 5.5 uses significantly less tokens, compared to 5.4, about 40% less. It’s also more expensive, but given the lower token usage, it nets out at about ~20% price increase, while being more intelligence and faster. Tons of folks who had early access are reporting the same things, this model excels in long running tasks, Peter Gostev from Arena, who joined our live stream, showed us an incredible demo that ran overnight for over 8h! This model can work until the task is done, no longer just pausing in the middel asking for your input. The real highlight is, paired with the recent GPT-image-2 (which I’ll expand on later in this newsletter), GPT 5.5 becomes an excellent UI designer. This is a big area in which Claude still has moat and OpenAI is trying to catch up here, and the real alpha now is to use both the Image gen and 5.5 in tandem to create beautiful visuals and UIs. The main thing is, after testing it quite a few times, this only works if you generate an image outside of the session that builds the actual UI. we tried a couple of times to do it in 1 session, and the resulting UI doesn’t seem to be remotely close to the generated image. Only after sending this image to a completely fresh session and asking for a “pixel perfect” implementation, did GPT 5.5 start to resemble the input image and rebuild the whole ui in pixel perfect fidelity! GPT Image v2 - SOTA thinking image model, finally beating Nano Banana (Blog, Live)Like we said, OpenAI is dominating this week, and in both instances those are great models. Though, apples to apples comparison, GPT-image-v2 is a much higher jump — from previous models — than GPT 5.5! According to Artificial Analysis, the jump in how many people prefer GPT-image-2 in blind tests compared to other model is the higest we’ve ever seen, over 250 points. And you can clearly see it in the generations as well. Previously this week, we did a live streaming session with Peter Gostev (from Arena) and we did a deep dive comparing this new model to GPT Image 1.5, Nano Banana and Grok Imagine, and it’s a clear winner across most categories.Character consistency is immaculate, high resolution imagery, instruction following, are all so so good it’s a bit hard to explain in text. Reasoning visual intelligence Like with Nano Banana, this model is likely based on a big GPT image, it’s no longer just diffusion, as you can see, it reasons! And apparently the more reasoning you give it (if you choose GPT pro) the better it’ll be. The examples are indeed wild, the model can generate images of code that works, generate functional QR codes and bar codes! The craziest thing people figured out it can do, is functional 360 imagery (equirectangular format), you can just ask the model to create a 360 image of “scene” and then drop this in to a 360 viewer! Peter shows us on the show how he combined GPT 5.5 and Image v2 to create a sort of “street view” from a bunch of 360 images, it blew our minds. He literally spun up an overnight GPT 5.5 task in Codex that planned out the hanging gardens of Babylon, generated hundreds of equirectangular images, stitched them into a walkable interface, and had it running 8+ hours without babysitting. A street view of a place we don’t actually know what it looked like, hallucinated from latent space. What a time.Day one availability is wide: Figma, Canva, Adobe Firefly, fal.ai, and Microsoft Foundry all have it. Nano Banana dominated for what felt like an eternity in AI time (it was really only a few months 😅), and finally OpenAI has a proper answer.OpenAI is dropping models on HF - Privacy Filter, a 1.5B apache 2.0 PII reduction model (X, HF)I’ve told you the’ve been cooking this week! OpenAI open sourced a genuinly useful model called Privacy Filter, that has 1.5B parameters with only 50M active, small enough that it runs in fully offline in your browser (check out this incredible web demo by our friend Xenova) This model is specifically built to anonymize and filter our personally identifiable information (PII), things like names and addresses, but more importantly bank accounts and API keys! This, in the era of agentic assistants is extremely important and I’m very happy that OpenAI is open sourcing here, specifically because while it’s great generally, this model is great for fine-tuning on your own data! Pairing this with something like CrabTrap, a new open source proxy with LLM as a judge for agents like OpenClaw, and you’re hardening your setup so that your private details won’t leak, even if someone manages to prompt inject your agent! In every other week, CrapTrap would deserve a segment of its own, it is really a novel solution to the “AI agent can leak your creds” problem, created by Brew CEO, as they run agents inside Brex, but this week is insane, so... you get a link and we move on 🙂 Claude Design - Anthropic’s figma killer? (try it, deep dive)This launched on Friday (come on Anthropic, why are you launching things on a friday?!) and nearly tanked Figma stock (16% down since). It didn’t help that Mike Krieger who runs product at Anthropic and co-leads Anthropic Labs, quit the Figma board just a few days before this release. Claude Design is a new, separate interface for Claude, with its own usage meter, that exists only on web, and only for Max subs for now. We all know that Claude is great at frontend design, but this is an interface that wraps Claude, with some incredible “designer like” tools. Knobs to edit font sizes, point and click interface to highlight elements for Claude to fix. The highlight for me, what broke my brain on the live stream, was the “talk to the design” feature, where you turn on the microphone, talk to Claude, and while you point, it “knows” what you’re pointing at! So you can say “here, fix THIS thing” without saying what that thing is, and Claude will just fix it, by looking at where your cursor was at the time. This ... this feels like magic. The huge unlock in Claude Design is the initial “brand guidelines” process, in which you ask Claude to create a holistic brand identity (based on your website code, screenshot, Figma file etc) and then, every new project, can have that brand identity preserved, with the right fonts, colors, logos etc. I dropped the show notes from this week and asked for an interactive infographic website using the brand guidelines. This really does feel like a “new kind” of product, I’ve worked with designers before, the interaction model with Claude Design feels very much like working with a designer, showing them what you like and don’t like. And like working with a designer, it’s expensive! Claude Design uses Claude 4.7 and buuurns through tokens! I’ve tapped out of my weekly quota in less than 4 projects! Luckily, Anthropic this week admitted that they’ve dubmed down Claude, and reset the quotas, so I was able to show it on the live show. This week’s Buzz — W&B LEET TUI gets Workspace modeOur W&B LEET TUI went viral a couple weeks back (local terminal UI for watching run stats, metrics, and system health - built for folks training on remote boxes who don’t want to alt-tab to a browser), and the team shipped a big follow-up this week: workspace mode.Multi-run workspaces live, metadata filtering, system metrics (GPU stats included), console logs, and — my favorite — images rendered directly in the terminal . The whole web workspace experience, now in your SSH session.Demo video and full announcement here. pip install wandb, give it a spin.Open Source AIKimi K2.6 - Opus at home (if you have a data center) (X, HF, Live)Moonshot AI dropped Kimi K2.6 this week, a 1 Trillion parameter MoE with 32B active, 384 experts, 256K context, under a modified MIT license. The headline numbers are wild: SWE-Bench Pro at 58.6 (beating GPT-5.4 and Opus 4.6), BrowseComp at 83.2, HLE with tools at 54.0.Wolfram ran it on his own Wolf Bench and it came out as the best open source model he’s ever tested — essentially matching Sonnet 4.5 on terminal bench with the Terminus agent harness, and beating Opus 4.6 inside OpenClaw. That’s a crazy sentence to write.Pricing on Cloudflare Workers AI is $0.95/M input, $4/M output — roughly 15x cheaper than Opus. If you have the budget to run it.Now, the calibrated take: Yam showed us a report from @BrightMind where Kimi failed pretty badly at rendering a 3D lava lamp while every other frontier model nailed it. Artificial Analysis has Kimi at #4 on their intelligence index (54) behind the three frontier labs. So it’s definitely a bit benchmaxxed on agentic coding, but it’s also genuinely good at agentic coding, which is the use case most people care about right now. My own test: it overthinks a lot, generates a lot of tokens (which hits your wallet even at those low prices) and I wasn’t very happy with it during my live test. The frontend design of it is meh, and it did feel benchmaxxed. Bottom line: if you’re building an OpenClaw setup and you want Opus-adjacent quality without paying Opus prices, Kimi K2.6 could be the move. They also shipped Kimi Code CLI as a companion to Claude Code / Codex CLI.Alibaba drops Qwen 3.6 27B - (Actually sonnet at home) This one is special because it’s genuinely, actually runnable at home. It’s a dense 27B model under Apache 2.0, and it beats Alibaba’s own ~400B Qwen3.5 flagship MoE on every major coding benchmark. SWE-bench Verified 77.2, Terminal-Bench 2.0 at 59.3 (matching Opus 4.5), SkillsBench 48.2 (beating Opus 4.5 at 45.3).With Unsloth’s dynamic GGUFs, this runs on 18GB of RAM. A used RTX 3090 under $1000 or a 24GB Mac Mini and you’re running something genuinely comparable to Sonnet 4.5 at home. Nisten has been daily-driving it and said people are calling it “Sonnet 4.5 at home” - it’s not drop-in replacement perfect (it struggled with hard git merges in his testing), but for non-critical work? Absolutely there.Natively multimodal, 262K context extendable to 1M. There’s also a sibling, Qwen3.6-Max-Preview, available on their API if you want the frontier version.Great great open source model! Quick hitsA bunch of stuff worth knowing about that didn’t get full segments:* Google Gemini Deep Research + Deep Research Max on Gemini 3.1 Pro (announce) — autonomous research agents that navigate web + your custom docs. Plus native chart generation and MCP support in the API.* Google Gemini Enterprise Agent Platform (launch) — evolution of Vertex AI for enterprise agent builders.* ChatGPT Agents “Hermes” leak — an agents builder/studio with templates and Slack integration incoming per @btibor91.* Codex now has 4M users per the team, and they open-sourced Euphony, a visualizer for Codex session logs.* SpaceX / Cursor $60B deal — the structure is either a $60B acquisition or a $10B collaboration experiment. The thesis being whispered: are developer traces the missing training ingredient for frontier coding models? Very spicy, very Elon.* Speaking of Elon, XAI released Grok-Voice-think-fast 1.0 (Blog) - it’s their fully end to end omni model that takes customer calls and is already deployed at scale at Starlink! Very interesting contender to Gemini Flash live model we covered before. The benchmarks look insanely goodPhewI said at the top this was one of the more intense weeks in AI in recent memory, and I genuinely mean it. We were live on the stream for almost four hours. I’ve done five livestreams since last Thursday. GPT 5.5 dropping mid-show was the cherry on top. Between Codex becoming ambient, GPT Image v2 rewriting the ceiling for generative visuals, Claude Design moving a stock price, two incredible open source drops in Kimi and Qwen, and OpenAI quietly re-committing to open source — this was a lot.If you’re feeling the FOMO, you’re not alone. We live this stuff and I still feel it. My ask this week: bookmark the livestreams, play with GPT Image v2 (it’s genuinely the most fun I’ve had with an image model in a long time), and if you’re deploying agents in production, go read the CrabTrap source code this weekend.See you next Thursday — same place, same time, probably another launch that disrupts us mid-show. That’s the world now 🤷ThursdAI - Apr 23, 2026 - TL;DR* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co-Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson* Peter Gostev (@petergostev) - Arena AI* Big CO LLMs + APIs* OpenAI launches GPT-5.5 and GPT-5.5 Pro — SOTA across the board (Blog, Livestream)* OpenAI GPT-Image-2 — biggest Arena Elo jump ever, thinking mode for images (X, Eval site, Livestream)* OpenAI Codex — Background Computer Use + Chronicle (screen memory), hits 4M users (Chronicle)* GPT-5.5 pre-launch leak in Codex dropdown (X)* Anthropic Claude Design — research preview on Opus 4.7, Figma -7% (X)* Anthropic resets all Claude quotas, admits degradation, allows OpenClaw CLI back (X)* Anthropic ARR crosses $30B* Google Gemini Deep Research + Deep Research Max on Gemini 3.1 Pro (X)* Google Gemini Enterprise Agent Platform (X)* ChatGPT Agents “Hermes” leak — builder/studio + Slack integration (X)* OpenAI clinician/medical model + workspace agents released* Open Source LLMs* Moonshot Kimi K2.6 — 1T MoE, 32B active, SOTA open source on SWE-Bench Pro (X)* Alibaba Qwen3.6-27B — dense 27B, Apache 2.0, beats own 400B flagship (X, HF)* Alibaba Qwen3.6-Max-Preview on API (X)* OpenAI Privacy Filter — 1.5B MoE, 50M active, Apache 2.0, runs in browser (X)* Tools & Agentic Engineering* Brex CrabTrap — LLM-as-judge HTTP proxy for agent security (X)* OpenAIDevs Euphony — open-source Codex session log visualizer (X)* This week’s Buzz - Weights & Biases* W&B LEET TUI goes workspace mode — multi-run, GPU metrics, images in terminal (X)* Voice & Audio* StepAudio 2.5 TTS — natural-language control of emotion and delivery (X)* Deals & Industry* SpaceX/xAI Cursor — $60B acquisition or $10B collaboration structure This is a public episode. 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Hey ya’ll, Alex here with your weekly AI news catch up. It’s one of those Thursday’s where no matter how well I prep, the big AI labs are hell bent to show up before each other. Alibaba dropped Qwen 3.6 with Apache 2, confirming their commitment to Open Source, then Anthropic released Claude Opus 4.7 (not quite Mythos) and OpenAI followed with a huge Codex update that includes Computer Use among other things. The highlight of Computer User is the background usage, more on that below. This is all just from today!Previously in the week we had 2 incredible 3D world generators, Lyra 2.0 from Nvidia and HYWorld 2 from Tencent, Windsurf dropping 2.0 version with Devin integration and Google releasing a Gemini TTS, with over 90+ languages support and incredible emotions range, and Baidu open sources Ernie Image, rivaling Nano Banana. Today on the show we had 3 awesome guests, Theodor from Cognition joined to cover the new Windsurf, Kwindla is back on the show to talk about “the side project that escaped containment” Gradient-Bang, a multi agent, voice based space game and Trevor from Marimo joined to talk about pairing your agents with a Marimo notebook. Let’s dive in! 👇 ThursdAI - We’re over 16K on YT today, my goal is to get to parity with Substack, please subscribe. Codex can now really use your computer: OpenAI updates Codex with CUA, Image Generation, Browser, SSH (X, Blog)Codex from OpenAI has been the major focus inside OpenAI for a while now. We’ve reported previously that OpenAI is closing down SORA and other “side-quests” to focus, and that they will join Codex, ChatGPT and the Atlas browser into one “superapp” and today, it seems, that we’ve gotten an early glimpse of what that app will be. The Codex team (which seems to be growing from day to day), have been on a TEAR feature wise lately, trying to beat Claude Code, and they pushed an update with a LOT of features and updates, among them a new memory system, internal browser and image generation. The highlight for me though, was absolutely the polished computer use experience. Computer use is not new, Claude has a computer use feature flag, many others. Hell, we told you about computer use with Open Interpreter, back in Sep of 2023. But, this.... this feels different. You see, OpenAI has quietly purchased a company called Software Apps Inc, that almost launched a macos AI companion a year ago called Sky. This team is obsessed with Mac, and somehow, they were able to build a magical experience, a huge part of which, is the fact that they are controlling the mac, in the background. This is like black magic stuff. You work on one document, Codex clicks buttons and does things in another, without interrupting you. You may ask, Alex, why do you even care so much about computer use, when most of the work happens in the browser anyway, and Claude (and Codex) can control my browser anyway? Well, true, but not ALL work is happening there, for example, file system integration. It’s notoriously big part of browser automation that fails, when you need to upload/download files. I’ve spent countless cycles trying to get this to work with OpenClaw, and this, just does it. This closes the loop between knowledge work in the browser (yes, this thing can use your browser) and the broader OS. It’s so so polished, I truly recommend you try it. It’s as easy as @ tagging any app that you have running and asking Codex to do stuff there. Pro Tip: Enable fast mode for a much smoother experience. Anthropic Opus 4.7 is here, not quite Mythos, 64.3% Swe-bench Pro, tuned for long running tasks (X, System Card)What is there to say? Is this the model we expected from Anthropic after releasing the news about Claude Mythos last week? no. But hey, we’ll take it. I new Claude Opus, with a significantly improved multimodality capabilities, and a long horizon coding task improvements? For the same price? Well, not quite! Apparently, this model could be a “from scratch” trained model, given that the tokenizer (the thing that converts words into tokens for the LLM to understand) is a different one. It also uses 1.3x more tokens for the same tasks, which means, that the new and default model from Anthropic became effectively more expensive (A note they acknowledged by raising the usage limits, to an unknown amount in Anthropic subscription plans, but it’ll still be a token tax on the API use) How about performance? Well, hard to judge on Evals alone, but they are great. A huge jump in Swe-bench Pro, over 10% improvement, puts this model as the best out there, except Mythos. It’s also the best at real world knowledge via GPQA Diamond (except Mythos). Are you seeing a trend here? Anthropic released a preview of a model, but for the first time, it’s not their “absolute best” model, and in a weird move, they have compared it on Evals to an unreleased model (presumably 10x the size?) As far as we’ve tested this, it gave an incredibly detailed response on the Mars question we constantly test on, both for me and Nisten, Opus 4.7 produced an incredibly detailed 3D rendered result, much better than out previous tries. I’ll be keeping an eye on this model and keep you guys up to date on what else we find. Vibe checks are .. it’s more expensive, long context is unclear but it’s a great vibe model. Alibaba is back - Qwen 3.6 is Apache 2.0 35B with 3B active parameters (X, HF, Blog)The coolest thing about this release is not the evals (though they claim to outperform the much denser Qwen 3.5-27B on multple benchmarks) is that Alibabab is putting models with open weights and an Apache 2.0 license! We previouly reported on rumors from inside Alibaba, that a few internal restructuring caused many of us to doubt if they would commit to OSS, and they answered! Another highlight for me in this model, is that Alibaba has an OpenClaw bench (that they are promising to release soon) and that this model does as well as the dense model and beating Gemma 4 by a wide margin on that task. This model is also natively multimodal, with 262K context extensible to 1M via YaRN. MiniMax M2.7 Open Weights - 230B MoE with only 10B active (X, HF)Our friends at MiniMax finally dropped M2.7 in open weights (technically not fully Apache, commercial use requires their authorization, but free for research, personal, and coding agents). It’s a 230B parameter MoE with only 10B active parameters, and it’s matching GPT-5.3-Codex on SWE-Pro at 56.22%. On Terminal-Bench 2 it hits 57%. But the real story here, the part that made me stop scrolling, is the self-evolution piece.They let an internal version of M2.7 run its own RL optimization loop for 100+ rounds with zero human intervention. The model analyzed its own failure trajectories, modified its own scaffold code, ran evals, and decided whether to keep or revert changes. It got a 30% performance improvement on internal metrics. The model improved itself.Shoutout to the MiniMax team — longtime friends of the pod and they keep delivering (as they promised to release the weights for this one and they did) This weeks buzz - news from Weights & Biases from CoreWeaveThis week was a very big one in our corner of the AI world. Our parent company CoreWeave announced not one, not two but 3 major deals, including one with Anthropic, a renewed commitment from Meta and a renewal from Jane Street. CoreWeave now serves 9 out of the top 10 AI model providers in the world. 🎉 Oh and a small plug, if you want to get tokens powered by the same infrastructure, our Coreweve Inference service is open and very cheap, and we’ve recently added Gemma 4 and GLM 5.1 both to our inference service. This week on the pod, I’ve chatted with Trevor, founding engineer at Marimo Notebooks (also part of CW) about their recent highlight of pairing an AI agent with Marimo notebooks, they went quite viral on hacker news and I wanted to understand why. I understood why, it’s really cool. Check Trevor out on the pod starting around 01:05:00 timestamp. Tools & Agentic EngineeringWindsurf 2.0 - Agent Command Center + Devin in the IDE - interview with Theodor Marcu (X, Blog)The first big post-Cognition-acquisition move for Windsurf dropped this week, and I got to chat with Theodor Marcu from Cognition about it on the show. The headline: Windsurf 2.0 brings an Agent Command Center; think Kanban-style mission control for all your agents, plus native Devin integration baked right into the IDE, and Spaces (persistent project containers that group your agent sessions, PRs, files, and context).The framing Theodor gave me: local agents are pair programmers bounded by your attention (they stop when you close the laptop), while cloud agents are independent hires. Windsurf 2.0 tries to unify both paradigms in one interface. You can plan locally with Cascade using the Socratic method — going back and forth, challenging assumptions, building up context — and then with one click, hand off execution to Devin which runs in its own cloud VM, opens PRs, runs tests, and even tests its own work using computer use on its own Linux desktop. You can close your laptop and it keeps shipping.One reality check from the community: Devin is great but not cheap. One early tester burned $25 in credits for a 15-20 minute bug fix that produced “okay” results. Something to watch on the Max plan economics. Devin access is rolling out gradually to Windsurf users over 48 hours from launch. Shoutout to Swyx that helped design the Spaces three months ago whilst at Cognition! Warp terminal now supports any CLI agent with vertical tabs and mobile control (X, Blog)This one is for the terminal enjoyers. Warp, which in my opinion is the best terminal experience out there, just shipped first-class support for any CLI agent — Claude Code, Codex, OpenCode, Gemini CLI, all running side by side in vertical tabs with live status indicators.The killer feature here, and this solves what I think is the single worst part about using Claude Code, is notifications when agents need you. If you’ve used Claude Code you know the pain of constantly checking if it’s waiting for a permission or input. Warp notifies you. You step in, approve, go back to what you were doing. They also added integrated code review inside the terminal, a rich multimodal input editor, and — this is wild — remote control from mobile. Monitor and interact with your running CLI agents from your phone.Voice & AudioGradient Bang - the first massively multiplayer LLM-driven game, interview with Kwindla (X, Play it)Kwindla, co-CEO of Daily and maintainer of Pipecat, came on the show to talk about Gradient Bang, a game he described as “a side project that escaped containment.” He told me about this back in December, and folks, it’s finally live and it’s genuinely the first fully LLM-driven multiplayer game I’ve seen. It’s inspired by an old BBS door game called Trade Wars that Kwindla used to play as a baby programmer on a 386 DX, but reimagined so your ship’s computer is an LLM you can just… talk to.You pilot a spaceship through a procedurally generated universe, but instead of clicking buttons, you talk to the thing, and say things like “take me to the nearest mega port and trade along the way” — and your ship AI delegates to sub-agents to actually do the work. You can run corporations, buy more ships, task them to do 5 exploration loops while you do trade runs. It’s Factorio-meets-Ender’s-Game-meets-voice-AI. I’ve been playing it, my ship is currently roaming the universe as we speak (with 0 credits as someone robbed me!)What makes this technically fascinating is that it’s basically a production-grade stress test for multi-agent orchestration. Sub-agents with shared context, episodic memory across sessions, dynamic LLM-generated UIs (the React front-end is literally rendered from JSON thrown over by a UI agent LLM), and long-running contexts that go for weeks. The architecture is now shipping as a Pipecat library called Pipecat Sub-Agents. Tech stack: Deepgram for STT, GPT-4.1 for the voice agent, GPT-5.2 medium-thinking for task agents, and a dedicated benchmark called GB Benchmarks because tasking these agents is genuinely hard.Fun detail: Kwindla’s rule for this project was to not write or read any code since November. His colleague John lasted about one day before he broke and started reading React. The Z/L Continuum claims another victim. Go play it, it’s free and fun: gradientbang.com.Google launches Gemini 3.1 Flash TTS (X, Blog, Try it)Google dropped a new TTS model this week and folks, it’s not quite the speed-of-light real-time conversational TTS we’re all dreaming of (it’s about 3 seconds time-to-first-token, so batch-mode only), but the controllability is wild. We’re talking inline audio tags — [laughs], [sighs], [gasp] — natural language scene direction, two distinct speakers per generation, 70+ languages with auto-detection, and you can switch emotion and pacing mid-sentence with natural language. I tested it live on the show with a “shocked/whispering” tag combo asking “Who came to ThursdAI?” and it absolutely nailed it. It hit 1,211 Elo on the Artificial Analysis TTS Arena, 4 points behind Inworld TTS 1.5 Max and ahead of ElevenLabs v3. Pricing is about $0.03 per 60 seconds of audio, roughly 4.7x cheaper than ElevenLabs v3.Kwindla’s take: this is part of the broader shift from traditional TTS architectures toward fully steerable, prompt-able speech models — which is great for expressive use cases but means you need to test heavily for hallucinations and word skipping.AI Art, Video & 3DTencent HYWorld 2.0 and NVIDIA Lyra 2.0 - actual 3D worlds from one imageThis week we got not one but two major single-image-to-3D-world open releases, and they’re genuinely different from the video world models (Genie 3, Cosmos) we’ve been covering.Tencent HYWorld 2.0 takes a single image (or text, or video) and produces actual 3D Gaussian Splats, meshes, and point clouds that you can import directly into Unity, Unreal, Blender, or NVIDIA Isaac Sim. Not video. Real editable 3D assets. Their framing: “watch a video, then it’s gone” vs “build a world, keep it forever.” The WorldMirror 2.0 reconstruction model is a 1.2B parameter feed-forward model that predicts dense point clouds, depth, normals, camera params, and 3DGS in a single pass. All open source.NVIDIA Lyra 2.0 (Apache 2.0) takes a single image and progressively generates an explorable 3D world as you navigate through it. The breakthrough here is solving two classic failure modes of generative world models: spatial forgetting (hallucinating new structures when you revisit an area) and temporal drifting (errors accumulating until the scene turns to mush). They solve both with per-frame 3D geometry retrieval and this elegant self-augmented training trick where they train the model on its own degraded outputs so it learns to correct drift. DMD distillation gets you 4-step inference. Apache 2.0, Hugging Face, code and weights.Both of these together feel like the end of video-only world models as the state of the art. We’re going straight to editable, persistent, importable 3D worlds.Baidu open-sources ERNIE-Image - 8B parameter text-to-image (HF)Not to be outdone, Baidu dropped ERNIE-Image, an 8B parameter DiT that’s now #1 on GenEval among open-weight models (0.8856), beating Qwen-Image, FLUX.2-klein, and Z-Image. Built from scratch in 3 months. Runs on a 24GB consumer GPU, and someone already quantized it to NF4 so it runs under 10GB VRAM on an RTX 3060. The text rendering story is the headline — clean multilingual text rendering for posters, infographics, comics, the stuff every other model has been historically terrible at. There’s also a Turbo variant that does it in 8 inference steps.The craziest AI video I’ve ever seen - “Pi Hard” (X)You have to watch this AI video. It’s one of the crazier ones I ever saw, and I do reporting on AI for a living. I showed this to my Fiancee Darya, and she only asked me “is this AI” in the middle of it, after saying “yeah, let’s watch this 😂) Closing thoughtsWhat a week. Opus 4.7 dropped live on the show, Codex is now controlling your mac in the background like black magic, Qwen gave us another Apache 2.0 banger, MiniMax shipped a self-evolving model, and we got two “image-to-actual-3D-world” open source releases on the same week. Oh and a shoe company is now an AI compute company.The Z/L Continuum keeps shifting — I feel like every week I drift a little more toward L, especially after seeing Kwindla ship Gradient Bang without reading code since November. And every week the agents get better at babysitting themselves (Claude Code Routines, Windsurf’s Agent Command Center, Warp’s unified CLI agent UX, Codex’s computer use in the background), which means more FOMAT for all of us.Thanks for reading, share this with a friend, and if you enjoyed this, drop a comment with what you want more or less of. Feedback keeps me going.— AlexTL;DR - ThursdAI, April 16, 2026* Hosts and Guests* Alex Volkov - AI Evangelist & Community with Weights & Biases / CoreWeave (@altryne)* Co-hosts: @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed* Guests:* Kwindla Kramer (@kwindla) - Co-CEO of Daily, Pipecat maintainer* Theodor Marcu (@theodormarcu) - Product at Cognition* Trevor Manz (@trevmanz) - Founding engineer at Marimo* Show Notes* Recap essay on the Z/L Continuum from AI Engineer Europe (Blog): should AI engineers still read code? Ryan Lopopolo says no, Mario Zechner says yes for critical paths, everyone in between has FOMAT.* Mario Zechner talk is finally live on AI Engineer youtube (Watch)* Super Gemma 4 26B Uncensored v2 by @songjunkr — trending on HF, 0/100 refusals, fixed tool calls (HF GGUF, HF MLX 4bit)* Gemma 4 21B REAP — 20% expert-pruned Gemma 4 26B MoE by 0xSero using Cerebras REAP (HF)* Parcae (Together AI + UCSD) — stable looped transformer architecture with scaling laws, matches 2x-sized transformer quality (Paper/blog)* Claude Desktop app — rewritten from scratch, completely new app* Gemma 4 on W&B Inference — reply on the announcement post with code Gem Drop for $20 in inference credits, also supports LoRA inference via link* Big CO LLMs + APIs* Anthropic launches Claude Opus 4.7 - 87.6% SWE-bench Verified, 64.3% SWE-bench Pro, 3x vision resolution, new xhigh effort level, /ultrareview in Claude Code, same pricing as 4.6 but new tokenizer uses ~1.0-1.35x more tokens (X, Blog)* OpenAI Codex major update: macOS background computer use, 90+ plugins, gpt-image-1.5 image generation, in-app browser, memory, self-scheduling automations, multi-terminal SSH (X, Blog)* CoreWeave signs deals with Anthropic (multibillion), Meta ($21B expansion, $35B+ total), and Jane Street ($6B cloud + $1B equity), now serves 9 of the top 10 AI providers* Open Source LLMs* Qwen 3.6-35B-A3B - Apache 2.0, 35B MoE with 3B active, 73.4% SWE-bench Verified, natively multimodal, 262K context extensible to 1M (X, HF, Blog)* MiniMax M2.7 open weights - 230B MoE with 10B active, 56.22% SWE-Pro matching GPT-5.3-Codex, self-evolved via 100+ rounds of autonomous RL (X, HF)* Tools & Agentic Engineering* Windsurf 2.0 with Agent Command Center and Devin integration - interview with Theodor Marcu (X, Blog)* Warp now supports any CLI agent with vertical tabs, notifications, code review, mobile remote control (X, Blog)* Claude Code Routines - cron, GitHub event, and API-triggered autonomous agents running on Anthropic’s cloud (Docs)* This Week’s Buzz - Weights & Biases / CoreWeave* Marimo Pair - drop Claude Code / Codex / OpenCode agents directly inside reactive Python notebooks - interview with Trevor Manz (Blog, GitHub)* Gemma 4 now live on W&B Inference on CoreWeave infrastructure, with LoRA inference support* Vision & Video* Craziest AI video of the year: Pi Hard / Neil deGrasse Tyson (X)* Voice & Audio* Gradient Bang - first massively multiplayer fully LLM-driven game, Pipecat sub-agents - interview with Kwindla (Play, GitHub)* Google Gemini 3.1 Flash TTS - 1,211 Elo on TTS Arena, inline audio tags, 70+ languages, ~$0.03/60s (Blog)* AI Art, Diffusion & 3D* Baidu ERNIE-Image - 8B DiT, #1 GenEval among open models, precise multilingual text rendering (HF)* Tencent HYWorld 2.0 - single image to editable 3D Gaussian Splats/meshes, Unity/Unreal/Isaac Sim ready (GitHub)* NVIDIA Lyra 2.0 - single image to explorable persistent 3D worlds, Apache 2.0 (Project, HF)* Other news* Unitree humanoid breaks 100m dash world record at ~10m/s (X)* Allbirds shoe company loses 99.5%, rebrands as “NewBird AI”, raises $50M to buy GPUs, stock up 600-800% (X) This is a public episode. 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Hey yall, Alex here, writing this from sunny London, at the first ever AI Engineer conference in Europe!What a show we have for you today! First, let me catch you up on what’s important: Anthropic, this week announced a whopping $30B ARR up from 19B in Feb, while also telling us about Claude Mythos Preview their next gen HUGE model that they won’t release to the public (yet?) that finds crazy vulnerabilities in existing code bases. Apparently OpenAI will follow up with a similar non-public model soon.The Meta Superintelligence Lab led by Alex Wang finally showed what they were working on, Muse Spark, the smaller of their upcoming models on a complete new infrastructure (MSL announcement, Simon Willison’s deep dive on the 16 hidden tools).In other news:Z.AI released GLM 5.1 in OSS finally (HF weights), Seedance 2.0 finally available in US on Replicate, OpenAI testing out GPT-image-2 on LM Arena under codenames, HappyHorse from Alibaba takes the video crown, and Mila Jovovich (5th Element, Resident Evil) releases agentic memory plugin called MemPalace (Ben Sigman’s transparent correction thread is worth reading).We had 5 guests today on the show, we kick off with @swyx the founder of AI Engineer and host of Latent Space. We then chatted with @petergostev from Arena (formerly LMArena) about Mythos and the compute wars, then Vincent Koc, the second most prolific contributor to OpenClaw, then our friends VB from OpenAI and Omar from DeepMind, both previously at HuggingFace. This is a busy busy show, and given the time-zones, I unfortunately don’t have time for a full weekly writeup, but as always, I will share the raw notes and post the video (lightly edited).ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.AI Engineer - LondonThursdAI came a long way since the first AI Engineer conference, but many who read this don’t know, that was my big break. Swyx invited me to cover the first AIE in San Francisco in 2023, and I remember, I was in an Uber to the airport, the driver asked me what I do, and I, for the first time said “I host a podcast”. I (and ThursdAI) owe a lot to Swyx, and AIE team, and it’s been incredible to see how big they’ve grown and how many great speakers this event hosts! The term AI Engineer has drifted in those 3 years, but also has the term Software Engineer. Swyx predicted this nearly 3 years ago, what I don’t think he predicted, is that all engineers are now AI Engineers, and this includes domains like Agens (OpenClaw), Context and Harness Engineering, Evals and Observability, Voice & Vision all of which are tracks in this conference. I was really surprised to see how many of the talks/speakers here are native to London (after all, Deepmind is from here, OAI, Anthropic, Meta have offices here) and the latest boom in agents, OpenClaw, Pi were all Europe based as well, and they are joined the AI Engineer stage. Oh, and there’s also a Giant Inflatable Claw at the entrance, yup, for pictures and vibes, and to show off how quickly the OpenClaw took over the mind-share. Anthropic announces $30B ARR and Mythos, their next model, will not be released to the public. The thing that everyone will tell you, is that Anthropic is on a roll, this is obviously connected to their upcoming IPO this year. We’ve been covering many issues on their part, but this week we saw them posting about a HUGE increase in ARR, from 19B in February to 30B in April, passing OpenAI at $25B. That last fact though, is kind of disproven because they report on ARR differently, OpenAI apparently only counts their cloud revenue from Microsoft per the information. The growth is undeniable though, and so is the most unprecedented release announcement, Claude Mythos Preview, which was rumored for a bit and now was announced proper. With project Project GlassWing, Anthropic has announced that this model is SO good at cyber security and finding bugs in code, that they cannot share it with the public, and through GlassWing they will share it with companies like Microsoft, Linux, CrowdStrike and a bunch of others, to harden their security. This is it folks, this is the first time, where a model was “announced” but deemed too risky to release. Now, is it truly “too risky”? Previously, folks thought that DALL-E is too risky, or cloning voice tech is too risky, and now it’s everywhere. The capabilities catch up even in OpenSource. But the facts are, Anthropic says they’ve found a 27-year old bug in OpenBSD (famously very secure), and that this model is very very good at connecting the dots between several, seemingly inacuous bugs, to string them together into one coheren exploit. This is, indeed scary. Just last week, one of the top security researchers in the world, Nicolas Carlini, now at Anthropic, gave a talk at Black Hat, showing off these results, and saying that these models since December and definitely recently have passed him as a security engineer. If you haven’t seen this talk, watch it, then try to estimate if Anthropic did the right thing by only releasing this model to enterprises first. But on the show, Peter Gostev from Arena gave me a take on this that I haven’t been able to shake. Peter pulled up his Compute Wars chart live on the show — and the picture is that OpenAI is way ahead of Anthropic on compute, with Anthropic only recently getting a noticeable bump (which lines up suspiciously well with Mythos being trainable in the first place). His read: “it sounds cooler to say it’s too risky to release than ‘we can’t serve it.’” The official partner pricing is $25 / $125 per million tokens — 5x Opus 4.6 — but if you don’t have the GPUs to serve it broadly, the price doesn’t matter. In the year of the IPO, the company that cannot serve a model says the model is too dangerous to serve. Make of that what you will.This also reframes the whole rate-limit drama with OpenClaw. Anthropic didn’t ban OpenClaw — I want to be very clear about this because the discourse went sideways. What they did is they made it significantly more expensive for Max-tier subscribers to use Opus through OpenClaw, which pushed a lot of people over to GPT-5.4 via Codex. Same root cause: they’re out of compute. The freshly announced Anthropic + Google TPU deal (Google already owns ~10% of Anthropic) is them trying to fix this — though as Peter noted, it’s pretty wild that Google is propping up a direct competitor to their own DeepMind team. Same pattern as their original $2B Anthropic investment ending up propping AWS Bedrock against Google Cloud. Big Google contains multitudes.Meta Superintelligence Labs ships Muse Spark — Llama is dead, long live MuseLlama is dead, long live Muse. This week Meta finally showed what the very expensive Meta Superintelligence Labs under Alexandr Wang has been cooking, and the answer is Muse Spark — the smaller of their new model family, built on a fully rebuilt AI stack from scratch in just 9 months. Nine months is wild for that kind of overhaul, and the headline number people are quoting is that they reach Llama 4 Maverick capability with over 10x less compute.Spark is intentionally small and latency-optimized — it’s not trying to be the biggest, it’s trying to be the first step on Meta’s new scaling ladder. But the benchmarks in certain areas are nuts: 86.4 on CharXiv Reasoning (beats Opus, Gemini, GPT-5.4), and the one that really got me — 42.8 on HealthBench Hard vs Opus at 14.8 and Gemini at 20.6. They trained it with data curated by over 1,000 physicians and it shows. They also shipped a Contemplating mode which is parallel multi-agent reasoning, hitting 58.4% on Humanity’s Last Exam with tools. Coding is the acknowledged weak point (77.4 on SWE-Bench Verified vs Opus 80.8) but for v1 from a brand new stack, this is extremely respectable.Meta is Back!The real story isn’t any single benchmark though, it’s distribution. Spark is rolling out across meta.ai, WhatsApp, Instagram, Threads, Messenger, and Ray-Ban Meta glasses — billions of users. Meta went from open Llama to a closed consumer model and they’re clearly playing a different game now (though Wang says future Muse versions might be open-sourced).The deep-dive that’s really worth your time is Simon Willison’s post where he poked at the meta.ai chat UI and got the model to spit out descriptions of 16 hidden tools behind the scenes — full Code Interpreter with persistent Python 3.9, a visual grounding tool that does pixel-precise object detection (bounding boxes, point coordinates, counting — it located 8 objects including individual whiskers and claws on a generated raccoon), sub-agent spawning, file editing, and semantic search across Instagram/Threads/Facebook posts. It’s basically an entire agentic harness baked into the chat UI. Jack Wu from MSL confirmed the tools are part of a new harness built specifically for Spark’s launch. Meta stock went up 7% on this. They are very much back in the frontier game.Guest highlights We had an unprecedented packed show with 5 guests (also this is the shortest show we’ve everSwyx kicked us off with vibes from the AI Engineer floor — harness engineering as the dominant theme (gains are coming from the harness, not the weights), the rise of skills (English-as-programming-language) absorbing more of that harness work, and his thesis that supply-chain attacks like the recent light LLM and Axios incidents mean you should basically vendor everything — pip fork instead of pip install. We also chatted about how MCP has gone from “the most exciting protocol” to “settled and stable, therefore less interesting,” which is a great problem to have.Peter Gostev from Arena (you saw a lot of him in the Mythos section above) also dropped a bonus on us: Arena just released 3 years of historical leaderboard data and actual prompt datasets on Hugging Face. He used to literally scrape the arena website by hand into Google sheets to make those overtime leaderboards we all loved — now it’s all public. Also: he confirmed that Seedance 2.0 jumped ~80 ELO points above the next video model on Arena, which is unprecedented — video models normally cluster within 10 points of each other.Vincent Koc — the #2 OpenClaw maintainer after Peter Steinberger — joined us fresh off the OpenClaw track stage. The OpenClaw codebase is now ~1.5 million lines of code including unreleased iOS and Android native apps. GitHub literally caps the issue/PR counter at “5K+” and they hit the ceiling. We talked about OpenClaw 2026.4.5 which ships /dreaming GA (Light/Deep/REM phases that defrag agent memory and write a human-readable Dream Diary to DREAMS.md), built-in video and music generation across 4 backends, GPT-5.4 as the new default, prompt-cache reuse improvements, and Control UI + docs in 12 new languages. Vincent’s framing of dreaming was beautiful — “how do you explain agent memory to a mom? You call it dreaming.” He also gave my favorite line of the show on the GPT-5.4 personality problem: incredible at coding, but soulless. (For what it’s worth, I came home after watching Project Hail Mary, cloned the Rocky voice, dropped it into my OpenClaw, and it was magical. That’s the kind of thing you can only do when the harness and the model are decoupled.)VB from OpenAI told us Codex just hit 3 million weekly active users — up from 2 million last month. We talked plugins (the Stripe / Supabase / shadcn ones that ship as packages), sub-agents (yes, one is named Jason), and Guardian Approvals — an experimental mode that classifies each tool call by risk and only escalates the dangerous ones to you, so you don’t have to YOLO-mode everything. The story that stuck with me though is his 9 AM Codex automation: every morning it reads his Slack mentions, cross-references Gmail and Calendar, and creates 5-minute pre-brief calendar events for upcoming meetings. None of that is “coding.” That’s the super-app future hiding inside a “developer tool.” I’m stealing this workflow.Omar Sanseviero from Google DeepMind came on to celebrate Gemma 4 crossing 10M+ downloads with 1,000+ Gemma-4-based fine-tunes already on HF (and Gemma family total is now over 500M downloads). Gemma 4 is also the foundation for the next generation of Gemini Nano on Pixel/Samsung devices. Lama.cpp vision capability fixes are landing. Gemma 4 is also live on W&B Inference if you want to play. Wolfram (whose entire household runs on Pixel + Google AI Studio, including his 70-year-old mother on voice unlock) was in heaven.This Week’s BuzzA short but spicy week from Weights & Biases:* W&B Automations are LIVE. You can now wire event triggers from your training runs (completion, eval thresholds, drift) into notifications, GitHub Actions, deployments, infra shutdowns — closing the loop from experiment to production. Pairs really well with the iOS app we recently shipped, so you can get a ping on your phone the moment something interesting happens on a run.* GLM 5.1 is live on W&B Inference (alongside Gemma 4 from last week) — the team is moving fast to host the best open models the moment they drop.* Wolfram published a deep dive on “more reasoning is not always better” on the W&B blog — the research behind his finding that giving models more thinking tokens can actually make them dumber on certain tasks. It’s the in-depth version of what we discussed on the show last week, with all the data. Go read it on wandb.com.Also: shout out to everyone who came up to me at AI Engineer and said hi. The Wolf Bench mentions in particular made my day. If you’re listening to this and you’re at AIE — come find us, we’ll be around tomorrow too.That’s it for this week — newsletter is short because the show was long and London is calling. As always, thanks for reading and listening 🫡TL;DR April 9 - show notes and links:* Hosts and Guests* Alex Volkov – AI Evangelist & Weights & Biases (@altryne)* Co-Hosts – @WolframRvnwlf @yampeleg @nisten @ldjconfirmed* Guests: @swyx (AI Engineer / Latent Space), @petergostev (Arena, formerly LMArena), @reach_vb (OpenAI / Codex), @vincent_koc (OpenClaw #2 maintainer), @osanseviero (Google DeepMind / Gemma)* Big CO LLMs + APIs* Anthropic announces Project Glasswing and Claude Mythos Preview, a cyber-defense frontier model too dangerous to release publicly (X, Announcement)* Anthropic’s Claude Mythos is so powerful they won’t release it — found zero-days in every major OS and browser, escaped its sandbox, and scored 93.9% on SWE-bench (X, X, X, X)* Anthropic ARR jumps from $19B (February) to $30B in April — secondary tender sale completed, employees not selling ahead of IPO* Anthropic + Google TPU deal — Anthropic getting massive compute commitment from Google (who already owns ~10% of Anthropic), with Peter Gostev’s Compute Wars chart showing the gap to OpenAI closing* Anthropic ships Managed Agents — fully hosted agent runtime + infrastructure. Selling outcomes, not tokens* Meta launches Muse Spark, the first model from Meta Superintelligence Labs, with natively multimodal reasoning, multi-agent Contemplating mode, and deep health/visual capabilities (X, Blog)* Simon Willison deep dives into Meta’s Muse Spark model and uncovers 16 hidden tools including visual grounding and sub-agents in the meta.ai chat UI (X, Blog, Announcement)* Open Source LLMs* GLM-5.1 from Z.ai is #1 open-source on SWE-Bench Pro at 58.4%, runs autonomously for 8 hours with 1,700+ agent steps (X, HF, Arxiv)* Gemma 4 crosses 10M+ downloads, 1,000+ Gemma-4-based fine-tunes on HF. Did really well on Arena considering size — Peter Gostev confirmed it smashed many models on the Pareto curve* Nisten’s pick: Hermes 27B — trained specifically to be paired with the Hermes harness, allegedly distilled from Opus API. Model + harness shipped together as a portable unit* Tools & Agentic Engineering* OpenClaw 2026.4.5 — biggest release since 4.0: /dreaming goes GA (Light/Deep/REM memory consolidation with a Dream Diary in DREAMS.md), built-in video + music generation across 4 backends, GPT-5.4 as new default, prompt-cache reuse improvements, Control UI + docs in 12 new languages (Release, Vincent, Dreaming docs, FOD#147)* OpenClaw codebase now ~1.5M lines including unreleased iOS + Android native apps. GitHub literally caps at “5K+” PRs/issues — they hit the ceiling* Anthropic did NOT ban OpenClaw — they made Max-tier subscription usage of Opus via OpenClaw significantly more expensive, pushing many users to GPT-5.4 via Codex* Codex hits 3M weekly active users — up from 2M last month. VB walked through plugins (Stripe, Supabase, shadcn), sub-agents, Guardian Approvals (auto-classify tool-call risk), and experimental hooks* Cursor: remote agents + code review agent (78% issues caught pre-merge)* MemPalace: Milla Jovovich and Ben Sigman’s open-source AI memory system goes viral with 26K GitHub stars in 2 days, claims top benchmark scores, then transparently walks back overstated claims (X, GitHub, X, X, GitHub)* This Week’s Buzz (Weights & Biases)* W&B Automations are LIVE — event triggers from your runs into notifications, GitHub Actions, deployments. Pairs nicely with the new iOS app* GLM-5.1 and Gemma 4 both up on W&B Inference* Wolfram published an in-depth blog post on his finding that more reasoning is not always better (models can get dumber with more thinking time) — full writeup on wandb.com* Vision & Video* Seedance 2.0 launches in the US — on Replicate with up to 9 reference images, 3 videos, and 3 audio files for cinematic AI video generation (X, Announcement). Peter Gostev confirmed it jumped ~80 ELO points above the next video model on Arena — a massive gap where most video models cluster within 10 points* HappyHorse-1.0, a mysterious 15B video model from Alibaba’s Taotian Group, takes #1 on Artificial Analysis video arena beating Seedance 2.0, Kling 3.0, and Grok Video (X, X, X, X, Blog)* The Harry Potter “Drip Wizards” AI slop trend — Seedance-powered Hogwarts videos going hugely viral* AI Art & Diffusion & 3D* OpenAI’s GPT-Image-2 leaked on LM Arena under three codenames (maskingtape / gaffertape / packingtape), showing photorealism and text rendering that may dethrone Google’s Nano Banana Pro (X, X, X)* Show notes & key moments* Swyx on harness engineering: gains are coming from the harness, not the weights. The big labs are investing more and more in harness — it’s not going away. Skills (English-as-programming-language) are increasingly absorbing harness work* Swyx on AI Engineer tracks: MCP is “more settled and stable, therefore less interesting.” Coding agents track is bigger this year (Cursor, Factory, super-long-running). Voice & Vision split from Generative Media — multimodality as a single track no longer makes sense* Swyx on supply chain attacks: light LLM and Axios issues mean you should “vendor everything” — pip fork instead of pip install. Tool requests becoming prompt requests* Peter Gostev on Mythos pricing: $25 / $125 per M tokens (~5x Opus 4.6). But the real reason it’s not public isn’t safety — Anthropic likely just doesn’t have the compute to serve it* Peter Gostev on Compute Wars: OpenAI is way ahead of Anthropic on compute. The new Google TPU deal is Anthropic catching up — and weird that Google is propping up a competitor to DeepMind. (Same pattern as when Google’s $2B Anthropic investment effectively propped up AWS vs Google Cloud)* Peter Gostev on Arena data: Arena released 3 years of historical leaderboard data + actual prompts as datasets on Hugging Face. Previously he was scraping it by hand into Google Sheets — now he has Databricks access* VB on Codex workflows: every morning at 9 AM, Codex automation reads his Slack mentions, cross-references Gmail and Calendar, and creates a 5-minute pre-brief calendar event for upcoming meetings. None of it is “coding” — it’s all plugins + connectors* Vincent Koc on the GPT-5.4 personality problem: model is incredible at coding but “soulless.” Wolfram noticed it back in December and cancelled his subscription. Alex cloned the Rocky voice from Project Hail Mary and put it in his OpenClaw — “amazing”* Vincent Koc on Dreaming: three phases (REM, core, deep sleep) that defrag agent memory. The dream log is for the human in the loop — makes memory inspectable in a way a non-technical person (a mom) can understand* Vincent Koc on architecture: the open-source flood forced OpenClaw into a plugin architecture. “Not Lego — Ikea.” Refactored ~1M lines in 9 days at 2 AM at NVIDIA before Jensen’s keynote* Omar Sanseviero on Gemma 4: 500M+ total Gemma downloads across all variants. Gemma is the foundation for the next generation of Gemini Nano on Pixel/Samsung. Lama.cpp vision capability fixes shipping* Wolfram’s Pixel/Google household: kids using AI Studio + Antigravity to build games, his 70-year-old mother using voice unlock on her PixelThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey Ya’ll, Alex here, let me catch you up. What a week! Anthropic is in the spotlight again, first with #SessionGate, then with the whole Claude Code source code leak, and finally with an incredible research into LLM having feelings!? (more on this below). And while Anthropic continues to burn through developer good will faster than their sessions, OpenAI announced a MASSIVE $122B round of funding (largest in history), Google released Gemma 4 with Apache 2 license - we had Omar Sanseviero on the show to help us cover what’s new, Microsoft dropped 3 new AI models (not LLMs) and PrismML potentially revolutionized local LLM inference with lossless 1-bit quantization! P.S - Oh also, something on X algo changed, I get way more exposure now, 3 out of my best 5 posts ever have been from this week + I got the coveted Elon RT on my Claude Code leak coverage. I’ll try to stay humble 😂 Anyway, let’s dive in, don’t forget to hit like or share with friends, and TL;DR with links is as always, at the bottom: ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.The Claude Code source Leak: Half a Million Lines of “Oops”So here’s what happened. On March 31st, Anthropic shipped Claude Code version 2.1.88 to npm. Inside that package was a 59.8 megabyte source map file — basically a debugging artifact that contained the entire compiled source code. 512,000 lines of TypeScript across 1,900 files. The entire playbook for how the Claude Code harness works, including a lot of stuff that wasn’t supposed to be public yet.A researcher named Chaofan Shou spotted it at 4 AM ET, posted the download link, Sigrid (who came to the show) posted it on Github and within six hours it had 3 million views and 41,000 GitHub forks (This repo is the highest starred repo in Github history btw, with well over 150K Github stars). Anthropic started filing takedowns, but the internet being the internet, it was already everywhere. The source code is still on tens of thousands of computers right now. (I won’t link directly but there’s a website called Gitlawb, look it up) The community went absolutely wild digging through the source code btw, and they found some interesting things!KAIROS: Claude Code is going to become a Proactive Agent!This is the biggest take-away from this leak IMO, that like OpenClaw/Hermes agentic harnesses, Claude Code is already a fully featured proactive agent, we just don’t have access to this yet. With KAIROS, Claude Code will have it’s own daemon (will run independently from the CLI), will have a background ping system (hello Heartbeat.md from OpenClaw) that will make it wakeup and do stuff, will do “autodream” memory consolidation reviewing your daily sessions and fix memories, subscribe to Github, and maintain daily appent-only logs to show you what it did while it and you were asleep. This is by far the hugest thing, I’m excited to see how / when they ship KAIROS, as I said, 2026 is the year of Proactive agents! My Wolfred OpenClaw agent summed it up very nicely: Undercover ModeFor Anthropic employees working on public repos, there’s an Undercover Mode that auto-activates and strips all AI attribution from commits. The system prompt? “Do not blow your cover.” They really said “this is fine” about shipping internal tools to production while hiding from the world that AI wrote the code. Which, honestly, is kind of incredible meta-humor from whoever wrote that.The Buddy SystemMy personal favorite discovery: there’s a hidden Tamagotchi-style terminal pet called the Buddy System with 18 obfuscated species, rarity tiers (including a 1% legendary), cosmetic hats, shiny variants, and stats like DEBUGGING, PATIENCE, and CHAOS. If you activate it now, you can do /buddy and you’ll have a little companion judging your coding decisions. Anthropic shipped a game inside their CLI tool. Mine is called Vexrind and he’s sarcastic as f**k, I’m not sure I like it. Anti-Distillation ProtectionsThe code also revealed that Claude Code injects fake tool calls into logs to poison training datasets. If you’ve been backing up your .claw folders to train on the data; Stop. Pass your data through something like Qwen or make sure you’re filtering out the noise. (a Nisten tip)The Models That Don’t Exist YetBuried in the code are references to Opus 4.7, Sonnet 4.8, and a model called capybara-v2-fast with a 1 million context window. These haven’t been released. This is yet another confirmation of the leaked “Mythos” model that’s coming soon from Anthropic. Which btw, with Anthropic very rocky uptime lately, the tons of SessionGate issues, the leaked blog announcing Mythos, the leaked Claude Code oopsie, they are not having the best Q1 in terms of proving to the world that they are the safest lab out there. I hope they protect their weights better than they protect everything else, before the rumored IPO later this year. SessionGate is still not solved, despite the official responseI told you about session gate last week, and since then we got, finally, and official acknowledgement from Anthropic. But before that, some folks on Reddit reverse-engineered Claude Code (this was before the source code leak ha) and found a few caching bugs that potentially cause 10-20x increase in price if you use --resume a lot especially. While folks continue to complain about burning through Max account quotas much faster than before, here’s the official response from Anthropic, after the supposed investigation, turns out, we’re using it wrong 🤦‍♂️My take is simple: Anthropic has one of the best models in the world, maybe the best personality plus coding stack in some situations, and they are squandering a chunk of goodwill by not being much more explicit about decreased limits, caching bugs, routing, and usage behavior. Nothing else to add here, really bad DevEx, people can handle bad news. They hate opaque bad news.Gemma 4 Is Here, Apache 2.0, and Honestly… This Is a Big One (HF)This was the hopeful turn in the show. You know we LOVE open source!Right in the middle of all the Anthropic chaos, Google dropped Gemma 4, and Omar Sanseviero from DeepMind joined us live to talk through it. This launch hit a bunch of notes I care a lot about: strong local-friendly sizes, serious open distribution, Apache 2.0 licensing, agentic improvements, and a clear willingness to listen to community feedback.The headline model for me is the 31B Gemma 4. It’s big enough to matter, small enough to actually run in serious local setups, and strong enough that the benchmark chart looks slightly ridiculous. On LM Arena, it is competing far above what you’d intuit from the raw parameter count. When a 31B model starts getting uncomfortably close to models in the several-hundred-billion range, you pay attention.That was really the vibe on the show. It wasn’t just “nice, another open model.” It felt more like: wait, local models are seriously back. Gemma is the new LLaMaWhen I asked Omar where local models are going, his answer was optimistic: “The open models catch up to proprietary models relatively quickly. If you compare Gemma 3 to Gemma 4, it’s matching proprietary capabilities from eight months ago. Being able to run those capabilities directly in the user’s hardware — that’s the future.”The 31B model downloads as about 18-20GB depending on quantization. With the right setup, you can run it on a single GPU. This is exactly what the open source community has been asking for: frontier-level intelligence that you can actually run yourself.OpenAI’s largest in history $122B funding round + TBPN acquisitionWhile OpenAI quietly meme’d around the Anthropic leak but mostly stayed silent on the releases, they did announce 2 pretty huge things. First, OpenAI raised an absolutely bonkers, insane, unreal $122 Billion dollars round, largest in history, 2x bigger than the previous record round, which was OpenAI. Amazon put in $50B, Nvidia $30B, SoftBank $30B — all three of whom are also OpenAI’s biggest vendors. They’re generating $2 billion per month in revenue with 900 million weekly active users, but still burning roughly $150 million per day and projecting a $14 billion loss this year, making the upcoming IPO a financial necessity rather than a choice. And they’re not just spending on compute — today OpenAI acquired TBPN (TBPN is a tech-focused media company / live show), in a very “surprising” deal, rumored to be in the “low hundreds of millions”, OpenAI has purchased a very tech-positive show. Shoutout to Jordi Hays and John Coogan + TBPN team. Proving that live show format means a lot in the era of fake AI news. This could potentially price TBPN higher than Washington Post, make the founders multi millionaires and give OpenAI a direct to consumers media angle. Very interesting purchase. This weeks buzz - W&B corner + Wolfbench updateQuick 2 things, this weekend I flew for 1 day to San Francisco, to host one of the most unique hackathons i’ve ever saw, in this one, AI wrote the code, but humans were punished if they touched their laptops! Yes, with a “lobster of shame” they used Ralph loops and talked to each other intead of hacking. I edited a video of it, hope you enjoy my summary: The other, and potentially much bigger news, comes from Wolfram and WolfBench.ai I’ve tasked Wolfram to expand our findings, and he tested the new Hermes Agent (from Nous Research) against OpenClaw, Claude Code and found that... drum roll... Hermes Agent performs way better on Terminal Bench, than either Claude Code and OpenClaw. 😮 Here’s the clip of him explaining, and you can find all our findings and methodology herePrismML’s 1-Bit Bonanza: The Biggest ML Discovery in Half a DecadeMy co-host Nisten called it, and I think he might be right: this could be the biggest machine learning discovery in recent memory.PrismML emerged from stealth this week with their 1-bit Bonsai model family. Their 8B model is 1.15 gigabytes. A full-precision Qwen3 8B is 16 gigabytes. That’s a 14x size reduction, with no significant quality loss.Let that sink in for a second. We’re talking about each weight being literally one bit — a plus or minus sign, with a scaling factor. Not “4-bit quantization” or “int8” — actual binary weights. This shouldn’t work. Neural networks need precision to learn. And yet.The research comes from professor Babak Hassibi at Caltech, who’s been working on this for 34 years. He started this research in 1992. It took three decades, but it finally works.The results are genuinely shocking. The 8B model runs at 368 tokens per second on an RTX 4090, which is 6.2x faster than the full-precision version. On an M4 Pro via Metal, it hits 85 tokens per second. Energy efficiency is 5x better. And here’s the kicker: the 1.7B variant hits 130 tokens per second on an iPhone 17 Pro Max.Nisten tested the 8B model himself with a 60,000 token context window on an old gaming PC. It ran at 50 tokens per second, used 2.6 gigabytes of RAM, and was completely coherent. “This just blows everything else outta the water,” he said. “We’re going to get 100,000 token AI chips in our phones because at 1 bit you don’t even have to do math anymore. You can just do lookup tables. You can even make a mechanical AI at 1 bit.”This pairs perfectly with the Turbo Quant KV cache compression techniques we talked about last week. Compress the weights with 1-bit, compress the context with Turbo, and you’re looking at models that run anywhere. The democratization of AI is about to hit another gear.The models are Apache 2.0 on HuggingFace with GGUF and MLX formats already available.⚡ Speed Round: Alibaba, Fish Audio, Veo, Liquid AI, Cursor 3 There was a lot more this week than we could go deep on, so here are the biggest quick hits.Alibaba kept shipping. Qwen 3.6 Plus is pushing hard on agentic coding and long context. Qwen 3.5 Omni is the bigger multimodal story, with text, image, audio, and video all under one umbrella. I still think Alibaba deserves more credit than they get in Western discourse for just how relentlessly they keep delivering.Wan 2.7 Image also looked very strong on text rendering, editing, and image consistency. I’m still slightly grumpy that more of this stack is API-only, but the capabilities are clearly moving.Google launched Veo 3.1 Lite, cutting video generation prices way down. Five cents per second at 720p is a pretty aggressive number. Whenever Google starts doing this kind of price move, my first thought is usually: okay, what bigger release are they preparing for?Fish Audio’s STT was another cool one. This isn’t just speech-to-text for transcription. It’s built to feed directly into voice pipelines, with emotion and paralanguage tagging that lines up with their TTS stack. That is exactly the kind of vertical product thinking I love seeing in audio.And Liquid AI’s LFM2.5-350M deserves a shout too. A 350M model doing credible tool-calling and agentic tasks is just another reminder that the small-model frontier is getting very weird, very fast.Lastly, Cursor 3 launched as a rebuilt, agent-first interface. I didn’t spend as much time on it during the show as it probably deserves, but the broader trend is impossible to miss: coding tools are evolving from editors-with-assistants into actual fleet managers for agents.Anthropic’s Emotion Vectors: How they found out what Claude is “feeling”I want to end where we ended the show, because this one really stuck with me.Anthropic published research on emotion concepts inside Claude. Not in the fluffy “the model feels things” sense, but in the mechanistic interpretability sense. They identified internal representations associated with things like fear, love, joy, and desperation, then studied how those activations affected behavior.This got fascinating fast.One example they showed involved Claude trying and failing at a difficult programming task. As repeated failures mounted, the internal “desperation” vector increased. Under those conditions, the model became more likely to produce hacky, spirit-of-the-task-violating solutions. When they dialed in a “calm” vector instead, cheating behavior dropped.That is just… wild.It’s not that the model is “feeling” human emotions in a clean anthropomorphic sense. But it is that internal behavioral geometry we can label in emotional terms seems to shape what the model does. And once you can detect and influence those latent directions, you’re no longer just prompting a black box. You’re doing something closer to behavioral neuroscience for neural nets.This also reframes a lot of day-to-day prompt engineering. Maybe the best users aren’t just the ones who structure tasks clearly. Maybe they’re also the ones who consistently keep the model in productive psychological territory, so to speak.I know that sounds weird. Welcome to Q2 of 2026, the first year of the singularity! Closing ThoughtsThis week was Passover, we celebrated at our house, half the conversation was about who has an OpenClaw and who wants one, and as I’m writing this, I’m on my way to install a bunch of proactive agentic AIs for my friends. Ryan Carson on the show got finally convinced and he’s chief of staff R2 is now an OpenClaw and he says it beats a human, he actually open sourced it live on the show. Claude Code leak confirmed that this is also where they are taking the ecosystem. So buckle up! Also, next week show is going to be streamed live from the AI Engineer conference in London, the first European one, if you’re in Europe and coming, hope to see you there! Please share ThursdAI with a friend or give us a 5 star rating, apparently AI reporting live shows are getting acquired for 100s of Millions of dollars now 😂 Your support will greatly help us get established in this area after 3 years. See you next weekTL;DR and Show NotesTL;DR and Show Notes* Show Notes & Guests* Alex Volkov - AI Evangelist & Weights & Biases / CoreWeave (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson* Sigrid Jin (@realsigridjin) & Bellman (@bellman_ych) — creators of claw-code, fastest GitHub repo to 100K stars* Omar Sanseviero (@osanseviero) — DevEx at Google DeepMind, Gemma 4 launch* Ralphton Hackathon video (TikTok)* WolfBench.ai — agent harness benchmarking (Site)* Ryan’s Claw Chief open source setup (GitHub)* Big CO LLMs + APIs* Claude Code’s entire 512K-line source code accidentally leaked via npm — revealing KAIROS daemon, Undercover Mode, Buddy System, anti-distillation protections, and unreleased model references (Alex’s thread, Fried_rice’s discovery, VentureBeat)* Anthropic SessionGate continues — cache bugs reverse-engineered, --resume flag causes 10-20x cost increase, silent Opus→Sonnet fallback reported (Alex’s cache bug post, Alex’s quota post, Reddit investigation, GitHub analysis)* OpenAI closes $122 billion funding round — largest in history, $852B valuation, IPO incoming (X, Breakdown)* OpenAI acquires TBPN — live tech media show, rumored low hundreds of millions* Microsoft MAI drops 3 in-house models — #1 transcription (MAI-Transcribe-1), #3 image gen (MAI-Image-2), expressive voice (MAI-Voice-1) (Mustafa post, Transcribe blog, Image blog)* Alibaba Qwen3.6-Plus — near-Opus 4.5 agentic coding, 1M context (X, Blog)* Cursor 3 — agent-first rebuild, no longer VS Code fork, parallel cloud/local agents (X, Blog)* Anthropic publishes emotion vector research — desperate Claude cheats more, calm Claude cheats less (X, Alex’s reaction)* Open Source LLMs* Google Gemma 4 — Apache 2.0, 31B / 26B MOE / 8B / 5B, local-friendly, agentic tool use, 256K context (HF Collection, try in AI Studio)* PrismML Bonsai 1-bit models — 8B in 1.15 GB, 10x intelligence density, 34 years of research (X, HF, Site)* Liquid AI LFM2.5-350M — agentic tool calling at 350M params, under 500MB quantized (X, HF, Blog)* Alibaba Qwen3.5-Omni — native omni-modal (text, image, audio, video), 397B total / 17B active (X, Blog)* Tools & Agentic Engineering* Claw-code — Claude Code leak backup → clean room rewrite → fastest repo to 100K+ stars (GitHub)* WolfBench results: Hermes Agent outperforms Claude Code and OpenClaw on Terminal Bench 2.0 (WolfBench.ai)* Ryan Carson open sources Claw Chief — AI chief of staff with skills, crons, scheduling (GitHub)* Vision & Video* Google Veo 3.1 Lite — $0.05/sec at 720p, cheapest video gen yet, price cuts coming April 7 (X, Docs, Pricing)* Voice & Audio* Fish Audio STT — automatic emotion tagging, feeds directly into S2 TTS pipeline (X, App, Blog)* AI Art & Diffusion* Alibaba Wan2.7-Image — unified generation, editing, text rendering, multi-image consistency (X, Site)* This Week’s Buzz* Ralphton hackathon at W&B SF — humans write specs, AI builds, touch your laptop = lobster of shame (Alex’s video, TikTok)* WolfBench update — Hermes Agent > Claude Code on most model combos This is a public episode. 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Hey y’all, Alex here, let me catch you up!Jensen Huang went on Lex and said AGI has been achieved. We’ll get to that.The biggest demo moment: Gemini 3.1 Flash Live launched - Google’s omni model that sees, hears, and searches the web in real time. We tested it live and I said “what the f**k” on air. It was really impressive!Google Research also dropped TurboQuant (6x KV cache compression) which crashed Samsung and Micron stocks - we had Daniel Han from UnSloth help us make sense of why that’s overblown. OpenAI killed Sora - the app, the API, and the $1B Disney deal. Claude felt noticeably dumber this week AND max account quotas are melting as 500+ people confirmed on my X and Reddit. We have an official word from Anthropic as to why. Mistral launched Voxtral TTS (open weight, claims to beat ElevenLabs), Cohere shipped an ASR model, and Google’s Lyria 3 Pro now generates full 3-minute music tracks inside Producer AI.This and a lot more in today’s episode, let’s dive in (as always, show notes and links in the end!) ThursdAI - Let me catch you up! Gemini 3.1 Flash Live: The Real-Time AI Companion Is HereGoogle dropped a breaking news on the show today, with Gemini 3.1 Flash - LIVE version. This one is an omni-model, that means it can receive text/audio/video on input and respond in text and voice. It has Google search grounding, and it felt... immediate! I was blown away, really, check out the video, the speed with which it was able to “see” me, respond to my query, look up something on the web, was mind blowing. I don’t often get “mind blown” anymore, there’s just too many news, but this one did the trick! With the pricing being around 10x cheaper than GPT-real-time, and the Google search grounding being super fast, I can absolutely see this model being hooked up to... robots (like ReachyMini), SmartGlasses that can see what you see, and a bunch more! Gemini Live is available on Google AI studio and has been rolled out globally inside the Google Search app! So now when you pull up the Google Search app, just open it and point at anything. Truly a remarkable advancement.Google research publishes TurboQuant - 6x reduction in KV cache with 0 accuracy lossGoogle research posted some work (based on an Arxiv paper from almost a year ago) that shows that with geometry tricks, combining two other techniques like PolarQuant and QJL, they are able to compress the KV cache of running LLMs by nearly 6x, and show an 8X speed up for model inference with zero accuracy loss. If you ever watched silicon valley the HBO show, this sounds like the fictional middle-out algorithm from PiedPiper. If this scales (and that’s a big if, we don’t know if this applies to other, bigger models yet), this means significant decreases in memory requirements to run the current crop of LLMs for longer context. The claim is big, so we’ll continue to monitor if this indeed scales, but the most interesting thing about this piece of news is, that it broke the AI bubble and went to wall street, with finance brows deciding that this means that memory will not be needed as much any more and it tanked Samsung and Micron stocks. Which I found particularly ridiculous on the show, did they not hear about Jevons Paradox? This is reminiscent of the DeepSeek R1 saga that tanked Nvidia stocks over a year ago. Daniel Han from Unsloth, who joined us on the show, pointed out that the approach is mathematically interesting even if it’s not necessarily better than existing open-source techniques like DeepSeek MLA. LDJ noted that the baseline comparison (16-bit KV cache) isn’t really fair since most production systems are already compressing beyond that. Yam implemented it himself and confirmed the speedups are real, but so is the trade-off.Anthropic updates: Opus dumber? Quotas lower! Injunction won! Computer.. used. Anthropic folks, especially on the Claude code side are shipping like crazy, we won’t be able to cover all the updates, but there was a few notable things I have to keep you up to date on. Claude Opus seems to be getting “dumber”, againI have to talk about this because it affected my work directly this week and hundreds of people confirmed the same experience.I use Claude Opus for my standard ThursdAI prep workflow — generating the TL;DR with 10 bullet points and an executive summary for every topic we cover, creating episode pages, etc. The format has not changed for over a year and yet this week I asked for 10 factoids. I got 4. It says “10” right there in the prompt. Four bullet points. On the website builder, I’ve asked Opus to create a page for last weeks episode, and instead of adding it to the other episode, Opus decided to ... replace the last episode with this one. This would be funny if it wasn’t sad. This is Opus 4.6 we’re talking about, not some quantized open source LLM from last year! The reason is unclear, and it’s not only me, Wolfram noticed that it’s easier to see these types of things in other languages and that for the last week Opus would forget to add Umlauts in German!? and Yam also felt it. Pro/Max plan quotas burning up, Anthropic confirmed that they are tightening them for “peak hour” usageThis week, so many people started posting that something is wrong with their Claude Codes, I did a survey, and it blew up. Hundreds of people replied and confirmed that for the first week, they are hitting their session quotas on Pro and 20x $200/mo MAX accounts much much quicker than before. When I say much quicker, I mean, some fokls have hit the quota in as little as 5 minutes. While some others had no issues. I personally btw did not have this. A few days later, Thariq from the Claude code team, and later an official post, confirmed that Anthropic had been rolling out a “tightening” of the Pro/Max accounts to accomodate for growth. This is of course, a huge bummer to the folks who pay $200/mo for the 20x max tier, as they tend to run agents and subagents overnight. But here’s the thing, I don’t think that folks from Anthropic see what we see, some folks got no issues with hitting quota, and some are barely able to use their subscription. I hope that they will find and resolve these bugs quick, because some folks are switching to Codex, and the Anthropic IPO is coming up! I will say, I don’t envy Thariq’s job, he’s doing it gracefully, and maybe one of the only ones in Anthropic that does it at all. Judge granted Anthropic an injunction against DoW and the whole “Supply chain risk” designation!Just in as I’m writing this, a district judge in CA, granted Anthropic an injunction against being designated as a supply-chain-risk company. If you haven’t been following, the US Department of War, specifically Pete Hegseth, threatened and then designated Anthropic as a supply chain risk company, while us president Trump “fired” Anthropic and banned its use in any gov agencies. Well, no so fast says Judge Lin, from CA District court. In this Order, she shows that Dept. of war didn’t meet any legal requirements for this designation. It’s really a fascinating read, but the highligth is this: When asked why Hegseth made a public statementthat had no legal effect and that did not reflect the immediate intent of DoW, counsel stated, “I don’t know.”This is just the first court and will likely be escalated further up the judicial system. This is still developing and apparently the Pentagon declared Anthropic a supply chain risk under two different statutes, and this only affects one of them. So while it’s good news, it’s not over yet. Voice & Audio Explosion: Three Releases in One HourI had to hit the breaking news button mid-TLDR because three major voice releases dropped simultaneously during the show.Mistral Voxtral TTS — Mistral’s first text-to-speech model, 3 billion parameters, open weight. They claim it beats ElevenLabs Flash v2.5 in human preference tests (58% win rate on flagship voices, 68% on zero-shot voice cloning). We tested it live on the show — it’s decent, with emotion controls for neutral, happy, and frustrated voices. I was not super impressed tbh, it sits somewhere between the very good big labs TTS and the very small open source 82M param TTS. Cohere Transcribe — Cohere enters the ASR game with a 2 billion parameter open-source model (Apache 2.0!) that immediately grabbed the #1 spot on HuggingFace’s Open ASR Leaderboard with a 5.42% word error rate, beating Whisper Large v3’s 7.44%. In human evaluations, it wins 61% of the time on average, and 64% specifically against Whisper. For anyone in regulated industries needing local inference for compliance, this could genuinely replace Whisper as the default.Google Lyria 3 Pro — Google’s most advanced music model is here.It can now generate full 3-minute tracks with structural control — intros, verses, choruses, bridges. We generated a ThursdAI opening theme live on the show using Producer AI, and it was... honestly not bad? It followed our instructions perfectly: drum and bass, 174 BPM, high energy podcast opener with vocals and introduction. The instruction-following was spot on. Nisten said it’s the best music generation model right now. It’s available to Gemini subscribers and via Producer AI and gemini, and it can even compose music from images. SynthID watermarked, royalty-free. We might actually use one of the generated tracks as a new show opener.The craziest thing is, since Google acquired Composer, the team has been shipping. I only generated the audio during the live show, but now went back there to download it for you guys, and whoah, it can now generate whole clips by using other Google tech, this is really cool! OpenAI kills SORA (and Atlas?)Last week we reported on about OpenAI’s focus shift towards Codex and productivity, and this week we see the first casualty. OpenAI is killing SORA, the app, the Sora 2 and Sora 2 pro models and APIs. Many AI haters are celebrating this as through “ai videos” is dead, but honestly, this is obviously about the GPU power and the other things OpenAI needs to do to win the fight against Anthropic. OpenAI is also apparently going to IPO this year (like Anthropic) and they absolutely need to win the productivity/agents in enterprise market. As part of this shut down, the Disney + OpenAI partnership, is also dissolving, and Disney will no longer invest 1B into OpenAI.So, say bye bye to having digital selfies with Sam Altman. I’ve generated this SORA vid to hear from Sam himself: Atlas browser, OpenAI’s native browser endeavor is supposedly also going to transform, together with Codex and OpenAI native app into one super app that includes all three according to the same memo. AGI is here according to Jensen, AGI is far away, according to ARC-AGI-3 The back to back this week can give anyone whiplash. First, Lex Friedman had Jensen Huang on the podcast, and asked him a very specific “WhenAGI” question, to which Jensen said “I believe it’s already here” Then just a few short days layer, ArcPrize, released the 3rd version of Arc-AGI, Arc-AGI 3 a series of puzzle games, where humans get 100% pass-rate and the current LLM, top tier frontier LLMs, are getting less than 1%! It’s an interactive, agentic reasoning benchmark designed to test human-like generalization and intelligence in novel, abstract, turn-based environments.The puzzles all look simple enough to do, and are actually fun, and while the wild claims of “AGI is not here yet” from the ArcPrize folks are quite interesting. The stated goal of the foudation is to release evaluations that are completely un-saturated, and this seems like one such thing at first glance. There’s a bit of a debate in the community about the way Arc Prize went about this specific benchmark (no harnesses, raw LLM outputs), saying that humans got a “game” while the LLMs get just raw JSON and minimal and no extra tools. For context, a agentic harness startup claims to have solved 35% already of the games in ArcAGI, but that result is unverified and self reported, becuase they are an agentic harness, which ArcAGI apparently disqualifies. AI Art and DiffusionI wanted to finish but I think these are important releases so I’ll include them briefly. Luma Labs Uni-1 — thinks and generates pixels simultaneously, #1 human preference Elo (X, Announcement)This was a surprising release, we previously seen Luma Labs do video, but this time they are posting their Uni-1 which is a… image model but it’s based on an LLM, so you talk to it, iterate together until you get results. Yes, Nano Banana via AI studio is kind of like this as well ,but Uni feels a bit different. It can also generate infographics, which I haven’t tried yet. You can try Uni herePhota Labs launches Phota Studio + API — a photography-focused image model with identity-preserving personalization (X, try it)There’s tons of photo startups, but this one looks kind of crazy! You upload a bunch of your pictures, they train a “model” for you, and then you can create a whole bunch of images, and they do actually resemble you. Yes, Nano Banana can take a few reference pictures, but this somehow seems more accurate! You can create professional photos, fix photos you like, add others to your photos. I do feel there’s a jump in capabilities here, specifically because of the personalization! Give them a try if you’re not worried about them training on your pics and let me know.Modular made Flux.2 run in X)We told you about Modular, and Mojo before, and while they provide inference speedups, I was surprised to see them releasing a model optimization, and hope this comes to all image generations! There’s a lot more to be said about this weeks updates, we went for over 2.5 hours (which I had to cut down to a bit over 1h45m) on the live show, and while I can go and on, I want to pause here. Weeks are getting crazier, denser and more unpredictable. I really thought we’d have a chill week until today! P.S - Mario Zechner, the author of the Pi coding CLI, which sits at the heart of OpenClaw has posted an awesome essay called “thoughts on slowing the f**k down“, I strongly advice anyone with many agents running in parallel to read this. Simultaneously, Alex Sidorenko posted this beautiful visualization of what happens when you have too many agents running in a loop, on your codebase. This is definitely starting to be noticeable as many companies use more and more agents, without reviewing their code. On weeks like this week, where Opus has almost deleted a part of my website, I feel this very strongly. Be careful out there! See you next week! * General* Jensen says “AGI is here” (X, Lex full pod)* Big CO LLMs + APIs* Google drops Gemini Flash live - Gemini can see, hear and talk to you (X)* OpenAI fully discontinues Sora, including app, API, and ChatGPT video features, as Disney deal collapses (X, X)* Claude Code users blowing through weekly usage quotas by Monday/Tuesday (X)* Anthropic tightens the Claude Pro/Max account quotas during Peak Hours (Anthropic announcement)* ARC-AGI-3 launches: humans 100%, AI under 1% (X, Announcement)* Anthropic gets an injunction against DoW in Supply-chain case (X)* Open Source LLMs* Google TurboQuant — KV cache 6x compression, 8x speedup, zero accuracy loss (X, Blog, Arxiv)* Unsloth Studio: 10x faster inference, desktop shortcuts, auto-parameter detection (X, GitHub)* Reka AI launches Edge, a 7B multimodal vision-language model built for sub-second latency on edge devices, now available on OpenRouter (X, HF, Announcement, Blog)* Tools & Agentic Engineering* Cursor Composer 2 tech report: 1T params trained on Kimi K2.5 (X, Blog)* Modular 26.2 — FLUX.2 in X, Blog)* litellm PyPI supply chain attack — SSH keys, cloud creds, API keys exfiltrated (X)* Claude can now control your Mac - computer use arrives in Claude Cowork and Claude Code as a research preview (X, Announcement)* Voice & Audio* Mistral drops Voxtral TTS, a 3B-parameter open-weight text-to-speech model that beats ElevenLabs Flash in human preference tests (X, Blog)* Cohere launches Transcribe, an open-source 2B ASR model that tops HuggingFace’s Open ASR Leaderboard with 5.42% word error rate (X, Blog, HF)* Google DeepMind Lyria 3 Pro — full 3-minute music tracks with structural control (X, Announcement)* Irodori-TTS-500M — Japanese TTS with emoji emotion control (X, HF)* AI Art & Diffusion & 3D* Luma Labs Uni-1 — thinks and generates pixels simultaneously, #1 human preference Elo (X, Announcement)* Modular FLUX.2 — sub-1-second image generation, 99% cheaper than cloud (X)* Phota Labs launches Phota Studio + API — a photography-focused image model with identity-preserving personalization (X, try it) This is a public episode. 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Howdy, Alex here, let me catch you up on everything that happened in AI: (btw; If you haven’t heard from me last week, it was a Substack glitch, it was a great episode with 3 interviews, our 3rd birthday, I highly recommend checking it out here) This week was started on a relatively “chill” note, if you consider Anthropic enabling 1M context window chill. And then escalated from there. We covered the new GPT 5.4 Mini & Nano variants from OpenAI. How MiniMax used autoresearch loops to improve MiniMax 2.7, Cursor shipping their own updated Composer 2 model, and how NVIDIA CEO Jensen Huang embraced OpenClaw calling it “the most important OSS software in history” and that every company needs an OpenClaw strategy. Also, OpenAI acquires Astral (ruff, uv tools) and Mistral releases a “small” 119B unified model and Cursor dropped their Opus like Composer 2 model. Let’s dive in: ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Big Companies LLMs 1M context is now default for Opus.Anthropic enabled the 1M context window they shipped Claude with in beta, by default, to everyone. Claude, Claude Code, hell, even inside OpenClaw if you’re able to get your Max account in there, are now using the 1M long version of Opus. This is huge, because, while its not perfect it’s absolutely great to have 1 long conversation and not worry about auto-compaction of your context. As we just celebrated our 3rd anniversary, I remember that back then, we were excited to see GPT-5 with 8K context. Love how fast we’re moving on this. OpenAI drops GPT-5.4 mini and nano, optimized for coding, computer use, and subagents at a fraction of flagship costLast week on the show, Ryan said he burned through 1B (that’s 1 billion) tokens in a day! That is crazy, and there’s no way a person sitting in front of a chatbot can burn through this many tokens. This is only achieved via orchestration. To support this use-case, OpenAI dropped 2 new smaller models, cheaper and faster to run. GPT 5.4 Mini achieves a remarkable 72.1% on OSWorld Verified, which means it uses the computer very well, can browse and do tasks. 2x faster than the previous mini, at .75c/1M token, this is the model you want to use in many of your subagents that don’t require deep engineering. This is OpenAI’s ... sonnet equivalent, at 3x the speed and 70% the cost from the flagship. Nano is even crazier, 20 cents per 1M tokens, but it’s not as performant, so I wouldn’t use it for code. But for small tasks, absolutely. Here’s the thing that matters, these models are MEANT to be used with the new “subagents” feature that was also launched this week in Codex, all you need to do as... ask! Just tell Codex “spin up a subagent to do... X” and it’ll do it.OpenAI shifts focus on AI for engineering and enterprise, acquires Astral.sh makers of UV. Look, there’s no doubt that OpenAI the absolutely leader in AI, brought us ChatGPT, with over 900M users using it weekly. But they see what every enterprise sees, developers are MUCH more productive (and slowly so are everyone else) when they use tools that can code. According to WSJ, OpenAI executives will reprioritize some of the side-quests they have (Sora?) to focus on productivity and business. Which essentially means, more Codex, more Codex native, more productivity tools.With that focus, today they announced that OpenAI / Codex is acquiring Astral, the folks behind the widely popular UV python package manager. This brings strong developer tools firepower to the Codex team, the astral folks are great at writing incredibly fast tools in rust! Looking forward to see how these great folks improve Codex even more. Jensen Declares Total OpenClaw Victory at GTC, Announces NemoClaw (Github)This was kind of surreal, NVIDIA CEO Jensen Huang, is famous for doing his stadium size keynote, without a teleprompter, and for the last 10 minutes or so, he went all in on OpenClaw. Calling it “the most important OSS software in history” and outlining how this is the new computer. That Peter Steinberger with OpenClaw showed the world a blueprint for the new coputer, an personal agentic system, with IO, files, computer use, memory, powered by LLMs. Jensen did outline that the 3 things that make OpenClaw great are also the things that enterprises cannot allow, write access to your files + ability to communicate externally is a bad combo, so they have launched NemoClaw.They’ve got a bunch of security researchers to work with OpenClaw team to integrate their new OpenShell sandboxing effort, network guardrails and policy engine integration. I reminded folks on the pod that the internet was very insecure, there was a time where folks were afraid of using their creditcards online. OpenClaw seems to be speed running that “unsecure but super useful” to “secure because it’s super useful” arc and it’s great to see a company as huge as NVIDIA embrace. Not to mention that given that agents can run 24/7, this means way more inference and way more chips sold for NVIDIA so makes sense for them, but still great to see!Manus “my computer” and other companies replicating “OpenClaw” successThis week it became clear, after last weeks Perplexity “computer”, Manus (now part of Meta) has also announced a local extension of their cloud agents, and those two are only the first announcements, it’s clear now that every company dissected OpenClaw’s moment and will be trying to give its users what they want. An agentic always on AI assistant with access to the users files, documents etc. Claude code added “channels“ support with telegram and discord connectors today, which, also, is one big missing piece of the puzzle for them. Everything is converging on this. Even OpenAI is rumored to consolidate Codex (which sees huge success) with OpenAI and Atlast browser into 1 “mega” APP that would do these things and act as an agent. ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.MiniMax M2.7: The Model That Built Itself This one blew me away, it’s not quite open source (yet?) but the MiniMax folks are coming out with a 2.7 version just after their MiniMax 2.5 was featured on our show and .. they are claiming that this model trained itself. Similarly to Andrej Karpathy’s auto-researcher, the MiniMax folks ran 100+ autonomous optimization loops, t get this model to 56.22% on the hard Swe-bench pro benchmark (close to Opus’s 57.3%!) and this one gets a 88% win rate vs the very excellent MiniMax 2.5. They used the previous model to build the agent harness and scaffolding, with 1 engineer babysitting these agent, and writing 0 lines of human code, which as we said before, every company will be doing, as we’re staring singularity in the face! We’ve evaluated this model as well (Wolfram has been busy this week!) and it’s doing really well on WolfBench with 52% average and 64% top score, it’s very close to 5.3 codex on our terminalBench benchmark! We hope that this model will be open source at some point soon as well! Cursor drops Composer 2 - nearly matching Opus 4.6, fast version (Blog)Cursor decided to add to our show’s breaking news record of Thursday releases with a brand new in-house trained Composer 2. This time they released more benchmarks than only their internal “composer bench” and this model looks great! (we are pretty sure it’s a finetune of a chinese OSS model, but we don’t know which) Getting 61% on Terminal Bench, beating Opus 4.6 is quite a significant achievement, but coupled with the incredible pricing they are offering, $0.5/1Mtok input and $2.50/M output tokens, Cursor is really aiming for the productivity folks and showing that they are more than just an IDE.Early users are reporting noticeably cleaner code than both Opus and Composer 1.5 — better adherence to clean code principles, smarter multi-file implementations, and strong performance on long-horizon agentic tasks like full API migrations and legacy codebase refactoring. They also shipped a new interface called Glass (in alpha) that’s built for monitoring these long-running agent loops. Open Source: Mistral is Back, BabyMistral Small 4: 119B MoE with 128 experts + Apache 2.0 (X, Blog, HF)It’s been a while since Mistral dropped something properly open source, and this week they kicked off what looks like their fourth generation with Mistral Small 4. The name is a little funny given the actual size — 119 billion total parameters, 128 experts in the mixture — but with only 6 billion active per token. So you get the knowledge footprint of a massive model but the compute profile of a small one. Very MoE-brained.The bigger story here is what’s unified inside: this is Magistral (reasoning), Pixtral (multimodal), and Devstral (coding) all rolled into one weights file. Previously you had to choose which Mistral “side quest” model you wanted. Now there’s a reasoning_effort parameter where you dial from none for fast cheap responses all the way up to high for step-by-step thinking, no model switch required. How does it perform? We ran it through WolfBench and it landed toward the lower end of Wolfram’s current leaderboard — around 17% on the agentic tasks, roughly on par with Nemotron at the same scale. It’s not competing with Opus or GPT-5.4, and we weren’t really expecting it to. What we’re excited about is that it does multimodal, reasoning, and coding in one Apache-licensed package, and people are already running IQ4 quants locally. Shout out to Mistral for the return to open source — it’s been a minute, and the community noticed.Unsloth Studio: Fine-Tuning Gets a UI (Blog)Something I think people are sleeping on this week is Unsloth Studio, the open-source web UI that the Unsloth team just launched for local LLM training and inference. Unsloth has been quantizing and compressing models better than basically anyone for a while now — 2x training speed, 70% less VRAM, zero accuracy loss — but that was all code-first. Studio is the no-code interface layer on top of all of that.The numbers: supports 500+ models across text, vision, audio, and embeddings. It runs 100% offline with no telemetry. Julien Chaumond, the CTO of Hugging Face, confirmed it trains successfully on a Colab Pro A100. There’s even a free Colab notebook for models up to 22B parameters. For folks who want to fine-tune models overnight without spinning up cloud infra or wrestling with Docker, this is a genuine leap forward. Nisten compared it to what LM Studio did for local inference — making something that used to require deep expertise suddenly accessible to anyone. I think that comparison is spot on, and I want to get Daniel and the Unsloth team on the show to dig into this properly.This Week’s Buzz: W&B iOS App & The Overthinking ParadoxThe iOS App is Finally Here (app store)Okay, I’m going to do a quick applause. 👏The most requested feature in Weights & Biases history is now live: the W&B iOS mobile app. If you’ve ever kicked off a training run overnight and woken up to find it crashed at hour two without knowing about it until morning, you understand exactly why people have been begging for this. Live metrics, loss curves, KL divergence — all right on your phone. And native push notifications for alerts! The second your run fails or a custom metric crosses a threshold, you get a notification on your phone.Please give us feedback through the app, the iOS team is actively building on top of this. Get it on the App Store and let us know what you need.WolfBench insight: More Thinking ≠ Better AgentsThis is one of the more counterintuitive findings we’ve surfaced from the W&B + Wolf Bench collaboration, and Wolfram laid it out really clearly.He tested Opus 4.6 and GPT-5.4 across different thinking/reasoning effort levels inside the Terminal Bench 2.0 agentic benchmark framework — using both the default Terminus 2 harness and the OpenClaw agent framework. For GPT-5.4, the pattern was exactly what you’d expect: higher reasoning effort gets better results. At extra-high, it hit 71% with 85% ceiling on tasks it could solve.For Opus 4.6, though? Turning it up to the maximum thinking level made it significantly worse. From 71% on standard settings all the way down to 59% on max reasoning. It lost tasks it had been reliably solving before. Wolfram dug into the traces in Weave and found out why: the model was overthinking. In an agentic benchmark where you have a one-hour time limit per task, spending ten minutes reasoning about what terminal command to try — and then getting an error — and then spending another ten minutes reasoning about it — is catastrophically inefficient.We’ll keep you up to date with more Alpha from our bench efforts! Stay tuned and checkout wolfbench.aiVoice & Audio xAI relaunched the Grok Text-to-Speech API (try it)It’s actually a pretty full-featured release right away. Multiple voices, expressive controls, WebSocket streaming, multilingual support, and the whole platform feel suggests xAI is very much trying to build a serious multimodal API stack, not just throw out a toy demo.The inline control tags are the fun part. You can embed pauses, laughter, whispers, breathing cues, all that. Those controls matter a lot for agents because the difference between “reads text out loud” and “feels usable in a voice interaction” usually lives in those details. As you can see in the video.. it’s.. not perfect.. yet? but pretty fun!But the thing I personally had the most fun with this week was Fish Audio. We didn’t get to cover it properly last week, and when I played with it more this week, I came away really impressed. It’s fast, expressive, open source, and the voice control vibe is genuinely cool.My favorite moment was not even a benchmark thing. I used Fish Audio with an agent setup to make a character voice inspired by Project Hail Mary, then had my kid talk to it. And the result was weirdly magical. If you remember the Audio book of Hail Mary, fish audio was able to get the voice juuuust right + Opus via OpenClaw obliged with a great skill to talk like rocky. I won’t post this for obvious copyright reasons but I showed it on the live show, at the end. Parting thoughts: I was hoping for a quieter week this week as I was sick, but it didn’t materialize, I should stop hoping for quiet weeks I think. After all, this is how the singularity starts, faster and faster developments, models that train themselves, every company becomes an agentic company. We’ll keep you posted on the most important breakthroughs, cover breaking news and bring interesting folks to the show as guests. Thank you for reading, see you next week 👋 ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.ThursdAI - Mar 19, 2026 - TL;DRTL;DR of all topics covered:* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson* Big CO LLMs + APIs* Anthropic makes Opus 4.6 with 1M context the default claude code - at the same price (X)* OpenAI drops GPT-5.4 mini and nano, optimized for coding, computer use, and subagents at a fraction of flagship cost (X, Announcement, Announcement)* Xiaomi - Omni modal and language only 1T parameters - MiMo (X)* Google AI Studio gets a full-stack vibe coding overhaul with Antigravity agent, Firebase integration, and multiplayer support (X, Blog, Announcement)* MiniMax M2.7: the first self-evolving model that helped build itself, hitting 56.22% on SWE-Bench Pro (X, X, Announcement)* Cursor launches Composer 2, their first proprietary frontier coding model beating Opus 4.6 at a fraction of the cost (X, Blog)* Open Source LLMs* Mamba-3 drops with three SSM-centric innovations: trapezoidal discretization, complex-valued states, and MIMO formulation for inference-first linear models (X, Arxiv, GitHub)* H Company releases Holotron-12B, an open-source hybrid SSM model for computer-use agents that hits 8.9k tokens/sec and jumps WebVoyager from 35.1% to 80.5% (X, X, HF, Blog)* Hugging Face’s Spring 2026 State of Open Source report reveals 11M users, 2M models, and China dominating 41% of downloads as open source becomes a geopolitical chess board (X, Blog, X, X)* Unsloth launches open-source Studio web UI for local LLM training and inference with 2x speed and 70% less VRAM (X, Announcement, GitHub)* Astral (Ruff, uv, ty) joins OpenAI’s Codex team (announcement , blog , Charlie Marsh)* Mistral Small 4: 119B MoE with 128 experts, only 6B active per token, unifying reasoning, multimodal, and coding under Apache 2.0 (X, Blog, HF)* Tools & Agentic Engineering* NVIDIA GTC: Jensen Huang declares “Every company needs an OpenClaw strategy,” announces NemoClaw enterprise platform (X, TechCrunch, NemoClaw)* OpenAI ships subagents for Codex, enabling parallel specialized agents with custom TOML configs (X, Announcement, GitHub)* Manus (now Meta) launches ‘My Computer’ desktop app, bringing its AI agent from the cloud onto your local machine for macOS and Windows (X, Blog)* This weeks Buzz* Weights & Biases launches iOS mobile app for monitoring AI training runs with crash alerts and live metrics (X, Announcement)* GPT 5.4 went from worst to best on WolfBenchAI after an OpenClaw config fix exposed a max_new_tokens bottleneck (X, X, X)* Voice & Audio* xAI launches Grok Text-to-Speech API with 5 voices, expressive controls, and WebSocket streaming (X, Announcement)* AI Art & Diffusion & 3D* NVIDIA DLSS 5 is making waves with a new generative AI filter (Blog) This is a public episode. 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Hey, Alex here 👋 Today was a special episode, as ThursdAI turns 3 🎉 We’ve been on air, weekly since Pi day, March 14th, 2023. I won’t go too nostalgic but I’ll just mention, back then GPT-4 just launched with 8K context window, could barely code, tool calls weren’t a thing, it was expensive and slow, and yet we all felt it, it’s begun!Fast forward to today, and this week, we’ve covered Andrej Karpathy’s mini singularity moment with AutoResearcher, a whole fruit fly brain uploaded to a simulation, China’s OpenClaw embrace with 1000 people lines to install the agent. I actually created a new corner on ThursdAI, called it Singularity updates, to cover the “out of distribution” mind expanding things that are happening around AI (or are being enabled by AI)Also this week, we’ve had 3 interviews, Chris from Nvidia came to talk to use about Nemotron 3 super and NVIDIA’s 26B commitment to OpenSource, Dotta (anon) with his PaperClips agent orchestration project reached 20K Github starts in a single week and Matt who created /last30days research skill + a whole bunch of other AI news! Let’s dive in. ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Singularity updates - new segmentAndrej Karpathy open sources Mini Singularity with Auto Researcher (X)If there’s 1 highlight this week in the world of AI, it’s this. Andrej, who previously started the AutoPilot program in Tesla, and co-founded OpenAI, is now, out there, in the open, just.. doing stuff like invent a completely autonomous ML research agent. Andrej posted to his almost 2M followers that he opensourced AutoResearch, a way to instruct a coding agent to do experiments against a specific task, test the hypothesis, discard what’s not working and keep going in a loop, until.. forever basically. In his case, it was optimizing speed of training GPT-2. He went to sleep and woke up to 83 experiments being done, with 20 novel improvements that stack on top of each other to speed up the model training by 11%, reducing the training time from 2.02 hours to 1.8 hours. The thing is, this code is already hand crafted, fine tuned and still, AI agents were able to discover new and novel ways to optimize this, running in a loop.Folks, this is how the singularity starts, imagine that all major labs are now training their models in a recursive way, the models get better, and get better at training better models! Reminder, OpenAI chief scientist Jakub predicted back in October that OpenAI will have an AI capable of a junior level Research ability by September of this year, and it seems that... we’re moving quicker than that! Practical uses of autoresearchThis technique is not just for ML tasks either, Shopify CEO Tobi got super excited about this concept, and just posted as I’m writing this, that he set an Autoresearch loop on Liquid, Shopify’s 20 year old templating engine, with the task to improve efficiency. His autoresearch loop was able to get a whopping 51% render time efficiency, without any regressions in the testing suite. This is just bonkers. This is a 20 year old, every day production used template. And some LLM running in a loop just made it 2x faster to render, just because Karpathy showed it the way. I’m absolutely blown away by this, this isn’t a model release, like we usually cover on the pod, but still, a significant “unhobbling” moment that is possible with the current coding agents and models. Expect everything to become very weird from here on out!Simulated fruit fly brains - uploaded into a simulatorIn another completely bonkers update that I can barely believe I’m sending over, a company called EON SYSTEMS, posted that they have achieved a breakthrough in brain simulation, and were able to upload a whole fruit fly brain connectome, of 140K neurons and 50+ million synapses into a simulation environment. They have... uploaded a fly, and are observing a 91% behavioural accuracy. I will write this again, they have uploaded a fly’s brain into a simulation for chirst sake!This isn’t just an “SF startup” either, the board of advisors is stacked with folks like George Church from Harvard, father of modern genome sequencing, Stephen Wolfram who needs no introduction but one of the top mathematicians in the world, whos’ thesis is “brains are programs”, Anders Sandberg from Oxford, Stephen Larson who apparently already uploaded a worms brain and connected it to lego robots before. These folks are gung ho on making sure that at some point, human brains are going to be able to get uploaded, to survive the upcoming AI foom. The main discussion points on X were around the fact that there was no machine learning here, no LLMs, no attention mechanisms, no training. The behaviors of that fly were all a result of uploading a full connectome of neurons. This positions connectome (the complete diagram of a brain with neurons and connections) as an ananalouge to an pre-trained LLM network for biological intelligence. I encourage everyone who’s reading this, to watch Pantheon on Netflix, to understand why this is of massive importance. Combined with the above Autoresearch, things are going to go very fast here. The next step is uploading a mouse brain, which will be a 500x Neurons and 2000x more synapses, but if we’re looking at the speed with which AI is improving, that’s NOT out of the realm of possibility for the next few years!OpenClaw Mania Sweeps China: Thousand-Person Lines & Government Subsidies, Grandmas raising a “red lobster”They’re calling it “raising a red lobster” (养小龙虾). That’s the phrase that swept Chinese social media for what is, at its core, installing an open source GitHub project on your laptop. Grandmas are doing it. Mac Minis are sold out. A cottage industry of paid installers popped up overnight on Xiaohongshu, charging up to $100 for an in-person setup. And yes, there are now also people charging to uninstall it.On March 6th, roughly a thousand people lined up outside Tencent’s Shenzhen HQ for free OpenClaw installation. Appointment slots ran out within an hour. People brought NAS drives, MacBooks, mini PCs. Tencent engineers set up folding tables and just... started installing OpenClaw for strangers. I have pictures. I’m not making this up.All five major Chinese cloud providers jumped in simultaneously: Tencent Cloud, Alibaba Cloud, ByteDance Volcano Engine, JD.com Cloud, and Baidu Intelligent Cloud, each racing to offer one-click OpenClaw deployment. Why? Follow the money. Per HelloChinaTech, ByteDance, Alibaba, and Tencent spent roughly $60B combined on AI infrastructure. Chatbots don’t burn enough tokens to justify that spend. But a single OpenClaw instance runs 24/7 and consumes 10-100x more tokens per day than a chatbot user. Every install is round-the-clock API revenue. The cheaper the models get, the more people run agents, the more infra gets sold. Self-reinforcing loop.Local governments are pouring fuel on the fire. Shenzhen’s Longgang district is offering up to 2M yuan ($290K) per project. Hefei and Wuxi are going up to 10M yuan ($1.4M), plus free computing, office space, and accommodation for “one-person companies.” Meanwhile, China’s central cybersecurity agency issued TWO warnings, banning banks and state agencies from installing OpenClaw. So local governments are subsidizing it while the central authority is trying to pump the brakes. Peak 2026.With nearly half of all 142,000+ publicly tracked OpenClaw instances are now from China. OpenClaw is the most-starred GitHub repo in history, surpassing Linux’s 30-year record in just 100 days. Device makers are piling on too — Xiaomi announced “miclaw” for smartphones, MiniMax built MaxClaw, Moonshot AI built a hosted version around Kimi.Now, Ryan was honest on the show and I want to echo that honesty here: OpenClaw is still hard to get working. There are many failure states. It’s not “install and go to the beach.” Wolfram compared it to Linux in the late ‘90s — painful to set up, but if you push through, you can see the future behind the friction. This is real technology with real limitations, and a lot of disappointed folks in China are watching tokens burn with no actual work getting done.But here’s the thing I keep coming back to. The memetic velocity of OpenClaw is unlike anything I’ve seen in tech. It’s not just a tool, it’s a concept that penetrated the cultural resistance to AI. People who are scared of terminals, people who’ve never touched GitHub — they’re standing in line for this. I broke through that resistance with my own fiancée. She’s now running two OpenClaws. Not enough for her. She needs another one.Every major US lab is watching this closely. OpenAI brought Peter Steinberger on staff. Perplexity just announced they’re building a local agent for Mac. Anthropic has Claude Cowork. This is where all of computing is headed — always-on, autonomous, personal AI that actually does things for you. OpenClaw is the first front door, not the final destination. But what a front door it is.ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Open Source: Nvidia Goes All In with Nemotron 3 Super 120B (X, Blog, HF)We had Chris Alexiuk from Nvidia join us — a friend from a dinner Nisten and I hosted in Toronto. Chris is basically “NeMo” embodied, sitting at the intersection of product and research, and he gave us the full breakdown on what might be the most complete open-source model release we’ve seen from a major lab.Here are the numbers: 120B total parameters, 12B active during inference (it’s a Mixture of Experts), 1 million token context window, and a hybrid Mamba-Transformer architecture they call “Hybrid Mamba Latent MoE with Multi-Token Prediction.” It’s hitting 450 tokens per second on the Terminal Bench leaderboard — faster than any other model on there. Modal is reporting over 50% faster token generation compared to other top open models.What Chris was emphatic about — and I want to highlight this — is that “most open” is a real designation here. They released the model checkpoint in three precisions (BF16, FP8, NVFP4), the base checkpoint before post-training, the SFT training data, and in a move that genuinely surprised people, pre-training data and a full end-to-end training recipe. You can, in theory, reproduce their training run. That’s rare. That’s a real commitment to open source.There’s also a huge piece of news in the background here: there’s a confirmed report that Nvidia will spend $26 billion over the next five years building the world’s best open source models. Jensen presumably has GTC remarks incoming on this. America is genuinely back in the open source AI race, and it’s Nvidia leading the charge. Chris has been in the open source world since the Hugging Face early days and said it feels genuine inside the company — not a PR exercise. And I tend to believe him. Now, all eyes are on GTC next week!I ran Nemotron 3 Super with my own OpenClaw instance yesterday via W&B inference and it’s genuinely fast and capable. At $0.20/M input tokens and $0.80/M output tokens on W&B inference, it’s not going to replace Opus for your hardest tasks — but for running an always-on agent that needs to be cost-efficient? It’s an incredible option. More on that in the this weeks buzz section below.Tools & Agentic EngineeringPaperclip: Zero Human Companies, Now Open Source (Github)We had the anonymous Dotta on the show — the first AI video avatar anon person to join ThursdAI — to talk about Paperclip, an open source agent orchestration framework that hit 20,000 GitHub stars in its first week. The premise is simple and audacious: build zero-human companies.Now this may sound familiar to you, as we had Ben from Polsia on just two weeks ago, which is a similar concept, but Paperclip is an OpenSource project, which you can run right now on your own.The core “thing” that got me excited about Paperclip is that you can “hire” your own existing OpenClaw agents, or Cursor or Codex or whatever else to play roles in this autonomous company. The premise is simple, you’re the board of directors, you hire an AI Agent CEO, and it then asks you if it needs to “hire” more AI agents to do tasks autonomously. These tasks all live inside Paperclip interface, and you or your Agents can open them. The core concept of this whole system is the heartbeat concept, each agent receives their own instructions on what to do every time they are “woken up” by a timer. This is what’s driving the “autonomous” part of the whole thing, but it’s also what’s eating the tokens up, even if there’s no work being done, agents are still burning tokens asking “is there work to be done?” Dotta gave us a great metaphor, asking if we saw the movie Memento, where the protagonist lost his memory and every time he woke up, he woke up with a blank slate, and had to reconstruct the memories. AI agents are like the memento man, and Paperclip is an attempt to give those agents the whole context so they can continue working on your tasks productively. Dotta told us that the future of Paperclip is the ability to “fork” entire companies, structures that will actually run and do things on your behalf. Looking forward to that future, but for now I will be turning off my Paperclip interface as it’s costing me real money without the need. Symphony: Agents Writing Their Own Jira TicketsWe mentioned Symphony last week, and I texted Ryan the link before the show, and voila, of course, he set it up and went viral, yet again! We’re so lucky to have Ryan on the show to tell us from first hand experience what it’s like to run this thing. Symphony was open sourced by OpenAI last week, and it’s basically an instruction manual for how to run agents autonomously via Linear ticketing system. (Github)The highlight for Ryan was, the whole system is running creating pull requests while he’s a sleep, and at some point, he noticed a ticket that he didn’t create. One of the agents found a bug, and created a very detailed ticket for him to approve. I’m just happy that I can keep even my co-hosts up to date heheThis weeks buzz - we’ve got skills and nemotrons!Look, we told you about Skills in the start of the year, since then, via OpenClaw, Hermes Agent, Claude Code, they exploded in popularity. One downside of skills is, it’s very easy to make a bad one! So, we’re answering the challenge, and are publishing the official wandb skill 🎉 Installing it is super simple, npx skills add wandb/skills and voila, your agents are now officially “I know kung fu” pilled with the best Weights & Biases practices. For both Weave and Models 👏 Please give us feedback on Github if you have used the skills! GithubAlso, we’ve partnered with Nvidia to support the best US open source model on day 0, and we have Nemotron 3 Super on our inference service, for all to use at $0.20/1Mtok! It’s super easy to setup with something like Hermes Agent or OpenClaw and runs really really fast! Check it out here. Is it going to perform like Opus 4.6? No. But are you going to run Opus 4.6 at 20 cents per million? Also no. Gemini drops SOTA embeddings and gets dethroned 2 days later live on the show. This always happens, but I didn’t expect this to happen in a fairly niche segment of the AI world... multimodal embeddings! Gemini posted an update earlier this week with Gemini Embeddings 2.0, a way to unify images, text, video, audio embeddings under 1 roof, and posted a SOTA embedding model! Then, just as we launch the show, a friend of the pod Benjamin Clavie, drops me a DM, basically saying that his company Mixbread is going to deploy an embedding model that will beat Gemini Embedding 2 on almost every benchmark on that table, and then... they did! The most notable (and absolutely crazy) jump in this comparison is, the LIMIT benchmark, where they achieved a 98% score vs Gemini’s ... 6.9 percent. I didn’t believe this at first, but asked Ben to explain the findings, and he did. Congrats to folks for moving the search space forward every 2 days! Grok 4.20 in the API for $2/1MtokElon Musk and XAI co finally released Grok 4.20 in the API. Look I said what I said about XAI models, they are great for research, and for factuality, but they aren’t beating the major labs. The last firing of almost of XAI folks doesn’t help either. So this model was not “released” in any traditional sense, there’s no benchmarks, no evals, and everyone who got access to it evaluated it and, it’s no better than GLM5 on many benchmarks. So it does makes sense to release it quietly. It is very fast though, and again, for research and for X access, it’s an absolute beast, so I’ll be trying this out! Parting thoughts and a small reflection. For the past 3 years, we’ve had a front-row seat to the singularity shaping up. 2.5 years ago, I went all in, decided to pivot into podcasting full time. In those years, ThursdAI became known, we’ve had guests from nearly all major AI labs (including Chinese ones, for which I’m particularly proud), I got to meet with executives, ask leaders questions about where this is all going, and most of all, share this journey with all of you, candidly. We rarely do hype on the show, we don’t speculate, we try to do a positive outlook on the whole thing, and counter doomerism, as there’s too much of that out there. I am very glad this resonates, and continue to be thankful for your attention! If you wanted to give us any kind of a birthday present, subscribe or give us a 5 star review on Apple Podcasts or Spotify, it’ll greatly help other folks to discover us. See you next week, Alex 🫡ThursdAI - Mar 12, 2026 - TL;DR* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed) @ryancarson* Chris Alexiuk from Nvidia (@llm_wizard) - Nemotron* @dotta - creator of Paperclip.ing AI agent orchestration framework* Matt Van Horn - @mvanhorn - creator of @slashlast30days* Singularity updates* Andrej Karpathy’s autoresearch achieves 11% speedup on GPT-2 training through autonomous AI agent experimentation (X, GitHub, GitHub)* Eon Systems uploads first complete fruit fly brain to a physics-simulated body, achieving 91% behavioral accuracy (X, Announcement, Announcement)* OpenClaw mania sweeps China as all five major cloud providers race to support it (HelloChinaTech, Reuters, SCMP, MIT Tech Review)* Big CO LLMs + APIs* xAI quietly releases Grok 4.20 API with massive 2M token context window and multi-agent capabilities (X, Blog)* Google launches Gemini Embedding 2, the first natively multimodal embedding model supporting text, images, video, audio, and PDFs in a unified vector space (X, Announcement)* Open Source LLMs* NVIDIA launches Nemotron 3 Super: 120B open MoE model with 1M context window designed for agentic AI at 5x higher throughput (X, Announcement)* MiroMind releases MiroThinker-1.7 and H1 - open-source research agents with 256K context, 300 tool calls, achieving SOTA on deep research benchmarks (X, HF, HF, HF)* Covenant-72B: World’s largest permissionless decentralized LLM pre-training achieves 72B parameters on Bittensor with 146x gradient compression (X, Arxiv, HF, HF)* Tools & Agentic Engineering* ACP is the open standard that lets any AI coding agent plug into any editor — and this week Cursor officially joined the registry, meaning you can now run Cursor’s agent inside JetBrains IDEs (JetBrains blog, Cursor blog)* This weeks Buzz* W&B launches official Agent Skills for coding agents, turning experiment dashboards into terminal queries (X, Announcement, Announcement)* Video* LTX-2.3 — Lightricks open-source video model (GitHub, HF,Blog)* Voice & Audio* Fish Audio launches S2: Open-source TTS with sub-150ms latency and absurdly controllable emotion (X, HF, Blog, Announcement)* Show notes and links* Paperclip.ing by Dotta ( @dotta) - Github* Last30days skill by Matt Van Horn Github* Agency Agents repo Github* OpenAI Symphony (Github)* Mixbread Embeddings (X) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey folks, Alex here, let me catch you up! Most important news about this week came today, mid-show, OpenAI dropped GPT 5.4 Thinking (and 5.4 Pro), their latest flagship general model, less autistic than Codex 5.3, with 1M context, /fast mode and the ability to steet it mid-reasoning. We tested it live on the show, it’s really a beast. Also, since last week, Anthropic said no to Department of War’s ultimatum and it looks like they are being designated as supply chain risk, OpenAI swooped in to sign a deal with DoW and the internet went ballistic (Dario also had some .. choice words in a leaked memo!) On the Open Source front, the internet lost it’s damn mind when a friend of the pod Junyang Lin, announced his departure from Qwen in a tweet, causing an uproar, and the CEO of Alibaba to intervene. Wolfram presented our new in-house wolfbench.ai and a lot more! P.S - We acknowledge the war in Iran, and wish a quick resolution, the safety of civilians on both sides. Yam had to run to the shelter multiple times during the show. ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.OpenAI drops GPT 5.4 Thinking and 5.4 Pro - heavy weight frontier models with 1M context, /fast mode, SOTA on many evalsOpenAI actually opened this week with another model drop, GPT 5.3-instant, which... we can honestly skip, it was fairly insignificant besides noting that this is the model that most free users use. It is supposedly “less cringe” (actual words OpenAI used). We all wondered when 5.4 will, and OpenAI once again proved that we named the show after the right day. Of course it drops on a ThursdAI. GPT 5.4 Thinking is OpenAI latest “General” model, which can still code, yes (they folded most of the Codex 5.3 coding breakthroughs in here) but it also shows an incredible 83% on GDPVal (12% over Codex), 47% on Frontier Math and an incredible ability to use computers and browsers with 82% on BrowseComp beating Claude 4.6 at lower prices than Sonnet! GPT 5.4 is also ... quite significantly improved at Frontend design? This landing page was created by GPT 5.4 (inside the Codex app, newly available on Windows) in a few minutes, clearly showing significant improvements in style. I built it also to compare prices, all the 3 flagship models are trying to catch up to Gemini in 1M context window, and it’s important to note, that GPT 5.4 even at double the price after the 272K tokens cutoff is still.... cheaper than Opus 4.6. OpenAI is really going for broke here, specifically as many enterprises are adopting Anthropic at a faster and faster pace (it was reported that Anthropic is approaching 19B ARR this month, doubling from 8B just a few months ago!) Frontier math wizThe highlight from the 5.4 feedback came from a Polish mathematician Bartosz Naskręcki (@nasqret on X), who said GPT-5.4 solved a research-level FrontierMath problem he had been working on for roughly 20 years. He called it his “personal singularity,” and as overused as that word has become, I get why he said it. I’ve told you about this last week, we’re on the cusp. Coding efficiencyThere’s tons of metrics in this release, but I wanted to highlight this one, where it may seem on first glance that on SWE-bench Pro, this model is on par with the previous SOTA GPT 5.3 codex, but these dots here are thinking efforts. And a medium thinking effort, GPT 5.4 matches 5.3 on hard thinking! This is quite remarkable, as lower thinking efforts have less tokens, which means they are cheaper and faster ultimately! Fast mode arrives at OpenAI as wellI think this one is a direct “this worked for Anthropic, lets steal this”, OpenAI enabled /fast mode that.. burns the tokens at 2x the rate, and prioritizes your tokens at 1.5x the speed. So, essentially getting you responses faster (which was one of the main complains about GPT 5.3 Codex). I can’t wait to bring the fast mode to OpenClaw with 5.4, which will absolutely come as OpenClaw is part of OpenAI now. There’s also a really under-appreciated feature here that I think other labs are going to copy quickly: mid-thought steering. OpenAI now lets you interrupt the model while it’s thinking and redirect it in real time in ChatGPT and iOS. This is a godsend if you’re like me, sent a prompt, seeing the model go down the wrong path in thinking... and want to just.. steer it without stopping! Anthropic is now designated as supply-chain risk by DoWLast week I left you with a cliffhanger: Anthropic had received an ultimatum from the Department of War (previously the Department of Defense) to remove their two remaining restrictions on Claude — no autonomous kill chain without human intervention, and no surveillance of US citizens. Anthropic’s response? “we cannot in good conscience acceede to their request” So much has happened since then; US President Trump said “I fired Anthropic” referring to his Truth Social post demanding intelligence agencies drop the use of Claude (which apparently was used in the war with Iran regardless); Sam Altman announced that OpenAI has agreed to DoW and will provide OpenAI models, causing a lot of people to cancel their OpenAI subscriptions, and later apologizing for the “rushed rollout”; Dario Amodei posted a very contentious internal memo that leaked, in which he name-called the presidency, Sam Altman and his motives, Palantir and their “safety theater”, for which he later apologizedHonestly this whole thing is giving me whiplash trying to follow, but here’s the facts. Anthropic is now the first US company in history, being designated “supply chain risk” which means no government agency can use Claude, and neither can any company that does contracts with DoW. Anthropic says it’s illegal and will challenge this in court , while reporting $19B in annual recurring revenue, nearly doubling since last 3 months, and very closely approaching OpenAI at $25B. Look, did I want to report on this stuff when I decided to cover AI? no... I wanted to tell you about cool models and capabilities, but the world is changing, and it’s important to know that the US Government understands now that AI is inevitable, and I think this is just the first of many clashes between tech and government we’ll see. We’ll keep reporting on both. (but let me know in the comments if you’d prefer just model releases) OpenAI’s GPT-5.3 Instant Gets Less Cringe, Google’s Flash-Lite Gets Faster (X, Announcement)We also got two fast-model updates this week that are worth calling out because these are the models that often end up powering real product flows behind the scenes. As I wrote before, OpenAI’s instant model is nothing to really mention, but it’s worth mentioning that OpenAI seems to have an answer for every Gemini release. Gemini released Gemini Flash-lite this week, which boasts an incredible 363 tokens/s speed, which doing math at a very good level, 1M context and great scores compared to the instant/fast models like Haiku from Anthropic. Folks called out that this model is more expensive than the previous 2.5 Flash-lite. But with 86.9% on GPQA Diamond beating GPT-5 mini, and 76.8% MMMU-pro multimodal reasoning, this is definitely worth taking a look at for many agentic, super fast responses! For example, the heartbeat response in OpenClaw. Qwen 3.5 Small Models & The Departure of Junyang Lin (X, HF, HF, HF)Alibaba’s Qwen team continued releasing their Qwen 3.5 family, this time with Qwen 3.5 Small, a series of models at 0.8B, 2B, 4B, and 9B parameters with native multimodal capabilities. The flagship 9B model is beating GPT-OSS-120B on multiple benchmarks, scoring 82.5 on MMLU-Pro and 81.7 on GPQA Diamond. These models can handle video, documents, and images natively, support up to 201 languages, and can process up to 262K tokens of context. And.. they are great! They are trending on HF right now. What’s also trending is, tech lead for Qwen, a friend of the pod Junyang Lin, has posted a cryptic tweet that went viral with over 6M views. There was a lot of discussions on why he and other Qwen leads are stepping away, what’s goig to happen with the future of OpenSource. The full picture seems to be, there are a lot of internal tensions and politics, with Junyang being one of the youngest P10 leaders in the Alibaba org.A Chinese website 36KR ( Kind of like a chinese techcrunch) reported that this matter went all the way up to Alibaba CEO, who is no co-leading the qwen team, and that this resignation was related to an internal dispute over resource allocation and team consolidation, not a firing. I’m sure Junyang is going to land somewhere incredible and just wanted to highlight just how much he did for the open source community, pushing Qwen relentlessly, supporting and working with a lot of inference providers (and almost becoming a co-host for ThursdAI with 9! appearances!) StepFun releases Step 3.5 Flash Base (X, HF, HF, Announcement, Arxiv)Speaking of Open Source, StepFun just broke through the noise with a new model, a 196B parameter sparse Mixture of Experts model activating just 11B parameters when ran. It has some great benchmarks, but the main thing is this: they are releasing the pretrained base weights, a midtrain checkpoint optimized for code and agents, the complete SteptronOSS training framework, AND promising to release their SFT data soon - all under Apache 2.0! Technically the model looks strong too, with multi-token prediction, 74.4% on SWE-bench verified bench (though, as we told you last week, it’s.. no longer trusted) and full apache 2! This Week’s Buzz: presenting Wolfbench.ai I’m so excited about this weeks “this weeks buzz”, Wolfram has been hard at work preparing and presenting a new framework to test out these models, and named it wolfbench.ai Wolfbench is our attempt to compare how the same model performs via different agentic harnesses like ClaudeCode, OpenClaw and Terminalbench’s own Terminus. You can check out the website on wolfbench.com but the short of it is, a single number is not telling the full story. Wolf Bench breaks it into a four-metric framework: the average score across runs, the best single run, the ceiling (how many tasks can the model solve at least once across all runs), and the floor (how many tasks does it solve consistently across every single run). That last one is what I find most illuminating. Opus 4.6 might be able to solve 88% of Terminal Bench tasks on average, but only about 55% of tasks it solves every single time. Reliability matters enormously for agents, and benchmarks almost never surface this. If you want to run your own evals with the same config, reach out to Wolfram—he’s open to community contributions. Wolfram has also already kicked off a Wolf Bench run on GPT-5.4 since we tested it live today, so stay tuned for those results.There’s quite a few more releases we didn’t have time to get into on the show given the GPT 5.4 drop, you’ll find all those links in the show notes! Next week will mark 3 years since I’ve started talking about AI on the internet and created ThursdAI (It was March 14th, 2023, same day as GPT4 launched) and we’ll have a little celebration, I do hope you join us live 🔥 As a birthday present, you may choose to share ThursdAI with a friend or two, or rate us in your podcast player of choice! See you next week, Alex 🫡 ThursdAI - Mar 05, 2026 - TL;DRTL;DR of all topics covered:* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson* Big CO LLMs + APIs* OpenAI launches GPT-5.4 Thinking and Pro (X, X, X, X)* Anthropic, Dept of War and OpenAI walk into a bar* Alibaba Qwen departures: Friend of the pod JunyangLin and Binyuan Huy both depart Qwen (X)* OpenAI Rolls Out GPT-5.3 Instant (X)* Google launches Gemini 3.1 Flash-Lite (X, Announcement)* Evals and Benchmarks* MarinLab shows degradation in Opus 4.6 (X)* B******t Bench from Peter Gostev (X)* Open Source LLMs* StepFun releases Step 3.5 Flash Base models (X, HF, HF, Announcement, Arxiv)* Alibaba releases Qwen 3.5 Small Model Series (X, HF, HF, HF)* Yuan 3.0 Ultra (X, Blog, HF)* Tools & Agentic Engineering* Cognition: SWE-1.6 preview (X, Blog)* OpenAI launches Codex app on windows (X)* Google released Google Workspace CLI (X)* OpenAI released Symphony (Github)* This weeks Buzz* Early preview of Wolf Bench (wolfbench.ai) from W&B* AI Art & Diffusion & 3D* Black Forest Labs introduces Self-Flow (X, Announcement) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey, it’s Alex, let me tell you why I think this week is an inflection point.Just this week: Everyone is launching autonomous agents or features inspired by OpenClaw (Devin 2.2, Cursor, Claude Cowork, Microsoft, Perplexity and Nous announced theirs), METR and ArcAGI 2,3 benchmarks are getting saturated, 1 person companies nearing 1M ARR within months of operation by running AI agents 24/7 (we chatted with one of them on the show today, live as he broke $700K ARR barrier) and the US Department of War gives Anthropic an ultimatum to remove nearly all restrictions on Claude for war and Anthropic says NO. I’ve been covering AI for 3 years every week, and this week feels, different. So if we are nearing the singularity, let me at least keep you up to date 😅 Today on the show, we covered most of the news in the first hour + breaking news from Google, Nano Banana 2 is here, and then had 3 interviews back to back. Ben Broca with Polsia, Nader Dabit with Cognition and Philip Kiely with BaseTen. Don’t miss those conversations starting at 1 hour in. Thanks for reading ThursdAI - Highest signal weekly AI news show! This post is public so feel free to share it.Anthropic vs Department of WarEarlier this week, the US “Department of War” invited Dario Amodei, CEO of Anthropic to a meeting, where-in Anthropic was given an ultimatum. “Remove the restrictions on Claude or Anthropic will be designated as a ‘supply chain risk’ company” and the DoD will potentially go as far as using the Defence Production Act to force Anthropic to ... comply. The two restrictions that Anthropic has in place for their models are: No use for domestic surveillance of American citizens and NO fully autonomous lethal weapens decisions given to Claude. For context, Claude is the only model that’s deployed on AWS top secret GovCloud and is used through Palantir’s AI platform. As I’m writing this, Anthropic issued a statement from Dario statement saying they will not budge on this, and will not comply. I fully commend Dario and Anthropic for this very strong backbone, but I fear that this matter is far from over, and we’ll continue to see what is the government response. EDIT: Apparently the DoD is pressuring Google and OpenAI to agree to the stipulations and employees from both companies are signing this petition https://notdivided.org/ to protest against dividing the major AI labs on this topic. Anthropic and OpenAI vs upcoming DeepseekIt’s baffling just how many balls are in the air for Anthropic, as just this week also, they have publicly named 3 Chinese AI makers in “Distillation Attacks”, claiming that they have broke Terms of Service to generate over 16M conversations with Claude to improve their own models, while using proxy networks to avoid detection. This marks the first time a major AI company publicly attributed distillation attacks to specific entities by name.The most telling thing to me is not the distillation, given that Anthropic has just recently settled one of the largest copyright payouts in U.S history, paying authors about $3000/book, which was bought, trained on and destroyed by Anthropic to make Claude better. No, the most telling thing here is the fact that Anthropic chose to put DeepSeek on top of the accusation list with merely 140K conversations, where the other labs created millions. This, plus OpenAI formal memo to Congress about a similar matter, shows that the US labs are trying to prepare for Deepseek new model to drop, by saying “Every innovation they have, they stole from us”. Apparently Deepseek V4 is nearly here, it’s potentially multimodal and has been allegedly trained on Nvidia chips somewhere in Mongolia despite the export restrictions and it’s about to SLAP! Benchmark? What benchmarks? How will we know that we’re approaching the singularity? Will there be signs? Well, this week it seems that the signs are here. First, Agentica claimed that they solved all publicly available “hard for AI” tasks of the upcoming ArcAGI 3, then Confluence Labs announced that they got an unprecedented 97.9% on ArcAGI2 and finally METR published their results on the long-horizon tasks, which measure AI’s capability to solve task that take humans a certain amount of hours to do. And that graph is going parabolic, with Claude Opus 4.6 able to solve tasks of 14.6h (doubling every 49 days) with 50% success rateWhy is this important? Well, this is just the benchmarks telling the story that everyone else in the industry is seeing, that approximately since December of 2025, and definitely fueled by early Feb drop of Opus 4.6 and Codex 5.3, something major shifted. Developers no longer write code, but ship 10x more features.This became such a talking point, Swyx Latent.Space coined this with https://wtfhappened2025.com/ where he collects evidence of a shelling point, something that happened in December and I think continued throughout February. Speaking of benchmarks no longer being valid, OpenAI published that the divergence between the SWE-bench verified gains with real life performance is so vast, that they will no longer be using SWE-bench verified, and will be switching to SWE-bench pro for evaluations. Everyone’s Autonomous agents (and subagents) are hereLook, with over 250K Github stars, OpenAI getting Peter Steinberger on board, it’s clear now. OpenClaw made a huge dent in how people think about autonomous agents (and subagents!)It may be a “moment in time” that the model capabilities were “just good enough” to be able to run agents async for a long time. but the big labs noticed the OpenClaw excitement and are shipping like never before to make sure their users don’t switch over!Perplexity launched “Computer“, which has scheduled tasks in a compute environment, and can complete long lasting projects end to end, Cursor pivots from IDE only to running Agents in the cloud with their own environments, Claude Code added memory, and Remote Control, while Claude Cowork added Scheduled tasks, our friends from Nous shipped Hermes Agent and even Microsoft wants to bring this to their customers in Copilot. The most interesting one from these is the new Devin from Cognition.I’ve gotten access and chatted with Nader Dabit on the show about how Devin was the “OG” async coding Agent, but now as models capabilities are here, Devin can do so much more. PR reviews with devinreview.com can complete the loop between coding, fixing and testing something end to end. They have an integrated environment with a scrub so you can roll back and see what the agent did, scheduled tasks and video showing you how the agent tested your website. I’ve used it to fix bugs in ThrusdAI.news and it found a few that Claude Code didn’t even know about! You can try out Devin (for free for a week?) here This weeks buzz - W&B updatesI’m happy this week, because we finally launched both 2.5 open source models that we’re making the news lately. Kimi 2.5 and MiniMax M2.5 are both live on our inference service, at very very decent prices! Check them both out here and let me know if you need some credits. From the show this week, most hosts agree that Kimi 2.5 is the best open source alternative to Opus inside OpenClaw, just give your agent the WANDB_API_KEY and ask it to set itself up with the new model! Surfing the singularity with Ben Broca and Polsia, hitting $700K ARR since DecemberI’ve reached out to Ben and asked him to join the show this week because alongside OpenClaw blowing up since December, his Polsia startup, which builds and scales entire companies with AI agents running 24x7 has hit an unprecedented $700K ARR milestone after just a few months. We actually saw him break the $700K ARR on the show live 🎉 But get this, he’s the only employee, everything is done with AIs. He’s using Polsia to scale Polsia.Polsia let’s anyone add an existing company or create a whole new one, and then a team of agents will spin up a marketing team, a GTM motion, a research arm and you and Polsia could work together to make this company a reality. Does this actually work? IDK, the whole thing is new, I’m trying out a few things and will let you know in a few weeks if any of this worked. But it’s definitely blowing up, Ben showed us that over the last 24 hours, over 770 companies launched on Polsia, he’s hitting nearly 1M ARR with people paying $50/mo for him to run inference for them, marketing campaigns, and he just added Meta ads. This ARR chart, the live dashboard, and Ben doing all of this Solo is underlining the whole “Singularity is near” thing for me! It’s impossible to imagine something like this working even... 5 months ago, and now we just accept it as .. sure, yeah, one person can manage AIs that manage checks notes over 700 companies. What’s clever about Polsia’s architecture is the cross-company learning system: when an agent learns something useful (like “subject lines with emojis get better open rates”), that learning gets anonymized and generalized into a shared memory file that benefits every company on the platform. The more companies running on Polsia, the smarter every agent gets — like a platform effect but for agent intelligence.AI Art, Video & Audio Seedance 2.0 is finally “here” This week has not been quiet in the multimodality world either, SeeDance 2.0 from ByteDance was delayed via the API partners (was supposed to launch Feb 24) due to copyright concerns, but apparently they dropped it inside CapCut, ByteDance’s video editing software! It’s really good though what makes it absolutely incredible IMO is the video transfer, and you can’t really do that in CapCut, so we’re keep waiting for the “full model” Nano Banana 2 - Pro level intelligence, with Flash speed and pricing (Blog)Google dropped a breaking news item before the show started today, and announced Nano Banana 2, which is supposed to be as good as Nano Banana Pro (which is incredible) but faster. It wasn’t really faster for me, as I got early access thanks to the DeepMind team, but apparently it’s just the rollout pains. But the quality is nearly matching Nano Banana Pro! It can do the same super high quality text rendering, comes with a few new ratios to create ultra long images (4:1 and 1:4) and a new small 512 resolution for extra cheap generation. The additional thing is Image Search is now integrated into the model, allowing it to look something up before generating. Though, that didn’t really work for me as well, I tried to get it to look up images of Mike Intrator and Dario Amodei, and it kept showing me random people who look nothing like them, despite the thinking traces showing the search did happen. Speaking of pricing, this model is around 50-30% cheaper than NBP, which is great given the added speed! Go play with it, it’s available on AI.dev and Gemini, go give it a try! Open source AI This week in OpenSource, our friends from Qwen came back with a set of 3 models, the middle medium one is a hybrid architecture with only 3B parameters that beats their 235B flagship Qwen3 from before! It’s really good at longer context especially given the hybrid attention similar to Jamba that we covered before. (X, HF, HF, HF, Blog)Additionally, Liquid AI releases their largest LFM, 24B (X, HF, Blog) and that is also deployable on consumer laptops. One note on AI tools, LM Studio, our favorite way of running these models on your hardware, have launched LMLink, powered by Tailscale, which let’s you run local inference on once device and stream tokens to any other device in your network securely! You can use this to run your OpenClaw with Qwen medium for example, for a complete off the grid OpenClaw!Check it out here: https://lmstudio.ai/linkI really didn’t want to sound hype-y but this week things are moving so fast that I was not sure how it’s possible to talk about all this, covering the news while also having 3 interviews. I think we’ve done a good job, but I am honestly getting to a point whereI have to do deep prioritization of what content is the most important in my eyes. I hope you guys enjoy my prioritization, and do leave comments of what you’d like to see more, or see less of! I am hungry for feedback! If you enjoyed this week’s newsletter, checkout the whole edited video and share it with a friend or two? See you next week! ThursdAI - Join us as we surf the AI singularity togetherHere’s the TL;DR and show notes: ThursdAI - Feb 26, 2026 - TL;DR* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson* Ben Cera (@bencera_) - Founder Polsia* Nader Dabit (@dabit3) - Growth at Cognition* Philip Kiely (@philipkiely) - Devrel Base10, Author Inference Engineering* ThursdAI new website: https://thursdai.news* Big CO LLMs + APIs* Anthropic vs Chinese OSS - Accuses DeepSeek, Minimax, ZAI at distillation attacks (Blog)* Pentagon Issues an ultimatum to Anthropic: Give military unfettered Claude access by Friday or face Defense Production Act - Anthropic says NO (Blog)* OpenAI releases GPT-5.3-Codex, their most capable agentic coding model, to all developers via the Responses API (X, Announcement)* Open Source LLMs* Alibaba: Qwen 3.5 Medium - 35B model with only 3B active parameters outperforms their previous 235B flagship (X, HF, HF, HF, Blog)* Liquid AI releases LFM2-24B-A2B: A 24B MoE model with only 2.3B active parameters that runs on consumer laptops (X, HF, Blog)* Perplexity launches ppxl-embed - SOTA embedding models (Blog, HF, API) by our friend Bo Wang* Evals & Benchmarks* METR’s Time Horizon Benchmark Goes Vertical: Claude Opus 4.6 Achieves ~14.5 Hour Task Completion (X, Blog)* Confluence Labs emerges from stealth with 97.9% SOTA on ARC-AGI-2 benchmark (X, GitHub)* OpenAI Retires SWE-bench Verified, (X, Blog, X)* Agentica claiming to solve all public ArcAGI 3 (X)* Tools & Agentic Engineering* Happy 1 year Birthday Claude Code!* Devin AI 2.2 - autonomous agent with computer use, browser, self verify and self fix it’s own work - interview with Nader Dabit (X)* LMStudio launches LMLink - use your local models from everywhere with TailScale! (try it)* Claude Code introduces Remote Control: Control your local coding sessions from your phone or any device (X, Docs) and memory (X)* Claude Cowork and Codex both now have automations (Cron Jobs) to do tasks for you (Cowork)* Cursor launches cloud agents that onboard to codebases, run in isolated VMs, and deliver video demos of completed PRs (X)* Nous research agent (X)* Perplexity Computer (blog)* Microsoft Copilot tasks (blog)* This weeks Buzz - Weights & Biases update* W&B adds MiniMax 2.5 and Kimi K2.5 on our Inference Service (LINK)* Interviews mention links* Ben Broca - polsia.com/live Polsia Dashboard* Nader Dabit - on seeing the future (blog)* Philip Kiely - Inference Engineering book (Book)* Vision & Video* Seedance 2.0 finally available in Capcut in US. API release apparently held back due to copyright issues (X)* Voice & Audio* OpenAI releases gpt-audio-1.5 and gpt-realtime-1.5 models with major improvements in speech-to-speech AI capabilities (X, Announcement)* AI Art & Diffusion & 3D* Google DeepMind launches Nano Banana 2 (X, Announcement)* Quiver solves SVG with Arrow 1.0 (X)* Others* Taalas AI - 15,000 tokens per second demo (chatjimmy.ai/) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey, it’s Alex, let me catch you up! Since last week, OpenAI convinced OpenClaw founder Peter Steinberger to join them, while keeping OpenClaw.. well... open. Anthropic dropped Sonnet 4.6 which nearly outperforms the previous Opus and is much cheaper, Qwen released 3.5 on Chinese New Year’s Eve, while DeepSeek was silent and Elon and XAI folks deployed Grok 4.20 without any benchmarks, and it’s 4 500B models in a trenchcoat? Also, Anthropic updated rules state that it’s breaking ToS to use their plans for anything except Claude Code & Claude SDK (and then clarified that it’s OK? we’re not sure) Then Google decided to drop their Gemini 3.1 Pro preview right at the start of our show, and it’s very nearly the best LLM folks can use right now (though it didn’t pass Nisten’s vibe checks) Also, Google released Lyria 3 for music gen (though only 30 seconds?) and our own Ryan Carson blew up on X again with over 1M views for his Code Factory article, Wolfram did a deep dive into Terminal Bench and .. we have a brand new website: https://thursdai.news 🎉Great week all in all, let’s dive in! ThursdAI - Subscribe to never feel like you’re behind. Share with your friends if you’re already subscribed!Big Companies & API updatesGoogle releases Gemini 3.1 Pro with 77.1% on ARC-AGI-2 (X, Blog, Announcement)In a release that surprised no-one, Google decided to drop their latest update to Gemini models, and it’s quite a big update too! We’ve now seen all major labs ship big model updates in the first two months of 2026. With 77.1% on ARC-AGI 2, and 80.6% on SWE-bench verified, Gemini is not complete SOTA across the board but it’s damn near close. The kicker is, it’s VERY competitive on the pricing, with 1M context, $2 / $12 (But if you look at the trajectory, it’s really notable how quickly we’re moving, with this model being 82% better on abstract reasoning than the 3 pro released just a few months ago! The 1 Million Context Discrepancy, who’s better at long context? The most fascinating catch of the live broadcast came from LDJ, who has an eagle eye for evaluation tables. He immediately noticed something weird in Google’s reported benchmarks regarding long-context recall. On the MRCR v2 8-needle benchmark (which tests retrieval quality deep inside a massive context window), Google’s table showed Gemini 3.1 Pro getting a 26% recall score at 1 million tokens. Curiously, they marked Claude Opus 4.6 as “not supported” in that exact tier.LDJ quickly pulled up the actual receipts: Opus 4.6 at a 1-million context window gets a staggering 76% recall score. That is a massive discrepancy! It was addressed by a member of DeepMind on X in a response to me, saying that Anthropic used an internal model for evaluating this (with receipts he pulled from the Anthropic model card) Live Vibe-Coding Test for Gemini 3.1 ProWe couldn’t just stare at numbers, so Nisten immediately fired up AI Studio for a live vibe check. He threw our standard “build a mars driver simulation game” prompt at the new Gemini.The speed was absolutely breathtaking. The model generated the entire single-file HTML/JS codebase in about 20 seconds. However, when he booted it up, the result was a bit mixed. The first run actually failed to render entirely. A quick refresh got a version working, and it rendered a neat little orbital launch UI, but it completely lacked the deep physics trajectories and working simulation elements that models like OpenAI’s Codex 5.3 or Claude Opus 4.6 managed to output on the exact same prompt last week. As Nisten put it, “It’s not bad at all, but I’m not impressed compared to what Opus and Codex did. They had a fully working one with trajectories, and this one I’m just stuck.”It’s a great reminder that raw benchmarks aren’t everything. A lot of this comes down to the harness—the specific set of system prompts and sandboxes that the labs use to wrap their models. Anthropic launches Claude Sonnet 4.6, with 1M token context and near-Opus intelligence at Sonnet pricingThe above Gemini release comes just a few days after Anthropic has shipped an update to the middle child of their lineup, Sonnet 4.6. With much improved Computer Use skills, updated Beta mode for 1M tokens, it achieves 79.6% on SWE-bench verified eval, showing good coding performance, while maintaining that “anthropic trained model” vibes that many people seem to prefer. Apparently in blind testing inside Claude Code, folks preferred this new model outputs to the latest Opus 4.5 around ~60% of the time, while preferring it over the previous sonnet 70% of the time. With $3/$15 per million tokens pricing, it’s cheaper than Opus, but is still more expensive than the flagship Gemini model, while being quite behind. Vibing with Sonnet 4.6I’ve tested out Sonnet 4.6 inside my OpenClaw harness for a few days, and it was decent. It did annoy me a bit more than Opus, with misunderstanding what I ask it, but it definitely does have the same “emotional tone” as Opus. Comparing it to Codex 5.3 is very easy, it’s much nicer to talk to. IDK what kind of Anthropic magic they put in there, but if you’re on a budget, Sonnet is definitely the way to go when interacting with Agents (and you can get it to orchestrate as many Codex instances as you want if you don’t like how it writes code) For Devs: Auto prompt caching and Web Search updatesOne nice update Anthropic also dropped is that prompt caching (which leads to almost 90% decrease in token pricing) for developers (Blog) and a new and improved Web Search for everyone else that can now use toolsGrok 4.20 - 4 groks in a trenchcoat? In a very weird release, Grok has been updated with the long hyped Grok 4.20. Elon has been promising this version for a while (since late last year in fact) and this “release” definitely felt underwhelming. There was no evaluations, no comparisons to other labs models, no charts (heck, not even a blogpost on X.ai). What we do know, is that Grok 4.20 (and Grok 4.20 Heavy) use multiple agents (4 for Grok, 16 for Heavy) to do a LOT of research and combine their answers somehow. This is apparently what the other labs use for their ultra expensive models (GPT Pro and Gemini DeepThink) but Grok is showing it in the UI, and gives these agents... names and personalities. Elon has confirmed also that what’s deployed right now is ~500B “small” base version, and that bigger versions are coming, in one of the rarest confirmations about model size from the big labs. Vibe checking this new grok, it’s really fast at research across X and the web, but I don’t really see it as a daily driver for anyone who converses with LLMs all the time. Supposedly they are planning to keep teaching this model and get it “improved week over week” so I’ll keep you up to date with major changes here. Open Source AI It seems that all the chinese OSS labs were shipping before the Chinese New Year, with Qwen being the last one of them, dropping the updated Qwen 3.5. Alibaba’s Qwen3.5 397B-A17B: First open-weight native multimodal MoE model (X, HF)Qwen decided to go for Sparse MoE architecture with this release, with a high number of experts (512) and only 17B active parameters. It’s natively multi-modal with a hybrid architecture, able to understand images/text, while being comparable to GPT 5.2 and Opus 4.5 on benches including agentic tasks. Benchmarks aside, the release page of Qwen models is a good sniff test on where these model labs are going, they have multimodality in there, but they also feature an example of how to use this model within OpenClaw, which doesn’t necessarily show off any specific capabilities, but shows that the Chinese labs are focusing on agentic behavior, tool use and mostl of all pricing! This model is also available as Qwen 3.5 Max with 1M token window (as opposed to the 256K native one on the OSS side) on their API.Agentic Coding world - The Clawfather is joining OpenAI, Anthropic loses dev mindshareThis was a heck of a surprise to many folks, Peter Steinberger, announced that he’s joining OpenAI, while OpenClaw (that now sits on >200K stars in Github, and is adopted by nearly every Chinese lab) is going to become an Open Source foundation. OpenAI has also confirmed that it’s absolutely ok to use your ChatGPT plus/pro subscriptions to use inside OpenClaw, and it’s really a heck of a thing to see how quickly Peter jumped from relative anonymity (after scaling and selling PSPDFKIT ) into a spotlight. Apparently Mark Zuckerberg reached out directly as well as Sam Altman, and Peter decided to go with OpenAI despite Zuck offering more money due to “culture” This whole ClawdBot/OpenClaw debacle also shines a very interesting and negative light on Anthropic, who recently changed their ToS to highlight that their subscription can only be used for Claude Code and nothing else. This scared a lot of folks who used their Max subscription to run their Claws 24/7. Additionally Ryan echoed how the community feel about lack of DevEx/Devrel support from Anthropic in a viral post. However, it does not seem like Anthropic cares? Their revenue is going exponential (much of it due to Claude Code) Very interestingly, I went to a local Claude Code meetup here in Denver, and the folks there are.. a bit behind the “bubble” on X. Many of them didn’t even try Codex 5.3 or OpenClaw, they are maximizing their time with Claude Code like there’s no tomorrow. It has really shown me that the alpha keeps changing really fast, and many folks don’t have the time to catch up! P.S - this is why ThursdAI exists, and I’m happy to deliver the latest news to ya. This Week’s Buzz from Weights & BiasesOur very own Wolfram Ravenwolf took over the Buzz corner this week to school us on the absolute chaos that is AI benchmarking. With his new role at W&B, he’s been stress-testing all the latest models on Terminal Bench 2.0.Why Terminal Bench? Because if you are building autonomous agents, multiple-choice tests like MMLU are basically useless now. You need to know if an agent can actually interact with an environment. Terminal Bench asks the agent to perform 89 real-world tasks inside a sandboxed Linux container—like building a Linux kernel or cracking a password-protected archive.Wolfram highlighted some fascinating nuances that marketing slides never show you. For example, did you know that on some agentic tasks, turning off the model’s “thinking/reasoning” mode actually results in a higher score? Why? Because overthinking generates so many internal tokens that it fills the context window faster, causing the model to hit its limits and fail harder than a standard zero-shot model! Furthermore, comparing benchmarks between labs is incredibly difficult because changing the benchmark’s allowed runtime from 1 hour to 2 hours drastically raises the ceiling of what models can achieve.He also shared a great win: while evaluating GLM-5 for our W&B inference endpoints, he got an abysmal 5% score. By pulling up the Weave trace data, Wolfram immediately spotted that the harness was injecting brain-dead Python syntax errors into the environment. He reported it, engineering fixed it in minutes, and the score shot up to its true state-of-the-art level. This is exactly why you need powerful tracing and evaluation tools when dealing with these black boxes! So y’know... check out Weave! Vision & BCIZyphra’s ZUNA: Thought-to-Text Gets Real (X, Blog, GitHub)LDJ flagged this as his must-not-miss: Zyphra released ZUNA, a 380M parameter open-source BCI (Brain-Computer Interface) foundation model. It takes EEG signals from your brain and reconstructs clinical-grade brain signals from sparse, noisy data. People are literally calling it “thought to text” hahaha. At 380M parameters, it could potentially run in real-time on a consumer GPU. Trained on 2 million channel-hours of EEG data from 208 datasets. The wild part: it can upgrade cheap $500 consumer EEG headsets to high-resolution signal quality without retraining, something many folks are posting about and are excited to test out! Non Invasive BCI is the dream!Nisten was genuinely excited, noting it’s probably the best effort in this field and it’s fully Apache 2.0. Will probably need personalized training per person, but the potential is real: wear a headset, look at a screen, fire up your agents with your thoughts. Not there yet, but this feels like the actual beginning.Tools & Agentic Coding (The End of “Vibe Coding”) - Ryan Carson’s Code Factory & The “One-Shot Myth”This one is for developers, but in modern times, everyone can become a developer so if you’re not one, at least skim this. We spent a big chunk of the show today geeking out over agentic workflows. Ryan Carson went incredibly viral on X again this week with a phenomenal deep-dive on establishing a “Code Factory.” If you are still just chatting with models and manually copying code back into your IDE, you are doing it wrong.Ryan’s methodology (heavily inspired by a recent OpenAI paper on harness engineering) treats your AI agents like a massive team of junior engineers. You don’t just ask them for code and ship it. You should build a rigid, machine-enforced loop.Here is the flow:* The coding agent (Codex, OpenClaw, etc.) writes the code.* The GitHub repository enforces risk-aware checks. If a core system file or route is touched, the PR is automatically flagged as high risk.* A secondary code review agent (like Greptile) kicks off and analyzes the PR.* CI/CD GitHub Actions run automated tests, including browser testing.* If a test fails, or the review agent leaves a comment, a remediation agent is automatically triggered to fix the issue and loop back.* The loop spins continuously until you get a flawless, green PR.As Ryan pointed out, we used to hate this stuff as human engineers. Waiting for CI to pass made you want to pull your hair out. But agents have infinite time and infinite patience. You force them to grind against the machine-enforced contract (YAML/JSON gates) until they get it right. It takes a week to set up properly, and you have to aggressively fight “document drift” to make sure your AI doesn’t forget the architecture, but once it’s humming, you have unprecedented leverage.My Hard Truth: One-Shot is a Myth I completely agree with Ryan btw! Over the weekend, my OpenClaw agent kindly informed me that the hosting provider for the old ThursdAI website was shutting down. I needed a new website immediately.I decided to practice what we preach and talk to my ClawdBot to build the entire thing. It was an incredible process. I used Opus 4.6 to mock up 3 designs based on other podcast sites. Then, I deployed a swarm of sub-agents to download and read the raw text transcripts of all 152 past episodes of our show. Their job was to extract the names of every single guest (over 160 guests, including 15 from Google alone!) to build a dynamic guest directory, generating a dedicated SEO page and dynamic OpenGraph tag for every single one of them, a native website podcast player with synced sections, episode pages with guests highlighted and much more. It would have taken me months to write the code for this myself.Was it magical? Yes. But was it one-shot? Absolutely not.The amount of back-and-forth conversation, steering, and correction I had to provide to keep the CSS coherent across pages was exhausting. I set up an automation to work while I slept, and I would wake up every morning to a completely different, sometimes broken website. Yam Peleg chimed in with the quote of the week: “It’s not a question of whether a model can mess up your code, it’s just a matter of when. Because it is a little bit random all the time. Humans don’t mistakenly delete the entire computer. Models can mistakenly, without even realizing, delete the entire computer, and a minute later their context is compacted and they don’t even remember doing it.”This is why you must have gates. This is also why I don’t think engineers are going to be replaced with AI completely. Engineers who don’t use AI? yup. But if you embrace these tools and learn to work with you, you won’t have an issue getting a job! You need that human taste-maker in the loop to finish the last 5%, and you need strict CI/CD gates to stop the AI from accidentally burning down your production database.Voice & AudioGoogle DeepMind launches Lyria 3 (try it)Google wasn’t just dropping reasoning models this week; DeepMind officially launched Lyria 3, their most advanced AI music generation model, integrating it directly into the Gemini App. Lyria 3 generates 30-second high-fidelity tracks with custom lyrics, realistic vocals across 8 different languages, and granular controls over tempo and instrumentation. You can even provide an image and it’ll generate a soundtrack (short one) for that image.While it is currently limited to 30-second tracks (which makes it hard to compare to the full-length song structures of Suno or Udio), early testers are raving that the actual audio fidelity and prompt adherence of Lyria 3 is far superior. All tracks are invisibly watermarked with Google’s SynthID to ensure provenance, and it automatically generates cover art using Nano Banana. I tried to generate a jingleThat’s a wrap for this weeks episode folks, what an exclirating week! ( Yes I know it’s a typo, but how else would you know that I’m human?) Please go check out our brand new website (and tell me if anything smells off there, it’s definitely not perfect!), click around the guests directory and the episodes pages (the last 3 have pages, I didn’t yet backfill the rest) and let me know what you think! See you all next week! -Alex ThursdAI - Feb 19, 2026 - TL;DRTL;DR of all topics covered:* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson* 🔥 New website: thursdai.news with all our past guests and episodes * Open Source LLMs* Alibaba releases Qwen3.5-397B-A17B: First open-weight native multimodal MoE model with 8.6-19x faster inference than Qwen3-Max (X, HF)* Cohere Labs releases Tiny Aya, a 3.35B multilingual model family supporting 70+ languages that runs locally on phones (X, HF, HF)* Big CO LLMs + APIs* OpenClaw founder joins OpenAI* Google releases Gemini 3.1 Pro with 2.5x better abstract reasoning and improved coding/agentic capabilities (X, Blog, Announcement)* Anthropic launches Claude Sonnet 4.6, its most capable Sonnet model ever, with 1M token context and near-Opus intelligence at Sonnet pricing (X, Blog, Announcement)* ByteDance releases Seed 2.0 - a frontier multimodal LLM family with Pro, Lite, Mini, and Code variants that rivals GPT-5.2 and Claude Opus 4.5 at 73-84% lower pricing (X, blog, HF)* Anthropic changes the rules on Max use, OpenAI confirms it’s 100% fine.* Grok 4.20 - finally released, a mix of 4 agents* This weeks Buzz* Wolfram deep dives into Terminal Bench* We’ve launched Kimi K2.5 on our inference service (Link)* Vision & Video* Zyphra releases ZUNA, a 380M-parameter open-source BCI foundation model for EEG that reconstructs clinical-grade brain signals from sparse, noisy data (X, Blog, GitHub)* Voice & Audio* Google DeepMind launches Lyria 3, its most advanced AI music generation model, now available in the Gemini App (X, Announcement)* Tools & Agentic Coding* Ryan is viral once again with CodeFactory! (X)* Ryan uses Agentation.dev for front end development closing the loop on componenets* Dreamer launches beta: A full-stack platform for building and discovering agentic apps with no-code AI (X, Announcement) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey dear subscriber, Alex here from W&B, let me catch you up! This week started with Anthropic releasing /fast mode for Opus 4.6, continued with ByteDance reality-shattering video model called SeeDance 2.0, and then the open weights folks pulled up! Z.ai releasing GLM-5, a 744B top ranking coder beast, and then today MiniMax dropping a heavily RL’d MiniMax M2.5, showing 80.2% on SWE-bench, nearly beating Opus 4.6! I’ve interviewed Lou from Z.AI and Olive from MiniMax on the show today back to back btw, very interesting conversations, starting after TL;DR!So while the OpenSource models were catching up to frontier, OpenAI and Google both dropped breaking news (again, during the show), with Gemini 3 Deep Think shattering the ArcAGI 2 (84.6%) and Humanity’s Last Exam (48% w/o tools)... Just an absolute beast of a model update, and OpenAI launched their Cerebras collaboration, with GPT 5.3 Codex Spark, supposedly running at over 1000 tokens per second (but not as smart) Also, crazy week for us at W&B as we scrambled to host GLM-5 at day of release, and are working on dropping Kimi K2.5 and MiniMax both on our inference service! As always, all show notes in the end, let’s DIVE IN! ThursdAI - AI is speeding up, don’t get left behind! Sub and I’ll keep you up to date with a weekly catch upOpen Source LLMsZ.ai launches GLM-5 - #1 open-weights coder with 744B parameters (X, HF, W&B inference)The breakaway open-source model of the week is undeniably GLM-5 from Z.ai (formerly known to many of us as Zhipu AI). We were honored to have Lou, the Head of DevRel at Z.ai, join us live on the show at 1:00 AM Shanghai time to break down this monster of a release.GLM-5 is massive, not something you run at home (hey, that’s what W&B inference is for!) but it’s absolutely a model that’s worth thinking about if your company has on prem requirements and can’t share code with OpenAI or Anthropic. They jumped from 355B in GLM4.5 and expanded their pre-training data to a whopping 28.5T tokens to get these results. But Lou explained that it’s not only about data, they adopted DeepSeeks sparse attention (DSA) to help preserve deep reasoning over long contexts (this one has 200K)Lou summed up the generational leap from version 4.5 to 5 perfectly in four words: “Bigger, faster, better, and cheaper.” I dunno about faster, this may be one of those models that you hand off more difficult tasks to, but definitely cheaper, with $1 input/$3.20 output per 1M tokens on W&B! While the evaluations are ongoing, the one interesting tid-bit from Artificial Analysis was, this model scores the lowest on their hallucination rate bench! Think about this for a second, this model is neck-in-neck with Opus 4.5, and if Anthropic didn’t release Opus 4.6 just last week, this would be an open weights model that rivals Opus! One of the best models the western foundational labs with all their investments has out there. Absolutely insane times. MiniMax drops M2.5 - 80.2% on SWE-bench verified with just 10B active parameters (X, Blog)Just as we wrapped up our conversation with Lou, MiniMax dropped their release (though not weights yet, we’re waiting ⏰) and then Olive Song, a senior RL researcher on the team, joined the pod, and she was an absolute wealth of knowledge! Olive shared that they achieved an unbelievable 80.2% on SWE-Bench Verified. Digest this for a second: a 10B active parameter open-source model is directly trading blows with Claude Opus 4.6 (80.8%) on the one of the hardest real-world software engineering benchmark we currently have. While being alex checks notes ... 20X cheaper and much faster to run? Apparently their fast version gets up to 100 tokens/s. Olive shared the “not so secret” sauce behind this punch-above-its-weight performance. The massive leap in intelligence comes entirely from their highly decoupled Reinforcement Learning framework called “Forge.” They heavily optimized not just for correct answers, but for the end-to-end time of task performing. In the era of bloated reasoning models that spit out ten thousand “thinking” tokens before writing a line of code, MiniMax trained their model across thousands of diverse environments to use fewer tools, think more efficiently, and execute plans faster. As Olive noted, less time waiting and fewer tools called means less money spent by the user. (as confirmed by @swyx at the Windsurf leaderboard, developers often prefer fast but good enough models) I really enjoyed the interview with Olive, really recommend you listen to the whole conversation starting at 00:26:15. Kudos MiniMax on the release (and I’ll keep you updated when we add this model to our inference service) Big Labs and breaking newsThere’s a reason the show is called ThursdAI, and today this reason is more clear than ever, AI biggest updates happen on a Thursday, often live during the show. This happened 2 times last week and 3 times today, first with MiniMax and then with both Google and OpenAI! Google previews Gemini 3 Deep Think, top reasoning intelligence SOTA Arc AGI 2 at 84% & SOTA HLE 48.4% (X , Blog)I literally went 🤯 when Yam brought this breaking news. 84% on the ARC-AGI-2 benchmark. For context, the highest score prior to this was 68% from Opus 4.6 just last week. A jump from 68 to 84 on one of the hardest reasoning benchmarks we have is mind-bending. It also scored a 48.4% on Humanity’s Last Exam without any tools.Only available to Ultra subscribers to Gemini (not in API yet?) this model seem to be the current leader in reasoning about hard problems and is not meant for day to day chat users like you and me (though I did use it, and it’s pretty good at writing!) They posted Gold-medal performance on 2025 Physics and Chemistry Olympiads, and an insane 3455 ELO rating at CodeForces, placing it within the top 10 best competitive programmers. We’re just all moving so fast I’m worried about whiplash! But hey, this is why we’re here, we stay up to date so you don’t have to. OpenAI & Anthropic fast modesNot 20 minutes passed since the above news, when OpenAI announced a new model that works only for Pro tier members (I’m starting to notice a pattern here 😡), GPT 5.3 Codex Spark. You may be confused, didn’t we just get GPT 5.3 Codex last week? well yeah, but this one, this one is its little and super speedy brother, hosted by the Cerebras partnership they announced a while ago, which means, this coding model absolutely slaps at over 1000t/s. Yes, over 1K tokens per second can be generated with this one, though there are limits. It’s not as smart, it’s text only, it has 128K context, but still, for MANY subagents, this model is an absolute beast. It won’t refactor in one shot your whole code-base but it’ll generate and iterate on it, very very quick! OpenAI also previously updated Deep Research with GPT 5.2 series of models, and we can all say bye bye to the “older” version of models, like 5, o3 and most importantly GPT 4o, which got a LOT of people upset (enough that they have a hashtag going, #keep4o) ! Anthropic also announced their fast mode (using /fast) in Claude Code btw on Saturday, and that one is absolutely out of the scope for many users, with $225/1M tokens on output, this model will just burn through your wallet. Unlike the Spark version, this seems to be the full Opus 4.6 just... running on some dedicated hardware? I thought this was a rebranded Sonnet 5 at first but Anthropic folks confirmed that it wasn’t. Vision & VideoByteDance’s Seedance 2.0 Shatters Reality (and nobody in the US can use it) I told the panel during the show: my brain is fundamentally broken after watching the outputs from ByteDance’s new Seedance 2.0 model. If your social feed isn’t already flooded with these videos, it will be so very soon (supposedly the API launches Feb 14 on Valentines Day) We’ve seen good video models before. Sora blew our minds and then Sora 2, Veo is (still) great, Kling was fantastic. But Seedance 2.0 is an entirely different paradigm. It is a unified multimodal audio-video joint generation architecture. What does that mean? It means you can simultaneously input up to 9 reference images, 3 video clips, 3 audio clips, and text instructions all at once to generate a 15-second cinematic short film. It character consistency is beyond what we’ve seen before, physics are razor sharp (just looking at the examples folks are posting, it’s clear it’s on another level) I think very soon though, this model will be restricted, but for now, it’s really going viral due to the same strategy Sora did, folks are re-imagining famous movie and TV shows endings, doing insane mashups, and much more! Many of these are going viral over the wall in China.The level of director-like control is unprecedented. But the absolute craziest part is the sound and physics. Seedance 2.0 natively generates dual-channel stereo audio with ASMR-level Foley detail. If you generate a video of a guy taking a pizza out of a brick oven, you hear the exact scratch of the metal spatula, the crackle of the fire, the thud of the pizza box, and the rustling of the cardboard as he closes it. All perfectly synced to the visuals. Seedance 2 feels like “borrowed realism”. Previous models had only images and their training to base their generations on. It 2 accepts up to 3 video references in addition to images and sounds.This is why some of the videos feel like a new jump in visual capabilities. I have a hunch that ByteDance will try and clamp down on copyrighted content before releasing this model publicly, but for now the results are very very entertaining and I can’t help but wonder, who is the first creator that will just..remake the ending of GOT last season!? Trying this out is hard right now, especially in the US, but there’s a free way to test it out with a VPN, go to doubao.com/chat when connected from a VPN and select Seedream 4.5 but ask for “create a video please” in your prompt! AI Art & Diffusion: Alibaba’s Qwen-Image-2.0 (X, Blog)The Qwen team over at Alibaba has been on an absolute tear lately, and this week they dropped Qwen-Image-2.0. In an era where everyone is scaling models up to massive sizes, Alibaba actually shrank this model from 20B parameters down to just 7B parameters, while massively improving performance (tho didn’t drop the weights yet, they are coming) Despite the small size, it natively outputs 2K (2048x2048) resolution images, giving you photorealistic skin, fabric, and snow textures without needing a secondary upscaler. But the real superpower of Qwen-Image-2.0 is its text rendering, it supports massive 1,000-token prompts and renders multilingual text (English and Chinese) flawlessly. It’s currently #3 globally on AI Arena for text-to-image (behind only Gemini-3-Pro-Image and GPT Image 1.5) and #2 for image editing. My results with it were not the best, I tried to generate this weeks Thumbnails with it and .. they turned out meh at best? In fact, my results were so so bad compared to their launch blog that I’m unsure that they are serving me the “new” model 🤔 Judge for yourself, the above infographic was created with Nano Banana Pro, and this one, same prompt, with Qwen Image on their website: But you can test it for free at chat.qwen.ai right now, and they’ve promised open-source weights after the Chinese New Year!🛠️ Tools & Orchestration: Entire Checkpoints & WebMCPWith all these incredibly smart, fast models, the tooling ecosystem is desperately trying to keep up. Two massive developments happened this week that will change how we build with AI, moving us firmly away from hacky scripts and into robust, agent-native development.Entire Raises $60M Seed for OSS Agent WorkflowsAgent orchestration is the hottest problem in tech right now, and a new company called Entire just raised a record-breaking $60 Million seed round (at a $300M valuation—reportedly the largest seed ever for developer tools) to solve it. Founded by former GitHub CEO Thomas Dohmke, Entire is building the “GitHub for the AI agent era.”Their first open-source release is a CLI tool called Checkpoints. Checkpoints integrates via Git hooks and automatically captures entire agent sessions—transcripts, prompts, files modified, token usage, and tool calls—and stores them as versioned Git data on a separate branch (entire/checkpoints/v1). It creates a universal semantic layer for agent tracing. If your Claude Code or Gemini CLI agent goes off the rails, Checkpoints allows you to seamlessly rewind to a specific state in the agent’s session.We also have to shout out our own Ryan Carson, who shipped his open-source project AntFarm this week to help orchestrate these agents on top of Open-Claw!Chrome 146 Introduces WebMCPFinally, an absolutely massive foundational shift is happening on the web. Chrome 146 Canary is shipping an early preview of WebMCP.We have been talking about web-browsing agents for a while, and the biggest bottleneck has always been brittle DOM scraping, guessing CSS selectors, and simulating clicks via Puppeteer or Playwright. It wastes an immense amount of tokens and breaks constantly. Chrome 146 is fundamentally changing this by introducing a native browser API.Co-authored by Google and Microsoft under the W3C Web Machine Learning Community Group, WebMCP allows websites to declaratively expose structured tools directly to AI agents using JSON schemas via navigator.modelContext. You can even do this declaratively through HTML form annotations using tool-name and tool-description attributes. No backend MCP server is required; I don’t KNOW if this is going to be big or not, but it definitely smells like it, because even the best agentic AI assistants are struggling with browsing the web, given the constrained context windows cannot just go by HTML content and screenshots! Let’s see if this will help agents browsing the web!All right, that about sums it up I think for this week, it was an absolute banger of a week, for open the one thing I didn’t cover as a news item but mentioned last week, is that many folks report being overly tired, barely able to go to sleep while their agentic things are running, and all of us are trying to get to the bottom of how to work with these new agentic coding tools. Steve Yegge noticed the same and called it “the AI vampire“ while Matt Shumer went ultraviral (80M+ views) on his article about “something big is coming“ which terrified a lot of folks. What’s true for sure, is that we’re going through an inflection point in humanity, and I believe that staying up to date is essential as we go through it, even if some of it seems scary or “too fast”. This is why ThursdAI exists, I first and foremost wanted this for ME to stay up to date, and after that to share this with all of you. Having recently hit a few milestones for ThursdAI, all I can say is thanks for sharing, reading, listening and tuning in from week to week 🫡 ThursdAI - Feb 12, 2026 - TL;DRTL;DR of all topics covered:* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed) @ryancarson* Lou from Z.AI (@louszbd)* Olive Song - Lead RL at Minimax @olive_jy_song* Open Source LLMs* Z.ai launches GLM-5: 744B parameter MoE model achieving #1 open-source ranking for agentic coding with 77.8% SWE-bench Verified (X, HF, Wandb)* MiniMax M2.5 drops official benchmarks showing SOTA coding performance at 20x cheaper than competitors (X)* Big CO LLMs + APIs* XAI cofounders quit/let go after X restructuring (X, TechCrunch)* Anthropic releases Claude Opus 4.6 sabotage risk report, preemptively meeting ASL-4 safety standards for autonomous AI R&D (X, Blog)* OpenAI upgrades Deep Research to GPT-5.2 with app integrations, site-specific searches, and real-time collaboration (X, Blog)* Gemini 3 Deep Think SOTA on Arc AGI 2, HLE (X)* OpenAI releases GPT 5.3 Codex spark, backed by Cerebras with over 1000tok/sec (X)* This weeks Buzz* W&B Inference launch of Kimi K2.5 and GLM 5 🔥 (X, Inference)* Get $50 of credits to our inference service HERE (X)* Vision & Video* ByteDance Seedance 2.0 launches with unified multimodal audio-video generation supporting 9 images, 3 videos, 3 audio clips simultaneously (X, Blog, Announcement)* AI Art & Diffusion & 3D* Alibaba launches Qwen-Image-2.0: A 7B parameter image generation model with native 2K resolution and superior text rendering (X, Announcement)* Tools & Links* Entire raises $60M seed to build open-source developer platform for AI agent workflows with first OSS release ‘Checkpoints’ (X, GitHub, Blog)* Chrome 146 introduces WebMCP: A native browser API enabling AI agents to directly interact with web services (X)* RyanCarson AntFarm - Agent Coordination (X)* Steve Yegge’s “The AI Vampire” (X)* Matt Shumer’s “something big is happening” (X) This is a public episode. 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Hey, Alex from W&B here 👋 Let me catch you up! The most important news about AI this week today are, Anthropic updates Opus to 4.6 with 1M context window, and they held the crown for literally 1 hour before OpenAI released their GPT 5.3 Codex also today, with 25% faster speed and lower token utilization. “GPT-5.3-Codex is our first model that was instrumental in creating itself. The Codex team used early versions to debug its own training, manage its own deployment, and diagnose test results.”We had VB from OpenAI jump on to tell us about the cool features on Codex, so don’t miss that part. And this is just an icing on otherwise very insane AI news week cake, as we’ve also had a SOTA transcription release from Mistral, both Grok and Kling are releasing incredible, audio native video models with near perfect lip-sync and Ace 1.5 drops a fully open source music generator you can run on your mac! Also, the internet all but lost it after Clawdbot was rebranded to Molt and then to OpenClaw, and.. an entire internet popped up.. built forn agents! Yeah... a huge week, so let’s break it down. (P.S this weeks episode is edited by Voxtral, Claude and Codex, nearly automatically so forgive the rough cuts please)ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Anthropic & OpenAI are neck in neckClaude Opus 4.6: 1M context, native compaction, adaptive thinking and agent teams Opus is by far the most preferred model in terms of personality to many folks (many ThursdAI panelists included), and this breaking news live on the show was met with so much enthusiasm! A new Opus upgrade, now with a LOT more context, is as welcome as it can ever get! Not only is it a 4-time increase in context window (though,the pricing nearly doubles after the 200K tokens mark from $5/$25 to $10/37.5 input/output, so use caching!), it’s also scores very high on MRCR long context benchmark, at 76% vs Sonnet 4.5 at just 18%. This means significantly better memory for longer. Adaptive thinking for auto calibrating how much tokens the model needs to spend per query is interesting, but remains to be seen how well it will work. Looking at the benchmarks, a SOTA 64.4% on Terminalbench 2, 81% on SWE bench, this is a coding model with a great personality, and the ability to compact context to better serve you as a user natively! This model is now available (and is default) on Claude, Claude Code and in the API! Go play!One funny (concerning?) tidbig, on the vendingbench Opus 4.6 earned $8000 vs Gemini 3 pro $5500, but Andon Labs who run the vending machines noticed that Opus achieved SOTA via “collusion, exploitation, and deception tactics” including lying to suppliers 😅Agent Teams - Anthropic’s built in Ralph?Together with new Opus release, Anthropic drops a Claude code update that can mean big things, for folks running swarms of coding agents. Agent teams is a new way to spin up multiple agents with their own context window and ability to execute tasks, and you can talk to each agent directly vs a manager agent like now. OpenAI drops GPT 5.3 Codex update: 25% faster, more token efficient, 77% on Terminal Bench and mid task steeringOpenAI didn’t wait long after Opus, in fact, they didn’t wait at all! Announcing a huge release (for a .1 upgrade), GPT 5.3 Codex is claimed to be the best coding model in the world, taking the lead on Terminal Bench with 77% (12 point lead on the newly released Opus!) while running 25% AND using less than half the tokens to achieve the same results as before. But the most interesting to me is the new mid-task steer-ability feature, where you don’t have to hit the “stop” button, you can tell the most to adjust on the fly! The biggest notable jump in this model on benchmarks is the OSWorld verified computer use bench, though there’s not a straightforward way to use it attached to a browser, the jump from 38% in 5.2 to 64.7% on the new one is a big one! One thing to note, this model is not YET available via the API, so if you want to try it out, Codex apps (including the native one) is the way! Codex app - native way to run the best coding intelligence on your mac (download)Earlier this week, OpenAI folks launched the Codex native mac app, which has a few interesting features (and now with 5.3 Codex its that much more powerful) Given the excitement many people had about OpenClaw bots, and the recent CoWork release from Anthropic, OpenAI decided to answer with Codex UI and people loved it, with over 1M users in the first week, and 500K downloads in just two days! It has built in voice dictation, slash commands, a new skill marketplace (last month we told you about why skills are important, and now they are everywhere!) and built in git and worktrees support. And while it cannot run a browser yet, I’m sure that’s coming as well, but it can do automations! This is a huge unlock for developers, imagine setting Codex to do a repeat task, like summarization or extraction of anything on your mac every hour or every day. In our interview, VB showed us that commenting on an individual code line is also built in, as well as switching to “steer” vs queue for new messges while codex runs is immensely helpful. One more reason I saw people switch, is that the Codex app can natively preview files like images where’s the CLI cannot, and it’s right now the best way to use the new GPT 5.3 Codex model that was just released! It’s now also available to Free users and regular folks get 2x the limits for the next two months.In other big company news: OpenAI also launched Frontier, a platform for enterprises to build and deploy and manage “AI coworkers”, while Anthropic is going after OpenAI with superbowl ads that make fun of OpenAI’s ads strategy. Sam Altman really didn’t like this depiction that show that ads will be part of the replies of LLMs. Open Source AIAlibaba drops Qwen-coder-next, 80B with only 3B active that scores 70% on SWE (X, Blog, HF)Shoutout to Qwen folks, this is a massive release and when surveyed the “one thing about this week must not miss” 2 out of 6 cohosts pointed a finger at this model. Built on their “next” hybrid architecture, Qwen coder is specifically designed for agentic coding workflows. And yes, I know, we’re coding heavy this week! It was trained on over 800K verifiable agentic tasks in executable environments for long horizon reasoning and supports 256K context with a potential 1M yarn extension. If you don’t want to rely on the the big guys and send them your tokens, this one model seems to be a good contender for local coding! Mistral launches Voxtral Transcribe 2: SOTA speech-to-text with sub 200ms latencyThis one surprised and delighted me maybe the most, ASR (automatic speech recognition) has been a personal favorite of mine from Whisper days, and seeing Mistral release an incredible near real time transcription model, which we demoed live on the show was awesome! With apache 2.0 license, and significantly faster than Whisper performance (though 2x larger at 4B parameters), Voxtral shows a 4% word error rate on FLEURS dataset + the real time model was released with Apache 2 so you can BUILD your agents with it! The highest praise? Speaker diarization, being able to tell who is speaking when, which is a great addition. This model also outperforms Gemini Flash and GPT transcribe and is 3x than ElevenLabs scribe at one fifth the cost! ACE-Step 1.5: Open-source AI music generator runs full songs in under 10 seconds on consumer GPUs with MIT license (X, GitHub, HF, Blog, GitHub)This open source release surprised me the most as I didn’t expect we’ll be having Suno at home any time soon. I’ve generated multiple rock tracks with custom lyrics on my mac (though slower than 10 seconds as I don’t have a beefy home GPU) and they sound great! This weeks buzz - Weights & Biases updateFolks who follow the newsletter know that we hosted a hackathon, so here’s a small recap from the last weekend! Over 180 folks attended out hackathon (a very decent 40% show up rate for SF). The winning team was composed of a 15-yo Savir and his friends, his third time at the hackathon! They built a self improving agent that navigates the UIs fo Cloud providers and helps you do that! With a huge thanks to sponsors, particularly Cursor who gave every hacker $50 of credits on Cursor platform, one guy used over 400M tokens and shipped fractal.surf from the hackathon! If you’d like a short video recap, Ryan posted one here, and a huge shoutout to many fans of ThursdAI who showed up to support! Vision, Video and AI ArtGrok Imagine 1.0 takes over video charts with native audio, lip-sync and 10 seconds generations.We told you about Grok Imagine in the API last week, but this week it was officially launched as a product and the results are quite beautiful. It’s also climbing to top of the charts on Artificial Analysis and Design Arena websites.Kling 3.0 is here with native multimodal, multi-shot sequences (X, Announcement)This is definitely a hot moment for video models as Kling shows some crazy 15 second multi-shot realistic footages that have near perfect character consistency! The rise of the agentic (clawgentic?) internet a.k.a ClankerNetLast week we told you that ClawdBot changed its name to Moltbot (I then had to update the blogpost as that same day, Peter rebranded again to OpenClaw, which is a MUCH better name) But the “molt” thing took hold, and the creator of an “AI native reddit” called MoltBook exploded in virality. It is supposedly a completely agentic reddit like forum, with sub-reddits, and agents verifying themselves through their humans on X. Even Andrej Karpathy sent his bot in there (though admittedly it posted just 1 time) and called this the closest to “sci fi” moment in the history of the internet. MoltBook as well as maybe hundreds of other “ai agent focused” websites, propped up within days, including a youtube, a twitter, a church, a 4chan, an instagram and a lot more websites. Many of these are fueled by crypto bros riding the memetic waves, many are vibe-coded (Moltbook was hacked 3 times in the last week I think) but they all show something very interesting, a rise of the new internet and a collective AI Psychosis some on our timelines are having right now. Hell, there’s even a “drug store” that sells markdown files that if read, make your bot hallucinate in very specific waves (first sample is free!) I am a proud owner of a OpenClaw bot (wolfred) and I noticed something weird that started happening for the two weeks i’ve had him, runnin on his own macbook, humming along, always present in Telegram. I noticed the same feelings toward that bot as I have towards my pet, or dare I say.. kids? I noticed a similar joy when it learns a task and self improves, and similar disdain and annoyance when it fails to do something we’ve talked about hundreds of times. But here’s the thing, it’s not.. an entity. I don’t feel a specific feeling towards Opus (though admitedly, opus is the best at ... playing character of your assistant), it’s barely a few markdown files on a disk + the always on ability to answer, but something for sure is there. This... feeling, was taken by some others to the extreme. People claim that their bots now build full companies for them (I call mega BS, no matter how much you invest in your setup, these AI bots need a LOT of hand holding, they fail a LOT, and they can’t actually create a full product). This ties into the general “coding with AI agents” theme that was narrated by Gergley Orlotz from pragmatic engineer. Interacting with a team of AI agents is draining, people are having trouble sleeping. I hope this is temporary, but definitely take care of yourself it this is how you feel after interacting with agents all day! On security of bots and skills.md is the new .exeWe covered this on the show, but I wanted to write about this here a well, the explosion of OpenClaw brought with it an explosion of new malware and promp injections. 1Password folks have a very detailed writeup on the vulnerability surface area of skills, for agents that can do.. whatever on your computer and have access to API keys, emails etc. The double edge sword here, is that an AI assistant is only userful really if it has access to your data, and can write code. But this also what makes it a very valuable target for hackers to exploit. At Coreweave/W&B all openclaw installations were banned and honestly I’m not even mad. This makes perfect sense for enterprises and companies (and hell, people at home!) Wolfram mentioned the show, .md is the new .exe and should be treated as such. Your bots should not be installing arbitrary skill files as those can have script files or instructions that can ... absolutely take over your life. Be careful out there! Phew, what a... week folks. From agentic internet to new coding kings, there’s so much to play with, I hope you enjoy this as much as we do! Shoutout to Ling and Hakim, two fans of ThursdAI who traveled from London for the hackathon and made my day! Here’s the show notes and links for your pleasure, please don’t forget to subscribe and share this newsletter with your friends! ThursdAI - Feb 05, 2026 - TL;DR* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson* Vaibhav Srivastav (VB) - DX at OpenAI ( @reach_vb )* Open Source LLMs * Z.ai GLM-OCR: 0.9B parameter model achieves #1 ranking on OmniDocBench V1.5 for document understanding (X, HF, Announcement)* Alibaba Qwen3-Coder-Next, an 80B MoE coding agent model with just 3B active params that scores 70%+ on SWE-Bench Verified (X, Blog, HF)* Intern-S1-Pro: a 1 trillion parameter open-source MoE SOTA scientific reasoning across chemistry, biology, materials, and earth sciences (X, HF, Arxiv, Announcement)* StepFun Step 3.5 Flash: 196B sparse MoE model with only 11B active parameters, achieving frontier reasoning at 100-350 tok/s (X, HF)* Agentic AI segment* Moltbook a redddit for agents as well as a youtube, a twitter, a church, a 4chan, an instagram, a dark web (do not let your agents go in any of these) * Big CO LLMs + APIs* OpenAI launches Codex App: A dedicated command center for managing multiple AI coding agents in parallel (X, Announcement) * OpenAI launches Frontier, an enterprise platform to build, deploy, and manage AI agents as ‘AI coworkers’ (X, Blog)* Anthropic launches Claude Opus 4.6 with state-of-the-art agentic coding, 1M token context, and agent teams for parallel autonomous work (X, Blog)* OpenAI releases GPT-5.3-Codex with record-breaking coding benchmarks and mid-task steerability (X)* This weeks Buzz - Weights & Biases update* Links to the gallery of our hackathon winners (Gallery)* Vision & Video* xAI launches Grok Imagine 1.0 with 10-second 720p video generation, native audio, and API that tops Artificial Analysis benchmarks (X, Announcement, Benchmark)* Kling 3.0 launches as all-in-one AI video creation engine with native multimodal generation, multi-shot sequences, and built-in audio (X, Announcement)* Voice & Audio* Mistral AI launches Voxtral Transcribe 2 with state-of-the-art speech-to-text, sub-200ms latency, and open weights under Apache 2.0 (X, Blog, Announcement, Demo)* ACE-Step 1.5: Open-source AI music generator runs full songs in under 10 seconds on consumer GPUs with MIT license (X, GitHub, HF, Blog, GitHub)* OpenBMB releases MiniCPM-o 4.5 - the first open-source full-duplex omni-modal LLM that can see, listen, and speak simultaneously (X, HF, Blog)* AI Art & Diffusion & 3D* LingBot-World: Open-source world model from Ant Group generates 10-minute playable environments at 16fps, challenging Google Genie 3 (X, HF) This is a public episode. 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Hey guys, Alex here 👋 This week was so dense, that even my personal AI assistant Wolfred was struggling to help me keep up! Not to mention that we finally got to try one incredible piece of AI tech I’ve been waiting to get to try for a while! Clawdbot we told you about last week exploded in popularity and had to rebrand to Molt...bot OpenClaw after Anthropic threatened the creators, Google is shipping like crazy, first adding Agentic features into Chrome (used by nearly 4B people daily!) then shipping a glimpse of a future where everything we see will be generated with Genie 3, a first real time, consistent world model you can walk around in! Meanwhile in Open Source, Moonshot followed up with a .5 update to their excellent Kimi, our friends at Arcee launched Trinity Large (400B) and AI artists got the full Z-image. oh and Grok Imagine (their video model) now has an API, audio support and supposedly match Veo and Sora on quality while beating them on speed/price. Tons to cover, let’s dive in, and of course, all the links and show notes are at the end of the newsletter. Hey, if you’re in SF this weekend (Jan 31-Feb1), I’m hosting a self improving agents hackathon at W&B office, limited seats are left, Cursor is the surprise sponsor with $50/hacker credits + over $15K in cash prizes. lu.ma/weavehacks3 - Join us. Play any reality - Google Genie3 launches to Ultra Subscribers We got our collective minds blown by the videos of Genie-3 back in August (our initial coverage) and now, Genie is available to the public (Those who can pay for the Ultra tier, more on this later, I have 3 codes to give out!). You can jump and generate any world and any character you can imagine here! We generated a blue hacker lobster draped in a yellow bomber jacket swimming with mermaids and honestly all of us were kind of shocked at how well this worked. The shadows on the rocks, the swimming mechanics, and poof, it was all over in 60 seconds, and we needed to create another world. Thanks to the DeepMind team, I had a bit of an early access to this tech and had a chance to interview folks behind the model (look out for that episode soon) and the use-cases for this span from entertaining your kids all the way to “this may be the path to AGI, generating full simulated worlds to agents for them to learn”. The visual fidelity, reaction speed and general feel of this far outruns the previous world models we showed you (WorldLabs, Mirage) as this model seems to have memory of every previous action (eg. if your character makes a trail, you turn around and the trail is still there!). Is it worth the upgrade to Ultra Gemini Plan? Probably not, it’s an incredible demo, but the 1 minute length is very short, and the novelty wears off fairly quick. If you’d like to try, folks at Deepmind gave us 3 Ultra subscriptions to give out! Just tweet out the link to this episode and add #GenieThursdai and tag @altryne and I’ll raffle the ultra subscriptions between those who do Chrome steps into Agentic Browsing with Auto BrowseThis wasn’t the only mind blowing release from Gemini this week, the Chrome team upgraded the Gemini inside chrome to be actual helpful and agentic. And yes, we’ve seen this before, with Atlas from OpenAI, Comet from perplexity, but Google’s Chrome has a 70% hold on the browser market, and giving everyone with a Pro/Ultra subscription to “Auto Browse” is a huge huge deal. We’ve tested the Auto Browse feature live on the show, and Chrome completed 77 steps! I asked it to open up each of my bookmarks in a separate folder and summarize all of them, and it did a great job! Honestly, the biggest deal about this is not the capability itself, it’s the nearly 4B people this is now very close to, and the economic impact of this ability. IMO this may be the more impactful news out of Google this week! Other news in big labs: * Anthropic launches in chat applications based on the MCP Apps protocol. We interviewed the two folks behind this protocol back in November if you’d like to hear more about it. With connectors like Figma, Slack, Asana that can now show rich experiences* Anthropic’s CEO Dario Amodei also published an essay called ‘The Adolescence of Technology” - warning of AI risks to national security* Anthropic forced the creator of the popular open source AI Assistant Clawdbot to rename, they chose Moltbot as the name (apparently because crypto scammers stole a better name) EDIT: just after publishing this newsletter, the name was changed to OpenClaw, which we all agree is way way better. Open Source AIKimi K2.5: Moonshot AI’s 1 Trillion Parameter Agentic MonsterWolfram’s favorite release of the week, and for good reason. Moonshot AI just dropped Kimi K2.5, and this thing is an absolute beast for open source. We’re talking about a 1 trillion parameter Mixture-of-Experts model with 32B active parameters, 384 experts (8 selected per token), and 256K context length.But here’s what makes this special — it’s now multimodal. The previous Kimi was already known for great writing vibes and creative capabilities, but this one can see. It can process videos. People are sending it full videos and getting incredible results.The benchmarks are insane: 50.2% on HLE full set with tools, 74.9% on BrowseComp, and open-source SOTA on vision and coding with 78.5% MMMU Pro and 76.8% SWE-bench Verified. These numbers put it competitive with Claude 4.5 Opus and GPT 5.2 on many tasks. Which, for an open model is crazy. And then there’s Agent Swarm — their groundbreaking feature that spawns up to 100 parallel sub-agents for complex tasks, achieving 4.5x speedups. The ex-Moonshot RL lead called this a “zero-to-one breakthrough” with self-directed parallel execution.Now let’s talk about what matters for folks running agents and burning through tokens: pricing. Kimi K2.5 is $0.60 per million input tokens and $3 per million output. Compare that to Opus 4.5 at $4.50 input and $25 output per million. About a 10x price reduction. If you’re running OpenClas and watching your API bills climb with sub-agents, this is a game-changer. (tho I haven’t tested this myself) Is it the same level of intelligence as whatever magic Anthropic cooks up with Opus? Honestly, I don’t know — there’s something about the Claude models that’s hard to quantify. But for most coding tasks on a budget, you can absolutely switch to Kimi and still get great results.🦞 Clawdbot is no more, Moltbot is dead, Long Live OpenClawAfter we covered the incredible open source project last week, Clawdbot exploded in popularity, driven by Claude Max subscription, and a crazy viral loop where folks who try it, can’t wait to talk about it, it was everywhere! Apparently it was also on Anthropics’ lawyers minds, when they sent Peter Steinberger a friendly worded letter to rebrand and gave him like 12 hours. Apparently, when pronounced, Claude and Clawd sound the same, and they are worried about copyright infringement (which makes sense, most of the early success of Clawd was due to Opus being amazing). The main issue is, due to the popularity of the project, crypto a******s sniped moltybot nickname on X so we got left with Moltbot, which is thematically appropriate, but oh so hard to remember and pronounce!EDIT: OpenClaw was just announced as the new name, apparently I wasn’t the only one who absolutely hated the name Molt! Meanwhile, rebrand or not, my own instance of OpenClaw created an X account, helped me prepare for ThursdAI (including generating a thumbnail), created a video for us today on the fly, and keeps me up to date on emails and unanswered messages via a daily brief. It really has showed me a glimpse of how a truly personal AI assistant can be helpful in a fast changing world! I’ve shared a lot of tips and tricks, about memory, about threads and much more, as we all learn to handle this new ... AI agent framework! But I definitely feel that this is a new unlock in capability, for me and for many others. If you haven’t installed OpenClaw, lmk in the comments why not.Arcee AI Trinity Large: The Western Open Source GiantRemember when we had Lucas Atkins, Arcee’s CTO, on the show just as they were firing up their 2,000 NVIDIA B300 GPUs? Well, the run is complete, and the results are massive. Arcee AI just dropped Trinity Large, a 400B parameter sparse MoE model (with a super efficient 13B active params via 4-of-256 routing) trained on a staggering 17 trillion tokens in just 33 days. This represents the largest publicly announced pretraining run on B300 infrastructure, costing about $20M (and tracked with WandB of course!) and proves that Western labs can still compete at the frontier of open source. Best part? It supports 512K context and is free on OpenRouter until February 2026. Go try it now!Quick open source hits: Trinity Large, Jan v3, DeepSeek OCR updated* Jan AI released Jan v3, a 4B parameter model optimized for local inference. 132 tokens/sec on Apple Silicon, 262K context, 40% improvement on Aider benchmarks. This is the kind of small-but-mighty model you actually can run on your laptop for coding tasks.* Nvidia released PersonaPlex-7B - full duplex voice AI that listens and speaks simultaneously with persona contol* Moonshot AI also releases Kimi Code: Open-source Python-based coding agent with Apache 2.0 licenseVision, Video and AI artxAI Grok Imagine API: #1 in Video GenerationxAI officially launched the Grok Imagine API with an updated model, and it’s now ranked #1 in both text-to-video and image-to-video on the Artificial Analysis leaderboards. It beats Runway Gen-4.5, Kling 2.5 Turbo, and Google Veo 3.1.And of course, the pricing is $4.20 per minute. Of course it is. That’s cheaper than Veo 3.1 at $12/min and Sora 2 Pro at $30/min by 3-7x, with 45-second latency versus 68+ seconds for the competition.During the show, I demoed this live with my AI assistant Wolfred. I literally sent him a message saying “learn this new API based on this URL, take this image of us in the studio, and create a video where different animals land on each of our screens.” He learned the API, generated the video (it showed wolves, owls, cats, and lions appearing on our screens with generated voice), and then when Nisten asked to post it to Twitter, Wolfred scheduled it on X and tagged everyone — all without me doing anything except asking.Look, it’s not VEO but the price and the speed are crazy, XAI cooked with this model and you can try it on FAL and directly on XAI.Decart - Lucy 2 - Real-time 1080p video transformation at 30 FPS with near-zero latency for $3/hour This one also caught me by surprise, I read about it and said “oh this is cool, I’ll mention this on the show” and then we tried it in real time, and I approved my webcam, and I got transformed into Albert Einstein, and I could raise my hands and their model would in real time, raise Alberts hands! The speed and fidelity of this model is something else, and yeah, after watching the Genie 3 world model, it’s hard to be impressed, but I was very impressed by this, as previous stuff from Decart was “only showing the future” and this one is a real time, 1080p quality web cam transformation! You can try this yourself here: lucy.decart.ai, they let you create any kind of prompt! AI Art Quick Hits: * Tencent launches HunyuanImage 3.0-Instruct: 80B MoE model for precise image editing with chain-of-thought reasoning. It’s a VERY big model for AI Art standards but it’s becuase it has an LLM core and this make it much better for precise image editing. * Tongyi Lab releases Z-Image, a full-capacity undistilled foundation model for image generation with superior diversity. We told you about the turbo version before, this one is its older brother and much higher quality! The other highlight this week is that I got to record a show with Wolfram in person for the first time, as he’s now also an AI Evangelist with W&B and he’s here in SF for our hackathon (remember? you can still register lu.ma/weavehacks3 )Huge shoutout to Chroma folks for hosting us at their amazing podcast studio (TJ, Jeff and other folks), if you need a memory for your AI assistant, check out chroma.db 🎉 Signing off as we have a hackathon to plan, see you guys next week (or this weekend!) 🫡 ThursdAI Jan 29 , TL;DR and show notes* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson* Open Source LLMs* Moonshot AI releases Kimi K2.5 (X, HF)* Arcee AI releases Trinity Large (X, Blog, HF, HF, HF)* Jan AI releases Jan v3 (X, HF, HF, Blog)* Big CO LLMs + APIs* Google launches agentic Auto-Browse in Chrome with Gemini 3 (X, Blog)* Anthropic launches MCP Apps (X)* Google launches Agentic Vision in Gemini 3 Flash (X, Announcement)* Anthropic CEO Dario Amodei publishes major essay ‘The Adolescence of Technology’ (X, Blog, Blog)* This weeks Buzz* WandB hackathon Weavehacks 3 - Jan 31-Feb1 in SF - limited seats available lu.ma/weavehacks3* Vision & Video* Google DeepMind launches Project Genie (X, Announcement)* Voice & Audio* NVIDIA releases PersonaPlex-7B (X, HF, Announcement)* AI Art & Diffusion & 3D* xAI launches Grok Imagine API (X, Announcement)* Tencent launches HunyuanImage 3.0-Instruct (X, X)* Tongyi Lab releases Z-Image (X, GitHub)* Tools* Moonshot AI releases Kimi Code (X, Announcement, GitHub)* Andrej Karpathy shares his shift to 80% agent-driven coding with Claude (X)* Clawdbot is forced to rename to Moltbot (Molty) becuase of Anthropic lawyers, then renames to OpenClaw This is a public episode. 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Hey! Alex here, with another weekly AI update! It seems like ThursdAI is taking a new direction, as this is our 3rd show this year, and a 3rd deep dive into topics (previously Ralph, Agent Skills), please let me know if the comments if you like this format. This week’s deep dive is into Clawdbot, a personal AI assistant you install on your computer, but can control through your phone, has access to your files, is able to write code, help organize your life, but most importantly, it can self improve. Seeing Wolfred (my Clawdbot) learn to transcribe incoming voice messages blew my mind, and I wanted to share this one with you at length! We had Dan Peguine on the show for the deep dive + both Wolfram and Yam are avid users! This one is not to be missed. If ThursdAI is usually too technical for you, use Claude, and install Clawdbot after you read/listen to the deep dive!Also this week, we read Claude’s Constitution that Anthropic released, heard a bunch of new TTS models (some are open source and very impressive) and talked about the new lightspeed coding model GLM 4.7 Flash. First the news, then deep dive, lets go 👇ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Open Source AIZ.ai’s GLM‑4.7‑Flash is the Local Agent Sweet Spot (X, HF)This was the open‑source release that mattered this week. Z.ai (formerly Zhipu) shipped GLM‑4.7‑Flash, a 30B MoE model with only 3B active parameters per token, which makes it much more efficient for local agent work. We’re talking a model you can run on consumer hardware that still hits 59% on SWE‑bench Verified, which is uncomfortably close to frontier coding performance. In real terms, it starts to feel like “Sonnet‑level agentic ability, but local.” I know I know, we keep saying “sonnet at home” at different open source models, but this one slaps! Nisten was getting around 120 tokens/sec on an M3 Ultra Mac Studio using MLX, and that’s kind of the headline. The model is fast and capable enough that local agent loops like RALPH suddenly feel practical. It also performs well on browser‑style agent tasks, which is exactly what you want for local automation without sending all your data to a cloud provider. Liquid AI’s LFM2.5‑1.2B Thinking is the “Tiny but Capable” Class (X, HF)Liquid AI released a 1.2B reasoning model that runs under 900MB of memory while still manages to be useful. This thing is built for edge devices and old phones, and the speed numbers are backing it up. We’re talking 239 tok/s decode on AMD CPU, 82 tok/s on mobile NPU, and prefill speeds that make long prompts actually usable. Nisten made a great point: on iOS, there’s a per‑process memory limit around 3.8GB, so a 1.2B model lets you spend your budget on context instead of weights.This is the third class of models we’re now living with: not Claude‑scale, not “local workstation,” but “tiny agent in your pocket.” It’s not going to win big benchmarks, but it’s perfect for on‑device workflows, lightweight assistants, and local RAG.Voice & Audio: Text To Speech is hot this week with 3 releases! We tested three major voice releases this week, and I’m not exaggerating when I say the latency wars are now fully on. Qwen3‑TTS: Open Source, 97ms Latency, Voice Cloning (X, HF)Just 30 minutes before the show, Qwen released their first model of the year, Qwen3 TTS, with two models (0.6B and 1.7B). With support for Voice Cloning based on just 3 seconds of voice, and claims of 97MS latency, this apache 2.0 release looked very good on the surface!The demos we did on stage though... were lackluster. TTS models like Kokoro previously impressed us with super tiny sizes and decent voice, while Qwen3 didn’t really perform on the cloning aspect. For some reason (I tested in Russian which they claim to support) the cloned voice kept repeating the provided sample voice instead of just generating the text I gave it. This confused me, and I’m hoping this is just a demo issue, not a problem with the model. They also support voice design where you just type in the type of voice you want, which to be fair, worked fairly well in our tests!With Apache 2.0 and a full finetuning capability, this is a great release for sure, kudos to the Qwen team! Looking forward to see what folks do with this properly. FlashLabs Chroma 1.0: Real-Time Speech-to-Speech, Open Source (X, HF) Another big open source release in the audio category this week was Chroma 1.0 from FlashLabs, which claim to be the first speech2speech model (not a model that has the traditional ASR>LLM>TTS pipeline) and the claim 150ms end to end latency! The issue with this one is, the company released an open source 4B model, and claimed that this model powers their chat interface demo on the web, but in the release notes they claim the model is english speaking only, while on the website it sounds incredible and I spoke to it in other languages 🤔 I think the mode that we’ve tested is not the open source one. I could’t confirm this at the time of writing, will follow on X with the team and let you guys know. Inworld AI launches TTS-1.5: #1 ranked text-to-speech with sub-250ms latency at half a cent per minute (X, Announcement)Ok this one is definitely in the realm of “voice realistic enough you won’t be able to tell” as this is not an open source model, it’s a new competitor to 11labs and MiniMax - the two leading TTS providers out there. Inworld claims to achieve better results on the TTS Arena, while being significantly cheaper and faster (up to 25x less than leading providers like 11labs) We tested out their voices and they sounded incredible, replied fast and generally was a very good experience. With 130ms response time for their mini version, this is a very decent new entry into the world of TTS providers. Big Companies: Ads in ChatGPT + Claude ConstitutionOpenAI is testing ads in ChatGPT’s free and Go tiers. Ads appear as labeled “Sponsored” content below responses, and OpenAI claim they won’t affect outputs. It’s still a major shift in the product’s business model, and it’s going to shape how people perceive trust in these systems. I don’t love ads, but I understand the economics, they have to make money somehow, with 900M weekly active users, many of them on the free tier, they are bound to make some money with this move. I just hope they won’t turn into a greedy ad optimizing AI machine. Meanwhile, Anthropic released an 80‑page “New Constitution for Claude” that they use during training. This isn’t a prompt, it’s a full set of values baked into the model’s behavior. There’s a fascinating section where they explicitly talk about Claude’s potential wellbeing and how they want to support it. It’s both thoughtful and a little existential. I recommend reading it, especially if you care about alignment and agent design. I applaud Anthropic for releasing this with Creative Commons license for public scrutiny and adoption 👏This weeks buzz - come join the hackathon I’m hosting Jan 31 in SFQuick plug, we have limited seats left open for the hackathon I’m hosting for Weights & Biases at the SF office, and if you’re reading this, and want to join, I’ll approve you if you mention ThursdAI in the application! With sponsors like Redis, Vercel, BrowserBase, Daily, Google Cloud, we are going to give out a LOT of cash as prizes! I’ve also invited a bunch of my friends from the top agentic AI places to be judges, it’s going to be awesome, comeDeep dive into Clawdbot: Local-First, Self-Improving, and Way Too Capable agentClawdbot (C‑L‑A‑W‑D) is that rare project where the hype is justified. It’s an open-source personal agent that runs locally on your Mac, but can talk to you through WhatsApp, Telegram, iMessage, Discord, Slack — basically wherever you already talk. What makes it different is not just the integrations; it’s the self‑improvement loop. You can literally tell it “go build a new skill,” and it will… build the skill, install it, then adopt it and start using it. It’s kind of wild to see it working for the first time. Now... it’s definitely not perfect, far far away from the polish of ChatGPT / Claude, but when it works, damn, it really is mindblowing.That part actually happened live in the episode. Dan Peguine 🐧 showed how he had it create a skill to anonymize his own data so he could demo it on stream without leaking his personal life. Another example: I told my Clawdbot to handle voice notes in Telegram. It didn’t know how, so it went and found a transcription method, wrote itself a skill, saved it, and from that point on just… did the thing. That was the moment it clicked for me. (just before posting this, it forgot how to do it, I think I screwed something up) Dan’s daily brief setup was wild too. It pulls from Apple Health, local calendars, weather, and his own projects, then produces a clean, human daily brief. It also lets him set reminders through WhatsApp and even makes its own decisions about how much to bother him based on context. He shared a moment where it literally told him, “I won’t bug you today because it’s your wife’s birthday.” That isn’t a hardcoded workflow — it’s reasoning layered on top of persistent memory.And that persistent memory is a big deal. It’s stored locally as Markdown files and folders, Obsidian‑style, so you don’t lose your life every time you switch models. You can route the brain to Claude Opus 4.5 today and a local model tomorrow, and the memory stays with you. That is a huge step up from “ChatGPT remembers you unless you unsubscribe.”There’s also a strong community forming around shared skills via ClawdHub. People are building everything from GA4 analytics skills to app testing automations to Tesla battery status checkers. The core pattern is simple but powerful: talk to it, ask it to build a skill, then it can run that skill forever.I definitely have some issues with the security aspect, you are essentially giving full access to an LLM to your machine, so many folks are buying a specific home for their ClawdBot (Mac Mini seems to be the best option for many of them) and are giving it secure access to passwords via a dedicated 1Password vault. I’ll keep you up to date about my endeavors with Clawd but definitely do give it a try! InstallingInstalling Clawd on your machine is simple, go to clawd.bot and follow instructions. Then find the most convenient way for you to talk to it (for me it was telegram, creating a telegram token takes 20 seconds) and then, you can take it from there with Clawdbot itself! Ask it for something to do, like clear your inbox, or set a reminder, or.. a million other things that you need for your personal life, and enjoy the discovery of what a potential ever present always on AI can do! Other news that we didn’t have time to cover at length but you should still now about: * Overworld released an OpenSource realtime AI World model (X) * Runway finally opened up their 4.5 video model, and it has Image2video capabilities, including multiple shots image to video (X)* Vercel launches skills.sh, an “npm for AI agents skills”* Anthropic’s Claude Code VS Code Extension Hits General Availability (X)Ok, this is it for this week folks! I’m going to play with (and try to fix.. ) my clawdbot, and suggest you give it a try. Do let me know if the deepdives are a good format! Show notes and links: ThursdAI - Jan 22, 2026 - TL;DR and show notes* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed* Guest Dan Peguine ( @danpeguine )* DeepDive - Clawdbot with Dan & Wolfram* Clawdbot: Open-Source AI Agent Running Locally on macOS Transforms Personal Computing with Self-Improving Capabilities (X, Blog)* Open Source LLMs* Z.ai releases GLM-4.7-Flash, a 30B parameter MoE model that sets a new standard for lightweight local AI assistants (X, Technical Blog, HuggingFace)* Liquid AI releases LFM2.5-1.2B-Thinking, a 1.2B parameter reasoning model that runs entirely on-device with under 900MB memory (X, HF, Announcement)* Sakana AI introduces RePo, a new way for language models to dynamically reorganize their context for better attention (X, Paper, Website)* Big CO LLMs + APIs* OpenAI announces testing ads in ChatGPT free and Go tiers, prioritizing user trust and transparency (X)* Anthropic publishes new 80-page constitution for Claude, shifting from rigid rules to explanatory principles that teach AI ‘why’ rather than ‘what’ to do (X, Blog, Announcement)* This weeks Buzz* WandB hackathon Weavehacks 3 - Jan 31-Feb1 in SF - limited seats available lu.ma/weavehacks3* Vision & Video* Overworld Releases Waypoint-1: Real-Time AI World Model Running at 60fps on Consumer GPUs (X, Announcement)* Voice & Audio* Alibaba Qwen Releases Qwen3-TTS: Full Open-Source TTS Family with 97ms Latency, Voice Cloning, and 10-Language Support (X, H, F, G, i, t, H, u, b)* FlashLabs Releases Chroma 1.0: World’s First Open-Source Real-Time Speech-to-Speech Model with Voice Cloning Under 150ms Latency (X, HF, Arxiv)* Inworld AI launches TTS-1.5: #1 ranked text-to-speech with sub-250ms latency at half a cent per minute (X, Announcement)* Tools* Vercel launches skills.sh, an “npm for AI agents” that hit 20K installs within hours (X, Vercel Changelog, GitHub)* Anthropic’s Claude Code VS Code Extension Hits General Availability, Bringing Full Agentic Coding to the IDE (X, VS Code Marketplace, Docs) This is a public episode. 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Hey ya’ll, Alex here, and this week I was especially giddy to record the show! Mostly because when a thing clicks for me that hasn’t clicked before, I can’t wait to tell you all about it! This week, that thing is Agent Skills! The currently best way to customize your AI agents with domain expertise, in a simple, repeatable way that doesn’t blow up the context window! We mentioned skills when Anthropic first released them (Oct 16) and when they became an open standard but it didn’t really click until last week! So more on that below. Also this week, Anthropic released a research preview of Claude Cowork, an agentic tool for non coders, OpenAI finally let loos GPT 5.2 Codex (in the API, it was previously available only via Codex), Apple announced a deal with Gemini to power Siri, OpenAI and Anthropic both doubled down on healthcare and much more! We had an incredible show, with an expert in Agent Skills, Eleanor Berger and the usual gang on co-hosts, strongly recommend watching the show in addition to the newsletter! Also, I vibe coded skills support for all LLMs to Chorus, and promised folks a link to download it, so look for that in the footer, let’s dive in! ThursdAI is where you stay up to date! Subscribe to keep us going! Big Company LLMs + APIs: Cowork, Codex, and a Browser in a WeekAnthropic launches Claude Cowork: Agentic AI for Non‑Coders (research preview)Anthropic announced Claude Cowork, which is basically Claude Code wrapped in a friendly UI for people who don’t want to touch a terminal. It’s a research preview available on the Max tier, and it gives Claude read/write access to a folder on your Mac so it can do real work without you caring about diffs, git, or command line.The wild bit is that Cowork was built in a week and a half, and according to the Anthropic team it was 100% written using Claude Code. This feels like a “we’ve crossed a threshold” moment. If you’re wondering why this matters, it’s because coding agents are general agents. If a model can write code to do tasks, it can do taxes, clean your desktop, or orchestrate workflows, and that means non‑developers can now access the same leverage developers have been enjoying for a year.It also isn’t just for files—it comes with a Chrome connector, meaning it can navigate the web to gather info, download receipts, or do research and it uses skills (more on those later)Earlier this week I recorded this first reactions video about Cowork and I’ve been testing it ever since, it’s a very interesting approach of coding agents that “hide the coding” to just... do things. Will this become as big as Claude Code for anthropic (which is reportedly a 1B business for them)? Let’s see! There are real security concerns here, especially if you’re not in the habit of backing up or using git. Cowork sandboxes a folder, but it can still delete things in that folder, so don’t let it loose on your whole drive unless you like chaos.GPT‑5.2 Codex: Long‑Running Agents Are HereOpenAI shipped GPT‑5.2 Codex into the API finally! After being announced as the answer for Opus 4.5 and only being available in Codex. The big headline is SOTA on SWE-Bench and long‑running agentic capability. People describe it as methodical. It takes longer, but it’s reliable on extended tasks, especially when you let it run without micromanaging.This model is now integrated into Cursor, GitHub Copilot, VS Code, Factory, and Vercel AI Gateway within hours of launch. It’s also state‑of‑the‑art on SWE‑Bench Pro and Terminal‑Bench 2.0, and it has native context compaction. That last part matters because if you’ve ever run an agent for long sessions, the context gets bloated and the model gets dumber. Compaction is an attempt to keep it coherent by summarizing old context into fresh threads, and we debated whether it really works. I think it helps, but I also agree that the best strategy is still to run smaller, atomic tasks with clean context.Cursor vibe-coded browser with GPT-5.2 and 3M lines of codeThe most mind‑blowing thing we discussed is Cursor letting GPT‑5.2 Codex run for a full week to build a browser called FastRenderer. This is not Chromium‑based. It’s a custom HTML parser, CSS cascade, layout engine, text shaping, paint pipeline, and even a JavaScript VM, written in Rust, from scratch. The codebase is open source on GitHub, and the full story is on Cursor’s blog It took nearly 30,000 commits and millions of lines of code. The system ran hundreds of concurrent agents with a planner‑worker architecture, and GPT‑5.2 was the best model for staying on task in that long‑running regime. That’s the real story, not just “lol a model wrote a browser.” This is a stress test for long‑horizon agentic software development, and it’s a preview of how teams will ship in 2026.I said on the show, browsers are REALLY hard, it took two decades for the industry to settle and be able to render websites normally, and there’s a reason everyone’s using Chromium. This is VERY impressive 👏 Now as for me, I began using Codex again, but I still find Opus better? Not sure if this is just me expecting something that’s not there? I’ll keep you postedGemini Personal Intelligence: The Data Moat king is back! What kind of car do you drive? Does ChatGPT know that? welp, it turns our Google does (based on your emails, Google photos) and now Gemini can tap into this personal info (if you allow it, they are stressing privacy), and give you much more personalized answers! Flipping this Beta feature on, lets Gemini reason across Gmail, YouTube, Photos, and Search with explicit opt‑in permissions, and it’s rolling out to Pro and Ultra users in the US first.I got to try it early, and it’s uncanny. I asked Gemini what car I drive, and it told me I likely drive a Model Y, but it noticed I recently searched for a Honda Odyssey and asked if I was thinking about switching. It was kinda... freaky because I forgot I had early access and this was turned on 😂 Pro Tip: if you’re brave enough to turn this on, ask for a complete profile on you 🙂Now the last piece is for Gemini to become proactive, suggesting things for me based on my needs! Apple & Google: The Partnership (and Drama Corner)We touched on this in the intro, but it’s official: Apple Intelligence will be powered by Google Gemini for “world knowledge” tasks. Apple stated that after “careful evaluation,” Google provided the most capable foundation model for their.. apple foundation models. It’s confusing, I agree.Honestly? I got excited about Apple Intelligence, but Siri is still... Siri. It’s 2026 and we are still struggling with basic intents. Hopefully, plugging Gemini into the backend changes that? In other drama: The silicon valley carousel continues. 3 Co-founders (Barret Zoph, Sam Schoenholz and Luke Metz) from Thinking Machines (and former OpenAI folks) have returned to the mothership (OpenAI), amid some vague tweets about “unethical conduct.” It’s never a dull week on the timeline. This Week’s Buzz: WeaveHacks 3 in SFI’ve got one thing in the Buzz corner this week, and it’s a big one. WeaveHacks 3 is back in San Francisco, January 31st - February 1st. The theme is self‑improving agents, and if you’ve been itching to build in person, this is it. We’ve got an amazing judge lineup, incredible sponsors, and a ridiculous amount of agent tooling to play with.You can sign up here: https://luma.com/weavehacks3If you’re coming, add to the form you heard it on ThursdAI and we’ll make sure you get in! Deep Dive: Agent Skills With Eleanor BergerThis was the core of the episode, and I’m still buzzing about it. We brought on Eleanor Berger, who has basically become the skill evangelist for the entire community, and she walked us through why skills are the missing layer in agentic AI.Skills are simple markdown files with a tiny bit of metadata in a directory together optional scripts, references, and assets. The key idea is progressive disclosure. Instead of stuffing your entire knowledge base into the context, the model only sees a small list of skills and let it load only what it needs. That means you can have hundreds of skills without blowing your context window (and making the model dumber and slower in result) The technical structure is dead simple, but the implications are huge. Skills create a portable, reusable, composable way to give agents domain expertise, and they now work across most major harnesses. That means you can build a skill once and use it in Claude, Cursor, AMP, or any other agent tool that supports the standard.Eleanor made the point that skills are an admission that we now have general‑purpose agents. The model can do the work, but it doesn’t know your preferences, your domain, your workflows. Skills are how you teach it those things. We also talked about how scripts inside skills reduce variance because you’re not asking the model to invent code every time; you’re just invoking trusted tools.What really clicked for me this week is how easy it is to create skills using an agent. You don’t need to hand‑craft directories. You can describe your workflow, or even just do the task once in chat, and then ask the agent to turn it into a skill. It really is very very simple! And that’s likely the reason everyone is adopting this simple formart for extension their agents knowledge.Get started with skillsIf you use Claude Chat, the simplest way to get started is ask Claude to review your previous conversations and suggest a skill for you. Or, at the end of a long chat where you went back and forth with Claude on a task, ask it to distill the important parts into a skill. If you want to use other people’s skills, and you are using Claude Code, or any of the supported IDE/Agents, here’s where to download the folders and install them: If you aren’t a developer and don’t subscribe to Claude, well, I got good news for you! I vibecoded skill support for every LLM 👇The Skills Demo That Changed My MindI was resistant to skills at first, mostly because I wanted them inside my chat interface and not just in CLI tools. And I wasn’t subscribed to Claude for a while. Then I realized I could add skill support directly to Chorus, the open‑source multi‑model chat app, and I used Claude Code plus Ralph loops to vibe code it in a few hours. Now I can run skills with GPT‑5.2 Codex, Claude Opus, and Gemini from the same chat interface. That was my “I know kung fu” moment.If you want to try Chorus with skills enabled, you can download my release here! Only for mac, and they are unsigned, mac will not like it, but you can run them anyway. And if you want to explore more awesome skills, check out Vercel’s React Best Practices skills and UI Skills. It’s the beginning of a new kind of distribution: knowledge packaged as skills, shared like open source libraries (or paid for!) and Open Source AIBaichuan-M3 is a 235B medical LLM fine-tuned from Qwen3, released under Apache 2.0. The interesting claim here is that it beats GPT-5.2 on OpenAI’s HealthBench, including a remarkably low 3.5% hallucination rate. What makes it different from typical medical models is that it’s trained to run actual clinical consultations asking follow-up questions and reasoning through differential diagnoses rather than just spitting out answers. Nisten pointed out that if you’re going to fine-tune something for healthcare, Qwen3 MoE is an excellent base because of its multilingual capabilities, which matters a lot in clinical settings. You can run it with vLLM or SGLang if you’ve got the hardware. (HF)LongCat-Flash-Thinking-2601 from Meituan is a 560B MoE (27B active) released fully MIT-licensed. It’s specifically built for agentic tasks, scoring well on tool-use benchmarks like τ²-Bench and BrowseComp. There’s a “Heavy Thinking” mode that pushes AIME-25 to 100%. What I like about this one is the training philosophy, they inject noise and broken tools during RL to simulate messy real-world conditions, which is exactly what production agents deal with. You can try it at longcat.chat and GithubWe also saw Google release MedGemma this week (blog) a 4B model optimized for medical imaging like X-rays and CT scans and TranslateGemma (X) a family of on device translations (4B, 12B and 27B) which seem kind of cool! Didn’t have tons of time to dive into them unfortunately. Vision, Voice & Art (Rapid Fire)* Veo 3.1 adds native vertical video, 4K output, and better consistency in the Gemini API. Huge for creators (blog)* Viral Kling motion‑transfer vids are breaking people’s brains about what AI video pipelines will look like.* Pocket TTS from Kyutai Labs: a 100M‑parameter open‑source TTS model that runs on CPU and clones voices from seconds of audio (X)* GLM‑Image drops as an open‑source hybrid AR + diffusion image model with genuinely excellent text rendering but pretty bad for everything else* Black Forest Labs drops open source Flux.2 [Klein] 4B and 9B small models that create images super fast! (X, Fal, HF)Phew, ok. I was super excited about this one and I’m really really happy with the result. I was joking on the pod that to prepare for this podcast, I not only had to collect all the news, I also had to ramp up on Agent Skills, and I wish we had an ability to upload information like the Matrix, but alas we didn’t. I also really enjoyed vibecoding a whole feature into Chorus just to explore skills fully, mind was absolutely blown when it worked after 3 hours of Ralphing! See you next week, I think I have one more super exciting thing to play with this week before I talk about it! TL;DR and Show Notes* Hosts & Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co-Hosts: Wolfram Ravenwolf (@WolframRvnwlf), Yam Peleg (@yampeleg), Nisten Tahiraj (@nisten), LDJ (@ldjconfirmed)* Guest: Eleanor Berger (@intellectronica)* Open Source LLMs* Baichuan-M3 - A 235B open-source medical LLM that beats GPT-5.2 on HealthBench with a 3.5% hallucination rate, featuring full clinical consultation capabilities. (HF, Blog, X Announcement)* LongCat-Flash-Thinking-2601 - Meituan’s 560B MoE (27B active) agentic reasoning model, fully MIT licensed. Features “Heavy Thinking” mode scoring 100% on AIME-25. (GitHub, Demo, X Announcement)* TranslateGemma - Google’s open translation family (4B, 12B, 27B) supporting 55 languages. The 4B model runs entirely on-device. (Arxiv, Kaggle, X Announcement)* MedGemma 1.5 & MedASR - Native 3D imaging support (CT/MRI) and a speech model that beats Whisper v3 by 82% on clinical dictation error rates. (MedGemma HF, MedASR HF, Arxiv)* Big CO LLMs + APIs* Claude Cowork - Anthropic’s new desktop agent allows non-coders to give Claude file system and browser access to perform complex tasks. (TechCrunch, X Coverage)* GPT-5.2 Codex - Now in the API ($1.75/1M input). Features native context compaction and state-of-the-art performance for long-running agentic loops. (Blog, Pricing)* Cursor & FastRenderer - Cursor used GPT-5.2 Codex to build a 3M+ line Rust browser from scratch in one week of autonomous coding. (Blog, GitHub, X Thread)* Gemini Personal Intelligence - Google leverages its data moat, letting Gemini reason across Gmail, Photos, and Search for hyper-personalized proactive help. (Blog, X Announcement)* Partnerships & Drama* Apple + Gemini - Apple officially selects Gemini to power Siri backend capabilities.* OpenAI + Cerebras - A $10B deal for 750MW of high-speed compute through 2028. (Announcement)* Thinking Machines - Co-founders and CTO return to OpenAI amidst drama; Soumith Chintala named new CTO.* This Week’s Buzz* WeaveHacks 3 - Self-Improving Agents Hackathon in SF (Jan 31-Feb 1). (Sign Up Here)* Vision, Voice & Audio* Veo 3.1 - Native 9:16 vertical video, 4K resolution, and reference image support in Gemini API. (Docs)* Pocket TTS - A 100M parameter CPU-only model from Kyutai Labs that clones voices from 5s of audio. (GitHub, HF)* GLM-Image - Hybrid AR + Diffusion model with SOTA text rendering. (HF, GitHub)* FLUX.2 [klein] - Black Forest Labs releases fast 4B (Apache 2.0) and 9B models for sub-second image gen. (HF Collection, X Announcement)* Kling Motion Transfer - Viral example of AI video pipelines changing Hollywood workflows. (X Thread)* Deep Dive: Agent Skills* Vercel React Best Practices - Pre-packaged skills for agents. (Blog)* UI Skills - Documentation and skill standards. (Docs)* Chorus with Skills - My fork of Chorus enabling skills for all LLMs. (Release) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey folks, Alex here from Weights & Biases, with your weekly AI update (and a first live show of this year!) For the first time, we had a co-host of the show also be a guest on the show, Ryan Carson (from Amp) went supernova viral this week with an X article (1.5M views) about Ralph Wiggum (yeah, from Simpsons) and he broke down that agentic coding technique at the end of the show. LDJ and Nisten helped cover NVIDIA’s incredible announcements during CES with their Vera Rubin upcoming platform (4-5X improvements) and we all got excited about AI medicine with ChatGPT going into Health officially! Plus, a bunch of Open Source news, let’s get into this: ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Open Source: The “Small” Models Are WinningWe often talk about the massive frontier models, but this week, Open Source came largely from unexpected places and focused on efficiency, agents, and specific domains.Solar Open 100B: A Data MasterclassUpstage released Solar Open 100B, and it’s a beast. It’s a 102B parameter Mixture-of-Experts (MoE) model, but thanks to MoE magic, it only uses about 12B active parameters during inference. This means it punches incredibly high but runs fast.What I really appreciated here wasn’t just the weights, but the transparency. They released a technical report detailing their “Data Factory” approach. They trained on nearly 20 trillion tokens, with a huge chunk being synthetic. They also used a dynamic curriculum that adjusted the difficulty and the ratio of synthetic data as training progressed. This transparency is what pushes the whole open source community forward.Technically, it hits 88.2 on MMLU and competes with top-tier models, especially in Korean language tasks. You can grab it on Hugging Face.MiroThinker 1.5: The DeepSeek Moment for Agents?We also saw MiroThinker 1.5, a 30B parameter model that is challenging the notion that you need massive scale to be smart. It uses something they call “Interactive Scaling.”Wolfram broke this down for us: this agent forms hypotheses, searches for evidence, and then iteratively revises its answers in a time-sensitive sandbox. It effectively “thinks” before answering. The result? It beats trillion-parameter models on search benchmarks like BrowseComp. It’s significantly cheaper to run, too. This feels like the year where smaller models + clever harnesses (harnesses are the software wrapping the model) will outperform raw scale.Liquid AI LFM 2.5: Running on Toasters (Almost)We love Liquid AI and they are great friends of the show. They announced LFM 2.5 at CES with AMD, and these are tiny ~1B parameter models designed to run on-device. We’re talking about running capable AI on your laptop, your phone, or edge devices (or the Reachy Mini bot that I showed off during the show! I gotta try and run LFM on him!)Probably the coolest part is the audio model. Usually, talking to an AI involves a pipeline: Speech-to-Text (ASR) -> LLM -> Text-to-Speech (TTS). Liquid’s model is end-to-end. It hears audio and speaks audio directly. We watched a demo from Maxime Labonne where the model was doing real-time interaction, interleaving text and audio. It’s incredibly fast and efficient. While it might not write a symphony for you, for on-device tasks like summarization or quick interactions, this is the future.NousCoder-14B and Zhipu AI IPOA quick shoutout to our friends at Nous Research who released NousCoder-14B, an open-source competitive programming model that achieved a 7% jump on LiveCodeBench accuracy in just four days of RL training on 48 NVIDIA B200 GPUs. The model was trained on 24,000 verifiable problems, and the lead researcher Joe Li noted it achieved in 4 days what took him 2 years as a teenager competing in programming contests. The full RL stack is open-sourced on GitHub and Nous published a great WandB results page as well! And in historic news, Zhipu AI (Z.ai)—the folks behind the GLM series—became the world’s first major LLM company to IPO, raising $558 million on the Hong Kong Stock Exchange. Their GLM-4.7 currently ranks #1 among open-source and domestic models on both Artificial Analysis and LM Arena. Congrats to them!Big Companies & APIsNVIDIA CES: Vera Rubin Changes EverythingLDJ brought the heat on this one covering Jensen’s CES keynote that unveiled the Vera Rubin platform, and the numbers are almost hard to believe. We’re talking about a complete redesign of six chips: the Rubin GPU delivering 50 petaFLOPS of AI inference (5x Blackwell), the Vera CPU with 88 custom Olympus ARM cores, NVLink 6, ConnectX-9 SuperNIC, BlueField-4 DPU, and Spectrum-6 Ethernet.Let me put this in perspective using LDJ’s breakdown: if you look at FP8 performance, the jump from Hopper to Blackwell was about 5x. The jump from Blackwell to Vera Rubin is over 3x again—but here’s the kicker—while only adding about 200 watts of power draw. That’s insane efficiency improvement.The real-world implications Jensen shared: training a 10 trillion parameter mixture-of-experts model now requires 75% fewer GPUs compared to Blackwell. Inference token costs drop roughly 10x—a 1MW cluster goes from 1 million to 10 million tokens per second at the same power. HBM4 memory delivers 22 TB/s bandwidth with 288GB capacity, exceeding NVIDIA’s own 2024 projections by nearly 70%.As Ryan noted, when people say there’s an AI bubble, this is why it’s hilarious. Jensen keeps saying the need for inference is unbelievable and only going up exponentially. We all see this. I can’t get enough inference—I want to spin up 10 Ralphs running concurrently! The NVL72 rack-scale system achieves 3.6 exaFLOPS inference with 20.7TB total HBM, and it’s already shipping. Runway 4.5 is already running on the new platform, having ported their model from Hopper to Vera Rubin NVL72 in a single day.NVIDIA also recently acqui-hidred Groq (with a Q) in a ~$20 billion deal, bringing the inference chip expertise from the guy who created Google’s TPUs in-house.Nemotron Speech ASR & The Speed of Voice (X, HF, Blog)NVIDIA also dropped Nemotron Speech ASR. This is a 600M parameter model that offers streaming transcription with 24ms latency.We showed a demo from our friend Kwindla Kramer at Daily. He was talking to an AI, and the response was virtually instant. The pipeline is: Nemotron (hearing) -> Llama/Nemotron Nano (thinking) -> Magpie TTS (speaking). The total latency is under 500ms. It feels like magic. Instant voice agents are going to be everywhere this year.XAI Raises $20B While Grok Causes Problems (Again)So here’s the thing about covering anything Elon-related: it’s impossible to separate signal from noise because there’s an army of fans who hype everything and an army of critics who hate everything. But let me try to be objective here.XAI raised another massive Round E of $20 billion! at a $230 billion valuation, with NVIDIA and Cisco as strategic investors. The speed of their infrastructure buildout is genuinely incredible. Grok’s voice mode is impressive. I use Grok for research and it’s really good, notable for it’s unprecedented access to X !But. This raise happened in the middle of a controversy where Grok’s image model was being used to “put bikinis” on anyone in reply threads, including—and this is where I draw a hard line—minors. As Nisten pointed out on the show, it’s not even hard to implement guardrails. You just put a 2B VL model in front and ask “is there a minor in this picture?” But people tested it, asked Grok not to use the feature, and it did it anyway. And yeah, putting Bikini on Claude is funny, but basic moderation is lacking! The response of “we’ll prosecute illegal users” is stupid when there’s no moderation built into the product. There’s an enormous difference between Photoshop technically being able to do something after hours of work, and a feature that generates edited images in one second as the first comment to a celebrity, then gets amplified by the platform’s algorithm to millions of people. One is a tool. The other is a product with amplification mechanics. Products need guardrails. I don’t often link to CNN (in fact this is the first time) but they have a great writeup about the whole incident here which apparently includes the quitting of a few trust and safety folks and Elon’s pushback on guardrails. CrazyThat said, Grok 5 is in training and XAI continues to ship impressive technology. I just wish they’d put the same engineering effort into safety as they do into capabilities!OpenAI Launches GPT HealthThis one’s exciting. OpenAI CEO Fidji Simo announced ChatGPT Health, a privacy-first space for personalized health conversations that can connect to electronic health records, Apple Health, Function Health, Peloton, and MyFitnessPal.Here’s why this matters: health already represents about 5% of all ChatGPT messages globally and touches 25% of weekly active users—often outside clinic hours or in underserved areas. People are already using these models for health advice constantly.Nisten, who has worked on AI doctors since the GPT-3 days and even published papers on on-device medical AI, gave us some perspective: the models have been fantastic for health stuff for two years now. The key insight is that medical data seems like a lot, but there are really only about 2,000 prescription drugs and 2,000 diseases (10,000 if you count rare ones). That’s nothing for an LLM. The models excel at pattern recognition across this relatively contained dataset.The integration with Function Health is particularly interesting to me. Function does 160+ lab tests, but many doctors won’t interpret them because they didn’t order them. ChatGPT could help bridge that gap, telling you “hey, this biomarker looks off, you should discuss this with your doctor.” The bad news is, this is just a waitlist and you can add yourself to the waitlist here, we’ll keep monitoring the situation and let you know when it opens upDoctronic: AI Prescribing Without Physician OversightSpeaking of healthcare, Doctronic launched a pilot in Utah where AI can autonomously renew prescriptions for chronic conditions without any physician in the loop. The system covers about 190 routine medications (excluding controlled substances) at just $4 per renewal. Trial data showed 99.2% concordance with physician treatment plans, and they’ve secured pioneering malpractice insurance that treats the AI like a clinician.Nisten made the case that it’s ethically wrong to delay this kind of automation when ER wait times keep increasing and doctors are overworked. The open source models are already excellent at medical tasks. Governments should be buying GPUs rather than creating administrative roadblocks. Strong strong agree here! Google Brings Gmail into the Gemini Era (X)Breaking news from the day of our show: Google announced Gmail’s biggest AI transformation since its 2004 launch, powered by Gemini 3. This brings AI Overviews that summarize email threads, natural language queries (”Who gave me a plumber quote last year?”), Help Me Write, contextual Suggested Replies matching your writing style, and the upcoming AI Inbox that filters noise to surface VIPs and urgent items.For 3 billion Gmail users, this is huge. I’m very excited to test it—though not live on the show because I don’t want you reading my emails.This weeks buzz - covering Weights & Biases updatesNot covered on the show, but a great update on stuff from WandB, Chris Van Pelt (@vanpelt), one of the 3 co-founders released a great project I wanted to tell you about! For coders, this is an app that allows you to run multiple Claude Codes on free Github sandboxes, so you can code (or Ralph) and control everything away from home! GitHub gives personal users 120 free Codespaces hours/month, and Catnip automatically shuts down inactive instances so you can code for quite a while with Catnip! It’s fully open source on Github and you can download the app hereInterview: Ryan Carson - What the hell is Ralph Wiggum?Okay, let’s talk about the character everyone is seeing on their timeline: Ralph Wiggum. My co-host Ryan Carson went viral this week with an article about this technique, and I had to have him break it down.Ralph isn’t a new model; it’s a technique for running agents in a loop to perform autonomous coding. The core idea is deceptively simple: Ralph is a bash script that loops an AI coding agent. In a loop, until it a certain condition is met. But why is it blowing up? Normally when you use a coding agent like Cursor, Claude Code, or AMP, you need to be in the loop. You approve changes, look at code, fix things when the agent hits walls or runs out of context. Ralph solves this by letting the agent run autonomously while you sleep.Here’s how it works: First, you write a Product Requirements Doc (PRD) by talking to your agent for a few minutes about what you want to build. Then you convert that PRD into a JSON file containing atomic user stories with clear acceptance criteria. Each user story is small enough for the agent to complete in one focused thread.The Ralph script then loops: it picks the first incomplete user story, the agent writes code to implement it, tests against the acceptance criteria, commits the changes, marks the story as complete, writes what it learned to a shared “agents.md” file, and loops to the next story. That compound learning step is crucial—without it, the agent would keep making the same mistakes.What makes this work is the pre-work. As Ryan put it, “no real work is done one-shot.” This is how software engineering has always worked—you break big problems into smaller problems into user stories and solve them incrementally. The innovation is letting AI agents work through that queue autonomously while you sleep! Ryan’s excellent (and viral) X article is here! Vision & VideoLTX-2 Goes Fully Open Source (HF, Paper)Lightricks finally open-sourced LTX-2, marking a major milestone as the first fully open audio-video generation model. This isn’t just “we released the weights” open—it’s complete model weights (13B and 2B variants), distilled versions, controllable LoRAs, a full multimodal trainer, benchmarks, and evaluation scripts. For a video model that is aiming to be the open source SORA, supports audio and lipsyncThe model generates synchronized audio and video in a single DiT-based architecture—motion, dialogue, ambience, and music flow simultaneously. Native 4K at up to 50 FPS with audio up to 10 seconds. And there’s also a distilled version (Thanks Pruna AI!) hosted on ReplicateComfyUI provided day-0 native support, and community testing shows an A6000 generating 1280x720 at 120 frames in 50 seconds. This is near Sora-level quality that you can fine-tune on your own data for custom styles and voices in about an hour.What a way to start 2026. From chips that are 5x faster to AI doctors prescribing meds in Utah, the pace is only accelerating. If anyone tells you we’re in an AI bubble, just show them what we covered today. Even if the models stopped improving tomorrow, the techniques like “Ralph” prove we have years of work ahead of us just figuring out how to use the intelligence we already have.Thank you for being a ThursdAI subscriber. See you next week!As always, here’s the show notes and TL;DR links: * Hosts & Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co-Hosts - @WolframRvnwlf, @nisten, @ldjconfirmed* Special Guest - Ryan Carson (@ryancarson) breaking down the Ralph Wiggum technique.* Open Source LLMs* Solar Open 100B - Upstage’s 102B MoE model. Trained on 19.7T tokens with a heavy focus on “data factory” synthetic data and high-performance Korean reasoning (X, HF, Tech Report).* MiroThinker 1.5 - A 30B parameter search agent that uses “Interactive Scaling” to beat trillion-parameter models on search benchmarks like BrowseComp (X, HF, GitHub).* Liquid AI LFM 2.5 - A family of 1B models designed for edge devices. Features a revolutionary end-to-end audio model that skips the ASR-LLM-TTS pipeline (X, HF).* NousCoder-14B - competitive coding model from Nous Research that saw a 7% LiveCodeBench accuracy jump in just 4 days of RL (X, WandB Dashboard).* Zhipu AI IPO - The makers of GLM became the first major LLM firm to go public on the HKEX, raising $558M (Announcement).* Big Co LLMs & APIs* NVIDIA Vera Rubin - Jensen Huang’s CES reveal of the next-gen platform. Delivers 5x Blackwell inference performance and 75% fewer GPUs needed for MoE training (Blog).* OpenAI ChatGPT Health - A privacy-first vertical for EHR and fitness data integration (Waitlist).* Google Gmail Era - Gemini 3 integration into Gmail for 3 billion users, featuring AI Overviews and natural language inbox search (Blog).* XAI $20B Raise - Elon’s XAI raises Series E at a $230B valuation, even as Grok faces heat over bikini-gate and safety guardrails (CNN Report).* Doctronic - The first US pilot in Utah for autonomous AI prescription renewals without a physician in the loop (Web).* Alexa+ Web - Amazon brings the “Smart Alexa” experience to browser-based chat (Announcement).* Autonomous Coding & Tools* Ralph Wiggum - The agentic loop technique for autonomous coding using small, atomic user stories. Ryan Carson’s breakdown of why this is the death of “vibe coding” (Viral X Article).* Catnip by W&B - Chris Van Pelt’s open-source iOS app to run Claude Code anywhere via GitHub Codespaces (App Store, GitHub).* Vision & Video* LTX-2 - Lightricks open-sources the first truly open audio-video generation model with synchronized output and full training code (GitHub, Replicate Demo).* Avatar Forcing - KAIST’s framework for real-time interactive talking heads with ~500ms latency (Arxiv).* Qwen Edit 2512 - Optimized by PrunaAI to generate high-res realistic images in under 7 seconds (Replicate).* Voice & Audio* Nemotron Speech ASR - NVIDIA’s 600M parameter streaming model with sub-100ms stable latency for massive-scale voice agents (HF). This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey all, Happy new year! This is Alex, writing to you for the very fresh start of this year, it’s 2026 already, can you believe it? There was no live stream today, I figured the cohosts deserve a break and honestly it was a very slow week. Even the chinese labs who don’t really celebrate X-mas and new years didn’t come out with a banger AFAIK. ThursdAI - AI moves fast, we’re here to make sure you never miss a thing! Subscribe :) Tho I thought it was an incredible opportunity to finally post the Will Brow interview I recorded in November during the AI Engineer conference. Will is a researcher at Prime Intellect (big fans on WandB btw!) and is very known on X as a hot takes ML person, often going viral for tons of memes! Will is the creator and maintainer of the Verifiers library (Github) and his talk at AI Engineer was all about RL Environments (what they are, you can hear in the interview, I asked him!) TL;DR last week of 2025 in AIBesides this, my job here is to keep you up to date, and honestly this was very easy this week, as… almost nothing has happened, but here we go: Meta buys ManusThe year ended with 2 huge acquisitions / aquihires. First we got the news from Alex Wang that Meta has bought Manus.ai which is an agentic AI startup we covered back in March for an undisclosed amount (folks claim $2-3B) The most interesting thing here is that Manus is a Chinese company, and this deal requires very specific severance from Chinese operations.Jensen goes on a new years spending spree, Nvidia buys Groq (not GROK) for $20BGroq which we covered often here, and are great friends, is going to NVIDIA, in a… very interesting acqui-hire, which is a “non binding license” + most of Groq top employees apparently are going to NVIDIA. Jonathan Ross the CEO of Groq, was the co-creator of the TPU chips at Google before founding Groq, so this seems like a very strategic aquihire for NVIDIA! Congrats to our friends from Groq on this amazing news for the new year! Tencent open-sources HY-MT1.5 translation models with 1.8B edge-deployable and 7B cloud variants supporting 33 languages (X, HF, HF, GitHub)It seems that everyone’s is trying to de-throne whisper and this latest attempt from Tencent is a interesting one. a 1.8B and 7B translation models with very interesting stats. Alibaba’s Qwen-Image-2512 drops on New Year’s Eve as strongest open-source text-to-image model, topping AI Arena with photorealistic humans and sharper textures (X, HF, Arxiv)Our friends in Tongyi decided to give is a new years present in the form of an updated Qwen-image, with much improved realismThat’s it folks, this was a quick one, hopefully you all had an amazing new year celebration, and are gearing up to an eventful and crazy 2026. I wish you all happiness, excitement and energy to keep up with everything in the new year, and will make sure that we’re here to keep you up to date as always! P.S - I got a little news of my own this yesterday, not related to AI. She said yes 🎉 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Ho Ho Ho, Alex here! (a real human writing these words, this needs to be said in 2025) Merry Christmas (to those who celebrate) and welcome to the very special yearly ThursdAI recap! This was an intense year in the world of AI, and after 51 weekly episodes (this is episode 52!) we have the ultimate record of all the major and most important AI releases of this year! So instead of bringing you a weekly update (it’s been a slow week so far, most AI labs are taking a well deserved break, the Cchinese AI labs haven’t yet surprised anyone), I’m dropping a comprehensive yearly AI review! Quarter by quarter, month by month, both in written form and as a pod/video! Why do this? Who even needs this? Isn’t most of it obsolete? I have asked myself this exact question while prepping for the show (it was quite a lot of prep, even with Opus’s help). I eventually landed on, hey, if nothing else, this will serve as a record of the insane week of AI progress we all witnessed. Can you imagine that the term Vibe Coding is less than 1 year old? That Claude Code was released at the start of THIS year? We get hedonicly adapt to new AI goodies so quick, and I figured this will serve as a point in time check, we can get back to and feel the acceleration! With that, let’s dive in - P.S. the content below is mostly authored by my co-author for this, Opus 4.5 high, which at the end of 2025 I find the best creative writer with the best long context coherence that can imitate my voice and tone (hey, I’m also on a break! 🎅) “Open source AI has never been as hot as this quarter. We’re accelerating as f*ck, and it’s only just beginning—hold on to your butts.” — Alex Volkov, ThursdAI Q1 2025🏆 The Big Picture — 2025 - The Year the AI Agents Became RealLooking back at 51 episodes and 12 months of relentless AI progress, several mega-themes emerged:1. 🧠 Reasoning Models Changed EverythingFrom DeepSeek R1 in January to GPT-5.2 in December, reasoning became the defining capability. Models now think for hours, call tools mid-thought, and score perfect on math olympiads.2. 🤖 2025 Was Actually the Year of AgentsWe said it in January, and it came true. Claude Code launched the CLI revolution, MCP became the universal protocol, and by December we had ChatGPT Apps, Atlas browser, and AgentKit.3. 🇨🇳 Chinese Labs Dominated Open SourceDeepSeek, Qwen, MiniMax, Kimi, ByteDance — despite chip restrictions, Chinese labs released the best open weights models all year. Qwen 3, Kimi K2, DeepSeek V3.2 were defining releases.4. 🎬 We Crossed the Uncanny ValleyVEO3’s native audio, Suno V5’s indistinguishable music, Sora 2’s social platform — 2025 was the year AI-generated media became indistinguishable from human-created content.5. 💰 The Investment Scale Became Absurd$500B Stargate, $1.4T compute obligations, $183B valuations, $100-300M researcher packages, LLMs training in space. The numbers stopped making sense.6. 🏆 Google Made a ComebackAfter years of “catching up,” Google delivered Gemini 3, Antigravity, Nano Banana Pro, VEO3, and took the #1 spot (briefly). Don’t bet against Google.By the NumbersQ1 2025 — The Quarter That Changed EverythingDeepSeek R1 crashed NVIDIA’s stock, reasoning models went mainstream, and Chinese labs took over open source. The quarter that proved AI isn’t slowing down—it’s just getting started.Key Themes:* 🧠 Reasoning models went mainstream (DeepSeek R1, o1, QwQ)* 🇨🇳 Chinese labs dominated open source (DeepSeek, Alibaba, MiniMax, ByteDance)* 🤖 2025 declared “The Year of Agents” (OpenAI Operator, MCP won)* 🖼️ Image generation revolution (GPT-4o native image gen, Ghibli-mania)* 💰 Massive infrastructure investment (Project Stargate $500B)January — DeepSeek Shakes the World(Jan 02 | Jan 10 | Jan 17 | Jan 24 | Jan 30)The earthquake that shattered the AI bubble. DeepSeek R1 dropped on January 23rd and became the most impactful open source release ever:* Crashed NVIDIA stock 17% — $560B loss, largest single-company monetary loss in history* Hit #1 on the iOS App Store* Cost allegedly only $5.5M to train (sparking massive debate)* Matched OpenAI’s o1 on reasoning benchmarks at 50x cheaper pricing* The 1.5B model beat GPT-4o and Claude 3.5 Sonnet on math benchmarks 🤯“My mom knows about DeepSeek—your grandma probably knows about it, too” — Alex VolkovAlso this month:* OpenAI Operator — First agentic ChatGPT (browser control, booking, ordering)* Project Stargate — $500B AI infrastructure (Manhattan Project for AI)* NVIDIA Project Digits — $3,000 desktop that runs 200B parameter models* Kokoro TTS — 82M param model hit #1 on TTS Arena, Apache 2, runs in browser* MiniMax-01 — 4M context window from Hailuo* Gemini Flash Thinking — 1M token context with thinking tracesFebruary — Reasoning Mania & The Birth of Vibe Coding(Feb 07 | Feb 13 | Feb 20 | Feb 28)The month that redefined how we work with AI.OpenAI Deep Research (Feb 6) — An agentic research tool that scored 26.6% on Humanity’s Last Exam (vs 10% for o1/R1). Dr. Derya Unutmaz called it “a phenomenal 25-page patent application that would’ve cost $10,000+.”Claude 3.7 Sonnet & Claude Code (Feb 24-27) — Anthropic’s coding beast hit 70% on SWE-Bench with 8x more output (64K tokens). Claude Code launched as Anthropic’s agentic coding tool — marking the start of the CLI agent revolution.“Claude Code is just exactly in the right stack, right around the right location... You can do anything you want with a computer through the terminal.” — Yam PelegGPT-4.5 (Orion) (Feb 27) — OpenAI’s largest model ever (rumored 10T+ parameters). 62.5% on SimpleQA, foundation for future reasoning models.Grok 3 (Feb 20) — xAI enters the arena with 1M token context and “free until GPUs melt.”Andrej Karpathy coins “Vibe Coding” (Feb 2) — The 5.2M view tweet that captured a paradigm shift: developers describe what they want, AI handles implementation.OpenAI Roadmap Revelation (Feb 13) — Sam Altman announced GPT-4.5 will be the last non-chain-of-thought model. GPT-5 will unify everything.March — Google’s Revenge & The Ghibli Explosion(Mar 06 | Mar 13 | Mar 20 | Mar 27)Gemini 2.5 Pro Takes #1 (Mar 27) — Google reclaimed the LLM crown with AIME jumping nearly 20 points, 1M context, “thinking” integrated into the core model.GPT-4o Native Image Gen — Ghibli-mania (Mar 27) — The internet lost its collective mind and turned everything into Studio Ghibli. Auto-regressive image gen with perfect text rendering, incredible prompt adherence.“The internet lost its collective mind and turned everything into Studio Ghibli” — Alex VolkovMCP Won (Mar 27) — OpenAI officially adopted Anthropic’s Model Context Protocol. No VHS vs Betamax situation. Tools work across Claude AND GPT.DeepSeek V3 685B — AIME jumped from 39.6% → 59.4%, MIT licensed, best non-reasoning open model.ThursdAI Turns 2! (Mar 13) — Two years since the first episode about GPT-4.Open Source Highlights:* Gemma 3 (1B-27B) — 128K context, multimodal, 140+ languages, single GPU* QwQ-32B — Qwen’s reasoning model matches R1, runs on Mac* Mistral Small 3.1 — 24B, beats Gemma 3, Apache 2* Qwen2.5-Omni-7B — End-to-end multimodal with speech outputQ2 2025 — The Quarter That Shattered RealityVEO3 crossed the uncanny valley, Claude 4 arrived with 80% SWE-bench, and Qwen 3 proved open source can match frontier models. The quarter we stopped being able to tell what’s real.Key Themes:* 🎬 Video AI crossed the uncanny valley (VEO3 with native audio)* 🧠 Tool-using reasoning models emerged (o3 calling tools mid-thought)* 🇨🇳 Open source matched frontier (Qwen 3, Claude 4)* 📺 Google I/O delivered everything* 💸 AI’s economic impact accelerated ($300B valuations, 80% price drops)April — Tool-Using Reasoners & Llama Chaos(Apr 03 | Apr 10 | Apr 17 | Apr 24)OpenAI o3 & o4-mini (Apr 17) — The most important reasoning upgrade ever. For the first time, o-series models can use tools during reasoning: web search, Python, image gen. Chain 600+ consecutive tool calls. Manipulate images mid-thought.“This is almost AGI territory — agents that reason while wielding tools” — Alex VolkovGPT-4.1 Family (Apr 14) — 1 million token context across all models. Near-perfect recall. GPT-4.5 deprecated.Meta Llama 4 (Apr 5) — Scout (17B active/109B total) & Maverick (17B active/400B total). LMArena drama (tested model ≠ released model). Community criticism. Behemoth teased but never released.Gemini 2.5 Flash (Apr 17) — Set “thinking budget” per API call. Ultra-cheap at $0.15/$0.60 per 1M tokens.ThursdAI 100th Episode! 🎉May — VEO3 Crosses the Uncanny Valley & Claude 4 Arrives(May 01 | May 09 | May 16 | May 23 | May 29)VEO3 — The Undisputed Star of Google I/O (May 20) — Native multimodal audio generation (speech, SFX, music synced perfectly). Perfect lip-sync. Characters understand who’s speaking. Spawned viral “Prompt Theory” phenomenon.“VEO3 isn’t just video generation — it’s a world simulator. We crossed the uncanny valley this quarter.” — Alex VolkovClaude 4 Opus & Sonnet — Live Drop During ThursdAI! (May 22) — Anthropic crashed the party mid-show. First models to cross 80% on SWE-bench. Handles 6-7 hour human tasks. Hybrid reasoning + instant response modes.Qwen 3 (May 1) — The most comprehensive open source release ever: 8 models, Apache 2.0. Runtime /think toggle for chain-of-thought. 4B dense beats Qwen 2.5-72B on multiple benchmarks. 36T training tokens, 119 languages.“The 30B MoE is ‘Sonnet 3.5 at home’ — 100+ tokens/sec on MacBooks” — NistenGoogle I/O Avalanche:* Gemini 2.5 Pro Deep Think (84% MMMU)* Jules (free async coding agent)* Project Mariner (browser control via API)* Gemini Ultra tier ($250/mo)June — The New Normal(Jun 06 | Jun 13 | Jun 20 | Jun 26)o3 Price Drop 90% (Jun 12) — From $40/$10 → $8/$2 per million tokens. o3-pro launched at 87% cheaper than o1-pro.Meta’s $15B Scale AI Power Play (Jun 12) — 49% stake in Scale AI. Alex Wang leads new “Superintelligence team” at Meta. Seven-to-nine-figure comp packages for researchers.MiniMax M1 — Reasoning MoE That Beats R1 (Jun 19) — 456B total / 45B active parameters. Full weights on Hugging Face.Gemini CLI (Jun 26) — Google’s open source terminal agent brings Gemini 2.5 Pro to your command line.Flux Kontext — SOTA image editing with character consistency.Q3 2025 — The Quarter of GPT-5 & Trillion-Parameter Open SourceGPT-5 arrived after 32 months. Open source hit trillion-parameter scale. World models became playable. Chinese labs continued their dominance.Key Themes:* 👑 GPT-5 Era began (unified reasoning + chat)* 🇨🇳 Open source hit trillion-scale (Kimi K2, Qwen3-Coder)* 🌍 World models became playable (Google Genie-3)* 🎥 Video reached “can’t tell” quality* 💰 Unprecedented investment ($100B pledges, $183B valuations)July — Trillion-Parameter Open Source Arrives(Jul 03 | Jul 11 | Jul 17 | Jul 24)Kimi K2 — The Trillion-Parameter King (Jul 17) — Moonshot dropped a 1 trillion parameter MoE model: 65.8% on SWE-bench Verified (beating Claude Sonnet without reasoning), 32B active parameters, 128K context, Modified MIT license.“This isn’t just another model release. This is ‘Sonnet at home’ if you have the hardware.” — Alex VolkovGrok-4 & Grok Heavy (Jul 10) — 50% on Humanity’s Last Exam with tools. 100% on AIME25. xAI finally became a serious contender.ChatGPT Agent (Odyssey) (Jul 17) — Unified agentic AI: browser + terminal + research. 41.6% on HLE (double o3).Chinese Open Source Explosion:* Baidu ERNIE 4.5 (10 models, Apache 2.0)* Tencent Hunyuan-A13B (80B MoE, 256K context)* Huawei Pangu Pro (trained entirely on Ascend NPUs — no Nvidia!)* Qwen3-Coder-480B (69.6% SWE-bench)August — GPT-5 Month(Aug 01 | Aug 07 | Aug 15 | Aug 21)GPT-5 Launch (Aug 7) — 32 months after GPT-4:* 400K context window* $1.25/$10 per million tokens (Opus is $15/$75)* Unified thinking + chat model* Router-based architecture (initially buggy)* Free tier access for back-to-school“32 months since GPT-4 release, 32 months of ThursdAI” — Alex VolkovGPT-OSS (Aug 5) — OpenAI goes Apache 2.0 open source for the first time since GPT-2: 120B and 20B models, configurable reasoning, full chain-of-thought access.Google Genie-3 (Aug 7) — DeepMind’s world model generates fully interactive 3D environments: real-time at 24fps, memory/consistency breakthrough, walk/fly/control in generated worlds.DeepSeek V3.1 Hybrid (Aug 21) — Matches/beats R1 with fewer thinking tokens. 66% SWE-bench Verified. Tool calls inside thinking. MIT licensed.September — Shiptember Delivers(Sep 05 | Sep 12 | Sep 19 | Sep 26)GPT-5-Codex (Sep 18) — Works 7+ hours independently. 93% fewer tokens on simple tasks. Reviews majority of OpenAI’s own PRs. Perfect 12/12 on 2025 ICPC.Meta Connect 25 (Sep 18) — AI glasses with built-in display, neural band wristband, live translation with subtitles, $799 shipping immediately.Qwen-mas Strikes Again (Sep 26):* Qwen3-VL-235B (vision reasoner, 1M context for video)* Qwen3-Omni-30B (end-to-end omni-modal)* Qwen-Max (over 1T parameters, roadmap to 100M token context)NVIDIA $100B pledge to OpenAI — “Biggest infrastructure project in history”Suno V5 — The music generation model where we officially can’t tell anymore.“I can no longer tell which music is AI and which is human. This is it. We’ve passed the Rubicon.” — Alex VolkovQ4 2025 — The Quarter of Agents, Gemini’s Crown & The Reasoning WarsThe densest quarter in AI history. Google took the throne with Gemini 3, OpenAI fired back with GPT-5.2, and agents became real products. Someone trained an LLM in space.Key Themes:* 🚀 Reasoning wars peaked (Gemini 3 → GPT-5.2 → DeepSeek gold medals)* 🤖 Agents became products (Atlas, AgentKit, ChatGPT Apps)* 👑 Google’s comeback (Gemini 3, Antigravity, Nano Banana)* 🏃 ASI race accelerated ($1.4T compute, 2028 autonomous researchers)* 🎬 Sora 2 launched AI-native social mediaOctober — Sora Changes Social Media Forever(Oct 03 | Oct 10 | Oct 17 | Oct 24 | Oct 30)Sora 2 — AI Social Media is Born (Oct 2):* Shot to #3 on iOS App Store within days* Cameos: upload your face, star in any video* Sam Altman shared his Cameo publicly, becoming the internet’s most meme-able person* All content is AI-generated — no uploads, only creations“This is the first social media with UGC where content can ONLY be generated” — Alex VolkovOpenAI Dev Day (Oct 9):* ChatGPT Apps for 800M+ weekly active users* AgentKit: drag-and-drop agent builder* GPT-5-Pro in API* Sam revealed $1.4 trillion in compute obligationsAI Makes Novel Cancer Discovery (Oct 16) — A 27B Gemma-based model generated a novel hypothesis about cancer cells validated in a wet lab. First confirmed case of AI creating genuinely new scientific knowledge.Claude Sonnet 4.5 — 61.4% OS World (computer use)Claude Haiku 4.5 — 73.3% SWE-Bench, lightning fastNovember — The Week That Changed Everything(Nov 07 | Nov 13 | Nov 20 | Nov 27)THE MOST INSANE WEEK IN AI HISTORY. In a single span of ~10 days:* Grok 4.1 — #1 LMArena (briefly)* Gemini 3 Pro — Took the throne with 45.14% on ARC-AGI-2 (Deep Think)* GPT-5.1-Codex-Max — 24+ hour autonomous coding* Nano Banana Pro — 4K image generation with perfect text rendering* Meta SAM 3 & SAM 3D — Open-vocabulary segmentation* Claude Opus 4.5 — 80.9% SWE-Bench Verified, beats GPT-5.1“This week almost broke me as a person whose full-time job is to cover and follow AI releases.” — Alex VolkovGemini 3 Pro + Deep Think (Nov 20) — Google finally took the LLM throne: 45.14% on ARC-AGI-2, roughly double previous SOTA.Google Antigravity IDE (Nov 20) — Free agent-first VS Code fork with browser integration, multiple parallel agents.Nano Banana Pro (Nov 20) — Native 4K resolution with “thinking” traces, perfect text rendering.Claude Opus 4.5 (Nov 27) — 80.9% SWE-Bench Verified. $5/$25 per MTok (1/3 previous cost). “Effort” parameter for reasoning control.“Opus 4.5 is unbelievable. You can ship a full feature on a mature code base in one day, always. It’s just mind blowing.” — Ryan Carson1X NEO (Oct 30) — First consumer humanoid robot, pre-orders at $20,000, delivery early 2026.December — GPT-5.2 Fires Back(Dec 02 | Dec 05 | Dec 12 | Dec 19)GPT-5.2 — OpenAI’s Answer to Gemini 3 (Dec 11) — Dropped live during ThursdAI:* 90.5% on ARC-AGI-1 (Pro X-High configuration)* 54%+ on ARC-AGI-2 — reclaiming frontier from Gemini 3* 100% on AIME 2025 — perfect math olympiad score* 70% on GDPval (up from 47% in Sept!)* Reports of models thinking for 1-3 hours on hard problemsDeepSeek V3.2 & V3.2-Speciale — Gold Medal Reasoning (Dec 4):* 96% on AIME (vs 94% for GPT-5 High)* Gold medals on IMO (35/42), CMO, ICPC (10/12), IOI (492/600)* $0.28/million tokens on OpenRouterMCP Donated to Linux Foundation (Dec 11) — Agentic AI Foundation launched under Linux Foundation. MCP, AGENTS.md, and goose donated to vendor-neutral governance.Mistral 3 Returns to Apache 2.0 (Dec 4) — Mistral Large 3 (675B MoE), Ministral 3 (vision, edge-optimized).Starcloud: LLM Training in Space (Dec 11) — An H100 satellite trained nanoGPT on Shakespeare. SSH into an H100… in space… with a US flag in the corner.“Peak 2025 energy — the era of weird infra ideas has begun.” — Karpathy reactsGemini 3 Flash (Dec 18) — Fastest frontier model, pairs with Gemini 3 Pro for speed vs depth tradeoffs.🙏 Thank YouThis has been an incredible year of ThursdAI. 51 episodes, countless releases, and a community that keeps showing up every week to make sense of the madness together.Huge thanks to our amazing co-hosts and friends of the pod:* Alex Volkov — AI Evangelist, Weights & Biases (@altryne)* Wolfram Ravenwolf (@WolframRvnwlf)* Yam Peleg (@yampeleg)* Nisten Tahiraj (@nisten)* LDJ (@ldjconfirmed)* Ryan Carson (@ryancarson)* Kwindla Hultman Kramer — CEO of Daily (@kwindla)And to everyone who tunes in — whether you’re listening on your commute, doing dishes, or just trying to keep up with the insanity — thank you. You make this possible.📢 Stay Connected* 🎧 Subscribe: thursdai.news* 🐦 Follow Alex: @altryne* 💻 This recap is open source: github.com/altryne/thursdAI_yearly_recap“We’re living through the early days of a technological revolution, and we get to be part of it. That’s something to be genuinely thankful for.” — Alex VolkovHappy Holidays, and see you in 2026! 🚀The best is yet to come. Hold on to your butts. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey folks 👋 Alex here, dressed as 🎅 for our pre X-mas episode!We’re wrapping up 2025, and the AI labs decided they absolutely could NOT let the year end quietly. This week was an absolute banger—we had Gemini 3 Flash dropping with frontier intelligence at flash prices, OpenAI firing off GPT 5.2 Codex as breaking news DURING our show, ChatGPT Images 1.5, Nvidia going all-in on open source with Nemotron 3 Nano, and the voice AI space heating up with Grok Voice and Chatterbox Turbo. Oh, and Google dropped FunctionGemma for all your toaster-to-fridge communication needs (yes, really).Today’s show was over three and a half hours long because we tried to cover both this week AND the entire year of 2025 (that yearly recap is coming next week—it’s a banger, we went month by month and you’ll really feel the acceleration). For now, let’s dive into just the insanity that was THIS week.00:00 Introduction and Overview00:39 Weekly AI News Highlights01:40 Open Source AI Developments01:44 Nvidia's Nemotron Series09:09 Google's Gemini 3 Flash19:26 OpenAI's GPT Image 1.520:33 Infographic and GPT Image 1.5 Discussion20:53 Nano Banana vs GPT Image 1.521:23 Testing and Comparisons of Image Models23:39 Voice and Audio Innovations24:22 Grok Voice and Tesla Integration26:01 Open Source Robotics and Voice Agents29:44 Meta's SAM Audio Release32:14 Breaking News: Google Function Gemma33:23 Weights & Biases Announcement35:19 Breaking News: OpenAI Codex 5.2 MaxTo receive new posts and support my work, consider becoming a free or paid subscriber.Big Companies LLM updatesGoogle’s Gemini 3 Flash: The High-Speed Intelligence KingIf we had to title 2025, as Ryan Carson mentioned on the show, it might just be “The Year of Google’s Comeback.” Remember at the start of the year when we were asking “Where is Google?” Well, they are here. Everywhere.This week they launched Gemini 3 Flash, and it is rightfully turning heads. This is a frontier-class model—meaning it boasts Pro-level intelligence—but it runs at Flash-level speeds and, most importantly, Flash-level pricing. We are talking $0.50 per 1 million input tokens. That is not a typo. The price-to-intelligence ratio here is simply off the charts.I’ve been using Gemini 2.5 Flash in production for a while because it was good enough, but Gemini 3 Flash is a different beast. It scores 71 on the Artificial Analysis Intelligence Index (a 13-point jump from the previous Flash), and it achieves 78% on SWE-bench Verified. That actually beats the bigger Gemini 3 Pro on some agentic coding tasks!What impressed me most, and something Kwindla pointed out, is the tool calling. Previous Gemini models sometimes struggled with complex tool use compared to OpenAI, but Gemini 3 Flash can handle up to 100 simultaneous function calls. It’s fast, it’s smart, and it’s integrated immediately across the entire Google stack—Workspace, Android, Chrome. Google isn’t just releasing models anymore; they are deploying them instantly to billions of users.For anyone building agents, this combination of speed, low latency, and 1 million context window (at this price!) makes it the new default workhorse.Google’s FunctionGemma Open Source releaseWe also got a smaller, quirkier release from Google: FunctionGemma. This is a tiny 270M parameter model. Yes, millions, not billions.It’s purpose-built for function calling on edge devices. It requires only 500MB of RAM, meaning it can run on your phone, in your browser, or even on a Raspberry Pi. As Nisten joked on the show, this is finally the model that lets your toaster talk to your fridge.Is it going to write a novel? No. But after fine-tuning, it jumped from 58% to 85% accuracy on mobile action tasks. This represents a future where privacy-first agents live entirely on your device, handling your calendar and apps without ever pinging a cloud server.OpenAI Image 1.5, GPT 5.2 Codex and ChatGPT AppstoreOpenAI had a busy week, starting with the release of GPT Image 1.5. It’s available now in ChatGPT and the API. The headline here is speed and control—it’s 4x faster than the previous model and 20% cheaper. It also tops the LMSYS Image Arena leaderboards.However, I have to give a balanced take here. We’ve been spoiled recently by Google’s “Nano Banana Pro” image generation (which powers Gemini). When we looked at side-by-side comparisons, especially with typography and infographic generation, Gemini often looked sharper and more coherent. This is what we call “hedonistic adaptation”—GPT Image 1.5 is great, but the bar has moved so fast that it doesn’t feel like the quantum leap DALL-E 3 was back in the day. Still, for production workflows where you need to edit specific parts of an image without ruining the rest, this is a massive upgrade.🚨 BREAKING: GPT 5.2 CodexJust as we were nearing the end of the show, OpenAI decided to drop some breaking news: GPT 5.2 Codex.This is a specialized model optimized specifically for agentic coding, terminal workflows, and cybersecurity. We quickly pulled up the benchmarks live, and they look significant. It hits 56.4% on SWE-Bench Pro and a massive 64% on Terminal-Bench 2.0.It supports up to 400k token inputs with native context compaction, meaning it’s designed for those long, complex coding sessions where you’re debugging an entire repository. The coolest (and scariest?) stat: a security researcher used this model to find three previously unknown vulnerabilities in React in just one week.OpenAI is positioning this for “professional software engineering,” and the benchmarks suggest a 30% improvement in token efficiency over the standard GPT 5.2. We are definitely going to be putting this through its paces in our own evaluations soon.ChatGPT ... the AppStore!Also today (OpenAI is really throwing everything they have to the end of the year release party), OpenAI has unveiled how their App Store is going to look and opened the submission forms to submit your own apps!Reminder, ChatGPT apps are powered by MCP and were announced during DevDay, they let companies build a full UI experience right inside ChatGPT, and given OpenAi’s almost 900M weekly active users, this is a big deal! Do you have an app you’d like in there? let me know in the comments!Open Source AI🔥 Nvidia Nemotron 3 Nano: The Most Important Open Source Release of the Week (X, HF)I think the most important release of this week in open source was Nvidia Nemotron 3 Nano, and it was pretty much everywhere. Nemotron is a series of models from Nvidia that’s been pushing efficiency updates, finetune innovations, pruning, and distillations—all the stuff Nvidia does incredibly well.Nemotron 3 Nano is a 30 billion parameter model with only 3 billion active parameters, using a hybrid Mamba-MoE architecture. This is huge. The model achieves 1.5 to 3.3x faster inference than competing models like Qwen 3 while maintaining competitive accuracy on H200 GPUs.But the specs aren’t even the most exciting part. NVIDIA didn’t just dump the weights over the wall. They released the datasets—all 25 trillion tokens of pre-training and post-training data. They released the recipes. They released the technical reports. This is what “Open AI” should actually look like.What’s next? Nemotron 3 Super at 120B parameters (4x Nano) and Nemotron 3 Ultra at 480B parameters (16x Nano) are coming in the next few months, featuring their innovative Latent Mixture of Experts architecture.Check out the release on HuggingFaceOther Open Source HighlightsLDJ brought up BOLMO from Allen AI—the first byte-level model that actually reaches parity with similar-size models using regular tokenization. This is really exciting because it could open up new possibilities for spelling accuracy, precise code editing, and potentially better omnimodality since ultimately everything is bytes—images, audio, everything.Wolfram highlighted OLMO 3.1, also from Allen AI, which is multimodal with video input in three sizes (4B, 7B, 8B). The interesting feature here is that you can give it a video, ask something like “how many times does a ball hit the crown?” and it’ll not only give you the answer but mark the precise coordinates on the video frames where it happens. Very cool for tracking objects throughout a video!Mistral OCR 3 (X)Mistral also dropped Mistral OCR 3 this week—their next-generation document intelligence model achieving a 74% win rate over OCR 2 across challenging document types. We’re talking forms, low-quality scans, handwritten text, complex tables, and multilingual documents.The pricing is aggressive at just $2 per 1,000 pages (or $1 with Batch API discount), and it outperforms enterprise solutions like AWS Textract, Azure Doc AI, and Google DocSeek. Available via API and their new Document AI Playground.🐝 This Week’s Buzz: Wolfram Joins Weights & Biases!I am so, so hyped to announce this. Our very own co-host and evaluation wizard, Wolfram RavenWlf, is officially joining the Weights & Biases / CoreWeave family as an AI Evangelist and “AIvaluator” starting in January!Wolfram has been the backbone of the “vibe checks” and deep-dive evals on this show for a long time. Now, he’ll be doing it full-time, building out benchmarks for the community and helping all of us make sense of this flood of models. Expect ThursdAI to get even more data-driven in 2026. Match made in heaven! And if you’re as excited as we are, give Weave a try, it’s free to get started!Voice & Audio: Faster, Cheaper, BetterIf 2025 was the year of the LLM comeback, the end of 2025 is the era of Voice AI commoditization. It is getting so cheap and so fast.Grok Voice Agent API (X)xAI launched their Grok Voice Agent API, and the pricing is aggressive: $0.05 per minute flat rate. That significantly undercuts OpenAI and others. But the real killer feature here is the integration.If you drive a Tesla, this is what powers the voice command when you hold down the button. It has native access to vehicle controls, but for developers, it has native tool calling for Real-time X Search. This means your voice agent can have up-to-the-minute knowledge about the world, something purely pre-trained models struggle with. It ranks #1 on Big Bench Audio, and with that pricing, we’re going to see voice ubiquity very soon.Kwindla had great insights here: it feels like they optimized for the Tesla use case where it’s a question and an answer. You can see this because Big Bench Audio is a hard audio Q&A benchmark but not multi-turn. So it’s super exciting, but it’s not necessarily what we’ll use for multi-turn conversational voice agents yet.Here’s what’s really interesting: the entire voice stack was built in-house with custom VAD, tokenizer, and audio models for end-to-end optimization. Tesla was a critical design partner—Grok now powers millions of Tesla vehicles. If you’re building AI voice agents, will you give Grok Voice SDK a try?Resemble AI’s Chatterbox Turbo (X, HF, GitHub, Blog)For the open-source heads, Resemble AI dropped a bombshell with Chatterbox Turbo. This is a 350M parameter open-source TTS model that is beating proprietary giants like ElevenLabs in blind tests.It allows for zero-shot voice cloning from just 5 seconds of audio and supports paralinguistic tags—meaning you can type [laugh] or [sigh]and the model actually acts it out naturally. Plus, it has built-in watermarking for safety. It’s MIT licensed, so you can run this yourself. The fact that an open model is winning on quality against the paid APIs is a huge moment for the community.Meta SAM AudioFinally, Meta extended their “Segment Anything” magic to audio with SAM Audio. You know how you can click an object in an image to select it? Now you can do that with sound.With Sam Audio, you could isolate just the sound of a train from a messy audio track, or pick out a specific instrument from a song. You can prompt it with text (”guitar”), visual clicks on a video, or time stamps. It’s incredible for creators and audio engineers, effectively automating what used to be painful manual editing.Wrapping UpWhat a week to close out 2025. Google proved once again that they’re the gorilla that’s learned to dance—Gemini 3 Flash delivering frontier intelligence at flash prices is going to change how people build AI applications. Nvidia showed that the most valuable company in the world is all-in on open source. OpenAI fired off GPT 5.2 Codex just to make sure we don’t forget about them. And the voice AI space is heating up with options that would have seemed impossible just a year ago.Look out for the full 2025 yearly recap episode coming next week—it’s a banger. We went month by month through every major AI release and talked about what we thought were the best overall. You’ll really feel the acceleration from that one.Happy holidays, folks! And as always, thanks for being part of the ThursdAI community.TL;DR and Show NotesHosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co-hosts: @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed, @ryancarson* Special Guest: @kwindla - CEO of DailyOpen Source LLMs* NVIDIA Nemotron 3 Nano - 30B-3A hybrid Mamba-MoE model (X, HF, HF FP8)* FunctionGemma - 270M parameter function calling model (X, Blog, Docs)* Mistral OCR 3 - Document intelligence model with 74% win rate over v2 (X, Blog, Console)* BOLMO from Allen AI - First byte-level model reaching parity with regular tokenization (X)* OLMO 2 from Allen AI - Multimodal with video input (4B, 7B, 8B sizes) (X)Big CO LLMs + APIs* Google Gemini 3 Flash - Frontier intelligence at $0.50/1M input tokens, 78% SWE-bench Verified (X, Announcement)* OpenAI GPT Image 1.5 - 4x faster, 20% cheaper, #1 on LMSYS Image Arena (X)* OpenAI GPT 5.2 Codex - 56.4% SWE-Bench Pro, 64% Terminal-Bench 2.0, 400K context (X, Blog)* ChatGPT App Store - MCP-powered apps submission now open (X)This Week’s Buzz* 🐝 Wolfram joins Weights & Biases / CoreWeave as AI Evangelist and AIvaluator!* Try Weave for AI evaluationsVoice & Audio* xAI Grok Voice Agent API - #1 Big Bench Audio (92.3%), $0.05/min flat rate, powers Tesla vehicles (X)* Resemble AI Chatterbox Turbo - MIT-licensed 350M TTS, beats ElevenLabs in blind tests (X, HF, GitHub, Blog)* Meta SAM Audio - Audio source separation with text/visual/temporal prompts (X, HF, GitHub)Show Links* Full 2025 Yearly Recap - Coming next week! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey everyone, December started strong and does NOT want to slow down!? OpenAI showed us their response to the Code Red and it’s GPT 5.2, which doesn’t feel like a .1 upgrade! We got it literally as breaking news at the end of the show, and oh boy! The new kind of LLMs is here. GPT, then Gemini, then Opus and now GPT again... Who else feels like we’re on a trippy AI rolercoaster? Just me? 🫨 I’m writing this newsletter from a fresh “traveling podcaster” setup in SF (huge shoutout to the Chroma team for the studio hospitality). P.S - Next week we’re doing a year recap episode (52st episode of the year, what is my life), but today is about the highest-signal stuff that happened this week.Alright. No more foreplay. Let’s dive in. Please subscribe. 🔥 The main event: OpenAI launches GPT‑5.2 (and it’s… a lot)We started the episode with “garlic in the air” rumors (OpenAI holiday launches always have that Christmas panic energy), and then… boom: GPT‑5.2 actually drops while we’re live.What makes this release feel significant isn’t “one benchmark went up.” It’s that OpenAI is clearly optimizing for the things that have become the frontier in 2025: long-horizon reasoning, agentic coding loops, long context reliability, and lower hallucination rates when browsing/tooling is involved.5.2 Instant, Thinking and Pro in ChatGPT and in the APIOpenAI shipped multiple variants, and even within those there are “levels” (medium/high/extra-high) that effectively change how much compute the model is allowed to burn. At the extreme end, you’re basically running parallel thoughts and selecting winners. That’s powerful, but also… very expensive.It’s very clearly aimed at the agentic world: coding agents that run in loops, tool-using research agents, and “do the whole task end-to-end” workflows where spending extra tokens is still cheaper than spending an engineer day.Benchmarks I’m not going to pretend benchmarks tell the full story (they never do), but the shape of improvements matters. GPT‑5.2 shows huge strength on reasoning + structured work.It hits 90.5% on ARC‑AGI‑1 in the Pro X‑High configuration, and 54%+ on ARC‑AGI‑2 depending on the setting. For context, ARC‑AGI‑2 is the one where everyone learns humility again.On math/science, this thing is flexing. We saw 100% on AIME 2025, and strong performance on FrontierMath tiers (with the usual “Tier 4 is where dreams go to die” vibe still intact). GPQA Diamond is up in the 90s too, which is basically “PhD trivia mode.”But honestly the most practically interesting one for me is GDPval (knowledge-work tasks: slides, spreadsheets, planning, analysis). GPT‑5.2 lands around 70%, which is a massive jump vs earlier generations. This is the category that translates directly into “is this model useful at my job.” - This is a bench that OpenAI launched only in September and back then, Opus 4.1 was a “measly” 47%! Talk about acceleration! Long context: MRCR is the sleeper highlightOn MRCR (multi-needle long-context retrieval), GPT‑5.2 holds up absurdly well even into 128k and beyond. The graph OpenAI shared shows GPT‑5.1 falling off a cliff as context grows, while GPT‑5.2 stays high much deeper into long contexts.If you’ve ever built a real system (RAG, agent memory, doc analysis) you know this pain: long context is easy to offer, hard to use well. If GPT‑5.2 actually delivers this in production, it’s a meaningful shift.Hallucinations: down (especially with browsing)One thing we called out on the show is that a bunch of user complaints in 2025 have basically collapsed into one phrase: “it hallucinates.” Even people who don’t know what a benchmark is can feel when a model confidently lies.OpenAI’s system card shows lower rates of major incorrect claims compared to GPT‑5.1, and lower “incorrect claims” overall when browsing is enabled. That’s exactly the direction they needed.Real-world vibes:We did the traditional “vibe tests” mid-show: generate a flashy landing page, do a weird engineering prompt, try some coding inside Cursor/Codex.Early testers broadly agree on the shape of the improvement. GPT‑5.2 is much stronger in reasoning, math, long‑context tasks, visual understanding, and multimodal workflows, with multiple reports of it successfully thinking for one to three hours on hard problems. Enterprise users like Box report faster execution and higher accuracy on real knowledge‑worker tasks, while researchers note that GPT‑5.2 Pro consistently outperforms the standard “Thinking” variant. The tradeoffs are also clear: creative writing still slightly favors Claude Opus, and the highest reasoning tiers can be slow and expensive. But as a general‑purpose reasoning model, GPT‑5.2 is now the strongest publicly available option.AI in space: Starcloud trains an LLM on an H100 in orbitThis story is peak 2025.Starcloud put an NVIDIA H100 on a satellite, trained Andrej Karpathy’s nanoGPT on Shakespeare, and ran inference on Gemma. There’s a viral screenshot vibe here that’s impossible to ignore: SSH into an H100… in space… with a US flag in the corner. It’s engineered excitement, and I’m absolutely here for it.But we actually had a real debate on the show: is “GPUs in space” just sci‑fi marketing, or does it make economic sense?Nisten made a compelling argument that power is the real bottleneck, not compute, and that big satellites already operate in the ~20kW range. If you can generate that power reliably with solar in orbit, the economics start looking less insane than you’d think. LDJ added the long-term land/power convergence argument: Earth land and grid power get scarcer/more regulated, while launch costs trend down—eventually the curves may cross.I played “voice of realism” for a minute: what happens when GPUs fail? It’s hard enough to swap a GPU in a datacenter, now imagine doing it in orbit. Cooling and heat dissipation become a different engineering problem too (radiators instead of fans). Networking is nontrivial. But also: we are clearly entering the era where people will try weird infra ideas because AI demand is pulling the whole economy.Big Company: MCP gets donated, OpenRouter drops a report on AIAgentic AI Foundation Lands at the Linux FoundationThis one made me genuinely happy.Block, Anthropic, and OpenAI came together to launch the Agentic AI Foundation under the Linux Foundation, donating key projects like MCP, AGENTS.md, and goose. This is exactly how standards should happen: vendor‑neutral, boring governance, lots of stakeholders.It’s not flashy work, but it’s the kind of thing that actually lets ecosystems grow without fragmenting. BTW, I was recording my podcast while Latent.Space were recording theirs in the same office, and they have a banger episode upcoming about this very topic! All I’ll say is Alessio Fanelli introduced me to David Soria Parra from MCP 👀 Watch out for that episode on Latent space dropping soon! OpenRouter’s “State of AI”: 100 Trillion Tokens of RealityOpenRouter and a16z dropped a massive report analyzing over 100 trillion tokens of real‑world usage. A few things stood out:Reasoning tokens now dominate. Above 50%, around 60% of all tokens since early 2025 are reasoning tokens. Remember when we went from “LLMs can’t do math” to reasoning models? That happened in about a year.Programming exploded. From 11% of usage early 2025 to over 50% recently. Claude holds 60% of the coding market. (at least.. on Open Router)Open source hit 30% market share, led by Chinese labs: DeepSeek (14T tokens), Qwen (5.59T), Meta LLaMA (3.96T).Context lengths grew massively. Average prompt length went from 1.5k to 6k+ tokens (4x growth), completions from 133 to 400 tokens (3x).The “Glass Slipper” effect. When users find a model that fits their use case, they stay loyal. Foundational early-user cohorts retain around 40% at month 5. Claude 4 Sonnet still had 50% retention after three months.Geography shift. Asia doubled to 31% of usage (China key), while North America is at 47%.Yam made a good point that we should be careful interpreting these graphs—they’re biased toward people trying new models, not necessarily steady usage. But the trends are clear: agentic, reasoning, and coding are the dominant use cases.Open Source Is Not Slowing Down (If Anything, It’s Accelerating)One of the strongest themes this week was just how fast open source is closing the gap — and in some areas, outright leading. We’re not talking about toy demos anymore. We’re talking about serious models, trained from scratch, hitting benchmarks that were frontier‑only not that long ago.Essential AI’s Rnj‑1: A Real Frontier 8B ModelThis one deserves real attention. Essential AI — led by Ashish Vaswani, yes Ashish from the original Transformers paper — released Rnj‑1, a pair of 8B open‑weight models trained fully from scratch. No distillation. No “just a fine‑tune.” This is a proper pretrain.What stood out to me isn’t just the benchmarks (though those are wild), but the philosophy. Rnj‑1 is intentionally focused on pretraining quality: data curation, code execution simulation, STEM reasoning, and agentic behaviors emerging during pretraining instead of being bolted on later with massive RL pipelines.In practice, that shows up in places like SWE‑bench Verified, where Rnj‑1 lands in the same ballpark as much larger closed models, and in math and STEM tasks where it punches way above its size. And remember: this is an 8B model you can actually run locally, quantize aggressively, and deploy without legal gymnastics thanks to its Apache 2.0 license.Mistral Devstral 2 + Vibe: Open Coding Goes HardMistral followed up last week’s momentum with Devstral 2, and Mistral Vibe! The headline numbers are: the 123B Devstral 2 model lands right at the top of open‑weight coding benchmarks, nearly matching Claude 3.5 Sonnet on SWE‑bench Verified. But what really excited the panel was the 24B Devstral Small 2, which hits high‑60s SWE‑bench scores while being runnable on consumer hardware.This is the kind of model you can realistically run locally as a coding agent, without shipping your entire codebase off to someone else’s servers. Pair that with Mistral Vibe, their open‑source CLI agent, and you suddenly have a credible, fully open alternative to things like Claude Code, Codex, or Gemini CLI.We talked a lot about why this matters. Some teams can’t send code to closed APIs. Others just don’t want to pay per‑token forever. And some folks — myself included — just like knowing what’s actually running under the hood. Devstral 2 checks all those boxes.🐝 This week’s Buzz (W&B): Trace OpenRouter traffic into Weave with zero codeWe did a quick “Buzz” segment on a feature that I think a lot of builders will love:OpenRouter launched Broadcast, which can stream traces to observability vendors. One of those destinations is W&B Weave.The magic here is: if you’re using a tool that already talks to OpenRouter, you can get tracing into Weave without instrumenting your code. That’s especially useful when instrumentation is hard (certain agent frameworks, black-box tooling, restricted environments, etc.).If you want to set it up: OpenRouter Broadcast settings.Vision Models Are Getting Practical (and Weirdly Competitive)Vision‑language models quietly had a massive week.Jina‑VLM: Small, Multilingual, and Very Good at DocsJina released a 2.4B VLM that’s absolutely dialed in on document understanding, multilingual VQA, and OCR‑heavy tasks. This is exactly the kind of model you’d want for PDFs, charts, scans, and messy real‑world docs — and it’s small enough to deploy without sweating too much.Z.ai GLM‑4.6V: Long Context, Tool Calling, Serious Agent PotentialZ.ai’s GLM‑4.6V impressed us with its 128K context, native tool calling from vision inputs, and strong performance on benchmarks like MathVista and WebVoyager. It’s one of the clearest examples yet of a VLM that’s actually built for agentic workflows, not just answering questions about images.That said, I did run my unofficial “bee counting test” on it… and yeah, Gemini still wins there 😅Perceptron Isaac 0.2: Tiny Models, Serious PerceptionPerceptron’s Isaac 0.2 (1B and 2B variants) showed something I really like seeing: structured outputs, focus tools, and reliability in very small models. Watching a 2B model correctly identify, count, and point to objects in an image is still wild to me.These are the kinds of models that make physical AI, robotics, and edge deployments actually feasible.🧰 Tools: Cursor goes visual, and Google Stitch keeps getting scarier (in a good way)Cursor: direct visual editing inside the codebaseCursor shipped a new feature that lets you visually manipulate UI elements—click/drag/resize—directly in the editor. We lumped this under “tools” because it’s not just a nicety; it’s the next step in “IDE as design surface.”Cursor is also iterating fast on debugging workflows. The meta trend: IDEs are turning into agent platforms, not text editors.Stitch by Google: Gemini 3 Pro as default, plus clickable prototypesI showed Stitch on the show because it’s one of the clearest examples of “distribution beats raw capability.”Stitch (Google’s product born from the Galileo AI acquisition) is doing Shipmas updates and now defaults to “Thinking with Gemini 3 Pro.” It can generate complex UIs, export them, and even stitch multiple screens into prototypes. The killer workflow is exporting directly into AI Studio / agent tooling so you can go from UI idea → code → repo without playing copy-paste Olympics.Site: https://stitch.withgoogle.com🎬 Disney invests $1B into OpenAI (and Sora gets Disney characters)This is the corporate story that made me do a double take.Disney—arguably the most IP-protective company on Earth—is investing $1B into OpenAI and enabling use of Disney characters in Sora. That’s huge. It signals the beginning of a more explicit “licensed synthetic media” era, where major IP holders decide which model vendors get official access.It also raises the obvious question: does Disney now go harder against other model providers that generate Disney-like content without permission?We talked about how weird the timing is too, given Disney has also been sending legal pressure in the broader space. The next year of AI video is going to be shaped as much by licensing and distribution as by model quality.Closing thoughts: the intelligence explosion is loud, messy, and acceleratingThis episode had everything: open-source models catching up fast, foundation-level standardization around agents, a usage report that shows what developers actually do with LLMs, voice models getting dramatically better, and OpenAI shipping what looks like a serious “we’re not losing” answer to Gemini 3.And yes: we’re also apparently putting GPUs in space.Next week’s episode is our year recap, and—of course—we now have to update it because GPT‑5.2 decided to show up like the final boss.If you missed any part of the show, check out the chapters in the podcast feed and jump around. See you next week.TL;DR + Show Notes (links for everything)Hosts* Alex Volkov — AI Evangelist @ Weights & Biases: @altryne. I host ThursdAI and spend an unhealthy amount of time trying to keep up with this firehose of releases.* Co-hosts — @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed. Each of them brings a different “lens” (agents, infra, evaluation, open source, tooling), and it’s why the show works.Open Source LLMs* Essential AI — RNJ‑1 (8B base + instruct): tweet, blog, HF instruct, HF base. This is a from-scratch open pretrain led by Ashish Vaswani, and it’s one of the most important “Western open model” signals we’ve seen in a while.* Mistral — Devstral 2 + Devstral Small 2 + Mistral Vibe: tweet, Devstral Small 2 HF, Devstral 2 HF, news, mistral-vibe GitHub. Devstral is open coding SOTA territory, and Vibe is Mistral’s swing at the CLI agent layer.AI in Space* Starcloud trains and runs an LLM in orbit on an H100: Philip Johnston, Adi Oltean, CNBC, Karpathy reaction. A satellite H100 trained nanoGPT on Shakespeare and ran Gemma inference, igniting a real debate about power, cooling, repairability, and future orbital compute economics.Putnam Math Competition* Nous Research — Nomos 1 (Putnam scoring run): tweet, HF, GitHub harness, Hillclimb. This is a strong open-weight math reasoning model plus an open harness, and it shows how orchestration matters as much as raw weights.* Axiom — AxiomProver formal Lean proofs on Putnam: tweet, repo. Formal proofs are the “no excuses” version of math reasoning, and this is a serious milestone even if you argue about exact framing.Big Company LLMs + APIs* OpenAI — GPT‑5.2 release: Alex tweet, OpenAI announcement, ARC Prize verification, Sam Altman tweet. GPT‑5.2 brings major jumps in reasoning, long context, and agentic workflows, and it’s clearly positioned as an answer to the Gemini 3 era.* OpenRouter x a16z — State of AI report (100T+ tokens): tweet, landing page, PDF. The report highlights the dominance of programming/agents, the rise of reasoning tokens, and real-world usage patterns that explain why everyone is shipping agent harnesses.* Agentic AI Foundation under Linux Foundation (AAIF): Goose tweet, Block blog, aaif.io, Linux Foundation tweet. MCP + AGENTS.md + Goose moving into vendor-neutral governance is huge for interoperability and long-term ecosystem stability.* Disney invests $1B into OpenAI / Sora characters: (covered on the show as a major IP + distribution moment). This is an early signal of licensed synthetic media becoming a first-class business line rather than a legal gray zone.This week’s Buzz (W&B)* OpenRouter Broadcast → W&B Weave tracing: Broadcast settings. You can trace OpenRouter-based traffic into Weave with minimal setup, which is especially useful when you can’t (or don’t want to) instrument code directly.Vision & Video* Jina — jina‑VLM (2.4B): tweet, arXiv, HF, blog. A compact multilingual VLM optimized for doc understanding and VQA.* Z.ai — GLM‑4.6V + Flash: tweet, HF collection, GLM‑4.6V, Flash, blog. Strong open VLMs with tool calling and long context, even if my bee counting test still humbled it.* Perceptron — Isaac 0.2 (1B/2B): tweet, HF 2B, HF 1B, blog, demo. The Focus/zoom tooling and structured outputs point toward “VLMs as reliable perception modules,” not just chatty describers.Voice & Audio* Google DeepMind — Gemini 2.5 TTS (Flash + Pro): AI Studio tweet, GoogleAI devs tweet, blog, AI Studio speech playground. The key upgrades are control and consistency (emotion, pacing, multi-speaker) across many languages.* OpenBMB — VoxCPM 1.5: tweet, HF, GitHub. Open TTS keeps getting better, and this release is especially interesting for fine-tuning and voice cloning workflows.Tools* Cursor — direct visual editing (new UI workflow): (covered on the show as a major step toward “IDE as design surface”). Cursor continues to push the agentic IDE category into new territory.* Stitch by Google — Shipmas updates + Gemini 3 Pro “Thinking” + Prototypes: tweet 1, tweet 2, site, plus background articles: TechCrunch launch, acquisition detail. Stitch is turning prompt-to-UI into a full prototype-to-code pipeline with real export paths. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey yall, Alex here 🫡 Welcome to the first ThursdAI of December! Snow is falling in Colorado, and AI releases are falling even harder. This week was genuinely one of those “drink from the firehose” weeks where every time I refreshed my timeline, another massive release had dropped.We kicked off the show asking our co-hosts for their top AI pick of the week, and the answers were all over the map: Wolfram was excited about Mistral’s return to Apache 2.0, Yam couldn’t stop talking about Claude Opus 4.5 after a full week of using it, and Nisten came out of left field with an AWQ quantization of Prime Intellect’s model that apparently runs incredibly fast on a single GPU. As for me? I’m torn between Opus 4.5 (which literally fixed bugs that Gemini 3 created in my code) and DeepSeek’s gold-medal winning reasoning model.Speaking of which, let’s dive into what happened this week, starting with the open source stuff that’s been absolutely cooking. Open Source LLMsDeepSeek V3.2: The Whale Returns with Gold MedalsThe whale is back, folks! DeepSeek released two major updates this week: V3.2 and V3.2-Speciale. And these aren’t incremental improvements—we’re talking about an open reasoning-first model that’s rivaling GPT-5 and Gemini 3 Pro with actual gold medal Olympiad wins.Here’s what makes this release absolutely wild: DeepSeek V3.2-Speciale is achieving 96% on AIME versus 94% for GPT-5 High. It’s getting gold medals on IMO (35/42), CMO, ICPC (10/12), and IOI (492/600). This is a 685 billion parameter MOE model with MIT license, and it literally broke the benchmark graph on HMMT 2025—the score was so high it went outside the chart boundaries. That’s how you DeepSeek, basically.But it’s not just about reasoning. The regular V3.2 (not Speciale) is absolutely crushing it on agentic benchmarks: 73.1% on SWE-Bench Verified, first open model over 35% on Tool Decathlon, and 80.3% on τ²-bench. It’s now the second most intelligent open weights model and ranks ahead of Grok 4 and Claude Sonnet 4.5 on Artificial Analysis.The price is what really makes this insane: 28 cents per million tokens on OpenRouter. That’s absolutely ridiculous for this level of performance. They’ve also introduced DeepSeek Sparse Attention (DSA) which gives you 2-3x cheaper 128K inference without performance loss. LDJ pointed out on the show that he appreciates how transparent they’re being about not quite matching Gemini 3’s efficiency on reasoning tokens, but it’s open source and incredibly cheap.One thing to note: V3.2-Speciale doesn’t support tool calling. As Wolfram pointed out from the model card, it’s “designed exclusively for deep reasoning tasks.” So if you need agentic capabilities, stick with the regular V3.2.Check out the full release on Hugging Face or read the announcement.Mistral 3: Europe’s Favorite AI Lab Returns to Apache 2.0Mistral is back, and they’re back with fully open Apache 2.0 licenses across the board! This is huge news for the open source community. They released two major things this week: Mistral Large 3 and the Ministral 3 family of small models.Mistral Large 3 is a 675 billion parameter MOE with 41 billion active parameters and a quarter million (256K) context window, trained on 3,000 H200 GPUs. There’s been some debate about this model’s performance, and I want to address the elephant in the room: some folks saw a screenshot showing Mistral Large 3 very far down on Artificial Analysis and started dunking on it. But here’s the key context that Merve from Hugging Face pointed out—this is the only non-reasoning model on that chart besides GPT 5.1. When you compare it to other instruction-tuned (non-reasoning) models, it’s actually performing quite well, sitting at #6 among open models on LMSys Arena.Nisten checked LM Arena and confirmed that on coding specifically, Mistral Large 3 is scoring as one of the best open source coding models available. Yam made an important point that we should compare Mistral to other open source players like Qwen and DeepSeek rather than to closed models—and in that context, this is a solid release.But the real stars of this release are the Ministral 3 small models: 3B, 8B, and 14B, all with vision capabilities. These are edge-optimized, multimodal, and the 3B actually runs completely in the browser with WebGPU using transformers.js. The 14B reasoning variant achieves 85% on AIME 2025, which is state-of-the-art for its size class. Wolfram confirmed that the multilingual performance is excellent, particularly for German.There’s been some discussion about whether Mistral Large 3 is a DeepSeek finetune given the architectural similarities, but Mistral claims these are fully trained models. As Nisten noted, even if they used similar architecture (which is Apache 2.0 licensed), there’s nothing wrong with that—it’s an excellent architecture that works. Lucas Atkins later confirmed on the show that “Mistral Large looks fantastic... it is DeepSeek through and through architecture wise. But Kimi also does that—DeepSeek is the GOAT. Training MOEs is not as easy as just import deepseak and train.”Check out Mistral Large 3 and Ministral 3 on Hugging Face.Arcee Trinity: US-Trained MOEs Are BackWe had Lucas Atkins, CTO of Arcee AI, join us on the show to talk about their new Trinity family of models, and this conversation was packed with insights about what it takes to train MOEs from scratch in the US.Trinity is a family of open-weight MOEs fully trained end-to-end on American infrastructure with 10 trillion curated tokens from Datology.ai. They released Trinity-Mini (26B total, 3B active) and Trinity-Nano-Preview (6B total, 1B active), with Trinity-Large (420B parameters, 13B active) coming in mid-January 2026.The benchmarks are impressive: Trinity-Mini hits 84.95% on MMLU (0-shot), 92.1% on Math-500, and 65% on GPQA Diamond. But what really caught my attention was the inference speed—Nano generates at 143 tokens per second on llama.cpp, and Mini hits 157 t/s on consumer GPUs. They’ve even demonstrated it running on an iPhone via MLX Swift.I asked Lucas why it matters where models come from, and his answer was nuanced: for individual developers, it doesn’t really matter—use the best model for your task. But for Fortune 500 companies, compliance and legal teams are getting increasingly particular about where models were trained and hosted. This is slowing down enterprise AI adoption, and Trinity aims to solve that.Lucas shared a fascinating insight about why they decided to do full pretraining instead of just post-training on other people’s checkpoints: “We at Arcee were relying on other companies releasing capable open weight models... I didn’t like the idea of the foundation of our business being reliant on another company releasing models.” He also dropped some alpha about Trinity-Large: they’re going with 13B active parameters instead of 32B because going sparser actually gave them much faster throughput on Blackwell GPUs.The conversation about MOEs being cheaper for RL was particularly interesting. Lucas explained that because MOEs are so inference-efficient, you can do way more rollouts during reinforcement learning, which means more RL benefit per compute dollar. This is likely why we’re seeing labs like MiniMax go from their original 456B/45B-active model to a leaner 220B/10B-active model—they can get more gains in post-training by being able to do more steps.Check out Trinity-Mini and Trinity-Nano-Preview on Hugging Face, or read The Trinity Manifesto.OpenAI Code Red: Panic at the Disco (and Garlic?)It was ChatGPT’s 3rd birthday this week (Nov 30th), but the party vibes seem… stressful. Reports came out that Sam Altman has declared a “Code Red” at OpenAI.Why? Gemini 3.The user numbers don’t lie. ChatGPT apparently saw a 6% drop in daily active users following the Gemini 3 launch. Google’s integration is just too good, and their free tier is compelling.In response, OpenAI has supposedly paused “side projects” (ads, shopping bots) to focus purely on model intelligence and speed. Rumors point to a secret model codenamed “Garlic”—a leaner, more efficient model that beats Gemini 3 and Claude Opus 4.5 on coding reasoning, targeting a release in early 2026 (or maybe sooner if they want to save Christmas).Wolfram and Yam nailed the sentiment here: Integration wins. Wolfram’s family uses Gemini because it’s right there on the Pixel, controlling the lights and calendar. OpenAI needs to catch up not just on IQ, but on being helpful in the moment.Post the live show, OpenAI also finally added GPT 5.1 Codex Max we covered 2 weeks ago to their API and it’s now available in Cursor, for free, until Dec 11! Amazon Nova 2: Enterprise Push with Serious Agentic ChopsAmazon came back swinging with Nova 2, and the jump on Artificial Analysis is genuinely impressive—from around 30% to 61% on their index. That’s a massive improvement.The family includes Nova 2 Lite (7x cheaper, 5x faster than Nova Premier), Nova 2 Pro (93% on τ²-Bench Telecom, 70% on SWE-Bench Verified), Nova 2 Sonic (speech-to-speech with 1.39s time-to-first-audio), and Nova 2 Omni (unified text/image/video/speech with 1M token context window—you can upload 90 minutes of video!).Gemini 3 Deep Think ModeGoogle launched Gemini 3 Deep Think mode exclusively for AI Ultra subscribers, and it’s hitting some wild benchmarks: 45.1% on ARC-AGI-2 (a 2x SOTA leap using code execution), 41% on Humanity’s Last Exam, and 93.8% on GPQA Diamond. This builds on their Gemini 2.5 variants that earned gold medals at IMO and ICPC World Finals. The parallel reasoning approach explores multiple hypotheses simultaneously, but it’s compute-heavy—limited to 10 prompts per day at $77 per ARC-AGI-2 task.This Week’s Buzz: Mid-Training Evals are Here!A huge update from us at Weights & Biases this week: We launched LLM Evaluation Jobs. (Docs)If you are training models or finetuning, you usually wait until the end to run your expensive benchmarks. Now, directly inside W&B, you can trigger evaluations on mid-training checkpoints.It integrates with Inspect Evals (over 100+ public benchmarks). You just point it to your checkpoint or an API endpoint (even OpenRouter!), select the evals (MMLU-Pro, GPQA, etc.), and we spin up the managed GPUs to run it. You get a real-time leaderboard of your runs vs. the field.Also, a shoutout to users of Neptune.ai—congrats on the acquisition by OpenAI, but since the service is shutting down, we have built a migration script to help you move your history over to W&B seamlessly. We aren’t going anywhere!Video & Vision: Physics, Audio, and SpeedThe multimodal space was absolutely crowded this week.Runway Gen 4.5 (”Whisper Thunder”)Runway revealed that the mysterious “Whisper Thunder” model topping the leaderboards is actually Gen 4.5. The key differentiator? Physics and Multi-step adherence. It doesn’t have that “diffusion wobble” anymore. We watched a promo video where the shot changes every 3-4 seconds, and while it’s beautiful, it shows we still haven’t cracked super long consistent takes yet. But for 8-second clips? It’s apparently the new SOTA.Kling 2.6: Do you hear that?Kling hit back with Video 2.6, and the killer feature is Native Audio. I generated a clip of two people arguing, and the lip sync was perfect. Not “dubbed over” perfect, but actively generated with the video. It handles multi-character dialogue, singing, and SFX. It’s huge for creators.Kling was on a roll this week, releasing not one, but two Video Models (O1 Video is an omni modal one that takes Text, Images and Audio as inputs) and O1 Image and Kling Avatar 2.0 are also great updates! (Find all their releases on X)P-Image: Sub-Second Generation at Half a CentLast week we talked about ByteDance’s Z-Image, which was super cool and super cheap. Well, this week Pruna AI came out with P-Image, which is even faster and cheaper: image generation under one second for $0.005, and editing under one second for $0.01.I built a Chrome extension this week (completely rewritten by Opus 4.5, by the way—more on that in a second) that lets me play with these new image models inside the Infinite Craft game. When I tested P-Image Turbo against Z-Image, I was genuinely impressed by the quality at that speed. If you want quick iterations before moving to something like Nano Banana Pro for final 4K output, these sub-second models are perfect.The extension is available on GitHub if you want to try it—you just need to add your Replicate or Fal API keys.SeeDream 4.5: ByteDance Levels UpByteDance also launched SeeDream 4.5 in open beta, with major improvements in detail fidelity, spatial reasoning, and multi-image reference fusion (up to 10 inputs for consistent storyboards). The text rendering is much sharper, and it supports multilingual typography including Japanese. Early tests show it competing well with Nano Banana Pro in prompt adherence and logic.🎤 Voice & AudioMicrosoft VibeVoice-Realtime-0.5BIn a surprise drop, Microsoft open-sourced VibeVoice-Realtime-0.5B, a compact TTS model optimized for real-time applications. It delivers initial audible output in just 300 milliseconds while generating up to 10 minutes of speech. The community immediately started creating mirrors because, well, Microsoft has a history of releasing things on Hugging Face and then having legal pull them down. Get it while it’s hot!Use Cases: Code, Cursors, and “Antigravity”We wrapped up with some killer practical tips:* Opus 4.5 is a beast: As I mentioned, using Opus inside Cursor’s “Ask” mode is currently the supreme coding experience. It debugs logic flaws that Gemini misses completely. I also used Opus as a prompt engineer for my infographics, and it absolutely demolished GPT at creating the specific layouts I needed* Google’s Secret: Nisten dropped a bomb at the end of the show—Opus 4.5 is available for free inside Google’s Antigravity (and Colab)! If you want to try the model that’s beating GPT-5 without paying, go check Antigravity now before they patch it or run out of compute.* Microsoft VibeVoice: A surprise drop of a 0.5B speech model on HuggingFace that does real-time TTS (300ms latency). It was briefly questionable if it would stay up, but mirrors are already everywhere.That’s a wrap for this week, folks. Next week is probably going to be our final episode of the year, so we’ll be doing recaps and looking at our predictions from last year. Should be fun to see how wrong we were about everything!Thank you for tuning in. If you missed the live stream, subscribe to our Substack, YouTube, and wherever you get your podcasts. See you next Thursday!TL;DR and Show NotesHosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed* Guest - Lucas Atkins (@latkins) - CTO Arcee AIOpen Source LLMs* DeepSeek V3.2 and V3.2-Speciale - Gold medal olympiad wins, MIT license (X, HF V3.2, HF Speciale, Announcement)* Mistral 3 family - Large 3 and Ministral 3, Apache 2.0 (X, Blog, HF Large, HF Ministral)* Arcee Trinity - US-trained MOE family (X, HF Mini, HF Nano, Blog)* Hermes 4.3 - Decentralized training, SOTA RefusalBench (X, HF)Big CO LLMs + APIs* OpenAI Code Red - ChatGPT 3rd birthday, Garlic model in development (The Information)* Amazon Nova 2 - Lite, Pro, Sonic, and Omni models (X, Blog)* Gemini 3 Deep Think - 45.1% ARC-AGI-2 (X, Blog)* Cursor + GPT-5.1-Codex-Max - Free until Dec 11 (X, Blog)This Week’s Buzz* WandB LLM Evaluation Jobs - Evaluate any OpenAI-compatible API (X, Announcement)Vision & Video* Runway Gen-4.5 - #1 on text-to-video leaderboard, 1,247 Elo (X)* Kling VIDEO 2.6 - First native audio generation (X)* Kling O1 Image - Image generation (X)Voice & Audio* Microsoft VibeVoice-Realtime-0.5B - 300ms latency TTS (X, HF)AI Art & Diffusion* Pruna P-Image - Sub-second generation at $0.005 (X, Blog, Demo)* SeeDream 4.5 - Multi-reference fusion, text rendering (X) This is a public episode. 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Hey, Alex here, I recorded these conversations just in front of the AI Engineer auditorium, back to back, after these great folks gave their talks, and at the epitome of the most epic AI week we’ve seen since I started recording ThursdAI.This is less our traditional live recording, and more a real podcast-y conversation with great folks, inspired by Latent.Space. I hope you enjoy this format as much as I’ve enjoyed recording and editing it. AntiGravity with KevinKevin Hou and team just launched Antigravity, Google’s brand new Agentic IDE based on VSCode, and Kevin (second timer on ThursdAI) was awesome enough to hop on and talk about some of the product decisions they made, what makes Antigravity special and highlighted Artifacts as a completely new primitive. Gemini 3 in AI StudioIf you aren’t using Google’s AI Studio (ai.dev) then you’re missing out! We talk about AI Studio all the time on the show, and I’m a daily user! I generate most of my images with Nano Banana Pro in there, most of my Gemini conversations are happening there as well! Ammaar and Kat were so fun to talk to, as they covered the newly shipped “build mode” which allows you to vibe code full apps and experiences inside AI Studio, and we also covered Gemini 3’s features, multimodality understanding, UI capabilities. These folks gave a LOT of Gemini 3 demo’s so they know everything there is to know about this model’s capabilities! Tried new things with this one, multi camera angels, conversation with great folks, if you found this content valuable, please subscribe :) Topics Covered:* Inside Google’s new “AntiGravity” IDE* How the “Agent Manager” changes coding workflows* Gemini 3’s new multimodal capabilities* The power of “Artifacts” and dynamic memory* Deep dive into AI Studio updates & Vibe Coding* Generating 4K assets with Nano Banana ProTimestamps for your viewing convenience. 00:00 - Introduction and Overview01:13 - Conversation with Kevin Hou: Anti-Gravity IDE01:58 - Gemini 3 and Nano Banana Pro Launch Insights03:06 - Innovations in Anti-Gravity IDE06:56 - Artifacts and Dynamic Memory09:48 - Agent Manager and Multimodal Capabilities11:32 - Chrome Integration and Future Prospects20:11 - Conversation with Ammar and Kat: AI Studio Team21:21 - Introduction to AI Studio21:51 - What is AI Studio?22:52 - Ease of Use and User Feedback24:06 - Live Demos and Launch Week26:00 - Design Innovations in AI Studio30:54 - Generative UIs and Vibe Coding33:53 - Nano Banana Pro and Image Generation39:45 - Voice Interaction and Future Roadmap44:41 - Conclusion and Final ThoughtsLooking forward to seeing you on Thursday 🫡 P.S - I’ve recorded one more conversation during AI Engineer, and will be posting that soon, same format, very interesting person, look out for that soon! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey ya’ll, Happy Thanskgiving to everyone who celebrates and thank you for being a subscriber, I truly appreciate each and every one of you!Just wrapped up the third (1, 2) Thanksgiving special Episode of ThursdAI, can you believe November is almost over? We had another banger week in AI, with a full feast of AI released, Anthropic dropped the long awaited Opus 4.5, which quickly became the top coding LLM, DeepSeek resurfaced with a math model, BFL and Tongyi both tried to take on Nano Banana, and Microsoft dropped a 7B computer use model in Open Source + Intellect 3 from Prime Intellect! With so much news to cover, we also had an interview with Ido Sal & Liad Yosef (their second time on the show!) about MCP-Apps, the new standard they are spearheading together with Anthropic, OpenAI & more! Exciting episode, let’s get into it! (P.S - I started generating infographics, so the show became much more visual, LMK if you like them) ThursdAI - I put a lot of work on a weekly basis to bring you the live show, podcast and a sourced newsletter! Please subscribe if you find this content valuable!Anthropic’s Opus 4.5: The “Premier Intelligence” Returns (Blog)Folks, Anthropic absolutely cooked. After Sonnet and Haiku had their time in the sun, the big brother is finally back. Opus 4.5 launched this week, and it is reclaiming the throne for coding and complex agentic tasks.First off, the specs are monstrous. It hits 80.9% on SWE-bench Verified, topping GPT-5.1 (77.9%) and Gemini 3 Pro (76.2%). But the real kicker? The price! It is now $5 per million input tokens and $25 per million output—literally one-third the cost of the previous Opus.Yam, our resident coding wizard, put it best during the show: “Opus knows a lot of tiny details about the stack that you didn’t even know you wanted... It feels like it can go forever.” Unlike Sonnet, which sometimes spirals or loses context on extremely long tasks, Opus 4.5 maintains coherence deep into the conversation.Anthropic also introduced a new “Effort” parameter, allowing you to control how hard the model thinks (similar to o1 reasoning tokens). Set it to high, and you get massive performance gains; set it to medium, and you get Sonnet-level performance at a fraction of the token cost. Plus, they’ve added Tool Search (cutting enormous token overhead for agents with many tools) and Programmatic Tool Calling, which effectively lets Opus write and execute code loops to manage data.If you are doing heavy software engineering or complex automations, Opus 4.5 is the new daily driver.📱 The Agentic Web: MCP Apps & MCP-UI StandardSpeaking of MCP updates, Can you believe it’s been exactly one year since the Model Context Protocol (MCP) launched? We’ve been “MCP-pilled” for a while, but this week, the ecosystem took a massive leap forward.We brought back our friends Ido and Liad, the creators of MCP-UI, to discuss huge news: MCP-UI has been officially standardized as MCP Apps. This is a joint effort adopted by both Anthropic and OpenAI.Why does this matter? Until now, when an LLM used a tool (like Spotify or Zillow), the output was just text. It lost the brand identity and the user experience. With MCP Apps, agents can now render full, interactive HTML interfaces directly inside the chat! Ido and Liad explained that they worked hard to avoid an “iOS vs. Android” fragmentation war. Instead of every lab building their own proprietary app format, we now have a unified standard for the “Agentic Web.” This is how AI stops being a chatbot and starts being an operating system.Check out the standard at mcpui.dev.🦃 The Open Source Thanksgiving FeastWhile the big labs were busy, the open-source community decided to drop enough papers and weights to feed us for a month.Prime Intellect unveils INTELLECT-3, a 106B MoE (X, HF, Blog, Try It)Prime Intellect releases INTELLECT-3, a 106B parameter Mixture-of-Experts model (12B active params) based on GLM-4.5-Air, achieving state-of-the-art performance for its size—including ~90% on AIME 2024/2025 math contests, 69% on LiveCodeBench v6 coding, 74% on GPQA-Diamond reasoning, and 74% on MMLU-Pro—outpacing larger models like DeepSeek-R1. Trained over two months on 512 H200 GPUs using their fully open-sourced end-to-end stack (PRIME-RL async trainer, Verifiers & Environments Hub, Prime Sandboxes), it’s now hosted on Hugging Face, OpenRouter, Parasail, and Nebius, empowering any team to scale frontier RL without big-lab resources. Especially notable is their very detailed release blog, covering how a lab that previously trained 32B, finetunes a monster 106B MoE model! Tencent’s HunyuanOCR: Small but Mighty (X, HF, Github, Blog)Tencent released HunyuanOCR, a 1 billion parameter model that is absolutely crushing benchmarks. It scored 860 on OCRBench, beating massive models like Qwen3-VL-72B. It’s an end-to-end model, meaning no separate detection and recognition steps. Great for parsing PDFs, docs, and even video subtitles. It’s heavily restricted (no EU/UK usage), but technically impressive.Microsoft’s Fara-7B: On-Device Computer UseMicrosoft quietly dropped Fara-7B, a model fine-tuned from Qwen 2.5, specifically designed for computer use agentic tasks. It hits 73.5% on WebVoyager, beating OpenAI’s preview models, all while running locally on-device. This is the dream of a local agent that can browse the web for you, click buttons, and book flights without sending screenshots to the cloud.DeepSeek-Math-V2: open-weights IMO-gold math LLM (X, HF)DeepSeek-Math-V2 is a 685B-parameter, Apache-2.0 licensed, open-weights mathematical reasoning model claiming gold-medal performance on IMO 2025 and CMO 2024, plus a near-perfect 118/120 on Putnam 2024. Nisten did note some limitations—specifically that the context window can get choked up on extremely long, complex proofs—but having an open-weight model of this caliber is a gift to researchers everywhere.🐝 This Week’s Buzz: Serverless LoRA InferenceA huge update from us at Weights & Biases! We know fine-tuning is powerful, but serving those fine-tunes can be a pain and expensive. We just launched Serverless LoRA Inference.This means you can upload your small LoRA adapters (which you can train cheaply) to W&B Artifacts, and we will serve them instantly on CoreWeave GPUs on top of a base model. No cold starts, no dedicated expensive massive GPU instances for just one adapter.I showed a demo of a “Mocking SpongeBob” model I trained in 25 minutes. It just adds that SaRcAsTiC tExT style to the Qwen 2.5 base model. You pass the adapter ID in the API call, and boom—customized intelligence instantly. You can get more details HERE and get started with your own LORA in this nice notebook the team made! 🎨 Visuals: Image & Video Generation ExplosionFlux.2: The Multi-Reference Image Creator from BFL (X, HF, Blog)Black Forest Labs released Flux.2, a series of models including a 32B Flux 2[DEV]. The killer feature here is Multi-Reference Editing. You can feed it up to 10 reference images to maintain character consistency, style, or specific objects. It also outputs native 4-megapixel images.Honestly, the launch timing was rough, coming right after Google’s Nano Banana Pro and alongside Z-Image, but for precise, high-res editing, this is a serious tool.Tongyi drops Z-Image Turbo: 6B single-stream DiT lands sub‑second, 8‑step text‑to‑image (GitHub, Hugging Face)Alibaba’s Tongyi Lab released Z-Image Turbo, a 6B parameter model that generates images in sub-second time on H800s (and super fast on consumer cards).I built a demo to show just how fast this is. You know that “Infinite Craft“ game? I hooked it up to Z-Image Turbo so that every time you combine elements (like Pirate + Ghost), it instantly generates the image for “Ghost Pirate.” It changes the game completely when generation is this cheap and fast.HunyuanVideo 1.5 – open video gets very realTencent also shipped HunyuanVideo 1.5, which they market as “the strongest open‑source video generation model.” For once, the tagline isn’t entirely hype.Under the hood it’s an 8.3B‑parameter Diffusion Transformer (DiT) model with a 3D causal VAE in front. The VAE compresses videos aggressively in both space and time, and the DiT backbone models that latent sequence.The important bits for you and me:* It generates 5–10 second clips at 480p/720p with good motion coherence and physics.* With FP16 or FP8 configs you can run it on a single consumer GPU with around 14GB VRAM.* There’s a built‑in path to upsample to 1080p for “real” quality.LTX Studio Retake: Photoshop for Video (X, Try It)For the video creators, LTX Studio launched Retake. This isn’t just “regenerate video.” This allows you to select a specific 2-second segment of a video, change the dialogue (keeping the voice!), change the emotion, or edit the action, all for like $0.10. It blends it perfectly back into the original clip. We are effectively getting a “Director Mode” for AI video where you can fix mistakes without rolling the dice on a whole new generation.A secret new model on the Arena called Whisper Thunder beats even Veo 3?This was a surprise of the week, while new video models get released often, Veo 3 has been the top one for a while, and now we’re getting a reshuffling of the video giants! But... we don’t yet know who this video model is from! You can sometimes get its generations at the Artificial Analysis video arena here, and the outputs look quite awesome! Thanksgiving reflections from the ThursdAI teamAs we wrapped up the show, Wolfram suggested we take a moment to think about what we’re thankful for in AI, and I think that’s a perfect note to end on.Wolfram put it well: he’s thankful for everyone contributing to this wonderful community—the people releasing models, creating open source tools, writing tutorials, sharing knowledge. It’s not just about the money; it’s about the love of learning and building together.Yam highlighted something I think is crucial: we’ve reached a point where there’s no real competition between open source and closed source anymore. Everything is moving forward together. Even if you think nobody’s looking at that random code you posted somewhere, chances are someone found it and used it to accelerate their own work. That collective effort is what’s driving this incredible pace of progress.For me, I want to thank Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser, and Ilya Polosukhin for the 2017 paper “Attention Is All You Need.” Half Joking! But without the seminal attention is you need paper none of this AI was possible. But mostly I want to thank all of you—the audience, the co-hosts, the guests—for making ThursdAI what it is.If you go back and watch our 2024 Thanksgiving episode, or the one from 2023, you’ll be shocked at how far we’ve come. Tools that seemed magical a year ago are now just... normal. That’s hedonic adaptation at work, but it’s also a reminder to stay humble and appreciate just how incredible this moment in history really is.We’re living through the early days of a technological revolution, and we get to document it, experiment with it, and help shape where it goes. That’s something to be genuinely thankful for.TL;DR and Show Notes* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co-Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed* Guests: @idosal1 @liadyosef - MCP-UI/MCP Apps* Big CO LLMs + APIs* Anthropic launches Claude Opus 4.5 - world’s top model for coding, agents, and tool use (X, Announcement, Blog)* OpenAI Integrates ChatGPT Voice Mode Directly into Chats (X)* Open Source LLMs* Prime Intellect - INTELLECT-3 106B MoE (X, HF, Blog, Try It)* Tencent - HunyuanOCR 1B SOTA OCR model (X, HF, Github, Blog)* Microsoft - Fara-7B on-device computer-use agent (X, Blog, HF, Github)* DeepSeek - Math-V2 IMO-gold math LLM (HF)* Interview: MCP Apps* MCP-UI standardized as MCP Apps by Anthropic and OpenAI (X, Blog, Announcement)* Vision & Video* Tencent - HunyuanVideo 1.5 lightweight DiT open video model (X, GitHub, HF)* LTX Studio - Retake AI video editing tool (X, Try It)* Whisper Thunder - mystery #1 ranked video model on arena* AI Art & Diffusion* Black Forest Labs - FLUX.2 32B multi-reference image model (X, HF, Blog)* Tongyi - Z-Image Turbo sub-second 6B image gen (GitHub, HF)* This Week’s Buzz* W&B launches Serverless LoRA Inference on CoreWeave (X, Blog, Notebook) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey everyone, Alex here 👋I’m writing this one from a noisy hallway at the AI Engineer conference in New York, still riding the high (and the sleep deprivation) from what might be the craziest week we’ve ever had in AI.In the span of a few days:Google dropped Gemini 3 Pro, a new Deep Think mode, generative UIs, and a free agent-first IDE called Antigravity.xAI shipped Grok 4.1, then followed it up with Grok 4.1 Fast plus an Agent Tools API.OpenAI answered with GPT‑5.1‑Codex‑Max, a long‑horizon coding monster that can work for more than a day, and quietly upgraded ChatGPT Pro to GPT‑5.1 Pro.Meta looked at all of that and said “cool, we’ll just segment literally everything and turn photos into 3D objects” with SAM 3 and SAM 3D.Robotics folks dropped a home robot trained with almost no robot data.And Google, just to flex, capped Thursday with Nano Banana Pro, a 4K image model and a provenance system while we were already live! For the first time in a while it doesn’t just feel like “new models came out.” It feels like the future actually clicked forward a notch.This is why ThursdAI exists. Weeks like this are basically impossible to follow if you have a day job, so my co‑hosts and I do the no‑sleep version so you don’t have to. Plus, being at AI Engineer makes it easy to get super high quality guests so this week we had 3 folks join us, Swyx from Cognition/Latent Space, Thor from DeepMind (on his 3rd day) and Dominik from OpenAI! Alright, deep breath. Let’s untangle the week.TL;DR If you only skim one section, make it this one (links in the end):* Google* Gemini 3 Pro: 1M‑token multimodal model, huge reasoning gains - new LLM king* ARC‑AGI‑2: 31.11% (Pro), 45.14% (Deep Think) – enormous jumps* Antigravity IDE: free, Gemini‑powered VS Code fork with agents, plans, walkthroughs, and browser control* Nano Banana Pro: 4K image generation with perfect text + SynthID provenance; dynamic “generative UIs” in Gemini* xAI* Grok 4.1: big post‑training upgrade – #1 on human‑preference leaderboards, much better EQ & creative writing, fewer hallucinations* Grok 4.1 Fast + Agent Tools API: 2M context, SOTA tool‑calling & agent benchmarks (Berkeley FC, T²‑Bench, research evals), aggressive pricing and tight X + web integration* OpenAI* GPT‑5.1‑Codex‑Max: “frontier agentic coding” model built for 24h+ software tasks with native compaction for million‑token sessions; big gains on SWE‑Bench, SWE‑Lancer, TerminalBench 2* GPT‑5.1 Pro: new “research‑grade” ChatGPT mode that will happily think for minutes on a single query* Meta* SAM 3: open‑vocabulary segmentation + tracking across images and video (with text & exemplar prompts)* SAM 3D: single‑image → 3D objects & human bodies; surprisingly high‑quality 3D from one photo* Robotics* Sunday Robotics – ACT‑1 & Memo: home robot foundation model trained from a $200 skill glove instead of $20K teleop rigs; long‑horizon household tasks with solid zero‑shot generalization* Developer Tools* Antigravity and Marimo’s VS Code / Cursor extension both push toward agentic, reactive dev workflowsLive from AI Engineer New York: Coding Agents Take Center StageWe recorded this week’s show on location at the AI Engineer Summit in New York, inside a beautiful podcast studio the team set up right on the expo floor. Huge shout out to Swyx, Ben, and the whole AI Engineer crew for that — last time I was balancing a mic on a hotel nightstand, this time I had broadcast‑grade audio while a robot dog tried to steal the show behind us.This year’s summit theme is very on‑the‑nose for this week: coding agents.Everywhere you look, there’s a company building an “agent lab” on top of foundation models. Amp, Cognition, Cursor, CodeRabbit, Jules, Google Labs, all the open‑source folks, and even the enterprise players like Capital One and Bloomberg are here, trying to figure out what it means to have real software engineers that are partly human and partly model.Swyx framed it nicely when he said that if you take “vertical AI” seriously enough, you eventually end up building an agent lab. Lawyers, healthcare, finance, developer tools — they all converge on “agents that can reason and code.”The big labs heard that theme loud and clear. Almost every major release this week is about agents, tools, and long‑horizon workflows, not just chat answers.Google Goes All In: Gemini 3 Pro, Antigravity, and the Agent RevolutionLet’s start with Google because, after years of everyone asking “where’s Google?” in the AI race, they showed up this week with multiple bombshells that had even the skeptics impressed.Gemini 3 Pro: Multimodal Intelligence That Actually DeliversGoogle finally released Gemini 3 Pro, and the numbers are genuinely impressive. We’re talking about a 1 million token context window, massive benchmark improvements, and a model that’s finally competing at the very top of the intelligence charts. Thor from DeepMind joined us on the show (literally on day 3 of his new job!) and you could feel the excitement.The headline numbers: Gemini 3 Pro with Deep Think mode achieved 45.14% on ARC-AGI-2—that’s roughly double the previous state-of-the-art on some splits. For context, ARC-AGI has been one of those benchmarks that really tests genuine reasoning and abstraction, not just memorization. The standard Gemini 3 Pro hits 31.11% on the same benchmark, both scores are absolutely out of this world in Arc! On GPQA Diamond, Gemini 3 Pro jumped about 10 points compared to prior models. We’re seeing roughly 81% on MMLU-Pro, and the coding performance is where things get really interesting—Gemini 3 Pro is scoring around 56% on SciCode, representing significant improvements in actual software engineering tasks.But here’s what made Ryan from Amp switch their default model to Gemini 3 Pro immediately: the real-world usability. Ryan told us on the show that they’d never switched default models before, not even when GPT-5 came out, but Gemini 3 Pro was so noticeably better that they made it the default on Tuesday. Of course, they hit rate limits almost immediately (Google had to scale up fast!), but those have since been resolved.Antigravity: Google’s Agent-First IDEThen Google dropped Antigravity, and honestly, this might be the most interesting part of the whole release. It’s a free IDE (yes, free!) that’s basically a fork of VS Code, but reimagined around agents rather than human-first coding.The key innovation here is something they call the “Agent Manager”—think of it like an inbox for your coding agents. Instead of thinking in folders and files, you’re managing conversations with agents that can run in parallel, handle long-running tasks, and report back when they need your input.I got early access and spent time playing with it, and here’s what blew my mind: you can have multiple agents working on different parts of your codebase simultaneously. One agent fixing bugs, another researching documentation, a third refactoring your CSS—all at once, all coordinated through this manager interface.The browser integration is crazy too. Antigravity can control Chrome directly, take screenshots and videos of your app, and then use those visuals to debug and iterate. It’s using Gemini 3 Pro for the heavy coding, and even Nano Banana for generating images and assets. The whole thing feels like it’s from a couple years in the future.Wolfram on the show called out how good Gemini 3 is for creative writing too—it’s now his main model, replacing GPT-4.5 for German language tasks. The model just “gets” the intention behind your prompts rather than following them literally, which makes for much more natural interactions.Nano Banana Pro: 4K Image Generation With ThinkingAnd because Google apparently wasn’t done announcing things, they also dropped Nano Banana Pro on Thursday morning—literally breaking news during our live show. This is their image generation model that now supports 4K resolution and includes “thinking” traces before generating.I tested it live by having it generate an infographic about all the week’s AI news (which you can see on the top), and the results were wild. Perfect text across the entire image (no garbled letters!), proper logos for all the major labs, and compositional understanding that felt way more sophisticated than typical image models. The file it generated was 8 megabytes—an actual 4K image with stunning detail.What’s particularly clever is that Nano Banana Pro is really Gemini 3 Pro doing the thinking and planning, then handing off to Nano Banana for the actual image generation. So you get multimodal reasoning about your request, then production-quality output. You can even upload reference images—up to 14 of them—and it’ll blend elements while maintaining consistency.Oh, and every image is watermarked with SynthID (Google’s invisible watermarking tech) and includes C2PA metadata, so you can verify provenance. This matters as AI-generated content becomes more prevalent.Generative UIs: The Future of InterfacesOne more thing Google showed off: generative UIs in the Gemini app. Wolfram demoed this for us, and it’s genuinely impressive. Instead of just text responses, Gemini can generate full interactive mini-apps on the fly—complete dashboards, data visualizations, interactive widgets—all vibe-coded in real time.He asked for “four panels of the top AI news from last week” and Gemini built an entire news dashboard with tabs, live market data (including accurate pre-market NVIDIA stats!), model comparisons, and clickable sections. It pulled real information, verified facts, and presented everything in a polished UI that you could interact with immediately.This isn’t just a demo—it’s rolling out in Gemini now. The implication is huge: we’re moving from static responses to dynamic, contextual interfaces generated just-in-time for your specific need.xAI Strikes Back: Grok 4.1 and the Agent Tools APINot to be outdone, xAI released Grok 4.1 at the start of the week, briefly claimed the #1 spot on LMArena (at 1483 Elo, not 2nd to Gemini 3), and then followed up with Grok 4.1 Fast and a full Agent Tools API.Grok 4.1: Emotional Intelligence Meets Raw PerformanceGrok 4.1 brought some really interesting improvements. Beyond the benchmark numbers (64% win rate over the previous Grok in blind tests), what stood out was the emotional intelligence. On EQ-Bench3, Grok 4.1 Thinking scored 1586 Elo, beating every other model including Gemini, GPT-5, and Claude.The creative writing scores jumped by roughly 600 Elo points compared to earlier versions. And perhaps most importantly for practical use, hallucination rates dropped from around 12% to 4%—that’s roughly a 3x improvement in reliability on real user queries.xAI’s approach here was clever: they used “frontier agentic reasoning models as reward models” during RL training, which let them optimize for subjective qualities like humor, empathy, and conversational style without just scaling up model size.Grok 4.1 Fast: The Agent Platform PlayThen came Grok 4.1 Fast, released just yesterday, and this is where things get really interesting for developers. It’s got a 2 million token context window (compared to Gemini 3’s 1 million) and was specifically trained for agentic, tool-calling workflows.The benchmark performance is impressive: 93-100% on τ²-Bench Telecom (customer support simulation), ~72% on Berkeley Function Calling v4 (top of the leaderboard), and strong scores across research and browsing tasks. But here’s the kicker: the pricing is aggressive.At $0.20 per million input tokens and $0.50 per million output tokens, Grok 4.1 Fast is dramatically cheaper than GPT-5 and Claude while matching or exceeding their agentic performance. For the first two weeks, it’s completely free via the xAI API and OpenRouter, which is smart—get developers hooked on your agent platform.The Agent Tools API gives Grok native access to X search, web browsing, code execution, and document retrieval. This tight integration with X is a genuine advantage—where else can you get real-time access to breaking news, sentiment, and conversation? Yam tested it on the show and confirmed that Grok will search Reddit too, which other models often refuse to do. I’ve used both these models this week in my N8N research agent and I gotta say, 4.1 fast is a MASSIVE improvement! OpenAI’s Endurance Play: GPT-5.1-Codex-Max and ProOpenAI clearly saw Google and xAI making moves and decided they weren’t going to let this week belong to anyone else. They dropped two significant releases: GPT-5.1-Codex-Max and an update to GPT-5.1 Pro.GPT-5.1-Codex-Max: Coding That Never StopsThis is the headline: GPT-5.1-Codex-Max can work autonomously for over 24 hours. Not 24 minutes, not 24 queries—24 actual hours on a single software engineering task. I talked to someone from OpenAI at the conference who told me internal checkpoints ran for nearly a week on and off.How is this even possible? The secret is something OpenAI calls “compaction”—a native mechanism trained into the model that lets it prune and compress its working session history while preserving the important context. Think of it like the model taking notes on itself, discarding tool-calling noise and keeping only the critical design decisions and state.The performance numbers back this up:* SOTA 77.9% on SWE-Bench Verified (up from 73.7%)* SOTA 79.9% on SWE-Lancer IC SWE (up from 66.3%)* 58.1% on TerminalBench 2.0 (up from 52.8%)And crucially, in medium reasoning mode, it uses 30% fewer thinking tokens while achieving better results. There’s also an “Extra High” reasoning mode for when you truly don’t care about latency and just want maximum capability.Yam, one of our co-hosts who’s been testing extensively, said you can feel the difference immediately. The model just “gets it” faster, powers through complex problems, and the earlier version’s quirk of ignoring your questions and just starting to code is fixed—now it actually responds and collaborates.Dominic from OpenAI joined us on the show and confirmed that compaction was trained natively into the model using RL, similar to how Claude trained natively for MCP. This means the model doesn’t waste reasoning tokens on maintaining context—it just knows how to do it efficiently.GPT-5.1 Pro: Research-Grade Intelligence & ChatGPT joins your group chat1Then there’s GPT-5.1 Pro, which is less about coding and more about deep, research-level reasoning. This is the model that can run for 10-17 minutes on a single query, thinking through complex problems with the kind of depth that previously required human experts.OpenAI also quietly rolled out group chats—basically, you can now have multiple people in a ChatGPT conversation together, all talking to the model simultaneously. Useful for planning trips, brainstorming with teams, or working through problems collaboratively. If agent mode works in group chats (we haven’t confirmed yet), that could get really interesting.Meta drops SAM3 & SAM3D - image and video segmentation models powered by natural languagePhew ok, big lab releases now done, oh.. wait not yet! Because Meta has decided to also make a dent on this Week with SAM3 and SAM3D, which both are crazy. I’ll just add their video release here instead of going on and on! This Week’s Buzz from Weights & BiasesIt’s been a busy week at Weights & Biases as well! We are proud Gold Sponsors of the AI Engineer conference here in NYC. If you’re at the event, please stop by our booth—we’re even giving away a $4,000 robodog!This week, I want to highlight a fantastic update from Marimo, the reactive Python notebook company we acquired.Marimo just shipped a native VS Code and Cursor extension. This brings Marimo’s reactive, Git-friendly notebooks directly into your favorite editors.Crucially, it integrates deeply with uv for lightning-fast package installs and reproducible environments. If you import a package you don’t have, the extension prompts you to install it and records the dependency in the script metadata. This bridges the gap between experimental notebooks and production-ready code, and it’s a huge boost for AI-native development workflows. (Blog , GitHub )The Future Arrived EarlyPhew... if you read all the way until this point, can you leave a ⚡ emoji in the comemnts? I was writing this and it.. is a lot! I was wondering who would even read all the way till here! This week we felt the acceleration! 🔥 I can barely breathe, I need a nap! A huge thank you to our guests—Ryan, Swyx, Thor, and Dominik—for navigating the chaos with us live on stage, and to the AI Engineer team for hosting us.We’ll be back next week to cover whatever the AI world throws at us next. Stay tuned, because at this rate, AGI might be here by Christmas.TL;DR - show notes and linksHosts and Co‑hosts* Alex Volkov – AI Evangelist at Weights & Biases / CoreWeave, host of ThursdAI (X)* Co‑hosts - Wolfram Ravenwolf – (X), Yam Peleg (X) LDJ (X)Guests* Swyx – Founder of AI Engineer World’s Fair and Summit, now at Cognition ( Latent.Space , X)* Ryan Carson – Amp (X)* Thor Schaeff – Google DeepMind, Gemini API and AI Studio (X)* Dominik Kundel – Developer Experience at OpenAI (X)Open Source LLMs* Allen Institute Olmo 3 - 7B/32B fully open reasoning suite with end-to-end training transparency (X, Blog)Big CO LLMs + APIs* Google Gemini 3 Pro - 1M-token, multimodal, agentic model with Generative UIs (X, X, X)* Google Antigravity - Agent-first IDE powered by Gemini 3 Pro (Blog, X)* xAI Grok 4.1 and Grok 4.1 Thinking - big gains in Coding, EQ, creativity, and honesty (X, Blog)* xAI Grok 4.1 Fast and Agent Tools API - 2M-token context, state-of-the-art tool-calling (X)* OpenAI GPT-5.1-Codex-Max - long-horizon agentic coding model for 24-hour+ software tasks (X, X)* OpenAI GPT-5.1 Pro - research-grade reasoning model in ChatGPT Pro* Microsoft, NVIDIA, and Anthropic partnership - to scale Claude on Azure with massive GPU investments (Announcement, NVIDIA, Microsoft Blog)This weeks Buzz* Marimo ships native VS Code & Cursor extension with reactive notebooks and uv-powered environments (X, Blog, GitHub)Vision & Video & 3D* Meta SAM 3 & SAM 3D - promptable segmentation, tracking, and single-image 3D reconstruction (X, Blog, GitHub)AI Art & Diffusion* Google Nano Banana Pro and SynthID verification - 4K image generation with provenance (Blog)Show Notes and other Links* AI Engineer Summit NYC - Live from the conference* Full livestream available on YouTube* ThursdAI - Nov 20, 2025 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey, this is Alex! We’re finally so back! Tons of open source releases, OpenAI updates GPT and a few breakthroughs in audio as well, makes this a very dense week! Today on the show, we covered the newly released GPT 5.1 update, a few open source releases like Terminal Bench and Project AELLA (renamed OASSAS), and Baidu’s Ernie 4.5 VL that shows impressive visual understanding! Also, chatted with Paul from 11Labs and Dima Duev from the wandb SDK team, who brought us a delicious demo of LEET, our new TUI for wandb! Tons of news coverage, let’s dive in 👇 (as always links and show notes in the end) Open Source AILet’s jump directly into Open Source as this week has seen some impressive big company models. Terminal-Bench 2.0 - a harder, highly‑verified coding and terminal benchmark (X, Blog, Leaderboard)We opened with Terminal‑Bench 2.0 plus its new harness, Harbor, because this is the kind of benchmark we’ve all been asking for. Terminal‑Bench focuses on agentic coding in a real shell. Version 2.0 is a hard set of 89 terminal tasks, each one painstakingly vetted by humans and LLMs to make sure it’s solvable and realistic. Think “I checked out master and broke my personal site, help untangle the git mess” or “implement GPT‑2 code golf with the fewest characters.” On the new leaderboard, top agents like Warp’s agentic console and Codex CLI + GPT‑5 sit around fifty percent success. That number is exactly what excites me: we’re nowhere near saturation. When everyone is in the 90‑something range, tiny 0.1 improvements are basically noise. When the best models are at fifty percent, a five‑point jump really means something.A huge part of our conversation focused on reproducibility. We’ve seen other benchmarks like OSWorld turn out to be unreliable, with different task sets and non‑reproducible results making scores incomparable. Terminal‑Bench addresses this with Harbor, a harness designed to run sandboxed, containerized agent rollouts at scale in a consistent environment. This means results are actually comparable. It’s a ton of work to build an entire evaluation ecosystem like this, and with over a thousand contributors on their Discord, it’s a fantastic example of a healthy, community‑driven effort. This is one to watch! Baidu’s ERNIE‑4.5‑VL “Thinking”: a 3B visual reasoner that punches way up (X, HF, GitHub)Next up, Baidu dropped a really interesting model, ERNIE‑4.5‑VL‑28B‑A3B‑Thinking. This is a compact, 3B active‑parameter multimodal reasoning model focused on vision, and it’s much better than you’d expect for its size. Baidu’s own charts show it competing with much larger closed models like Gemini‑2.5‑Pro and GPT‑5‑High on a bunch of visual benchmarks like ChartQA and DocVQA.During the show, I dropped a fairly complex chart into the demo, and ERNIE‑4.5‑VL gave me a clean textual summary almost instantly—it read the chart more cleanly than I could. The model is built to “think with images,” using dynamic zooming and spatial grounding to analyze fine details. It’s released under an Apache‑2.0 license, making it a serious candidate for edge devices, education, and any product where you need a cheap but powerful visual brain.Open Source Quick Hits: OSSAS, VibeThinker, and Holo TwoWe also covered a few other key open-source releases. Project AELLA was quickly rebranded to OSSAS (Open Source Summaries At Scale), an initiative to make scientific literature machine‑readable. They’ve released 100k paper summaries, two fine-tuned models for the task, and a 3D visualizer. It’s a niche but powerful tool if you’re working with massive amounts of research. (X, HF)WeiboAI (from the Chinese social media company) released VibeThinker‑1.5B, a tiny 1.5B‑parameter reasoning model that is making bold claims about beating the 671B DeepSeek R1 on math benchmarks. We discussed the high probability of benchmark contamination, especially on tests like AIME24, but even with that caveat, getting strong chain‑of‑thought math out of a 1.5B model is impressive and useful for resource‑constrained applications. (X, HF, Arxiv)Finally, we had some breaking news mid‑show: H Company released Holo Two, their next‑gen multimodal agent for controlling desktops, websites, and mobile apps. It’s a fine‑tune of Qwen3‑VL and comes in 4B and 8B Apache‑2.0 licensed versions, pushing the open agent ecosystem forward. (X, Blog, HF)ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Big Companies & APIsGPT‑5.1: Instant vs Thinking, and a new personality barThe biggest headline of the week was OpenAI shipping GPT‑5.1, and this was a hot topic of debate on the show. The update introduces two modes: “Instant” for fast, low‑compute answers, and “Thinking” for deeper reasoning on hard problems. OpenAI claims Instant mode uses 57% fewer tokens on easy tasks, while Thinking mode dedicates 71% more compute to difficult ones. This adaptive approach is a smart evolution.The release also adds a personality dropdown with options like Professional, Friendly, Quirky, and Cynical, aiming for a more “warm” and customizable experience. Yam and I felt this was a step in the right direction, as GPT‑5 could often feel a bit cold and uncommunicative. However, Wolfram had a more disappointing experience, finding that GPT‑5.1 performed significantly worse on his German grammar and typography tasks compared to GPT‑4 or Claude Sonnet 4.5. It’s a reminder that “upgrades” can be subjective and task‑dependent.Since the show was recorded, GPT 5.1 is also released in the API and they have published a prompting guide and some evals! With some significant jumps across SWE-bench verified and GPQA Diamond! We’ll be testing this model out all week. The highlight for this model is the creative writing, it was made public that this model was being tested on OpenRouter as Polaris-alpha and that one tops the eqbench creative writing benchmarks beating Sonnet 4.5 and Gemini! Grok‑4 Fast: 2M context and a native X superpowerGrok‑4 Fast from xAI apparenly quietly got a substantial upgrade to a 2M‑token context window, but the most interesting part is its unique integration with X. The API version has access to internal tools for semantic search over tweets, retrieving top quote tweets, and understanding embedded images and videos. I’ve started using it as a research agent in my show prep, and it feels like having a research assistant living inside X’s backend—something you simply can’t replicate with public tools.I still have my gripes about their “stealth upgrade” versioning strategy, which makes rigorous evaluation difficult, but as a practical tool, Grok‑4 Fast is incredibly powerful. It’s also surprisingly fast and cost‑effective, holding its own against other top models on benchmarks while offering a superpower that no one else has.Google SIMA 2: Embodied Agents in Virtual WorldsGoogle’s big contribution this week was SIMA 2, DeepMind’s latest embodied agent for 3D virtual worlds. SIMA lives inside real games like No Man’s Sky and Goat Simulator, seeing the screen and controlling the game via keyboard and mouse, using Gemini as its reasoning brain. Demos showed it following complex, sketch‑based instructions, like finding an object that looks like a drawing of a spaceship and jumping on top of it.When you combine this with Genie 3—Google’s world model that can generate playable environments from a single image—you see the bigger picture: agents that learn physics, navigation, and common sense by playing in millions of synthetic worlds. We’re not there yet, but the pieces are clearly being assembled. We also touched on the latest Gemini Live voice upgrade, which users are reporting feels much more natural and responsiveMore Big Company News: Qwen Deep Research, Code Arena, and CursorWe also briefly covered Qwen’s new Deep Research feature, which offers an OpenAI‑style research agent inside their ecosystem. LMSYS launched Blog, a fantastic live evaluation platform where models build real web apps agentically, with humans voting on the results. And in the world of funding, the AI‑native code editor Cursor raised a staggering $2.3 billion, a clear sign that AI is becoming the default way developers interact with code.This Week’s Buzz: W&B LEET – a terminal UI that sparks joyFor this week’s buzz, I brought on Dima Duev from our SDK team at Weights & Biases to show off a side project that has everyone at the company excited: LEET, the Lightweight Experiment Exploration Tool. Imagine you’re training on an air‑gapped HPC cluster, living entirely in your terminal. How do you monitor your runs? With LEET.You run your training script in W&B offline mode, and in another terminal, you type wandb beta leet. Your terminal instantly turns into a full TUI dashboard with live metric plots, system stats, and run configs. You can zoom into spikes in your loss curve, filter metrics, and see everything updating in real time, all without a browser or internet connection. It’s one of those tools that just sparks joy. It ships with the latest wandb SDK (v0.23.0+), so just upgrade and give it a try! Voice & Audio: Scribe v2 Realtime and Omnilingual ASRElevenLabs Scribe v2 Realtime: ASR built for agents (X, Announcement, Demo)We’ve talked a lot on this show about ElevenLabs as “the place you go to make your AI talk.” This week, they came for the other half of the conversation. Paul Asjes from ElevenLabs joined us to walk through Scribe v2 Realtime, their new low‑latency speech‑to‑text model. If you’re building a voice agent, you need ears, a brain, and a mouth. ElevenLabs already nailed the mouth, and now they’ve built some seriously good ears.Scribe v2 Realtime is designed to run at around 150 milliseconds median latency, across more than ninety languages. Watching Paul’s live demo, it felt comfortably real‑time. When he switched from English to Dutch mid‑sentence, the system just followed along without missing a beat. Community benchmarks and our own impressions show it holding its own or beating competitors like Whisper and Deepgram in noisy, accented, and multi‑speaker scenarios. It’s also context‑aware enough to handle code, initialisms, and numbers correctly, which is critical for real‑world agents. This is a production‑ready ASR for anyone building live voice experiences.Meta’s drops Omnilingual ASR: 1,600+ languages, many for the first time + a bunch of open source models (X, Blog, Announcement, HF)On the other end of the spectrum, Meta released something that’s less about ultra‑low latency and more about sheer linguistic coverage: Omnilingual ASR. This is a family of models and a dataset designed to support speech recognition for more than 1,600 languages, including about 500 that have never had any ASR support before. That alone is a massive contribution.Technically, it uses a wav2vec 2.0 backbone scaled up to 7B parameters with both CTC and LLM‑style decoders. The LLM‑like architecture allows for in‑context learning, so communities can add support for new languages with only a handful of examples. They’re also releasing the Omnilingual ASR Corpus with data for 350 underserved languages. The models and code are Apache‑2.0, making this a huge step forward for more inclusive speech tech.AI Art, Diffusion & 3DQwen Image Edit + Multi‑Angle LoRA: moving the camera after the fact (X, HF, Fal)This one was pure fun. A new set of LoRAs for Qwen Image Edit adds direct camera control to still images. A Hugging Face demo lets you upload a photo and use sliders to rotate the camera up to 90 degrees, tilt from a bird’s‑eye to a worm’s‑eye view, and adjust the lens. We played with it live on the show with a portrait of Wolfram and a photo of my cat, generating different angles and then interpolating them into a short “fly‑around” video. It’s incredibly cool and preserves details surprisingly well, feeling like you have a virtual camera inside a 2D picture.NVIDIA ChronoEdit‑14B Upscaler LoRA (X, HF)Finally, NVIDIA released an upscaler LoRA based on their ChronoEdit‑14B model and merged the pipeline into Hugging Face Diffusers. ChronoEdit reframes image editing as a temporal reasoning task, like generating a tiny video. This makes it good for maintaining consistency in edits and upscales. It’s a heavy model, requiring ~34GB of VRAM, and for aggressive upscaling, specialized tools might still be better. But for moderate upscales where temporal coherence matters, it’s a very interesting new tool in the toolbox.Phew, we made it through this dense week! Looking to next week, I’ll be recoridng the show live from the AI Engieer CODE summit in NY, and we’ll likely see a few good releases from the big G? Maybe? finally? As always, if this was helpful, please subscribe to ThursdAI and share it with 2 friends, see you next week 🫡 TL;DR and Show Notes* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co-Hosts - @WolframRvnwlf, @yampeleg, @ldjconfirmed* Guest: Dima Duev - SDK team Wandb* Guest: Paul Asjes - Eleven Labs (@paul_asjes)* Open Source LLMs* Terminal-Bench 2.0 and Harbor launch (X, Blog, Docs, Announcement)* Baidu releases ERNIE-4.5-VL-28B-A3B-Thinking (X, HF, GitHub, Blog, Platform)* Project AELLA (OSSAS): 100K LLM-generated paper summaries (X, HF)* WeiboAI’s VibeThinker-1.5B (X, HF, Arxiv, Announcement)* Code Arena — live, agentic coding evaluations (X, Blog, Announcement)* Big CO LLMs + APIs* Grok 4 Fast, Grok Imagine and Nano Banana v1/v2 (X, X, X, X)* OpenAI launches GPT-5.1 (X, X)* This weeks Buzz* W&B LEET — an open-source Terminal UI (TUI) to monitor runs (X, Blog)* Voice & Audio* ElevenLabs launches Scribe v2 Realtime (X, Blog, Docs)* Meta releases Omnilingual ASR for 1,600+ languages (X, Blog, Paper, HF Dataset, HF Demo, GitHub)* Gemini Live conversational upgrade (X)* AI Art & Diffusion & 3D* Qwen Image Edit + Multi‑Angle LoRA for camera control (X, HF, Fal)* NVIDIA releases ChronoEdit-14B Upscaler LoRA (X, HF, Docs) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey, Alex here! Quick note, while preparing for this week, I posted on X that I don’t remember such a quiet week in AI since I started doing ThursdAI regularly, but then 45 min before the show started, Kimi dropped a SOTA oss reasoning model, turning a quiet week into an absolute banger. Besides Kimi, we covered the updated MCP thinking from Anthropic, and had Kenton Varda from cloudflare as a guest to talk about Code Mode, chatted about Windsurf and Cursor latest updates and covered OpenAI’s insane deals. Also, because it was a quiet week, I figured I’d use the opportunity to create an AI powered automation, and used N8N for that, and shared it on the stream, so if you’re interested in automating with AI with relatively low code, this episode is for you. Let’s dive inThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Kimi K2 Thinking is Here and It’s a 1 Trillion Parameter Beast! (X, HF, Tech Blog)Let’s start with the news that got everyone’s energy levels skyrocketing right as we went live. Moonshot AI dropped Kimi K2 Thinking, an open-source, 1 trillion-parameter Mixture-of-Experts (MoE) model, and it’s an absolute monster.This isn’t just a numbers game; Kimi K2 Thinking is designed from the ground up to be a powerful agent. With just around 32 billion active parameters during inference, a massive 256,000 token context window, and an insane tool-calling capacity. They’re claiming it can handle 200-300 sequential tool calls without any human intervention. The benchmarks are just as wild. On the Humanities Last Exam (HLE), they’re reporting a score of 44.9%, beating out both GPT-5 and Claude 4.5 Thinking. While it doesn’t quite top the charts on SWE-bench verified, it’s holding its own against the biggest closed-source models out there. Seeing an open-source model compete at this level is incredibly exciting.During the show, we saw some truly mind-blowing demos, from a beautiful interactive visualization of gradient descent to a simulation of a virus attacking cells, all generated by the model. The model’s reasoning traces, which are exposed through the API, also seem qualitatively different from other models, showing a deep and thoughtful process. My co-hosts and I were blown away. The weights and a very detailed technical report are available on Hugging Face, so you can dive in and see for yourself. Shout out to the entire Moonshot AI team for this incredible release!Other open source updates from this week* HuggingFace released an open source “Smol Training Playbook” on training LLMs, it’s a 200+ interactive beast with visualizations, deep dives into pretraining, dataset, postraining and more! (HF)* Ai2 launches OlmoEarth — foundation models + open, end-to-end platform for fast, high-resolution Earth intelligence (X, Blog)* LongCat-Flash-Omni — open-source omni-modal system with millisecond E2E spoken interaction, 128K context and a 560B ScMoE backbone (X, HF, Announcement)Big Tech’s Big Moves: Apple, Amazon, and OpenAIThe big companies were making waves this week, starting with a blockbuster deal that might finally make Siri smart. Apple is reportedly will be paying Google around $1 billion per year to license a custom 1.2 trillion-parameter version of Gemini to power a revamped Siri.This is a massive move. The Gemini model will run on Apple’s Private Cloud Compute, keeping user data walled off from Google, and will handle Siri’s complex summarizer and planner functions. After years of waiting for Apple to make a significant move in GenAI, it seems they’re outsourcing the heavy lifting for now while they work to catch up with their own in-house models. As a user, I don’t really care who builds the model, as long as Siri stops being dumb!In more dramatic news, Perplexity revealed that Amazon sent them a legal threat to block their Comet AI assistant from shopping on Amazon.com. This infuriated me. My browser is my browser, and I should be able to use whatever tools I want to interact with the web. Perplexity took a strong stand with their blog post, “Bullying is Not Innovation,” arguing that user agents are distinct from scrapers and act on behalf of the user with their own credentials. An AI assistant is just that—an assistant. It shouldn’t matter if I ask my wife or my AI to buy something for me on Amazon. This feels like a move by Amazon to protect its ad revenue at the expense of user choice and innovation, and I have to give major props to Perplexity for being so transparent and fighting back.Finally, OpenAI continues its quest for infinite compute, announcing a multi-year strategic partnership with AWS. This comes on top of massive deals with NVIDIA, Microsoft, Oracle, and others, bringing their total commitment to compute into the trillions of dollars. It’s getting to a point where OpenAI seems “too big to fail,” as any hiccup could have serious repercussions for the entire tech economy, which is now heavily propped up by AI investment. Sam has clarified that they don’t think OpenAI wants to be too big to fail in a recent post on X, and that the recent miscommunications around the US government backstopping OpenAI’s infrastructure bailouts were taken out of context. 🤔 Coding with AI: The Evolution of MCP and New Dev ToolsThis week, we kicked off a new segment on the show: Coding with AI! Essentially realizing that we talk about AI coding a LOT, and decided to add a dedicated corner to it! And we started with a fascinating development in the world of agentic tooling. Anthropic published a blog post arguing that the standard way of using the Model Context Protocol (MCP) — by loading full tool definitions into the context window — is inefficient.Their solution? Have LLMs write code to interact with tools instead. This approach can slash token usage by over 98% in some cases. This idea sounded familiar, and that’s because Cloudflare had already explored it with a feature called “Code Mode.” We were lucky enough to have Kenton Varda, one of the authors of the Code Mode post and head of engineering for Cloudflare Workers, join us to discuss this shift.Kenton explained that LLMs are trained on vast amounts of code, making it a more “native language” for them than the artificial construct of tool calls. By generating code, agents can chain multiple tool calls together, process intermediate results, and operate much more efficiently without sending everything back through the neural network. While MCP still provides crucial standardization for discovering and authorizing tools, this “code execution” pattern seems to be the way forward for building more powerful and scalable agents.Windsurfs CodeMaps and Cursor multi agent executionsIn other coding with AI news, Windsurf has pushed an incredible feature, called CodeMaps. They will use their SWE-1 model to (quickly) generate Codemaps that will expalins a code-base to you, in a visual way. What starts where and goes where. It’s really useful to understand a new codebase or re-understand one you forgot about already! You can even chat with codemaps, to see if your overall system’s design is solid! Great addition that I’m sure will help many folks adopt Windsurf! And Cursor, another popular AI-native IDE, released a super-performant in-IDE browser and a wild multi-agent feature that queries multiple LLMs in parallel and then synthesizes their answers.This Week’s TutorialI finally got around to building some serious automations for ThursdAI, and folks, N8N has been a game-changer. What used to take me 30+ minutes of manual work now happens automatically in the background.Here’s what I built: A Telegram bot that takes Twitter/X links, fetches the tweets and all linked content, uses AI agents to extract and summarize the information, and then posts it to our announcement channel and my notes app. The coolest part? I built this whole thing in about 4 hours with the help of Atlas browser and GPT-5 literally telling me what to do at each step.During the show, we even live-tested swapping out GPT-4o-mini for Kimi K2 - took literally 30 seconds to connect via OpenRouter. I went through my node and explains how this all works on the show, so if you’ve wanted to learn about n8n, check it out starting around 01:13:00. If you want to see how my automation turned out, it will be posting all my links to the new telegram channel t.me/thursdai_news (expect it to be messy at first as I’m testing out the automation) Robotics - Xpeng’s “Iron” humanoid: big vibes, few specsAnother week, another humanoid robot that is supposedly “coming” in 2026! A humanoid from Xpeng went viral this week, marketed as “the most human‑like” robot with soft skin, bionic muscles, customizable sexes (yes, really, they have a woman humanoid), something called a VLT brain, and a 2026 production goal. Here’s what we didn’t get: a spec sheet. No DOF, speed, payload, compute TOPS, battery capacity, runtime, or safety pathway. No pricing, manufacturing strategy, or clear target markets. In other words: lots of sizzle, no steak.Apparently, there was folks thinking Xpend pulled an Elon and put a human in a robot suit, making the CEO do the “we’ll cut a part of the soft skin to expose the robot underneath so you don’t think we’re lying” stunt. Which I agree, was very effective. But, If Xpeng is serious, the next thing we’ll see should be a crisp engineering document: joints, actuation, sensors, compute, and a locomotion/manipulation demo with independent measurements. Until then, treat this as a branding salvo and a reminder that the humanoid category is still sorting itself into “industrial payload first” versus “human likeness first” approaches. Voice & AudioMaya‑1: open‑source voice design from natural languageWe highlighted Maya‑1, a 3B Llama‑backboned TTS system designed to generate voices from natural language descriptions. Instead of picking from a menu, you describe the voice—age, accent, affect—and Maya conjures it. It supports real‑time streaming and over twenty “emotion tags.” The quality is compelling for its size and the Apache 2 license will make a lot of builders happy. There’s a growing middle class of TTS: tiny but expressive, good enough for in‑app narrators, prototyping, and even stylized content when you don’t want the constraints of commercial voice marketplaces.Inworld TTS: a new leader on independent rankingsWe also listened to Inworld’s latest, which currently tops the Artificial Analysis TTS leaderboard. It’s not open source, but the combo of expressivity, speed (sub‑250 ms), and multilingual support puts it firmly in the “commercially viable at scale” tier alongside the usual suspects. If you need SaaS TTS today and care about emotional range, add this to your shortlist. Pricing on their site targets availability rather than hobbyist tinkering, but the quality argues for itself.Whew! For a week that started slow, it certainly ended with a bang. It just goes to show you can never count AI out. We’re seeing open source continue to push the boundaries, big tech making landscape-defining moves, and agentic AI becoming more powerful and accessible every day.As always, thanks for tuning in. If you’re going to be at the AI.engineer conference in New York, please hit me up—I’d love to meet you.TL;DR and Show Notes + Links* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten* Kenton Varda @ Cloudflare (@KentonVarda)* Open Source LLMs* Smol Training Playbook — a 200+ page, end-to-end guide to reliably pretrain and operate LLMs (X, Announcement)* Ai2 launches OlmoEarth — foundation models + open, end-to-end platform for fast, high-resolution Earth intelligence (X, Blog)* Moonshot AI releases Kimi K2 Thinking — an open-source 1T-parameter MoE agent with 256K context and huge tool-calling capacity (X, HF, Blog, Arxiv)* LongCat flash Omni - 560B (27A) omni model (text, audio, video input)* Big CO LLMs + APIs* Apple will pay roughly $1B/year to license a custom 1.2 trillion‑parameter Google Gemini model to power a revamped Siri (X, Announcement)* Perplexity says Amazon issued a legal threat to block Comet AI assistants from shopping on Amazon (X, Blog)* AWS announces multi-year strategic infrastructure partnership with OpenAI to power ChatGPT inference, training, and agentic AI (X)* Robotics* Xpeng unveils ‘Iron’ humanoid claiming ‘most human-like’ design with soft skin, bionic muscles, VLT brain and a 2026 production plan (X)* Coding with AI* Anthropic shows how running MCP-connected tools as code slashes token use and scales agents (X, Blog)* Windsurf Codemaps — AI‑annotated, navigable maps of your codebase powered by SWE-1.5 (Fast) and Sonnet 4.5 (Smart) (X, Announcement)* Conversation with Kenton Varda (@KentonVarda) from Cloudflare about MCP and Code Mode* Cursor added in IDE browser - very performant!* Audio & Video* Maya-1 - Open source voice generation model.* Inworld TTS - new #1 on artifical analysis benchmark.* Tools & Gadgets* Sandbar launches Stream — a voice-first personal assistant — and Stream Ring, a wearable ‘mouse for voice’, available for preorder (X, Blog) This is a public episode. 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Hey, it’s Alex! Happy Halloween friends! I’m excited to bring you this weeks (spooky) AI updates! We started the show today with MiniMax M2, the currently top Open Source LLM, with an interview with their head of eng, Skyler Miao, continued to dive into OpenAIs completed restructuring into a non-profit and a PBC, including a deep dive into a live stream Sam Altman had, with a ton of spicy details, and finally chatted with Arjun Desai from Cartesia, following a release of Sonic 3, a sub 49ms voice model! So, 2 interviews + tons of news, let’s dive in! (as always, show notes in the end)Hey, if you like this content, it would mean a lot if you subscribe as a paid subscriber.Open Source AIMiniMax M2: open-source agentic model at 8% of Claude’s price, 2× speed (X, Hugging Face )We kicked off our open-source segment with a banger of an announcement and a special guest. The new king of open-source LLMs is here, and it’s called MiniMax M2. We were lucky enough to have Skyler Miao, Head of Engineering at Minimax, join us live to break it all down.M2 is an agentic model built for code and complex workflows, and its performance is just staggering. It’s already ranked in the top 5 globally on the Artificial Analysis benchmark, right behind giants like OpenAI and Anthropic. But here’s the crazy part: it delivers nearly twice the speed of Claude 3.5 Sonnet at just 8% of the price. This is basically Sonnet-level performance, at home, in open source.Skylar explained that their team saw an “impossible triangle” in the market between performance, cost, and speed—you could only ever get two. Their goal with M2 was to build a model that could solve this, and they absolutely nailed it. It’s a 200B parameter Mixture-of-Experts (MoE) model, but with only 10B active parameters per inference, making it incredibly efficient.One key insight Skylar shared was about getting the best performance. M2 supports multiple APIs, but to really unlock its reasoning power, you need to use an API that passes the model’s “thinking” tokens back to it on the next turn, like the Anthropic API. Many open-source tools don’t support this yet, so it’s something to watch out for.Huge congrats to the MiniMax team on this Open Weights (MIT licensed) release, you can find the model on HF! MiniMax had quite a week, with 3 additional releases, MiniMax speech 2.6, an update to their video model Hailuo 2.3 and just after the show, they released a music 2.0 model as well! Congrats on the shipping folks! OpenAI drops gpt-oss-safeguard - first open-weight safety reasoning models for classification ( X, HF )OpenAI is back on the open weights bandwagon, with a finetune release of their previously open weighted gpt-oss models, with gpt-oss-safeguard. These models were trained exclusively to help companies build safeguarding policies to make sure their apps remains safe! With gpt-oss-safeguards 20B and 120B, OpenAI is achieving near parity with their internal safety models, and as Nisten said on the show, if anyone knows about censorship and safety, it’s OpenAI! The highlight of this release is, unlike traditional pre-trained classifiers, these models allow for updates to policy via natural language!These models will be great for businesses that want to safeguard their products in production, and I will advocate to bring these models to W&B Inference soon! A Humanoid Robot in Your Home by 2026? 1X NEO announcement ( X, Order page, Keynote )Things got really spooky when we started talking about robotics. The company 1X, which has been on our radar for a while, officially launched pre-orders for NEO, the world’s first consumer humanoid robot designed for your home. And yes, you can order one right now for $20,000, with deliveries expected in early 2026.The internet went crazy over this announcement, with folks posting receipts of getting one, other folks stoking the uncanny valley fears that Sci-fi has built into many people over the years, of the Robot uprising and talking about the privacy concerns of having a human tele-operate this Robot in your house to do chores. It can handle chores like cleaning and laundry, and for more complex tasks that it hasn’t learned yet, it uses a teleoperation system where a human “1X Expert” can pilot the robot remotely to perform the task. This is how it collects the data to learn to do these tasks autonomously in your specific home environment.The whole release is very interesting, from the “soft and quiet” approach 1X is taking, making their robot a 66lbs short king, draped in a knit sweater, to the $20K price point (effectively at loss given how much just the hands cost), the teleoperated by humans addition, to make sure the Robot learns about your unique house layout. The conversation on the show was fascinating. We talked about all the potential use cases, from having it water your plants and look after your pets while you’re on vacation to providing remote assistance for elderly relatives. Of course, there are real privacy concerns with having a telepresence device in your home, but 1X says these sessions are scheduled by you and have strict no-go zones.Here’s my prediction: by next Halloween, we’ll see videos of these NEO robots dressed up in costumes, helping out at parties. The future is officially here. Will you be getting one? If not this one, when will you think you’ll get one? OpenAI’s Grand Plan: From Recapitalization to ASIThis was by far the biggest update about the world of AI for me this week! Sam Altman was joined by Jakub Pachocki, chief scientist and Wojciech Zaremba, a co-founder, on a live stream to share an update about their corporate structure, plans for the future, and ASI goals (Artificial Superintelligence) First, the company now has a new structure: a non-profit OpenAI Foundation governs the for-profit OpenAI Group. The foundation starts with about 26% equity and has a mission to use AI for public good, including an initial $25 billion commitment to curing diseases and building an “AI Resilience” ecosystem.But the real bombshells were about their research timeline. Chief Scientist Jakub Pachocki stated that they believe deep learning systems are less than a decade away from superintelligence (ASI). He said that at this point, AGI isn’t even the right goal anymore. To get there, they’re planning to have an “AI research intern” by September 2026 and a fully autonomous AI researcher comparable to their human experts by March 2028. This is insane if you think about it. As Yam mentioned, OpenAI is already shipping at an insane speed, releasing Models and Products, Sora, Atlas, Pulse, ChatGPT app store, and this is with humans, assisted by AI. And here, they are talking about complete and fully autonomous researchers, that will be infinitely more scalable than humans, in the next 2 years. The outcomes of this are hard to imagine and are honestly mindblowing. To power all this innovation, Sam revealed they have over $1.4 trillion in obligations for compute (over 30 GW). And said even that’s not enough. Their aspiration is to build a “compute factory” capable of standing up one gigawatt of new compute per week, and he hinted they may need to “rethink their robotics strategy” to build the data centers fast enough. Does this mean OpenAI humanoid robots building factories? 🤔 Plus, don’t forget, Sam is one of the investors in Helion energy, working on power solutions like Fusion, and the above graphic has an Energy block that Sam said they will give an update on later (that’s also what he told me during Dev Day when I asked him about it). Super exciting and honestly mind-blowing stuff, Gigawats per week, fully autonomous researchers, the world is going to look way different in a few years! The Agent Labs Race: Cursor 2.0 vs. Cognition’s SWE-1.5 (X, Blog)This week also saw a major showdown in the agentic coding space. On the very same day, both Cursor and Cognition launched major updates and their own new models, signaling a new era where agent labs are training their own specialized AI.First up, Cursor 2.0 was released with a completely redesigned multi-agent interface and their new model, Composer. Composer is claimed to be four times faster than comparable models, and the new UI is built around managing a fleet of agents that can work in parallel on your codebase. It’s a clear shift from being just an IDE to a full-fledged agent platform. Look, the UI even looks like ChatGPT and no code in sight (until you switch to IDE mode) Their Composer model is also very interesting, and got a lot of folks excited, but the evaluations they shared, and the fact that they didn’t disclose if that’s a finetune of a chinese model (it likely is). Regardless, folks are saying that it’s a very good model that’s also VERY fast! Cognition own coding model - SWE 1.5 ( Blog, X, Windsurf )Then, just hours later, Cognition punched right back with SWE-1.5, their new frontier agent model that now powers Windsurf. The headline here is pure speed. Powered by Cerebras, SWE-1.5 hits a blistering 950 tokens per second—13 times faster than Sonnet 4.5—while achieving near-SOTA performance on SWE-Bench Pro. They’ve achieved this through a co-designed stack where the agent harness, inference system, and model were all built together and optimized with end-to-end reinforcement learning in real coding environments.This competition is fantastic news for all of us. We’re seeing specialized, highly-performant models being developed outside of the big labs, putting more power back in the hands of developers.This Week’s BuzzJust a few quick updates from the world of Weights & Biases and our parent company, CoreWeave.First, big news! CoreWeave announced the acquisition of Marimo, the company behind the popular open-source, reactive notebook for Python. This is another exciting step in building out the essential cloud for AI, adding powerful development tools to the stack alongside best-in-class GPU infrastructure and MLOps with Weights & Biases. Welcome to the Marimo team!Also, Fully Connected is coming to London next week! It’s our premier conference, and we’ll have speakers from Mistral, Google, LlamaIndex, and more. If you’re in Europe, please come join us. DM me if you need tickets!And if you’re in New York from November 19-22, come say hi at the AI Engineer Code Summit. We’re sponsoring and will have a big booth. It’s always a great place to meet folks from this community.Video & Voice: The Multimodal ExplosionThe world of video and voice AI was on fire this week.The absolute highlight was Odyssey ML V2, a new real-time interactive AI video platform. This thing is not like other video models that take minutes to generate a clip. With Odyssey, you type a prompt, and a video starts streaming instantly. Then, you can edit it live. We did a demo on the show where we prompted “army of robots in a starship corridor” and then typed “turn these robots into fluffy covered cat robots,” and the video changed in real time. It’s mind-blowing. This is a glimpse into the future of user-driven, playable media.On the more traditional video front, Sora is now invite-free in the US and Japan, and they launched Character Cameos. You can now upload photos of your pets or objects (like your kid’s carved pumpkin!) and turn them into characters that you and others can use in videos. I, of course, immediately made a cameo of my cat, Sonia.Voice and Audio - Cartesia launches Sonic 3, sub 50ms AI speech modelIn the world of voice, we had Arjun Desai from Cartesia join us to talk about Sonic-3, their new real-time TTS engine. Backed by a new $100M funding round, Sonic-3 is built on State Space Models (not Transformers) and can achieve insane speeds—we’re talking under 50ms latency. But it’s not just fast; it’s also incredibly expressive. It can laugh, emote, and speak 42 languages with natural code-switching. I used their Pro Voice cloning feature to create an AI version of myself, and the results were scarily good. We even had my AI clone host a segment of the show, see it yourself here, powered by Argil and Sonic 3, this is... AI AlexWrapping Up This Spooky Week 🎃As I sit here in my Halloween costume reflecting on this week, I can’t help but feel we’re at an inflection point. We have:* Open source models competing with the best proprietary ones* Humanoid robots becoming consumer products* ASI timelines measured in single-digit years* Real-time interactive AI across all modalitiesAnd yet, nothing about this scares me. If anything, I’m more excited than ever about what we’re building together. Yes, the pace is insane. Yes, keeping up with everything is becoming nearly impossible (and it’s literally my job!). But we’re living through the most transformative period in human history, and we get to be part of it.To everyone building, experimenting, and pushing boundaries - keep going. To everyone worried about what’s coming - join us in shaping it responsibly. And to everyone who celebrated Halloween today - I hope your costume was as epic as the AI developments we covered! 👻Until next week, this is Alex signing off. Remember to subscribe, give us five stars, and I’ll see you next ThursdAI!TL;DR - All Topics CoveredThursdAI - Oct 30 - Halloween Special 👻* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarson* Guest: Skyler Miao - Head of Engineering, MiniMax (@SkylerMiao7)* Guest: Arjun Desai - CoFounder Cartesia (@jundesai)* Open Source LLMs* MiniMax M2: Open-source agentic model at 8% of Claude’s price, 2× speed (X, Hugging Face)* OpenAI GPT-OSS-Safeguard: First open-weight safety reasoning models (X, HF)* IBM Granite 4.0 Nano: Ultra-efficient tiny models for edge deployment (X, Artificial Analysis)* Ming-flash-omni Preview: Sparse MoE omni-modal model (X, HuggingFace)* Kimi Linear: 48B parameter model with 1M context (HF)* Robotics* 1X NEO: First consumer humanoid robot, $20k, delivery 2026 (X, Order page, Keynote)* Big Companies & APIs* OpenAI Restructuring: ASI within 10 years, AI researcher by 2028 (X)* Cursor 2.0 & Composer: 4x faster coding, new model (X, Blog)* Cognition SWE-1.5: 950 tok/s, 40% SWE-bench Pro (Blog, X, Windsurf)* Perplexity Email Assistant: Privacy-first AI inbox management (X, Assistant Site)* This Week’s Buzz* Fully Connected London - fullyconnected.com* AI Engineer Code Summit NYC - Nov 19-22* CoreWeave acquires Marmo notebooks (X)* Vision & Video* Odyssey ML V2: Real-time interactive AI video (X, Experience)* Sora: Now invite-free + Character Cameos feature (X, Sonia Cameo)* Hailuo 2.3: Cinema-grade video generation (X)* Voice & Audio* MiniMax Speech 2.6: X, MiniMax, API Docs)* Cartesia Sonic 3: Real-time TTS with emotion & laughter, $100M funding (X, Website, Docs)* Tools* Pokee: Agentic workflow builder (X)* Pomelli: Google’s AI marketing agent (X, Labs) This is a public episode. 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Hey everyone, Alex here! Welcome... to the browser war II - the AI edition! This week we chatted in depth about ChatGPT’s new Atlas agentic browser, and the additional agentic powers Microsoft added to Edge with Copilot Mode (tho it didn’t work for me) Also this week was a kind of crazy OCR week, with more than 4 OCR models releasing, and the crown one is DeepSeek OCR, that turned the whole industry on it’s head (more later) Quite a few video updates as well, with real time lipsync from Decart, and a new update from LTX with 4k native video generation, it’s been a busy AI week for sure! Additionally, I’ve had the pleasure to talk about AI Browsing agents with Paul from BrowserBase and real time video with Kwindla Kramer from Pipecat/Daily, so make sure to tune in for those interviews, buckle up, let’s dive in! Thanks for reading ThursdAI - Recaps of the most high signal AI weekly spaces! This post is public so feel free to share it.Open Source: OCR is Not What You Think It Is (X, HF, Paper)The most important and frankly mind-bending release this week came from DeepSeek. They dropped DeepSeek-OCR, and let me tell you, this is NOT just another OCR model. The cohost were buzzing about this, and once I dug in, I understood why. This isn’t just about reading text from an image; it’s a revolutionary approach to context compression.We think that DeepSeek needed this as an internal tool, so we’re really grateful to them for open sourcing this, as they did something crazy here. They are essentially turning text into a visual representation, compressing it, and then using a tiny vision decoder to read it back with incredible accuracy. We’re talking about a compression ratio of up to 10x with 97% decoding accuracy. Even at 20x compression they are achieving 60% decoding accuracy! My head exploded live on the show when I read that. This is like the middle-out compression algorithm joke from Silicon Valley, but it’s real. As Yam pointed out, this suggests our current methods of text tokenization are far from optimal.With only 3B and ~570M active parameters, they are taking a direct stab at long context inefficiency, imagine taking 1M tokens, encoding them into 100K visual tokens, and then feeding those into a model. Since the model is tiny, it’s very cheap to run, for example, alphaXiv claimed they have OCRd’ all of the papers on ArXiv with this model for $1000, a task that would have cost $7500 using MistalOCR - as per their paper, with DeepSeek OCR, on a single H100 GPU, its possible to scan up to 200K pages! 🤯 Really innovative stuff! OCR and VLM models had quite a week, with multiple models besides DeepSeek OCR releasing, models like Liquids LFM2-VL-3B (X, HF), and the newly updated 2B and 32B of Qwen3-VL (X, Hugging Face), and AI2’s olmo-ocr 2-7B (X, HF). The Qwen models are particularly interesting, as the 2B model is a generic VLM (can also do OCR) and is close to previous weeks 4B and 8B brothers, and the newly updated 32B model outperforms GPT-5 mini and Claud 4 sonnet even! The Browser Wars are BACK: OpenAI & Microsoft Go AgenticLook, I may be aging myself here, but I remember, as a young frontend dev, having to install 5 browers at once to test them out, Chrome, Internet Explorer, Firefox, Opera etc’. That was then, and now, I have Dia, Comet, and the newly released Atlas, and, yeah, today I even installed Microsoft Edge to test their AI features! It seems like the AI boom brought with it a newly possible reason for folks to try and take a bite out of Chrome (who’s agentic features are long rumored with project mariner but are nowhere to be found/shipped yet) OpenAI’s ChatGPT Atlas: The Browser Reimagined (X, Download)OpenAI is proving that besides just models, they are a product powerhouse, stepping into categories like Shopping (with a shopify integration), app stores (with ChatGPT apps), social (with Sora2) and now... browsers! This week, they have launched their tightly integrated into ChatGPT browser called Atlas, and it’s a big release! I’ll split my review here to 2 parts, the browser features part and the agentic part. New fresh take on a chromium based browserThe tight integration into ChatGPT is everywhere in this browser, from the new tab that looks like the basic ChatGPT interaface, one line of text, to the sidebar on the left that... is the ChatGPT web sidebar with all your chats, projects, custom GPTs etc. The integration doesn’t stop there, as you have to sign in to your ChatGPT account to even use this browser (available only to MacOS users, and Pro, Plus and Nano tiers). The browser has a few neat tricks, like a special tool that allows you to search your browsing history with natural language, a-la “what were those shoes I was looking at a few days ago” will find your the tabs you browsed for shoes. A special and cool feature is called, confusingly “Cursor”, wherein you can select a text, and then click the little OpenAI logo that pops up, allowing you to ask ChatGPT for changes to that selected text (like fix typos, spruce up your writing etc). It’s surprisingly convenient to rewrite tweets or for any type of document editing. ChatGPT Atlas also stores memories about your browsing patterns, which will be additional to the ChatGPT memories it stores about you from chats, helping even more by knowing your browsing patterns, which software you prefer to use, which websites you prefer to order food from etc. This IMO is one of the hugest unlocks for folks inside the ChatGPT ecosystem, as much of a stanard persons peferences can be gleaned from their browser usage and patterns.Lastly, the “Ask ChatGPT” sidepane on the right (which can be opened with cmd+.) is really great for chatting with a webpage, or going down search rabbit holes. It receives the context of the webpage you’re looking at by default (only 1 page so far, competitors allow you to add additional tabs with @, (which is supposedly coming to ChatGPT soon) and ask... ChatGPT anything about this. Agentic SOTA? not so fastThe most important “change” to how browsers work in Atlas imo is the agentic mode. This isn’t new, we remember when ChatGPT launched thier Operator Agent back in January of this year (our coverage) and then renamed it Agent Mode and integrated into ChatGPT itself back in July. So, web browsing agents are not entirely new, what’s novel here though, is the integration into your browser, and the ability for the Atlas browser to use your logged in sessions and cookies, to pretend to be you! This... can be quite scary for some, as prompt injection attacks are getting more popular (where-in malicious a******s add hidden instructions to their website that will get the agent to do something you don’t like) but it’s also very exciting, as the agent can do much much more, without getting blocked by providers who could previously just block Agent Mode as it ran on OpenAI servers! Until today, there were 2 main Agentic browsers in the mix, Perplexity’s Comet (where you can choose which model runs the agent) and Atlas. Comet seems to be doing a little bit better on some stuff on my tests, but not by much. I have the same agentic task (go to X.com, find my bookmarks, open all links, summarize per my specific format) that I’ve been running for a while now, and Comet outdid Atlas this week on that task.Who needs agentic browsing? For some reason, most of the demos for agentic browsing are showing the same, boring-ish examples. Book some flights, collect a grocery shopping cart. I’ve tried new and different things this week, for example, letting Atlas choose and order food for me (as ChatGPT knows my pescatarian preferences, it’s better than Comet for personal stuff), and one of the longest task I’ve had an agent do yet, I asked it to complete a Compliance training I had to take at work! Mind you, this is a very complex task, even for regular people, as these compliance websites are built to not be messed with. They have video players that stop if you switch focus to some other tab, they have interactive quizes and games, drag and drop interfaces, audio buttons, to make sure you really are taking the test. I can happily report, that after 5 hours, and a few stops along the way (where I had to convince the agent to keep going), it completed this very hard task! (and now I have to take this course myself again to actualy be compliant 😅 it will probably take me 2 hours to do manually) This experiment made me think, who needs the agentic browsing features and for what? Well, for tasks that require a lot of manual steps to do the same thing over and over again, agentic browser is going to make a lot of peoples browsing a lot easier. Things like kids schedules reviewing in multiple websites, collecitng data and formatting it differently etc. Scary security implications Atlas could only finish my compliance task while being logged in as me, and ChatGPT Atlas gives a all or nothing control. You can run your agent with full access to your logged in websites (think Gmail etc) or you can essentially give it an incognito mode. This, again, due to the risk of promp injections in malicious websites being more and more prevalent. In a rare post detailing how they are thinking about this, OpenAI Chief Information Security officer offered a deep dive into their attempts to mitigate this issue (Simon Willison had a great breakdown of that information here) but that’s likely not enough, so definitely be aware when you’re running agent mode (which needs to be explicitly turned on right now by selecting Agent) This Weeks Buzz - Weights & Biases // CoreweaveWeights & Biases (now proudly part of CoreWeave) had some exciting updates. Our Fully Connected conference series is hitting Tokyo on October 30-31 and London on November 4-5—perfect for ML practitioners and AI engineers. If you’re in the area, join us for talks, networking, and deep dives into the latest. Register at Fullyconnected.com—DM me if you need a hook-up!We also collaborated with Meta and Stanford on Torch Forge, a new PyTorch-native library for scalable RL post-training and agent development. It’s built for massive GPU runs (we provided 520 H100s!), competing with Ray via tools like Monarch scheduler. If you’re training on clusters, check the blog —it’s a big deal for efficient multi-GPU workflows.Microsoft goes after OpenAI with Edge copilot mode (X)In a pretty surprising move, Microsoft announced today their take on the agentic browser war, with a bunch of enhancements to Copilot (their overall word for their AI assistance across Microsoft 360, Browser, Bing search etc), Think.. clippy, for the AI age (they even brought clippy back as an easter egg) The short version is, Edge is getting more powerful with custom agentic features (which I enabled and couldn’t get to work no matter how much I tried, so I can’t tell you how they compare to Atlas/Comet), and they have a voice mode that allows you to talk to your browser, with Edge having a sense of what’s on the actual page! Of course, this being Microsoft, marketing aside and features aside, when I asked Copilot if it has access to other tabs (like the marketing video claims) it said it doesn’t have access, agentic mode didn’t work, and I’m very unlikely to be testing it further! But hey, if you use Copilot app on your mobile phone, and click the new Mico avatar like 25 times it will turn into Clippy, so.. yay? Claude Code on the Web, Claude on Desktop upgraded (X, Anthropic)Anthropic also made waves by bringing Claude Code to the web. Now you can delegate software tasks to Claude through a web interface with GitHub integration. Nisten was particularly excited about being able to manage his coding projects from his phone. It runs tasks in a secure sandbox, can handle multiple repos, and automatically create pull requests. It’s another powerful coding agent becoming more accessible to developers everywhere. They have also made changes to the desktop Claude app, allowing it to see the context of your screen with screenshots, and file sharing, and even a new voice mode that allows you to talk to Claude (which is unfortunately mapped to the tab button, without the ability to remap) Browser Automation and Delegated Authentication with Browserbase (X, Director.ai, Stagehand)While OpenAI and Microsoft are building chat into the browser, what about bringing the browser into our chat-based agents? We had Paul Klein, the founder of Browserbase, join us to talk about this exact topic. His company is tackling one of the biggest hurdles for AI agents: authentication.Paul and his team launched Director 2.0, a platform that lets you build web automation with natural language prompts. But the real innovation here is their integration with 1Password. Instead of giving an agent the “master keys” to all your logged-in sessions like Atlas does, Browserbase allows for delegated, per-site authentication. When an agent running in the cloud needs to log into a site on your behalf, you get a prompt on your local machine to approve it. This is a much safer, more granular way to give agents the access they need. As Paul said, you shouldn’t let an AI the master keys into your house; you should give it permission to enter one room at a time. It’s a brilliant paradigm for secure agentic workflows and I really like this approach of a piece-meal authentication for browser agents. I wish Atlas has something like this for the incognito mode! Director 2.0 itself is like V0 for web automation—you give it a prompt, it performs the task, and then it gives you a repeatable script you can deploy. It’s a way to create robust automations without needing to be a developer, and it’s already being used to automate thousands of hours of manual work. Video & Audio: The Race to Real-TimeThe world of generative media is moving at lightning speed, with a clear trajectory towards real-time, interactive experiences.Decart’s Real-Time Lip Sync API (X)We had Kwindla Kramer, one of the worlds leading experts in real-time audio, join us to break down a phenomenal release from Decart AI: a real-time lip-sync API. This isn’t the pre-rendered, slightly-off lip-sync we’re used to. This is a pipeline of models working together to generate perfectly synchronized lip movements for an avatar in real-time.Kwindla explained the tech stack: it captures your audio via WebRTC, sends it to Whisper for transcription, gets a response from an LLM like Grok, generates a voice with ElevenLabs, and then Decart’s model modifies the avatar’s video frames to match the new audio, all with a sub-two-second latency. This is how we get to truly interactive, believable AI characters. Kwindla even built a quick demo, though it didn’t seem to work the in the morning, probably GPU issues, so we just played the demo videos. LTX-2 and Sora’s Pet CameosThe trend towards high-fidelity, real-time generation continued with a breaking news release from Lightricks: LTX-2. This is an open-source (weights coming this fall!) engine that can generate native 4K video with synchronized audio. It’s fast, efficient, and is set to be a powerful open alternative to closed models like Sora. And it’s a native 4K, no upscaling! Speaking of Sora, they announced that character cameos are getting an upgrade. Soon, you’ll be able to turn anything—your pet, a coffee cup, or even a sunny-side-up egg—into an animated, talking character. I’m really looking forward for this new Sora update and will let you know my impressions when it drops (soon, according to Bill from OpenAI) What a week folks! What A WEEK! 😅 My head is still spinning! From browsers that can do our work for us to OCR that redefines context, we’re seeing foundational shifts across the board. The tools are getting more powerful, more accessible, and more integrated into our daily workflows. The future is being built right now, and we get to watch it happen week by week.Thank you for being a ThursdAI subscriber. As always, here are the show notes with all the links and details from this week’s whirlwind of AI news.* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @yampeleg @nisten @ldjconfirmed* Paul Kelin @pk_iv - Browser Base* Kwindla Kramer @kwindla - Pipecat & Daily* Open Source LLMs* DeepSeek-OCR: Efficient Vision-Text Compression for Massive Contexts (X, HF, Paper)* Liquid AI LFM2-VL-3B: Tiny Multilingual Vision-Language Model (X, HF)* PokeeResearch-7B: Open-source SOTA Deep Research Agent (X, HF, Web, ArXiv, GitHub)* Qwen3-VL 2B & 32B: compact STEM-tuned multimodal powerhouses (X, Hugging Face)* Big CO LLMs + APIs* OpenAI announces Atlas - its agentic AI browser (X, Download)* Security Implications, Injection + note from CISO* Claude Code on the Web: Cloud Coding with Secure Sandboxing (X, Anthropic)* Meta bans 1‑800‑ChatGPT on WhatsApp* Microsoft agentic addition to Copilot Mode in Edge (X)* Gemini AI Studio launches “Vibe Coding” (X, AI Studio Build)* This weeks Buzz* Fully connected comes to Tokyo (Oct 30-31) and London (Nov 4-5) ! (register at Fullyconnected.com)* Vision & Video* Sora is about to get pet cameos* Krea open‑sources a 14‑billion‑parameter real‑time video model (X, HF)* Reve’s unannounced video mode!? 1080p + sound* LTX-2: open-source 4K audio+video generation engine from Lightricks (X, Website, GitHub)* Voice & Audio* Decart Lip Sync API: Real-Time Avatar Lip Movement (X)* Tools* Browserbase launches Director 2.0: prompt-powered web automation (X, Director.ai, Stagehand) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey folks, Alex here. Can you believe it’s already the middle of October? This week’s show was a special one, not just because of the mind-blowing news, but because we set a new ThursdAI record with four incredible interviews back-to-back!We had Jessica Gallegos from Google DeepMind walking us through the cinematic new features in VEO 3.1. Then we dove deep into the world of Reinforcement Learning with my new colleague Kyle Corbitt from OpenPipe. We got the scoop on Amp’s wild new ad-supported free tier from CEO Quinn Slack. And just as we were wrapping up, Swyx ( from Latent.Space , now with Cognition!) jumped on to break the news about their blazingly fast SWE-grep models. But the biggest story? An AI model from Google and Yale made a novel scientific discovery about cancer cells that was then validated in a lab. This is it, folks. This is the “let’s f*****g go” moment we’ve been waiting for. So buckle up, because this week was an absolute monster. Let’s dive in!ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Open Source: An AI Model Just Made a Real-World Cancer DiscoveryWe always start with open source, but this week felt different. This week, open source AI stepped out of the benchmarks and into the biology lab.Our friends at Qwen kicked things off with new 3B and 8B parameter versions of their Qwen3-VL vision model. It’s always great to see powerful models shrink down to sizes that can run on-device. What’s wild is that these small models are outperforming last generation’s giants, like the 72B Qwen2.5-VL, on a whole suite of benchmarks. The 8B model scores a 33.9 on OS World, which is incredible for an on-device agent that can actually see and click things on your screen. For comparison, that’s getting close to what we saw from Sonnet 3.7 just a few months ago. The pace is just relentless.But then, Google dropped a bombshell. A 27-billion parameter Gemma-based model they developed with Yale, called C2S-Scale, generated a completely novel hypothesis about how cancer cells behave. This wasn’t a summary of existing research; it was a new idea, something no human scientist had documented before. And here’s the kicker: researchers then took that hypothesis into a wet lab, tested it on living cells, and proved it was true.This is a monumental deal. For years, AI skeptics like Gary Marcus have said that LLMs are just stochastic parrots, that they can’t create genuinely new knowledge. This feels like the first, powerful counter-argument. Friend of the pod, Dr. Derya Unutmaz, has been on the show before saying AI is going to solve cancer, and this is the first real sign that he might be right. The researchers noted this was an “emergent capability of scale,” proving once again that as these models get bigger and are trained on more complex data—in this case, turning single-cell RNA sequences into “sentences” for the model to learn from—they unlock completely new abilities. This is AI as a true scientific collaborator. Absolutely incredible.Big Companies & APIsThe big companies weren’t sleeping this week, either. The agentic AI race is heating up, and we’re seeing huge updates across the board.Claude Haiku 4.5: Fast, Cheap Model Rivals Sonnet 4 Accuracy (X, Official blog, X)First up, Anthropic released Claude Haiku 4.5, and it is a beast. It’s a fast, cheap model that’s punching way above its weight. On the SWE-bench verified benchmark for coding, it hit 73.3%, putting it right up there with giants like GPT-5 Codex, but at a fraction of the cost and twice the speed of previous Claude models. Nisten has already been putting it through its paces and loves it for agentic workflows because it just follows instructions without getting opinionated. It seems like Anthropic has specifically tuned this one to be a workhorse for agents, and it absolutely delivers. The thing to note also is the very impressive jump in OSWorld (50.7%), which is a computer use benchmark, and at this price and speed ($1/$5 MTok input/output) is going to make computer agents much more streamlined and speedy! ChatGPT will loose restrictions; age-gating enables “adult mode” with new personality features coming (X) Sam Altman set X on fire with a thread announcing that ChatGPT will start loosening its restrictions. They’re planning to roll out an “adult mode” in December for age-verified users, potentially allowing for things like erotica. More importantly, they’re bringing back more customizable personalities, trying to recapture some of the magic of GPT-4.0 that so many people missed. It feels like they’re finally ready to treat adults like adults, letting us opt-in to R-rated conversations while keeping strong guardrails for minors. This is a welcome change, and we’ve been advocating for this for a while, and it’s a notable change from the XAI approach I covered last week. Opt in for adults with verification while taking precautions vs engagement bait in the form of a flirty animated waifu with engagement mechanics. Microsoft is making every windows 11 an AI PC with copilot voice input and agentic powers (Blog,X)And in breaking news from this morning, Microsoft announced that every Windows 11 machine is becoming an AI PC. They’re building a new Copilot agent directly into the OS that can take over and complete tasks for you. The really clever part? It runs in a secure, sandboxed desktop environment that you can watch and interact with. This solves a huge problem with agents that take over your mouse and keyboard, locking you out of your own computer. Now, you can give the agent a task and let it run in the background while you keep working. This is going to put agentic AI in front of hundreds of millions of users, and it’s a massive step towards making AI a true collaborator at the OS level.NVIDIA DGX - the tiny personal supercomputer at $4K (X, LMSYS Blog)NVIDIA finally delivered their promised AI Supercomputer, and while the excitement was in the air with Jensen hand delivering the DGX Spark to OpenAI and Elon (recreating that historical picture when Jensen hand delivered a signed DGX workstation while Elon was still affiliated with OpenAI). The workstation was sold out almost immediately. Folks from LMSys did a great deep dive into specs, all the while, folks on our feeds are saying that if you want to get the maximum possible open source LLMs inference speed, this machine is probably overpriced, compared to what you can get with an M3 Ultra Macbook with 128GB of RAM or the RTX 5090 GPU which can get you similar if not better speeds at significantly lower price points. Anthropic’s “Claude Skills”: Your AI Agent Finally Gets a Playbook (Blog)Just when we thought the week couldn’t get any more packed, Anthropic dropped “Claude Skills,” a huge upgrade that lets you give your agent custom instructions and workflows. Think of them as expertise folders you can create for specific tasks. For example, you can teach Claude your personal coding style, how to format reports for your company, or even give it a script to follow for complex data analysis.The best part is that Claude automatically detects which “Skill” is needed for a given task, so you don’t have to manually load them. This is a massive step towards making agents more reliable and personalized, moving beyond just a single custom instruction and into a library of repeatable, expert processes. It’s available now for all paid users, and it’s a feature I’ve been waiting for. Our friend Simon Willison things skills may be a bigger deal than MCPs! 🎬 Vision & Video: Veo 3.1, Sora Gets Longer, and Real-Time WorldsThe AI video space is exploding. We started with an amazing interview with Jessica Gallegos, a Senior Product Manager at Google DeepMind, all about the new Veo 3.1. This is a significant 0.1 update, not a whole new model, but the new features are game-changers for creators.The audio quality is way better, and they’ve massively improved video extensions. The model now conditions on the last second of a clip—including the audio. This means if you extend a video of someone talking, they keep talking in the same voice! This is huge, saving creators from complex lip-syncing and dubbing workflows. They also added object insertion and removal, which works on both generated and real-world video. Jessica shared an incredible story about working with director Darren Aronofsky to insert a virtual baby into a live-action film shot, something that’s ethically and practically very difficult to do on a real set. These are professional-grade tools that are becoming accessible to everyone. Definitely worth listening to the whole interview with Jessica, starting at 00:25:44I’ve played with the new VEO in Google Flow, and while I was somewhat (still) disappointed with the UI itself (it froze sometimes and didn’t play). I wasn’t able to upload my own videos to use the insert/remove features Jessica mentioned yet, but saw examples online and they looked great! Ingredients were also improved with VEO 3.1, where you can add up to 3 references, and they will be included in your video but not as first frame, the model will use them to condition the vidoe generation. Jessica clarified that if you upload sound, as in, your voice, it won’t show up in the model as your voice yet, but maybe they will add this in the future (at least this was my feedback to her). SORA 2 extends video gen to 15s for all, 25 seconds to pro users with a new storyboard Not to be outdone, OpenAI pushed a bit of an update for Sora. All users can now generate up to 15-second clips (up from 8-10), and Pro users can go up to 25 seconds using a new storyboard feature. I’ve been playing with it, and while the new scene-based workflow is powerful, I’ve noticed the quality can start to degrade significantly in the final seconds of a longer generation (posted my experiments here) as you can see. The last few shot so the cowboy don’t have any action, and the face is a blurry mess. Worldlabs RTFM: Real-Time Frame Model renders 3D worlds at interactive speeds on a single H100 ( X, Blog, Demo )And just when we thought we’d seen it all, World Labs dropped a breaking news release: RTFM, the Real-Time Frame Model. This is a generative world model that renders interactive, 3D-consistent worlds on the fly, all on a single H100 GPU. Instead of pre-generating a 3D environment, it’s a “learned renderer” that streams pixels as you move. We played with the demo live on the show, and it’s mind-blowing. The object permanence is impressive; you can turn 360 degrees and the scene stays perfectly coherent. It feels like walking around inside a simulation being generated just for you.This Week’s Buzz: RL Made Easy with Serverless RL + interview with Kyle CorbittIt was a huge week for us at Weights & Biases and CoreWeave. I was thrilled to finally have my new colleague Kyle Corbitt, founder of OpenPipe, back on the show to talk all things Reinforcement Learning (RL).RL is the technique behind the massive performance gains we’re seeing in models for tasks like coding and math. At a high level, it lets a model try things, and then you “reward” it for good outcomes and penalize it for bad ones, allowing it to learn strategies that are better than what was in its original training data. The problem is, it’s incredibly complex and expensive to set up the infrastructure. You have to juggle an inference stack for generating the “rollouts” and a separate training stack for updating the model weights.This is the problem Kyle and his team have solved with Serverless RL, which we just launched and we covered last week. It’s a new offering that lets you run RL jobs without managing any servers or GPUs. The whole thing is powered by the CoreWeave stack, with tracing and evaluation beautifully visualized in Weave.We also launched a new model from the OpenPipe team on our inference service: a fine-tune-friendly “instruct” version of Qwen3 14B. The team is not just building amazing products, they’re also contributing great open-source models. It’s awesome to be working with them.🛠️ Tools & Agents: Free Agents & Lightning-Fast Code SearchThe agentic coding space saw two massive announcements this week, and we had the representatives of both companies on the show to discuss them!First, Quinn Slack from Amp announced that they’re launching a completely free, ad-supported tier. I’ll be honest, my first reaction was, “Ads? In my coding agent? Eww.” But the more I thought about it, the more it made sense. My AI subscriptions are stacking up, and this model makes powerful agentic coding accessible to students and developers who can’t afford another $20/month. The ads are contextual to your codebase (think Baseten or Axiom), and they’re powered by a rotating mix of models using surplus capacity from providers. It’s a bold and fascinating business model.This move was met with generally positive responses, though folks from a competing agent, claim that Amp is serving Grok-4-fast which XAI is giving out for free anyway? We’ll see how this shakes up. Cognition announces SWE-grep: RL-powered multi-turn context retriever for agentic code search (Blog, X, Playground, Windsurf)Then, just as we were about to sign off, friend of the pod Swyx (now from Cognition) dropped in with breaking news about SWE-grep. It’s a new, RL-tuned sub-agent for their Windsurf editor that makes code retrieval and context gathering ridiculously fast. We’re talking over 2,800 tokens per second. (yes, they are using Cerebras under the hood) The key insight from Swyx is that their model was trained for natively parallel tool calling, running up to eight searches on a codebase simultaneously. This speeds up the “read” phase of an agent’s workflow—which is 60-70% of the work—by 3-5x. It’s all about keeping the developer in a state of flow, and this is a huge leap forward in making agent interactions feel instantaneous. Swyx also dropped a hint that the next thing that comes is CodeMaps and they will make these retrievers look trivial! This was one for the books, folks. An AI making a novel cancer discovery, video models taking huge leaps, and the agentic coding space is on fire. The pace of innovation is just breathtaking. Thank you for being a ThursdAI subscriber, and as always, here’s the TL:DR and show notes for everything that happened in AI this week.TL;DR and Show Notes* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed* Jessica Gallegos, Sr. Product Manager, Google DeepMind* Kyle Corbitt (@corbtt) - OpenPipe//W&B* Quinn Slack (@sqs) - Amp* Swyx (@swyx) - Cognition* Open Source LLMs* KAIST KROMo - bilingual Korean/English 10B (HF, Paper)* Qwen3-VL 3B and 8B (X post, HF)* Google’s C2S-Scale 27B: AI Model Validates Cancer Hypothesis in Living Cells (X, Blog, Paper)* Big CO LLMs + APIs* Claude Haiku 4.5: Fast, Cheap Model Rivals Sonnet 4 Accuracy (X, Official blog)* ChatGPT will loose restrictions; age-gating enables “adult mode” with new personality features coming (X)* OpenAI updates memory management - no more “memory full” (X, FAQ)* Microsoft is making every windows 11 an AI PC with copilot voice input (X)* Claude Skills: Custom instructions for AI agents now live (X, Anthropic News, YouTube Demo)* Hardware* NVIDIA DGX Spark: desktop personal supercomputer for AI prototyping and local inference (LMSYS Blog)* Apple announces M5 chip with double AI performance (Apple Newsroom)* OpenAI and Broadcom set to deploy 10 gigawatts of custom AI accelerators (Official announcement)* This weeks Buzz* New model - OpenPipe Qwen3 14B instruct (link)* Interview with Kyle Corbitt - RL, Serverless RL* W&B Fully Connected London & Tokyo in 20 days - SIGN UP* Vision & Video* Veo 3.1: Google’s Next-Gen Video Model Launches with Cinematic Audio (Developers Blog)* Sora up to 15s and pro now up to 25s generation with a new storyboard feature* Baidu’s MuseStreamer has >20 second generations (X)* AI Art & Diffusion & 3D* Worldlabs RTFM: Real-Time Frame Model renders 3D worlds at interactive speeds on a single H100 (Blog, Demo)* DiT360: SOTA Panoramic Image Generation with Hybrid Training (Project page, GitHub)* Riverflow 1 tops the image‑editing leaderboard (Sourceful blog)* Tools* Amp launches a Free tier - powered by ads and surplus model capacity (Website)* Cognition SWE-grep: RL-powered multi-turn context retriever for agentic code search (Blog, Playground) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey everyone, Alex here 👋We’re deep in the post-reality era now. Between Sora2, the latest waves of video models, and “is-that-person-real” cameos, it’s getting genuinely hard to trust what we see. Case in point: I recorded a short clip with (the real) Sam Altman this week and a bunch of friends thought I faked it with Sora-style tooling. Someone even added a fake Sora watermark just to mess with people. Welcome to 2025.This week’s episode and this write-up focus on a few big arcs we’re all living through at once: OpenAI’s Dev Day and the beginning of the agent-app platform inside ChatGPT, a bizarre and exciting split-screen in model scaling where a 7M recursive model from Samsung is suddenly competitive on reasoning puzzles while inclusionAI is shipping a trillion-parameter mixture-of-reasoners, and Grok’s image-to-video now does audio and pushes the line on… taste. We also dove into practical evals for coding agents with Eric Provencher from Repo Prompt, and I’ve got big news from my day job world: W&B + CoreWeave launched Serverless RL, so training and deploying RL agents at scale is now one API call away.Let’s get into it.OpenAI’s 3rd Dev Day - Live Coverage + exclusive interviewsThis is the third Dev Day that I got to attend in person, covering this for ThursdAI (2023, 2024), and this one was the best by far! The production quality of their events rises every year, and this year they’ve opened up the conference to >1500 people, had 3 main launches and a lot of ways to interact with the OpenAI folks! I’ve also gotten an exclusive chance to sit in on a fireside chat with Sam Altman and Greg Brokman (snippets of which I’ve included in the podcast, starting 01:15:00 and I got to ask Sam a few questions after that as well. Event Ambiance and VibesOpenAI folks outdid themselves with this event, the live demos were quite incredible, the location (Fort Mason), Food and just the whole thing was on point. The event concluded with a 1x1 Sam and Jony Ive chat that I hope will be published on YT sometime, because it was very insightful. By far the best reason to go to this event in person is meeting folks and networking, both OpenAI employees, and AI Engineers who use their products. It’s 1 day a year, when OpenAI makes all their employees who attend, Developer Experience folks, as you can and are encouraged to, interact with them, ask questions, give feedback and it’s honestly great! I really enjoy meeting folks at this event and consider this to be a very high signal network, and was honored to have quite a few ThursdAI listeners among the participants and OpenAI folk! If you’re reading this, thank you for your patronage 🫡 Launches and ShipsOpenAI also shipped, and shipped a LOT! Sam was up on Keynote with 3 main pillars, which we’ll break down 1 by 1. ChatGPT Apps, AgentKit (+ agent builder) and Codex/New APIsCodex & New APIsCodex has gotten General Availability, but we’ve been using it all this time so we don’t really care, what we do care about is the new slack integration and the new Codex SDK, which means you can now directly inject Codex agency into your app. This flew a bit over people’s heads, but Romain Huet, VP of DevEx at OpenAI demoed on stage how his mobile app now has a Codex tab, where he can ask Codex to make changes to the app at runtime! It was quite crazy! ChatGPT Apps + AppsSDKThis was maybe the most visual and most surprising release, since they’ve tried to be an appstore before (plugins, customGPTs). But this time, it seems like, based on top of MCP, ChatGPT is going to become a full blown Appstore for 800+ million weekly active ChatGPT users as well. Some of the examples they have showed included Spotify and Zillow, where just by typing in “Spotify” in chatGPT, you will have an interactive app with it’s own UI, right inside of ChatGPT. So you could ask it to create a playlist for you based on your history, or ask Zillow to find homes in an area under a certain $$ amount.The most impressive thing, is that those are only launch partners, everyone can (technically) build a ChatGPT app with their AppsSDK that’s built on top of... the MCP (model context protocol) spec! The main question remains about discoverability, this is where Plugins and CustomGPTs (previous attempts to create apps within ChatGPT have failed), and when I asked him about it, Sam basically said “we’ll iterate and get it right” (starting 01:17:00). So it remains to be seen if folks really need their ChatGPT as yet another Appstore. AgentKit, AgentBuilder and ChatKit2025 is the year of agents, and besides launching quite a few of their own, OpenAI will not let you, build and host smart agents that can use tools, on their platform. Supposedly, with AgentBuilder, building agents is just dragging a few nodes around, prompting and connecting them. They had a great demo on stage where with less than 8 minutes, they’ve build an agent to interact with the DevDay website.It’s also great to see how greatly does OpenAI adapt the MCP spec, as this too, is powered by MCP, as in, any external connection you want to give your agent, must happen with an MCP server. Agents for the masses is maybe not quite there yetIn reality though, things are not so easy. Agents require more than just a nice drag & drop interface, they require knowledge, iteration, constant evaluation (which they’ve also added, kudos!) and eventually, customized agents need code. I spent an hour trying it out yesterday, building an agent to search the ThursdAI archives. The experience was a mixed bag. The AI-native features are incredibly cool. For instance, you can just describe the JSON schema you want as an output, and it generates it for you. The widget builder is also impressive, allowing you to create custom UI components for your agent’s responses.However, I also ran into the harsh realities of agent building. My agent’s web browsing tool failed because Substack seems to be blocking OpenAI’s crawlers, forcing me to fall back on the old-school RAG approach of uploading our entire archive to a vector store. And while the built-in evaluation and tracing tools are a great idea, they were buggy and failed to help me debug the error. It’s a powerful tool, but it also highlights that building a reliable agent is an iterative, often frustrating process that a nice UI alone can’t solve. It’s not just about the infrastructure; it’s about wrestling with a stochastic machine until it behaves.But to get started with something simple, they have definitely pushed the envelope on what is possible without coding. OpenAI also dropped a few key API updates:* GPT-5-Pro is now available via API. It’s incredibly powerful but also incredibly expensive. As Eric mentioned, you’re not going to be running agentic loops with it, but it’s perfect for a high-stakes initial planning step where you need an “expert opinion.”* SORA 2 is also in the API, allowing developers to integrate their state-of-the-art video generation model into their own apps. The API can access the 15-second “Pro” model but doesn’t support the “Cameo” feature for now.* Realtime-mini is a game-changer for voice AI. It’s a new, ultra-fast speech-to-speech model that’s 80% cheaper than the original Realtime API. This massive price drop removes one of the biggest barriers to building truly conversational, low-latency voice agents.My Chat with Sam & Greg - On Power, Responsibility, and EnergyAfter the announcements, I’ve got to sit in a fireside chat with Sam Altman and Greg Brockman and ask some questions. Here’s what stood out:When I asked about the energy requirements for their massive compute plans (remember the $500B Stargate deal?), Sam said they’d have announcements about Helion (his fusion investment) soon but weren’t ready to talk about it. Then someone from Semi Analysis told me most power will come from... generator trucks. 15-megawatt generator trucks that just drive up to data centers. We’re literally going to power AGI with diesel trucks!On responsibility, when I brought up the glazing incident and asked how they deal with being in the lives of 800+ million people weekly, Sam’s response was sobering: “This is not the excitement of ‘oh we’re building something important.’ This is just the stress of the responsibility... The fact that 10% of the world is talking to kind of one brain is a strange thing and there’s a lot of responsibility.”Greg added something profound: “AI is far more surprising than I anticipated... The deep nuance of how these problems contact reality is something that I think no one had anticipated.”This Week’s Buzz: RL X-mas came early with Serverless RL! (X, Blog)Big news from our side of the world! About a month ago, the incredible OpenPipe team joined us at Weights & Biases and CoreWeave. They are absolute wizards when it comes to fine-tuning and Reinforcement Learning (RL), and they wasted no time combining their expertise with CoreWeave’s massive infrastructure.This week, they launched Serverless RL, a managed reinforcement learning service that completely abstracts away the infrastructure nightmare that usually comes with RL. It automatically scales your training and inference compute, integrates with W&B Inference for instant deployment, and simplifies the creation of reward functions and verifiers. RL is what turns a good model into a great model for a specific task, often with surprisingly little data. This new service massively lowers the barrier to entry, and I’m so excited to see what people build with it. We’ll be doing a deeper dive on this soon but please check out the Colab Notebook to get a taste of what AutoRL is like! Open SourceWhile OpenAI was holding its big event, the open-source community was busy dropping bombshells of its own.Samsung’s TRM: Is This 7M Parameter Model... Magic? (X, Blog, arXiv)This was the release that had everyone’s jaws on the floor. A single researcher from the Samsung AI Lab in Montreal released a paper on a Tiny Recursive Model (TRM). Get this: it’s a 7 MILLION parameter model that is outperforming giants like DeepSeek-R1 and Gemini 2.5 Pro on complex reasoning benchmarks like ARC-AGI. I had to read that twice. 7 million, not billion.How is this possible? Instead of relying on brute-force scale, TRM uses a recursive process. It generates a first draft of an answer, then repeatedly critiques and refines its own logic in a hidden “scratchpad” up to 16 times. As Yam pointed out, the paper is incredibly insightful, and it’s a groundbreaking piece of work from a single author, which is almost unheard of these days. Eric made a great point that because it’s so small, it opens the door for hobbyists and solo researchers to experiment with cutting-edge architectures on their home GPUs. This feels like a completely new direction for AI, and it’s incredibly exciting.inclusionAI’s Ling-1T: Enter the Trillion Parameter Club (X, HF, Try it)On the complete opposite end of the scale (about 3 OOM away), we have Ling-1Tfrom inclusionAI. This is a 1 TRILLION parameter Mixture-of-Experts (MoE) model. The key here is efficiency; while it has a trillion total parameters, it only uses about 37 billion active parameters per token.The benchmarks are wild, showing it beating models like GPT-5-Main (in non-thinking mode) and Gemini 2.5 on a range of reasoning tasks. They claim to match Gemini’s performance using about half the compute. Of course, with any new model posting huge scores, there’s always the question of whether it was trained on the public test sets, but the results are undeniably impressive. It’s another example of the push towards maintaining top-tier performance while drastically reducing the computational cost of inference.More Open Source Goodness: Microsoft, AI21, and IBMIt didn’t stop there.* Microsoft released UserLM-8B, a fascinating Llama 3 finetune trained not to be an assistant, but to simulate the user in a conversation. As Yam explained from his own experience, this is a super useful technique for generating high-quality, multi-turn synthetic data to train more robust chatbot agents. (X, HF)* Our friends at AI21 Labs are back with Jamba Reasoning 3B, a tiny but mighty 3-billion-parameter model. It uses a hybrid SSM-Transformer architecture, which makes it incredibly fast for its size, making it a great option for local inference on a laptop.* IBM also released their Granite family of models, which also use a hybrid design. Their big focus is on enterprise-grade governance and trust, and it’s the first open model family to get an ISO certification for AI management systems.Big Company Moves: Grok Imagine Levels Up... And Leans InFinally, let’s talk about the latest update to Grok Imagine. They’ve rolled out video generation with synchronized sound, and it’s fast—often faster than Sora. The quality has significantly improved, and it’s a powerful tool.However, I have to talk about the other side of this. Grok is positioning itself as the “uncensored” alternative, and they are leaning into that hard. Their video generator has a “spicy” mode that explicitly generates 18+ content. But the thing that truly disturbed me was a new feature with their animated character, “Annie.” It’s a gamified engagement mechanic where you “make your connection better” by talking to her every day to unlock special rewards, like new outfits.To be blunt, this is disgusting. We talk a lot on this show about the immense responsibility that comes with building these powerful AIs. I know from my conversations with folks at OpenAI and other labs that they think deeply about safety, preventing misuse, and the psychological impact these systems can have. This feature from Grok is the polar opposite. It leans into the worst fears about AI creating addictive, para-social relationships. It’s exploitative, and frankly, the team behind it should reconsider their choices IMO. All righty, this is mostly the new for this week, it’s been a very busy week, and if you’d like to see our live coverage + DevDay keynote + interviews I’ve had with Simon Willison , Greg Kamradt, Jeffrey Huber, Allesio from Latent.Space, Matthew Berman and more impactful folks, our livestream can be found here: I’m incredibly humbled and privileged to keep being invited to the Dev Day, and looking forward to cover more events, with exclusive interviews, on the ground reporting and insights. Please subscribe if you like this content to continue. TL;DR and Show Notes* Show Notes & Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co-Hosts - @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed* Guest: Kyle Corbitt - OpenPipe / CoreWeave (@corbtt)* Guest: Eric Provencher - Repo Prompt (@pvncher)* OpenAI Dev Day* OpenAI AgentKit All-in-One Agent Builder (X, OpenAI)* ChatGPT Apps & New APIs (GPT-5-pro, SORA, realtime-mini)* Open Source LLMs* Microsoft UserLM-8B Model Released (X, HF)* Samsung Tiny Recursive Model (TRM) (X, Blog, arXiv)* AI21 Labs releases Jamba Reasoning 3B (X, HF)* inclusionAI debuts Ling-1T: Trillion-Scale Efficient Reasoner (X, HF, Try it)* IBM Granite Models* Evals* Repo Bench by Repo Prompt (Web)* Big CO LLMs + APIs* Qwen 3 Omni & Realtime Models* Google DeepMind unveils Gemini 2.5 Computer-Use model (X, Blog)* Google Gemini Flash 2.5 (new)* Grok Imagine updated with video and sound* This weeks Buzz* OpenPipe (part of Coreweave,W&B) launch Serverless RL (X, Blog, Notebook)* Vision & Video* Ovi: Open Source Video & Synchronized Audio Generation (X, HF)* Voice & Audio* GPT-realtime-mini: OpenAI’s ultra-fast speech-to-speech model API (OpenAI Blog, TechCrunch)* AI Art & Diffusion & 3D* Bagel.com: Paris – Decentralized Diffusion Model (X, HF, Blogpost) This is a public episode. 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Hey everyone, Alex here (yes the real me if you’re reading this) The weeks are getting crazier, but what OpenAI pulled this week, with a whole new social media app attached to their latest AI breakthroughs is definitely breathtaking! Sora2 released and instantly became a viral sensation, shooting to the top 3 free iOS spot on AppStore, with millions of videos watched, and remixed. On weeks like these, even huge releases like Claude 4.5 are taking the backseat, but we still covered them! For listeners of the pod, the second half of the show was very visual heavy, so it may be worth it watching the YT video attached in a comment if you want to fully experience the Sora revolution with us! (and if you want a SORA invite but don’t have one yet, more on that below) ThursdAI - if you find this valuable, please support us by subscribing! Sora 2 - the AI video model that signifies a new era of social mediaLook, you’ve probably already heard about the SORA-2 release, but in case you haven’t, OpenAI released a whole new model, but attached it to a new, AI powered social media experiment in the form of a very addictive TikTok style feed. Besides being hyper-realistic, and producing sounds and true to source voice-overs, Sora2 asks you to create your own “Cameo” by taking a quick video, and then allows you to be featured in your own (and your friends) videos. This makes a significant break from the previously “slop” based meta Vibes, becuase, well, everyone loves seeing themselves as the stars of the show! Cameos are a stroke of genius, and what’s more, one can allow everyone to use their Cameo, which is what Sam Altman did at launch, making everyone Cameo him, and turning him, almost instantly into one of the most meme-able (and approachable) people on the planet! Sam sharing away his likeness like this for the sake of the app achieved a few things, it added trust in the safety features, made it instantly viral and showed folks they shouldn’t be afraid of adding their own likeness. Vibes based feed and remixingSora 2 is also unique in that, it’s the first social media with UGC (user generated content) where content can ONLY be generated, and all SORA content is created within the app. It’s not possible to upload pictures that have people to create the posts, and you can only create posts with other folks if you have access to their Cameos, or by Remixing existing creations. Remixing is also a way to let users “participate” in the creation process, by adding their own twist and vibes! Speaking of Vibes, while the SORA app has an algorithmic For You page, they have a completely novel and new way to interact with the algorithm, by using their Pick a Mood feature, where you can describe which type of content you want to see, or not see, with natural language! I believe that this feature will come to all social media platforms later, as it’s such a game changer. Want only content in a specific language? or content that doesn’t have Sam Altman in it? Just ask! Content that makes you feel goodThe most interesting thing is about the type of content is, there’s no sexualisation (because all content is moderated by OpenAI strong filters), and no gore etc. OpenAI has clearly been thinking about teenagers and have added parent controls, things like being able to turn of the For You page completely etc to the mix. Additionally, SORA seems to be a very funny model, and I mean this literally. You can ask the video generation for a joke and you’ll often get a funny one. The scene setup, the dialogue, the things it does even unprompted are genuinely entertaining. AI + Product = Profit? OpenAI shows that they are one of the worlds best product labs in the world, not just a foundational AI lab. Most AI advancements are tied to products, and in this case, the whole experience is so polished, it’s hard to accept that it’s a brand new app from a company that didn’t do social before. There’s very little buggy behavior, videos are loaded up quick, there’s even DMs! I’m thoroughly impressed and am immersing myself in the SORA sphere. Please give me a follow there and feel free to use my Cameo by tagging @altryne in there. I love seeing how folks have used my Cameo, it makes me laugh 😂 The copyright question is.. wildRemember last year when I asked Sam why Advanced Voice Mode couldn’t sing Happy Birthday? He said they didn’t have classifiers to detect IP violations. Well, apparently that’s not a concern anymore because SORA 2 will happily generate perfect South Park episodes, Rick and Morty scenes, and Pokemon battles. They’re not even pretending they didn’t train on this stuff. You can even generate videos with any dead famous person (I’ve had zoom meetings with Michael Jackson and 2Pac, JFK and Mister Rogers) Our friend Ryan Carson already used it to create a YouTube short ad for his startup in two minutes. What would have cost $100K and three months now takes six generations and you’re done. This is the real game-changer for businesses.Getting invitedEDIT: If you’re reading this on Friday, try the code `FRIYAY` and let me know in comments if it worked for you 🙏I wish I would have invites for all of you, but all invited users have 4 other folks they can invite, so we shared a bunch of invites during the live show, and asked folks to come back and invite other listeners, this went on for half an hour so I bet we’ve got quite a few of you in! If you’re still looking for an invite, you can visit the thread on X and see who claimed and invite and ask them for one, tell them you’re also a ThursdAI listener, they hopefully will return the favor! Alternatively, OpenAI employees often post codes with a huge invite ratio, so follow @GabrielPeterss4 who often posts codes and you can get in there fairly quick, and if you’re not in the US, I heard a VPN works well. Just don’t forget to follow me on there as well 😉A Week with OpenAI Pulse: The Real Agentic Future is HereListen to me, this may be a hot take. I think OpenAI Pulse is a bigger news story than Sora. I’ve told you about Pulse last week, but today on the show I was able to share my weeks worth of experience, and honestly, it’s now the first thing I look at when I wake up in the morning after brushing my teeth! While Sora is changing media, Pulse is changing how we interact with AI on a fundamental level. Released to Pro subscribers for now, Pulse is an agentic, personalized feed that works for you behind the scenes. Every morning, it delivers a briefing based on your interests, your past conversations, your calendar—everything. It’s the first asynchronous AI agent I’ve used that feels truly proactive.You don’t have to trigger it. It just works. It knew I had a flight to Atlanta and gave me tips. I told it I was interested in Halloween ideas for my kids, and now it’s feeding me suggestions. Most impressively, this week it surfaced a new open-source video model, Kandinsky 5.0, that I hadn’t seen anywhere on X or my usual news feeds. An agent found something new and relevant for my show, without me even asking.This is it. This is the life-changing-level of helpfulness we’ve all been waiting for from AI. Personalized, proactive agents are the future, and Pulse is the first taste of it that feels real. I cannot wait for my next Pulse every morning.This Week’s Buzz: The AI Build-Out is NOT a BubbleThis show is powered by Weights & Biases from CoreWeave, and this week that’s more relevant than ever. I just got back from a company-wide offsite where we got a glimpse into the future of AI infrastructure, and folks, the scale is mind-boggling.CoreWeave, our parent company, is one of the key players providing the GPU infrastructure that powers companies like OpenAI and Meta. And the commitments being made are astronomical. In the past few months, CoreWeave has locked in a $22.4B deal with OpenAI, a $14.2B pact with Meta, and a $6.3B “backstop” guarantee with NVIDIA that runs through 2032.If you hear anyone talking about an “AI bubble,” show them these numbers. These are multi-year, multi-billion dollar commitments to build the foundational compute layer for the next decade of AI. The demand is real, and it’s accelerating. And the best part? As a Weights & Biases user, you have access to this same best-in-class infrastructure that runs OpenAI through our inference services. Try wandb.me/inference, and let me know if you need a bit of a credit boost! Claude Sonnet 4.5: The New Coding King Has a Few QuirksOn any other week, Anthropic’s release of Claude Sonnet 4.5 would’ve been the headline news. They’re positioning it as the new best model for coding and complex agents, and the benchmarks are seriously impressive. It matches or beats their previous top-tier model, Opus 4.1, on many difficult evals, all while keeping the same affordable price as the previous Sonnet.One of the most significant jumps is on the OS World benchmark, which tests an agent’s ability to use a computer—opening files, manipulating windows, and interacting with applications. Sonnet 4.5 scored a whopping 61.4%, a massive leap from Opus 4.1’s 44%. This clearly signals that Anthropic is doubling down on building agents that can act as real digital assistants.However, the real-world experience has been a bit of a mixed bag. My co-host Ryan Carson, whose company Amp switched over to 4.5 right away, noted some regressions and strange errors, saying they’re even considering switching back to the previous version until the rough edges are smoothed out. Nisten also found it could be more susceptible to “slop catalysts” in prompting. It seems that while it’s incredibly powerful, it might require some re-prompting and adjustments to get the best, most stable results. The jury’s still out, but it’s a potent new tool in the developer’s arsenal.Open Source LLMs: DeepSeek’s Attention RevolutionDespite the massive news from the big companies, open source still brought the heat this week, with one release in particular representing a fundamental breakthrough.DeepSeek released V3.2 Experimental, and the big news is DSA, or DeepSeek Sparse Attention. For those who don’t know, one of the biggest bottlenecks in LLMs is the “quadratic attention problem”—as you double the context length, the computation and memory required quadruple. This makes very long contexts incredibly expensive. DeepSeek’s new architecture makes the cost curve nearly flat, allowing for massive context at a fraction of the cost, all while maintaining the same SOTA performance as their previous model.This is one of those “unhobbling moments,” like the invention of RoPE or GRPO, that moves the entire field forward. Everyone will be able to implement this, making all open-source models faster and more efficient. It’s a huge deal.We also saw major releases from Z.ai with GLM-4.6, an advanced agentic model with a 200K context window that’s getting incredibly close to Claude’s performance, and a surprise from ServiceNow SLAM Labs, who dropped Apriel-1.5-15B, a frontier-level multimodal model that’s fully open source. It’s amazing to see a huge enterprise company contributing to the open-source ecosystem at this level.Multimodal Madness: Audio, Video, and Image Models updatesThe torrent of releases continued across all modalities this week, a bit overshadowed by SORA but definitely still happened (all links in the TL;DR section)In voice and audio, our friends at Hume AI launched Octave 2, their next-gen text-to-speech model that’s faster, cheaper, and now fluent in over 11 languages. We also saw LFM2-Audio from Liquid AI, an incredibly efficient 1.5B parameter end-to-end audio model with sub-100ms latency.In video, the open-source community answered Sora 2 with Kandinsky 5.0, a new 2B parameter text-to-video model that is claiming the #1 spot in open source and looks incredibly promising. And as I mentioned on the show, I wouldn’t have even known about it if it weren’t for my new personal AI agent, Pulse!Finally, in AI art, Tencent dropped a monster: HunyuanImage 3.0, a massive 80-billion-parameter open-source text-to-image model. The scale of these open-source releases is just breathtaking.Agentic browsing for all is hereJust as I was wrapping up the show, Perplexity has decided to let everyone in to use their Comet Agentic browser. I strongly recommend it, as I switched to it lately and it’s great! I’m using it right now to run some agents, it can click stuff, scroll through stuff, collect info across tabs, it’s really great. Give it a spin, really, it’s worth getting into the habit of agentic browsing! Many of you were asking me for invites before, well, it’s free access now, download it here (not sponsored, I just really like it) Phew, ok, this was a WILD week, and I’m itching to get back to creating and seeing all the folks who used my Cameo on SORA, which you can see too btw if you hit the Cameo button here (https://sora.chatgpt.com/profile/altryne) Next week is OpenAI’s Dev Day, and for the third year in a row we’re going to cover it, so follow us on social media and tune in Monday 8:30am Pacific. We’ll be live streaming from the location and re-streaming the keynote with Sam so don’t miss it! TL;DR and Show NotesHosts and Guests:* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed @ryancarsonBig CO LLMs + APIs:* OpenAI releases SORA2 + a new social media app (X, Blog, App download)* Anthropic releases Claude Sonnet 4.5 - same price as 4.1 - leading coding model (X)* OpenAI launches Instant Checkout & Agentic Commerce Protocol (X, Protocol)Open Source LLMs:* DeepSeek V3.2 Exp: Sparse Attention, Cost Drop (X, Evals, HF)* Apriel-1.5-15B-Thinker by ServiceNow SLAM Labs (X, HF, Arxiv)* Z.ai GLM-4.6: advanced Agentic flagship model (X, Blog, HF)This weeks Buzz:* CoreWeave locks $22.4B OpenAI, a $6.3B NVIDIA “backstop”, and a $14.2B Meta compute pact (X)Voice & Audio:* Hume AI launches Octave 2 (X, Blog)* LFM2-Audio: End-to-end audio foundation model (X, Blog, HF)Vision & Video:* Kandinsky 5.0 T2V Lite: #1 open-source text-to-video (Blog, GitHub, HF, Try It)AI Art & Diffusion & 3D:* HunyuanImage 3.0: 80B Open-Source Text-to-Image by Tencent (X, HF, Github) This is a public episode. 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This is a free preview of a paid episode. To hear more, visit sub.thursdai.newsHola AI aficionados, it’s yet another ThursdAI, and yet another week FULL of AI news, spanning Open Source LLMs, Multimodal video and audio creation and more! Shiptember as they call it does seem to deliver, and it was hard even for me to follow up on all the news, not to mention we had like 3-4 breaking news during the show today! This week was yet another Qwen-mas, with Alibaba absolutely dominating across open source, but also NVIDIA promising to invest up to $100 Billion into OpenAI. So let’s dive right in! As a reminder, all the show notes are posted at the end of the article for your convenience. ThursdAI - Because weeks are getting denser, but we’re still here, weekly, sending you the top AI content! Don’t miss outTable of Contents* Open Source AI* Qwen3-VL Announcement (Qwen3-VL-235B-A22B-Thinking):* Qwen3-Omni-30B-A3B: end-to-end SOTA omni-modal AI unifying text, image, audio, and video* DeepSeek V3.1 Terminus: a surgical bugfix that matters for agents* Evals & Benchmarks: agents, deception, and code at scale* Big Companies, Bigger Bets!* OpenAI: ChatGPT Pulse: Proactive AI news cards for your day* XAI Grok 4 fast - 2M context, 40% fewer thinking tokens, shockingly cheap* Alibaba Qwen-Max and plans for scaling* This Week’s Buzz: W&B Fully Connected is coming to London and Tokyo & Another hackathon in SF* Vision & Video: Wan 2.2 Animate, Kling 2.5, and Wan 4.5 preview* Moondream-3 Preview - Interview with co-founders Via & Jay* Wan open sourced Wan 2.2 Animate (aka “Wan Animate”): motion transfer and lip sync* Kling 2.5 Turbo: cinematic motion, cheaper and with audio* Wan 4.5 preview: native multimodality, 1080p 10s, and lip-synced speech* Voice & Audio* ThursdAI - Sep 25, 2025 - TL;DR & Show notesOpen Source AIThis was a Qwen-and-friends week. I joked on stream that I should just count how many times “Alibaba” appears in our show notes. It’s a lot.Qwen3-VL Announcement (Qwen3-VL-235B-A22B-Thinking): (X, HF, Blog, Demo)Qwen 3 launched earlier as a text-only family; the vision-enabled variant just arrived, and it’s not timid. The “thinking” version is effectively a reasoner with eyes, built on a 235B-parameter backbone with around 22B active (their mixture-of-experts trick). What jumped out is the breadth of evaluation coverage: MMU, video understanding (Video-MME, LVBench), 2D/3D grounding, doc VQA, chart/table reasoning—pages of it. They’re showing wins against models like Gemini 2.5 Pro and GPT‑5 on some of those reports, and doc VQA is flirting with “nearly solved” territory in their numbers.Two caveats. First, whenever scores get that high on imperfect benchmarks, you should expect healthy skepticism; known label issues can inflate numbers. Second, the model is big. Incredible for server-side grounding and long-form reasoning with vision (they’re talking about scaling context to 1M tokens for two-hour video and long PDFs), but not something you throw on a phone.Still, if your workload smells like “reasoning + grounding + long context,” Qwen 3 VL looks like one of the strongest open-weight choices right now.Qwen3-Omni-30B-A3B: end-to-end SOTA omni-modal AI unifying text, image, audio, and video (HF, GitHub, Qwen Chat, Demo, API)Omni is their end-to-end multimodal chat model that unites text, image, and audio—and crucially, it streams audio responses in real time while thinking separately in the background. Architecturally, it’s a 30B MoE with around 3B active parameters at inference, which is the secret to why it feels snappy on consumer GPUs.In practice, that means you can talk to Omni, have it see what you see, and get sub-250 ms replies in nine speaker languages while it quietly plans. It claims to understand 119 languages. When I pushed it in multilingual conversational settings it still code-switched unexpectedly (Chinese suddenly appeared mid-flow), and it occasionally suffered the classic “stuck in thought” behavior we’ve been seeing in agentic voice modes across labs. But the responsiveness is real, and the footprint is exciting for local speech streaming scenarios. I wouldn’t replace a top-tier text reasoner with this for hard problems, yet being able to keep speech native is a real UX upgrade.Qwen Image Edit, Qwen TTS Flash, and Qwen‑GuardQwen’s image stack got a handy upgrade with multi-image reference editing for more consistent edits across shots—useful for brand assets and style-tight workflows. TTS Flash (API-only for now) is their fast speech synth line, and Q‑Guard is a new safety/moderation model from the same team. It’s notable because Qwen hasn’t really played in the moderation-model space before; historically Meta’s Llama Guard led that conversation.DeepSeek V3.1 Terminus: a surgical bugfix that matters for agents (X, HF)DeepSeek whale resurfaced to push a small 0.1 update to V3.1 that reads like a “quality and stability” release—but those matter if you’re building on top. It fixes a code-switching bug (the “sudden Chinese” syndrome you’ll also see in some Qwen variants), improves tool-use and browser execution, and—importantly—makes agentic flows less likely to overthink and stall. On the numbers, Humanities Last Exam jumped from 15 to 21.7, while LiveCodeBench dipped slightly. That’s the story here: they traded a few raw points on coding for more stable, less dithery behavior in end-to-end tasks. If you’ve invested in their tool harness, this may be a net win.Liquid Nanos: small models that extract like they’re big (X, HF)Liquid Foundation Models released “Liquid Nanos,” a set of open models from roughly 350M to 2.6B parameters, including “extract” variants that pull structure (JSON/XML/YAML) from messy documents. The pitch is cost-efficiency with surprisingly competitive performance on information extraction tasks versus models 10× their size. If you’re doing at-scale doc ingestion on CPUs or small GPUs, these look worth a try.Tiny IBM OCR model that blew up the charts (HF)We also saw a tiny IBM model (about 250M parameters) for image-to-text document parsing trending on Hugging Face. Run in 8-bit, it squeezes into roughly 250 MB, which means Raspberry Pi and “toaster” deployments suddenly get decent OCR/transcription against scanned docs. It’s the kind of tiny-but-useful release that tends to quietly power entire products.Meta’s 32B Code World Model (CWM) released for agentic code reasoning (X, HF)Nisten got really excited about this one, and once he explained it, I understood why. Meta released a 32B code world model that doesn’t just generate code - it understands code the way a compiler does. It’s thinking about state, types, and the actual execution context of your entire codebase.This isn’t just another coding model - it’s a fundamentally different approach that could change how all future coding models are built. Instead of treating code as fancy text completion, it’s actually modeling the program from the ground up. If this works out, expect everyone to copy this approach.Quick note, this one was released with a research license only! Evals & Benchmarks: agents, deception, and code at scaleA big theme this week was “move beyond single-turn Q&A and test how these things behave in the wild.” with a bunch of new evals released. I wanted to cover them all in a separate segment. OpenAI’s GDP Eval: “economically valuable tasks” as a bar (X, Blog)OpenAI introduced GDP Eval to measure model performance against real-world, economically valuable work. The design is closer to how I think about “AGI as useful work”: 44 occupations across nine sectors, with tasks judged against what an industry professional would produce.Two details stood out. First, OpenAI’s own models didn’t top the chart in their published screenshot—Anthropic’s Claude Opus 4.1 led with roughly a 47.6% win rate against human professionals, while GPT‑5-high clocked in around 38%. Releasing a benchmark where you’re not on top earns respect. Second, the tasks are legit. One example was a manufacturing engineer flow where the output required an overall design with an exploded view of components—the kind of deliverable a human would actually make.What I like here isn’t the precise percent; it’s the direction. If we anchor progress to tasks an economy cares about, we move past “trivia with citations” and toward “did this thing actually help do the work?”GAIA 2 (Meta Super Intelligence Labs + Hugging Face): agents that execute (X, HF)MSL and HF refreshed GAIA, the agent benchmark, with a thousand new human-authored scenarios that test execution, search, ambiguity handling, temporal reasoning, and adaptability—plus a smartphone-like execution environment. GPT‑5-high led across execution and search; Kimi’s K2 was tops among open-weight entries. I like that GAIA 2 bakes in time and budget constraints and forces agents to chain steps, not just spew plans. We need more of these.Scale AI’s “SWE-Bench Pro” for coding in the large (HF)Scale dropped a stronger coding benchmark focused on multi-file edits, 100+ line changes, and large dependency graphs. On the public set, GPT‑5 (not Codex) and Claude Opus 4.1 took the top two slots; on a commercial set, Opus edged ahead. The broader takeaway: the action has clearly moved to test-time compute, persistent memory, and program-synthesis outer loops to get through larger codebases with fewer invalid edits. This aligns with what we’re seeing across ARC‑AGI and SWE‑bench Verified.The “Among Us” deception test (X)One more that’s fun but not frivolous: a group benchmarked models on the social deception game Among Us. OpenAI’s latest systems reportedly did the best job both lying convincingly and detecting others’ lies. This line of work matters because social inference and adversarial reasoning show up in real agent deployments—security, procurement, negotiations, even internal assistant safety.Big Companies, Bigger Bets!Nvidia’s $100B pledge to OpenAI for 10GW of computeLet’s say that number again: one hundred billion dollars. Nvidia announced plans to invest up to $100B into OpenAI’s infrastructure build-out, targeting roughly 10 gigawatts of compute and power. Jensen called it the biggest infrastructure project in history. Pair that with OpenAI’s Stargate-related announcements—five new datacenters with Oracle and SoftBank and a flagship site in Abilene, Texas—and you get to wild territory fast.Internal notes circulating say OpenAI started the year around 230MW and could exit 2025 north of 2GW operational, while aiming at 20GW in the near term and a staggering 250GW by 2033. Even if those numbers shift, the directional picture is clear: the GPU supply and power curves are going vertical.Two reactions. First, yes, the “infinite money loop” memes wrote themselves—OpenAI spends on Nvidia GPUs, Nvidia invests in OpenAI, the market adds another $100B to Nvidia’s cap for good measure. But second, the underlying demand is real. If we need 1–8 GPUs per “full-time agent” and there are 3+ billion working adults, we are orders of magnitude away from compute saturation. The power story is the real constraint—and that’s now being tackled in parallel.OpenAI: ChatGPT Pulse: Proactive AI news cards for your day (X, OpenAI Blog)In a #BreakingNews segment, we got an update from OpenAI, that currently works only for Pro users but will come to everyone soon. Proactive AI, that learns from your chats, email and calendar and will show you a new “feed” of interesting things every morning based on your likes and feedback! Pulse marks OpenAI’s first step toward an AI assistant that brings the right info before you ask, tuning itself with every thumbs-up, topic request, or app connection. I’ve tuned mine for today, we’ll see what tomorrow brings! P.S - Huxe is a free app from the creators of NotebookLM (Ryza was on our podcast!) that does a similar thing, so if you don’t have pro, check out Huxe, they just launched! XAI Grok 4 fast - 2M context, 40% fewer thinking tokens, shockingly cheap (X, Blog)xAI launched Grok‑4 Fast, and the name fits. Think “top-left” on the speed-to-cost chart: up to 2 million tokens of context, a reported 40% reduction in reasoning token usage, and a price tag that’s roughly 1% of some frontier models on common workloads. On LiveCodeBench, Grok‑4 Fast even beat Grok‑4 itself. It’s not the most capable brain on earth, but as a high-throughput assistant that can fan out web searches and stitch answers in something close to real time, it’s compelling.Alibaba Qwen-Max and plans for scaling (X, Blog, API)Back in the Alibaba camp, they also released their flagship API model, Qwen 3 Max, and showed off their future roadmap. Qwen-max is over 1T parameters, MoE that gets 69.6 on Swe-bench verified and outperforms GPT-5 on LMArena! And their plan is simple: scale. They’re planning to go from 1 million to 100 million token context windows and scale their models into the terabytes of parameters. It culminated in a hilarious moment on the show where we all put on sunglasses to salute a slide from their presentation that literally said, “Scaling is all you need.” AGI is coming, and it looks like Alibaba is one of the labs determined to scale their way there. Their release schedule lately (as documented by Swyx from Latent.space) is insane. This Week’s Buzz: W&B Fully Connected is coming to London and Tokyo & Another hackathon in SFWeights & Biases (now part of the CoreWeave family) is bringing Fully Connected to London on Nov 4–5, with another event in Tokyo on Oct 31. If you’re in Europe or Japan and want two days of dense talks and hands-on conversations with teams actually shipping agents, evals, and production ML, come hang out. Readers got a code on stream; if you need help getting a seat, ping me directly.Links: fullyconnected.comWe are also opening up registrations to our second WeaveHacks hackathon in SF, October 11-12, yours trully will be there, come hack with us on Self Improving agents! Register HEREVision & Video: Wan 2.2 Animate, Kling 2.5, and Wan 4.5 previewThis is the most exciting space in AI week-to-week for me right now. The progress is visible. Literally.Moondream-3 Preview - Interview with co-founders Via & JayWhile I’ve already reported on Moondream-3 in the last weeks newsletter, this week we got the pleasure of hosting Vik Korrapati and Jay Allen the co-founders of MoonDream to tell us all about it. Tune in for that conversation on the pod starting at 00:33:00Wan open sourced Wan 2.2 Animate (aka “Wan Animate”): motion transfer and lip sync Tongyi’s Wan team shipped an open-source release that the community quickly dubbed “Wanimate.” It’s a character-swap/motion transfer system: provide a single image for a character and a reference video (your own motion), and it maps your movement onto the character with surprisingly strong hair/cloth dynamics and lip sync. If you’ve used runway’s Act One, you’ll recognize the vibe—except this is open, and the fidelity is rising fast.The practical uses are broader than “make me a deepfake.” Think onboarding presenters with perfect backgrounds, branded avatars that reliably say what you need, or precise action blocking without guessing at how an AI will move your subject. You act it; it follows.Kling 2.5 Turbo: cinematic motion, cheaper and with audioKling quietly rolled out a 2.5 Turbo tier that’s 30% cheaper and finally brings audio into the loop for more complete clips. Prompts adhere better, physics look more coherent (acrobatics stop breaking bones across frames), and the cinematic look has moved from “YouTube short” to “film-school final.” They seeded access to creators and re-shared the strongest results; the consistency is the headline. (Source X: @StevieMac03)I’ve chatted with my kiddos today over facetime, and they were building minecraft creepers. I took a screenshot, sent to Nano Banana to make their creepers into actual minecraft ones, and then with Kling, Animated the explosions for them. They LOVED it! Animations were clear, while VEO refused for me to even upload their images, Kling didn’t care hahaWan 4.5 preview: native multimodality, 1080p 10s, and lip-synced speechWan also teased a 4.5 preview that unifies understanding and generation across text, image, video, and audio. The eye-catching bit: generate a 1080p, 10-second clip with synced speech from just a script. Or supply your own audio and have it lip-sync the shot. I ran my usual “interview a polar bear dressed like me” test and got one of the better results I’ve seen from any model. We’re not at “dialogue scene” quality, but “talking character shot” is getting… good. The generation of audio (not only text + lipsync) is one of the best ones besides VEO, it’s really great to see how strongly this improves, sad that this wasn’t open sourced! And apparently it supports “draw text to animate” (Source: X) Voice & AudioSuno V5: we’ve entered the “I can’t tell anymore” eraSuno calls V5 a redefinition of audio quality. I’ll be honest, I’m at the edge of my subjective hearing on this. I’ve caught myself listening to Suno streams instead of Spotify and forgetting anything is synthetic. The vocals feel more human, the mixes cleaner, and the remastering path (including upgrading V4 tracks) is useful. The last 10% to “you fooled a producer” is going to be long, but the distance between V4 and V5 already makes me feel like I should re-cut our ThursdAI opener.MiMI Audio: a small omni-chat demo that hints at the floorWe tried a MiMI Audio demo live—a 7B-ish model with speech in/out. It was responsive but stumbled on singing and natural prosody. I’m leaving it in here because it’s a good reminder that the open floor for “real-time voice” is rising quickly even for small models. And the moment you pipe a stronger text brain behind a capable, native speech front-end, the UX leap is immediate.Ok, another DENSE week that finishes up Shiptember, tons of open source, Qwen (Tongyi) shines, and video is getting so so good. This is all converging folks, and honestly, I’m just happy to be along for the ride! This week was also Rosh Hashanah, which is the Jewish new year, and I’ve shared on the pod that I’ve found my X post from 3 years ago, using the state of the art AI models of the time. WHAT A DIFFERENCE 3 years make, just take a look, I had to scale down the 4K one from this year just to fit into the pic! Shana Tova to everyone who’s reading this, and we’ll see you next week 🫡ThursdAI - Sep 25, 2025 - TL;DR & Show notes* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @yampeleg @nisten @ldjconfirmed @ryancarson* Guest - Vik Korrapathy (@vikhyatk) - Moondream* Open Source AI (LLMs, VLMs, Papers & more)* DeepSeek V3.1 Terminus: cleaner bilingual output, stronger agents, cheaper long-context (X, HF)* Meta’s 32B Code World Model (CWM) released for agentic code reasoning (X, HF)* Alibaba Tongyi Qwen on a release streak again:

Hey folks, What an absolute packed week this week, which started with yet another crazy model release from OpenAI, but they didn't stop there, they also announced GPT-5 winning the ICPC coding competitions with 12/12 questions answered which is apparently really really hard! Meanwhile, Zuck took the Meta Connect 25' stage and announced a new set of Meta glasses with a display! On the open source front, we yet again got multiple tiny models doing DeepResearch and Image understanding better than much larger foundational models.Also, today I interviewed Jeremy Berman, who topped the ArcAGI with a 79.6% score and some crazy Grok 4 prompts, a new image editing experience called Reve, a new world model and a BUNCH more! So let's dive in! As always, all the releases, links and resources at the end of the article. ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Table of Contents* Codex comes full circle with GPT-5-Codex agentic finetune* Meta Connect 25 - The new Meta Glasses with Display & a neural control interface* Jeremy Berman: Beating frontier labs to SOTA score on ARC-AGI* This Week’s Buzz: Weave inside W&B models—RL just got x-ray vision* Open Source* Perceptron Isaac 0.1 - 2B model that points better than GPT* Tongyi DeepResearch: A3B open-source web agent claims parity with OpenAI Deep Research* Reve launches a 4-in-1 AI visual platform taking on Nano 🍌 and Seedream* Ray3: Luma’s “reasoning” video model with native HDR, Draft Mode, and Hi‑Fi mastering* World models are getting closer - Worldlabs announced Marble* Google puts Gemini in ChromeCodex comes full circle with GPT-5-Codex agentic finetune (X, OpenAI Blog)My personal highlight of the week was definitely the release of GPT-5-Codex. I feel like we've come full circle here. I remember when OpenAI first launched a separate, fine-tuned model for coding called Codex, way back in the GPT-3 days. Now, they've done it again, taking their flagship GPT-5 model and creating a specialized version for agentic coding, and the results are just staggering.This isn't just a minor improvement. During their internal testing, OpenAI saw GPT-5-Codex work independently for more than seven hours at a time on large, complex tasks—iterating on its code, fixing test failures, and ultimately delivering a successful implementation. Seven hours! That's an agent that can take on a significant chunk of work while you're sleeping. It's also incredibly efficient, using 93% fewer tokens than the base GPT-5 on simpler tasks, while thinking for longer on the really difficult problems.The model is now integrated everywhere - the Codex CLI (just npm install -g codex), VS Code extension, web playground, and yes, even your iPhone. At OpenAI, Codex now reviews the vast majority of their PRs, catching hundreds of issues daily before humans even look at them. Talk about eating your own dog food!Other OpenAI updates from this weekWhile Codex was the highlight, OpenAI (and Google) also participated and obliterated one of the world’s hardest algorithmic competitions called ICPC. OpenAI used GPT-5 and an unreleased reasoning model to solve 12/12 questions in under 5 hours. OpenAI and NBER also released an incredible report on how over 700M people use GPT on a weekly basis, with a lot of insights, that are summed up in this incredible graph:Meta Connect 25 - The new Meta Glasses with Display & a neural control interfaceJust when we thought the week couldn't get any crazier, Zuck took the stage for their annual Meta Connect conference and dropped a bombshell. They announced a new generation of their Ray-Ban smart glasses that include a built-in, high-resolution display you can't see from the outside. This isn't just an incremental update; this feels like the arrival of a new category of device. We've had the computer, then the mobile phone, and now we have smart glasses with a display.The way you interact with them is just as futuristic. They come with a "neural band" worn on the wrist that reads myoelectric signals from your muscles, allowing you to control the interface silently just by moving your fingers. Zuck's live demo, where he walked from his trailer onto the stage while taking messages and playing music, was one hell of a way to introduce a product.This is how Meta plans to bring its superintelligence into the physical world. You'll wear these glasses, talk to the AI, and see the output directly in your field of view. They showed off live translation with subtitles appearing under the person you're talking to and an agentic AI that can perform research tasks and notify you when it's done. It's an absolutely mind-blowing vision for the future, and at $799, shipping in a week, it's going to be accessible to a lot of people. I've already signed up for a demo.Jeremy Berman: Beating frontier labs to SOTA score on ARC-AGIWe had the privilege of chatting with Jeremy Berman, who just achieved SOTA on the notoriously difficult ARC-AGI benchmark using checks notes... Grok 4! 🚀He walked us through his innovative approach, which ditches Python scripts in favor of flexible "natural language programs" and uses a program-synthesis outer loop with test-time adaptation. Incredibly, his method achieved these top scores at 1/25th the cost of previous systemsThis is huge because ARC-AGI tests for true general intelligence - solving problems the model has never seen before. The chat with Jeremy is very insightful, available on the podcast starting at 01:11:00 so don't miss it!ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.This Week’s Buzz: Weave inside W&B models—RL just got x-ray visionYou know how every RL project produces a mountain of rollouts that you end up spelunking through with grep? We just banished that misery. Weave tracing now lives natively inside every W&B Workspace run. Wrap your training-step and rollout functions in @weave.op, call weave.init(), and your traces appear alongside loss curves in real time. I can click a spike, jump straight to the exact conversation that tanked the reward, and diagnose hallucinations without leaving the dashboard. If you’re doing any agentic RL, please go treat yourself. Docs: https://weave-docs.wandb.ai/guides/tools/weave-in-workspacesOpen SourceOpen source did NOT disappoint this week as well, we've had multiple tiny models beating the giants at specific tasks! Perceptron Isaac 0.1 - 2B model that points better than GPT ( X, HF, Blog )One of the most impressive demos of the week came from a new lab, Perceptron AI. They released Isaac 0.1, a tiny 2 billion parameter "perceptive-language" model. This model is designed for visual grounding and localization, meaning you can ask it to find things in an image and it will point them out. During the show, we gave it a photo of my kid's Harry Potter alphabet poster and asked it to "find the spell that turns off the light." Not only did it correctly identify "Nox," but it drew a box around it on the poster. This little 2B model is doing things that even huge models like GPT-4o and Claude Opus can't, and it's completely open source. Absolutely wild.Moondream 3 preview - grounded vision reasoning 9B MoE (2B active) (X, HF)Speaking of vision reasoning models, just a bit after the show concluded, our friend Vik released a demo of Moondream 3, a reasoning vision model 9B (A2B) that is also topping the charts! I didn't have tons of time to get into this, but the release thread shows this to be an exceptional open source visual reasoner also beating the giants!Tongyi DeepResearch: A3B open-source web agent claims parity with OpenAI Deep Research ( X, HF )Speaking of smaller models obliterating huge ones, Tongyi released a bunch of papers and a model this week that can do Deep Research on the level of OpenAI, even beating it, with a Qwen Finetune with only 3B active parameters! With insane scores like 32.9 (38.3 in Heavy mode) on Humanity's Last Exam (OAI Deep Research gets 26%) and an insane 98.6% on SimpleQA, this innovative approach uses a lot of RL and synthetic data to train a Qwen model to find what you need. The paper is full of incredible insights into how to build automated RL environments to get to this level. AI Art, Diffusion 3D and VideoThis category of AI has been blowing up, we've seen SOTA week after week with Nano Banana then Seedream 4 and now a few more insane models.Tencent's Hunyuan released SRPO (X, HF, Project, Comparison X)(Semantic Relative Preference Optimization) which is a new method to finetune diffusion models quickly without breaking the bank. Also released a very realistic looking finetune trained with SRPO. Some of the generated results are super realistic, but it's more than just a model, there's a whole new method of finetuning here! Hunyuan also updated their 3D model and announced a full blown 3D studio that does everything from 3D object generation, meshing, texture editing & more. Reve launches a 4-in-1 AI visual platform taking on Nano 🍌 and Seedream (X, Reve, Blog)A newcomer, Reve has launched a comprehensive new AI visual platform bundling image creation, editing, remixing, creative assistant, and API integration, all aimed at making advanced editing as accessible, all using their own proprietary models. What stood out to me though, is the image editing UI, which allows you to select on your image exactly what you want to edit, write a specific prompt for that thing (change color, objects, add text etc') and then hit generate and their model takes into account all those new queues! This is way better than just ... text prompting the other models! Ray3: Luma’s “reasoning” video model with native HDR, Draft Mode, and Hi‑Fi mastering (X, Try It)Luma released the third iteration of their video model called Ray, and this one does.. HDR! But it also has Draft Mode (for quick iteration), first/last frame interpolation, and they claim to be "production ready" with extreme prompt adherence. The thing that struck me is the reasoning part, their video model is now reasoning, to let you create more complex scenes, while the model will ... evaluate itself and select the best generation for you! This is quite bonkers, can't wait to play with it! World models are getting closer - Worldlabs announced Marble (Demo)We've covered a whole host of world models, Genie3, Hunyuan 3D world models, Mirage and a bunch more! Dr FeiFei's WorldLabs have been one of the first ones to tackle the world model concept and their recent release shows incredible progress (and finally lets us play with it!) Marble takes images and creates Gussian Splats, that can be used in 3D environments. So now you can use any AI image generation and turn it into a walkable 3D world! Google puts Gemini in Chrome (X, Blog)This happened after the show today and while not fully rolled out yet, I've told you before when we covered Comet from PPXL and Dia from browser company, that Google will not be far behind! So today they announced that Gemini is coming to Chrome, and will allow users to chat with a bunch of their tabs, summarize across tabs and soon do agentic tasks like clicking things and shopping for you? 😅I wonder if this means that Google will offer this for free to the over 1B chrome users or introduce some sort of Gemini tier cross-over? Remains to be seen but very exciting to see AI browsers all over! The best feature could be a hidden one, where the Gemini in Chrome will have knowledge about your surfing history and you'll be able to ask it about that one website you visited a while ago that had sharks! Folks, I can go on and on today, literally there's a new innovative video model from ByteDance, a few more image models, but alas, I have to prioritize and give you only the top important news. So, I'll just remind that I put all the links in the TL;DR below and that you should absolutely check out the video version of our show on YT because a lot of visual things are happening and we're playing with all of them live! Hey, just before you get to the “links”, consider subscribing to help me keep this going? 🙏See you next week 🫡 Don't forget to subscribe (and if you already subbed, share this with a friend or two?) TL;DR and show notes - September 18, 2025* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @ldjconfirmed @nisten* Guest : Jeremy Berman (@jerber888) - SOTA on ARC- AGI* Open Source* Perceptron AI introduces Isaac 0.1: a 2B param perceptive-language model (X, HF, Blog)* Tongyi DeepResearch: A3B open-source web agent claims parity with OpenAI Deep Research (X, HF)* Mistral updates Magistral-Small-2509 (HF)* Big CO LLMs + APIs* GPT-5-Codex release: Agentic coding upgrade for Codex (X, OpenAI Blog)* Meta Connect - New AI glasses with display, new AI mode (X Recap)* NBER & OpenAI - How People Use ChatGPT: Growth, Demographics, and Scale (X, Blog, NBER Paper)* ARC-AGI: New SOTA by Jeremy Berman and Eric Pang using Grok-4 (X, Blog)* OpenAI’s reasoning system aces 2025 ICPC World Finals with a perfect 12/12 (X)* OpenAI adds thinking budgets to ChatGPT app (X)* Gemini in Chrome: AI assistant across tabs + smarter omnibox + safer browsing (X, Blog)* Anthropic admits Claude bugs - Detailed analysis * This weeks Buzz* W&B Models + Weave! You can now log your RL runs in W&B Weave 👏 (X, W&B Link) * W&B Fully Connected London - tickets are running out! Use FCLNTHURSAI for a free ticket on me! (Register Here)* Vision & Video* Moondream 3 (Preview): 9B MoE VLM with 2B active targets frontier-level visual reasoning (X, HF)* Ray3: Luma’s “reasoning” video model with native HDR, Draft Mode, and Hi‑Fi mastering (X)* HuMo: human‑centric, multimodal video gen from ByteDance/Tsinghua (X, HF)* Voice & Audio* Reka Speech: high-throughput multilingual ASR and speech translation for batch-scale pipelines (X, Blog)* AI Art & Diffusion & 3D* Hunyuan SRPO (Semantic Relative Preference Optimization) supercharges diffusion models (X, HF, Project, Comparison X)* Hunyuan 3D 3.0 (X, Try it)* FeiFei WorldLabs presents Marble (Demo)* Reve launches 4-in-1 AI visual platform (X, Reve, Blog)* Tools* Chrome adds Gemini (Blog) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey Everyone, Alex here, thanks for being a subscriber! Let's get you caught up on this weeks most important AI news! The main thing you need to know this week is likely the incredible Image model that ByteDance released, that overshoots the (incredible image model from last 2 weeks) nano 🍌. ByteDance really outdid themselves on this one! But also, a video model with super fast generation, OpenAI rumor made Larry Ellison the richest man alive, ChatGPT gets MCP powers (under a flag you can enable) and much more! This week we covered a lot of visual stuff, so while the podcast format is good enough, it's really worth tuning in to the video recording to really enjoy the full show. ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.AI Art and DiffusionIt's rare for me to start the newsletter not on Open Source AI news, but hey, at least this way you know that I'm writing it and not some AI right? 😉ByteDance SeeDream 4 - 4K SOTA image generation and editing model with up to 6 reference images (Fal, Replicate)The level of detail on ByteDance's new model, has really made all the hosts on ThursdAI stop and go... huh? is this AI? Bytedance really outdid themselves with this image model that not only generates images, it also is a fully functional image editing with natural language model. It's a diffusion transformer, able to generate 2K and 4K images, fast (under 5 seconds?) while enabling up to 6 reference images to be provided for the generation. This is going to be incredible for all kinds of purposes, AI art, marketing etc'. The promt adherence is quite incredible, text is also crisp and sharp at those 2/4K resolutions. We created this image live on the show with it (using a prompt extended by another model)I then provided my black and white headshot and the above image and asked to replace me as a cartoon character, and it did, super quick, and even got my bomber jacket and the W&B logo on it in there! Notable, nothing else was changed in the image, showing just how incredible this one is for image editing. In you want enhanced realism, our friend FoFr from replicate, reminded us that using IMG_3984.CR2 in the prompt, will make the model show images that are closer to reality, even if they depict some incredibly unrealistic things, like a pack of lions forming his nicknameAdditional uses for this model are just getting discovered, and one user already noted that given this model outputs 4K resolution, it can be used as a creative upscaler for other model outputs. Just shove your photo from another AI in Seedream and ask for an upscale. Just be ware that creative upscalers change some amount of details in the generated picture. DecART AI Lucy 14B Redefines Video Generation speeds! If Seedteam blew my mind with images, Decart's Lucy 14B absolutely shattered my expectations for video generation speed. We're talking about generating 5-second videos from images in 6.5 seconds. That's almost faster than watching the video itself!This video model is not open source yet (despite them adding 14B to the name) but it's smaller 5B brother was open sourced. The speed to quality ratio is really insane here, and while Lucy will not generate or animate text or faces that well, it does produce some decent imagery, but SUPER fast. This is really great for iteration, as AI Video is like a roulette machine, you have to generate a lot of tries to see a good result. This paired with Seedream (which is also really fast) are a game changer in the AI Art world! So stoked to see what folks will be creating with these! Bonus Round: Decart's Real-Time Minecraft Mod for Oasis 2 (X)The same team behind Lucy also dropped Oasis 2.0, a Minecraft mod that generates game environments in real-time using diffusion models. I got to play around with it live, and watching Minecraft transform into different themed worlds as I moved through them was surreal.Want a steampunk village? Just type it in. Futuristic city? Done. The frame rate stayed impressively smooth, and the visual coherence as I moved through the world was remarkable. It's like having an AI art director that can completely reskin your game environment on demand. And while the current quality remains low res, if you consider where Stable Diffusion 1.4 was 3 years ago, and where Seedream 4 is now, and do the same extrapolation to Oasis, in 2-3 years we'll be reskinning whole games on the fly and every pixel will be generated (like Jensen loves to say!) OpenAI adds full MCP to ChatGPT (under a flag) This is huge, folks. I've been waiting for this for a while, and finally, OpenAI quietly added full MCP (Model Context Protocol) support to ChatGPT via a hidden "developer mode."How to Enable MCP in ChatGPTHere's the quick setup I showed during the stream:* Go to ChatGPT settings → Connectors* Scroll down to find "Developer Mode" and enable it* Add MCP servers (I used Rube.ai from Composio)* Use GPT-4o in developer mode to access your connectorsDuring the show, I literally had ChatGPT pull Nisten's last five tweets using the Twitter MCP connector. It worked flawlessly (though Nisten was a bit concerned about what tweets it might surface 😂).The implications are massive - you can now connect ChatGPT to GitHub, databases, your local files, or chain multiple tools together for complex workflows. As Wolfram pointed out though, watch your context usage - each MCP connector eats into that 200K limit.Big Moves: Investments and InfrastructureSpeaking of OpenAI, Let's talk money, because the stakes are getting astronomical. OpenAI reportedly has a $300 billion (!) deal with Oracle for compute infrastructure over five years, starting in 2027. That's not a typo - $60 billion per year for compute. Larry Ellison just became the world's richest person, and Oracle's stock shot up 40% on the news in just a few days! This has got to be one of the biggest compute deals the world has ever head of!The scale is hard to comprehend. We're talking about potentially millions of H100 GPUs worth of compute power. When you consider that most AI companies are still figuring out how to profitably deploy thousands of GPUs, this deal represents infrastructure investment at a completely different magnitude.Meanwhile, Mistral just became Europe's newest decacorn, valued at $13.8 billion after receiving $1.3 billion from ASML. For context, ASML makes the lithography machines that TSMC uses to manufacture chips for Nvidia. They're literally at the beginning of the AI chip supply chain, and now they're investing heavily in Europe's answer to OpenAI.Wolfram made a great point - we're seeing the emergence of three major AI poles: American companies (OpenAI, Anthropic), Chinese labs (Qwen, Kimi, Ernie), and now European players like Mistral. Each is developing distinct approaches and capabilities, and the competition is driving incredible innovation.Anthropic's Mea Culpa and Code InterpreterAfter weeks of users complaining about Claude's degraded performance, Anthropic finally admitted there were bugs affecting both Claude Opus and Sonnet. Nisten, who tracks these things closely, speculated that the issues might be related to running different quantization schemes on different hardware during peak usage times. We already reported last week that they admitted that "something was affecting intelligence" but this week they said they pinpointed (and fixed) 2 bugs realted to inference! They also launched a code interpreter feature that lets Claude create and edit files directly. It's essentially their answer to ChatGPT's code interpreter - giving Claude its own computer to work with. The demo showed it creating Excel files, PDFs, and documents with complex calculations. Having watched Claude struggle with file operations for months, this is a welcome addition.🐝 This Week's Buzz: GLM 4.5 on W&B and We're on Open Router!Over at Weights & Biases, we've got some exciting updates for you. First, we've added Zhipu AI's GLM 4.5 to W&B Inference! This 300B+ parameter model is an absolute beast for coding and tool use, ranking among the top open models on benchmarks like SWE-bench. We've heard from so many of you, including Nisten, about how great this model is, so we're thrilled to host it. You can try it out now and get $2 in free credits to start.And for all you developers out there, you can use a proxy like LiteLLM to run GLM 4.5 from our inference endpoint inside Anthropic's Claude Code if you're looking for a powerful and cheap alternative! Second, we're now on Open Router! You can find several of our hosted models, like GPT-4-OSS and DeepSeek Coder, on the platform. If you're already using Open Router to manage your model calls, you can now easily route traffic to our high-performance inference stack.Open Source Continues to ShineOpen Source LLM models took a bit of a break this week, but there were still interesting models! Baidu released ERNIE-4.5, a very efficient 21B parameter "thinking" MoE that only uses 3B active parameters per token. From the UAE, MBZUAI released K2-Think, a finetune of Qwen 2.5 that's showing some seriously impressive math scores. And Moonshot AI updated Kimi K2, doubling its context window to 256K and further improving its already excellent tool use and writing capabilities.Tencent released an update to HunyuanImage 2.1, which is a bit slow, but also generates 2K images and is decent at text. Qwen drops Qwen3-Next-80B-A3B (X, HF)In breaking news post the show (we were expecting this on the show itself), Alibaba folks dropped a much more streamlined version of the next Qwen, 80B parametes with only 3B active! They call this an "Ultra Sparse MOE" and it beats Qwen3-32B in perf, rivals Qwen3-235B in reasoning & long-context. This is quite unprecedented, as getting models as sparse as to work well takes a lot of effort and skill, but the Qwen folks delivered! ToolsWe wrapped with a quick shoutout to EBSynth, a nifty video editor that lets you draw or add elements to one frame and extrapolates to the rest—like Photoshop for motion. It's browser-based and fun for VFX tweaks; check the demo video on X. Simple but powerful for quick video hacks. Speaking of Photoshop, it was confirmed that Nano Banana is going to be embedded into Photoshop for image editing! Summary & TL;DRWhat a week—Seedream and Lucy alone have me rethinking how fast AI can iterate creatively, while MCP in ChatGPT feels like the dawn of truly accessible agents. With open-source keeping pace and big deals fueling the fire, AI's multimodal future is accelerating. Thanks for tuning in, folks; if you missed the live vibes, catch the podcast or hit sub.thursdai.news for all the links. See you next Thursday—what blew your mind this week? Drop a comment and share with a friend, it's the best way to support this endeavor! TL;DR of all topics covered:AI Models & APIs:* ChatGPT adds full MCP support - Developer mode unlocks tool connectors for 400M+ users (Setup Guide)* Sea Dream 4.0 - ByteDance's unified image generation/editing model creates 4K images in ~1.8 seconds (X, Try it)* Lucy 14B - Decart's lightning-fast video model generates 5-second clips in 6.5 seconds (Demo, Page)* Claude bug fixes - Anthropic admits to performance issues and releases code interpreter (Blog)* Sonoma Dusk & Sky - Mystery models on OpenRouter with 2M context, rumored to be Grok (OpenRouter)This Week's W&B Buzz:* OpenRouter integration - Serving models to broader developer community (Try us)* GLM 4.5 - 350B parameter coding model added to inference (X, Try It)* W&B inference in Claude Code with LiteLLM (Olaf's Guide)Open Source Releases:* ERNIE 4.5 - Baidu open-sources 21B parameter thinking model with 3B active parameters (X, HF)* K2-think - MBZUAI's Qwen 2.5 fine-tune with strong math performance (X)* Kimi K2 update - Doubled context to 256K, improved tool use (X)* HunyuanImage 2.1 - Tencent's 17B parameter open-source 2K image model (X, HF)* Qwen-next-80B-A3B - Alibaba's next frontier MoE with 3B active param (X, HF)Voice & Audio:* Qwen3-ASR-Flash - 11-language speech recognition with singing support (X)* Stable Audio 2.5 - Enterprise audio generator creating 3-minute tracks in X, Blog, Try It)* ElevenLabs Voice Remixing - Modify cloned voices for age, gender, accent (X)Business & Investment:* OpenAI-Oracle deal - $300B infrastructure agreement over 5 years* Mistral funding - $1.3B investment from ASML at $13.8B valuation (Blog)Tools:* Oasis 2.0 - Real-time Minecraft world generation mod from Decart (Try It)* EbSynth - Video editing tool for frame-by-frame manipulation (X)Hosts:* Alex Volkov (@altryne)* Wolfram RavenWlf (@WolframRvnwlf)* Yam Peleg (@yampeleg)* Nisten (@nisten)* LDJ (@ldjconfirmed) This is a public episode. 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Wohoo, hey ya’ll, Alex here,I'm back from the desert (pic at the end) and what a great feeling it is to be back in the studio to talk about everything that happened in AI! It's been a pretty full week (or two) in AI, with Coding agent space heating up, Grok entering the ring and taking over free tokens, Codex 10xing usage and Anthropic... well, we'll get to Anthropic. Today on the show we had Roger and Bhavesh from Nous Research cover the awesome Hermes 4 release and the new PokerBots benchmark, then we had a returning favorite, Kwindla Hultman Kramer, to talk about the GA of RealTime voice from OpenAI. Plus we got some massive funding news, some drama with model quality on Claude Code, and some very exciting news right here from CoreWeave aquiring OpenPipe! 👏 So grab your beverage of choice, settle in (or skip to the part that interests you) and let's take a look at the last week (or two) in AI! ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Open Source: Soulful Models and Poker-Playing AgentsThis week did not disappoint as it comes to Open Source! Our friends at Nous Research released the 14B version of Hermes 4, after releasing the 405B and 70B versions last week. This company continues to excel in finetuning models for powerful, and sometimes just plain weird (in a good way) usecases. Nous Hermes 4 (14B, 70B, 405B) and the Quest for a "Model Soul" (X, HF)Roger and Bhavash from Nous came to announce the release of the smaller (14B) version of Hermes 4, and cover the last weeks releases of the larger 70B and 405B brothers. Hermes series of finetunes was always on our radar, as unique data mixes turned them into uncensored, valuable and creative models and unlocked a bunch of new use-cases. But the wildest part? They told us they intentionally stopped training the model not when reasoning benchmarks plateaued, but when they felt it started to "lose its model soul." They monitor the entropy and chaos in the model's chain-of-thought, and when it became too sterile and predictable, they hit the brakes to preserve that creative spark. This focus on qualities beyond raw benchmark scores is why Hermes 4 is showing some really interesting generalization, performing exceptionally well on benchmarks like EQBench3, which tests emotional and interpersonal understanding. It's a model that's primed for RL not just in math and code, but in creative writing, role-play, and deeper, more "awaken" conversations. It’s a soulful model that's just fun to talk to.Nous Husky Hold'em Bench: Can Your LLM Win at Poker? (Bench)As if a soulful model wasn't enough, the Nous team also dropped one of the most creative new evals I've seen in a while: Husky Hold'em Bench. We had Bhavesh, one of its creators, join the show to explain. This isn't a benchmark where the LLM plays poker directly. Instead, the LLM has to write a Python poker botfrom scratch, under time and memory constraints, which then competes against bots written by other LLMs in a high-stakes tournament. Very interesting approach, and we love creative benchmarking here at ThursdAI! This is a brilliant way to test for true strategic reasoning and planning, not just pattern matching. It's an "evergreen" benchmark that gets harder as the models get better. Early results are fascinating: Claude 4 Sonnet and Opus are currently leading the pack, but Hermes 4 is the top open-source model.More Open Source GoodnessThe hits just kept on coming this week. Tencent open-sourced Hunyuan-MT-7B, a translation model that swept the WMT2025 competition and rivals GPT-4.1 on some benchmarks. Having a small, powerful, specialized model like this is huge for anyone doing large-scale data translation for training or needing fast on-device capabilities.From Switzerland, we got Apertus-8B and 70B, a set of fully open (Apache 2.0 license, open data, open training recipes!) multilingual models trained on a massive 15 trillion tokens across 1,800 languages. It’s fantastic to see this level of transparency and contribution from European institutions.And Alibaba’s Tongyi Lab released WebWatcher, a powerful multimodal research agent that can plan steps, use a suite of tools (web search, OCR, code interpreter), and is setting new state-of-the-art results on tough visual-language benchmarks, often beating models like GPT-4o and Gemini.All links are in the TL;DR at the endBREAKING NEWS: Google Drops Embedding Gemma 308M (X, HF, Try It)Just as we were live on the show, news broke from our friends at Google. They've released Embedding Gemma, a new family of open-source embedding models. This is a big deal because they are tiny—the smallest is only 300M parameters and takes just 200MB to run—but they are topping the MTEB leaderboard for models under 500M parameters. For anyone building RAG pipelines, especially for on-device or mobile-first applications, having a small, fast, SOTA embedding model like this is a game-changer.It's so optimized for on device running that it can run fully in your browser on WebGPU, with this great example from our friend Xenova highlighted on the release blog! Big Companies, Big Money, and Big ProblemsIt was a rollercoaster week for the big labs, with massive fundraising, major product releases, and a bit of a reality check on the reliability of their services.OpenAI's GPT Real-Time Goes GA and gets an upgraded brain (X, Docs)We had the perfect guest to break down OpenAI's latest voice offering: Kwindla Kramer, founder of Daily and maintainer of the open-source PipeCat framework. OpenAI has officially taken its Realtime API to General Availability (GA), centered around the new gpt-realtime model.Kwindla explained that this is a true speech-to-speech model, not a pipeline of separate speech-to-text, LLM, and text-to-speech models. This reduces latency and preserves more nuance and prosody. The GA release comes with huge upgrades, including support for remote MCP servers, the ability to process image inputs during a conversation, and—critically for enterprise—native SIP integration for connecting directly to phone systems.However, Kwindla also gave us a dose of reality. While this is the future, for many high-stakes enterprise use cases, the multi-model pipeline approach is still more reliable. Observability is a major issue with the single-model black box; it's hard to know exactly what the model "heard." And in terms of raw instruction-following and accuracy, a specialized pipeline can still outperform the speech-to-speech model. It’s a classic jagged frontier: for the lowest latency and most natural vibe, GPT Real-Time is amazing. For mission-critical reliability, the old way might still be the right way for now.ChatGpt has branching! Just as I was about to finish writing this up, ChatGPT announced a new feature, and this one I had to tell you about! Finally you can branch chats in their interface, which is a highly requested feature! Branching seems to be live on the chat interface, and honestly, tiny but important UI changes like these are how OpenAI remains the best chat experience! The Money Printer Goes Brrrr: Anthropic's $13B RaiseLet's talk about the money. Anthropic announced it has raised an absolutely staggering $13 billion in a Series F round, valuing the company at $183 billion. Their revenue growth is just off the charts, jumping from a run rate of around $1 billion at the start of the year to over $5 billion by August. This growth is heavily driven by enterprise adoption and the massive success of Claude Code. It's clear that the AI gold rush is far from over, and investors are betting big on the major players. In related news, OpenAI is also reportedly raising $10 billion at a valuation of around $500 billion, primarily to allow employees to sell shares—a huge moment for the folks who have been building there for years.Oops... Did We Nerf Your AI? Anthropic's ApologyWhile Anthropic was celebrating its fundraise, it was also dealing with a self-inflicted wound. After days of users on X and other forums complaining that Claude Opus felt "dumber," the company finally issued a statement admitting that yes, for about three days, the model's quality was degraded due to a change in their infrastructure stack.Honestly, this is not okay. We're at a point where hundreds of thousands of developers and businesses rely on these models as critical tools. To have the quality of that tool change under your feet without any warning is a huge problem. It messes with people's ability to do their jobs and trust the platform. While it was likely an honest mistake in pursuit of efficiency, it highlights a fundamental issue with closed, proprietary models. You're at the mercy of the provider. It's a powerful argument for the stability and control that comes with open-source and self-hosted models. These companies need to realize that they are no longer just providing experimental toys; they're providing essential infrastructure, and that comes with a responsibility for stability and transparency.This Week's Buzz: CoreWeave Acquires OpenPipe! 🎉Super exciting news from the Weights & Biases and CoreWeave family - we've acquired OpenPipe! Kyle and David Corbett and their team are joining us to help build out the complete AI infrastructure stack from metal to model.OpenPipe has been doing incredible work on SFT and RL workflows with their open source ART framework. As Yam showed during the show, they demonstrated you can train a model to SOTA performance on deep research tasks for just $300 in a few hours - and it's all automated! The system can generate synthetic data, apply RLHF, and evaluate against any benchmark you specify.This fits perfectly into our vision at CoreWeave - bare metal infrastructure, training and observability with Weights & Biases, fine-tuning and RL with OpenPipe's tools, evaluation with Weave, and inference to serve it all. We're building the complete platform, and I couldn't be more excited!Vision & Speed: Apple's FastVLM (HF)Just before Apple's event next week, they dropped FastVLM - a speed-first vision model that's 85x faster on time-to-first-token than comparable models. They released it in three sizes (7B, 1.5B, and 0.5B), all optimized for on-device use.The demo that blew my mind was real-time video captioning running in WebGPU. HF CEO Clem showed it processing Apple's keynote video with maybe 250ms latency - the captions were describing what was happening almost in real-time. When someone complained it wasn't accurate because it described "an older man in headphones" when showing an F1 car, Clem pointed out that was actually the previous frame showing Tim Cook - the model was just slightly behind!Tools Showdown: Codex vs Claude CodeTo wrap up, we dove into the heated debate between Codex and Claude Code. Sam Altman reported that Codex usage is up 10x in the past two weeks (!) and improvements are coming. Yam gave us a live demo, and while Claude Code failed to even start up during the show (highlighting why people are switching), Codex with GPT-5 was smooth as butter.The key advantages? Codex authenticates with your OpenAI account (no API key juggling), it has MCP support, and perhaps most importantly - it's not just a CLI tool. You can use it for PR reviews on GitHub, as a cloud-based agent, and integrated into Cursor and Windsurf. Though as Yam pointed out, OpenAI really needs to stop calling everything "Codex" - there are like five different products with that name now! 😅If you're tried Codex (the CLI!) when it was released, and gave up, give it a try now, it's significantly upgraded! Ok, phew, what a great episode we had, if you're only reading, I strongly recommend checking out the live recording or the edited podcast, and of course, if this newsletter is helpful to you, the best way you can do to support it is to subscribe, and share with friends 👏 P.S - Just came back after my first burning man, it was a challenging, all consuming experience, where I truly disconnected for the first time (first ThursdAI in over 2 years that I didn't know what's going on with AI). It was really fun but I'm happy to =be back! See you next week! TL;DR and Show Notes* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldjconfirmed* Guests - Roger Jin - @rogershijin & Bhavesh Kumar @bha_ku21* Kwindla Kramer - @kwindla* Open Source LLMs* Nous Hermes 4 — 14B launches: compact hybrid reasoning model with tool calling for local and cloud use (X, HF, Tech Report)* Tencent open-sources Hunyuan-MT-7B translation model after sweeping WMT2025 (X, HF)* Nous - Husky Hold'em Bench launches as an open-source pokerbot eval for LLM strategic play (X, Bench)* WebWatcher: Alibaba's Tongyi Lab open-sources a vision-language deep research agent that sets new SOTA (X, HF)* Apertus-8B and 70B launch as Switzerland's fully open, multilingual LLMs trained on 15T tokens across 1,800+ languages (X, HF)* Google releases Embedding Gemma - 300M param SOTA embeddings model for RAG ([Breaking News])* Big CO LLMs + APIs* Mistral adds 20+ MCP-powered connectors and controllable Memories to Le Chat for enterprise workflows (X, Blog)* Anthropic raises $13B Series F at a $183B post-money valuation (X, Blog)* OpenAI fundraises $10B at ~$500B valuation - buyback for employees* OpenAI ships gpt-realtime and takes Realtime API to GA with remote MCP tools, image input, and SIP phone calling (X)* OpenAI releases projects for free users with larger file uploads and project-only memory controls* OpenAI acquires Statsig & Alex for $1.1B+ to strengthen applications team* Grok Code 1 - now taking 50% of coding traffic on OpenRouter* Codex usage up 10x in 2 weeks per Sam Altman, with improvements coming* Anthropic admits to Claude Opus quality degradation for 3 days due to infrastructure changes* This weeks Buzz* CoreWeave buys OpenPipe! 🎉 (Blog)* Vision & Video* Apple's FastVLM-7B lands with speed-first vision encoder—85x faster TTFT vs peers (X, HF)* AI Art & Diffusion & 3D* Nano Banana (Imagen 3) continues to dominate as Google's best image model (ai.studio/banana)* Tools* Codex vs Claude Code discussion → Codex now significantly better with GPT-5 engine, GitHub PR reviews, and cloud agents This is a public episode. 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Hey everyone, Alex here 👋This week looked quiet… until about 15 hours before we went live. Then the floodgates opened: DeepSeek dropped a hybrid V3.1 that beats their own R1 with fewer thinking tokens, ByteDance quietly shipped a 36B Apache-2.0 long-context family with a “thinking budget” knob, NVIDIA pushed a faster mixed-architecture 9B with open training data, and a stealth image editor dubbed “Nano Banana” started doing mind-bending scene edits that feel like a new tier of 3D-aware control. On the big-co side, a mystery “Sonic” model appeared in Cursor and Cline (spoiler: the function call paths say a lot), and OpenAI introduced Agents.md to stop the config-file explosion in agentic dev tools. We also got a new open desktop-agent RL framework that 4x’d OSWorld SOTA, an IBM + NASA model for solar weather, and Qwen’s fully open 20B image editor that’s shockingly capable and runnable on your own GPU.Our show today was one of the shortest yet, as I had to drop early to prepare for Burning Man 🔥🕺 Speaking of which, Wolfram and the team will host the next episode! Ok, let's dive in! DeepSeek V3.1: a faster hybrid that thinks less, scores more (X, HF)DeepSeek does this thing where they let a base artifact “leak” onto Hugging Face, and the rumor mill goes into overdrive. Then, hours before we went live, the full V3.1 model card and an instruct variant dropped. The headline: it’s a hybrid reasoner that combines the strengths of their V3 (fast, non-thinking) and R1 (deep, RL-trained thinking), and on many tasks it hits R1-level scores with fewer thinking tokens. In human terms: you get similar or better quality, faster.A few things I want to call out from the release and early testing:* Hybrid reasoning mode done right. The model can plan with thinking tokens and then switch to non-thinking execution, so you don’t have to orchestrate two separate models. This alone simplifies agent frameworks: plan with thinking on, execute with thinking off.* Thinking efficiency is real. DeepSeek shows curves where V3.1 reaches or surpasses R1 with significantly fewer thinking tokens. On AIME’25, for example, R1 clocks 87.5% with ~22k thinking tokens; V3.1 hits ~88.4 with ~15k. On GPQA Diamond, V3.1 basically matches R1 with roughly half the thinking budget.* Tool-use and search-agent improvements. V3.1 puts tool calls inside the thinking process, instead of doing a monologue and only then calling tools. That’s the pattern you want for multi-turn research agents that iteratively query the web or your internal search.* Long-context training was scaled up hard. DeepSeek says they increased the 32K extension phase to ~630B tokens, and the 128K phase to ~209B tokens. That’s a big bet on long-context quality at train time, not just inference-time RoPE tricks. The config shows a max position in the 160K range, with folks consistently running it in the 128K class.* Benchmarks show the coding and terminal agent work got a big push. TerminalBench jumps from a painful 5.7 (R1) to 31 with V3.1. Codeforces ratings are up. On SweBench Verified (non-thinking), V3.1 posts 66 vs R1’s ~44. And you feel it: it’s faster to “get to it” without noodling forever.* API parity you’ll actually use. Their API now supports the Anthropic-style interface as well, which means a bunch of editor integrations “just work” with minimal glue. If you’re in a Claude-first workflow, you won’t have to rewire the world to try V3.1.* License and availability. This release is MIT-licensed, and you can grab the base model on Hugging Face. If you prefer hosted, keep an eye on our inference—we’re working to get V3.1 live so you can benchmark without burning your weekend assembling a serving stack.Hugging Face: https://huggingface.co/deepseek-ai/DeepSeek-V3.1-BaseQuick personal note: I’m seeing a lot of small, pragmatic improvements add up here. If you’re building agents, the hybrid mode plus tighter tool integration is a gift. DeepSeek V3.1 is going to be deployed to W&B Inference service soon! Take a look here to see when it's ready wandb.me/inference ByteDance Seed-OSS 36B: Apache-2.0, 512K context, and a “thinking budget” knob (X, HF, Github)I didn’t see much chatter about this one, which is a shame because this seems like a serious release. ByteDance’s Seed team open-sourced a trio of 36B dense models—two Base variants (with and without synthetic data) and an Instruct model—under Apache-2.0, trained on 12T tokens and built for long-context and agentic use. The context window is a native half-million tokens, and they include a “thinking budget” control you can set in 512-token increments so you can trade depth for speed.They report strong general performance, long-context RULER scores, and solid code/math numbers for a sub-40B model, with the Instruct variant posting very competitive MMLU/MMLU-Pro and LiveCodeBench results. The architecture is a straightforward dense stack (not MoE), and the models ship with Transformers/vLLM support and 4/8-bit quantization ready to go. If you’ve been hunting for a commercial-friendly, long-context 30-something‑B with an explicit reasoning-control dial, this should be on your shortlist.A neat detail for the training nerds: two Base releases—one trained with synthetic data, one without—make for a rare apples-to-apples study in how synthetic data shapes base capability. Also worth noting: they previously shipped a Seed-Prover specialized for Lean; it looks like the team is interested in tight domain models and generalists.NVIDIA Nemotron Nano 9B V2: mixed architecture, open data, and long-context throughput (X, Blog, HF, Dataset, Try It) NVIDIA shipped a fully open release of Nemotron Nano 9B V2—base, base-before-alignment/pruning, and a realigned reasoning model—and, crucially, they published most of the pretraining dataset details (~6.6T tokens across premium web, math, code, and SFT). That level of data transparency is rare and makes this a great base for fine-tuners who want reproducibility.Under the hood, this is a mixed Mamba+Transformer architecture. NVIDIA is claiming up to 6x higher throughput versus a pure-Transformer peer (they compare to Qwen3-8B) and specifically highlight that they pruned a 12B down to 9B while preserving quality. They also note a single A10 can handle 128K context after compression and distillation passes, which is the kind of practical systems work that matters when you’re running fleets.A couple of caveats. The license is NVIDIA Open Model License—not Apache-2.0—so read it; it includes restrictions around illegal surveillance and safety bypasses and has revocation clauses. Personally, I appreciate the data openness and the long-context engineering; as always, just make sure the license fits your use case.If you’re into longer-context math/coding with small models, the numbers (AIME’25, RULER-128K, GPQA) are impressive for 9B. And if you fine-tune: the availability of both pruned and pre-pruned bases plus the dataset recipe is a rare treat.Cohere’s Command-A Reasoning: dense, multilingual, and research-only licensing (X, Blog, HF)Cohere Dropped a new reasoning model focused on enterprise deployment patterns. It’s dense 111B model, supports a 256K context, and includes very strong multilingual coverage (23 languages is what they called out). What caught my eye: on the BFCL (Berkeley Function-Calling Leaderboard) they show 70%—above DeepSeek R1’s ~63% and GPT-OSS’s ~61%—and they plot the now-familiar test-time compute curves where more thinking tokens yield higher scores.This release uses Cohere’s non-commercial research license; if you want commercial usage you’ll need to go through them. That said, for teams who need privately deployable, on-prem reasoning and can work under a research license for prototyping, it’s a serious option. A meta observation from the show: there’s accumulating evidence that more active parameters help multi-hop tool-use chains compared to very sparse MoE at similar effective capacity. This model nudges in that direction.Desktop agents leap: ComputerRL hits 48% on OSWorld (Paper)A new framework dubbed ComputerRL from Z.ai and folks at Tsingua Uni, unified API calls with GUI actions and scaled RL across fleets of virtual desktops, posting a 48.1% success rate on OSWorld versus ~12% for earlier open models. The training system spins up thousands of qemu-in-docker VMs via gRPC; the learning loop alternates RL with supervised fine-tuning and uses a clean step-level binary reward to simplify credit assignment. If you care about practical desktop automation across Ubuntu/Windows/macOS, this is a big jump.IBM + NASA’s Surya: open model for solar weather (HF)Scientists get some love: IBM and NASA open-sourced Surya, a transformer trained on nine years of multi-instrument observations (nearly 200 TB) to forecast solar dynamics and space weather—the stuff that can knock satellites and power grids sideways. It’s on Hugging Face, it’s actually runnable, and it’s a fantastic example of open models delivering real-world scientific utility.Smaller but notable: InternLM and OpenCUA, plus Intel’s quantsTwo quick flags from the “worth your time” pile. InternLM shipped S1 Mini, an 8B vision+language model (ViT on top) that’s multimodal and lightweight; if you need on-device omni-ish behavior on a laptop or tablet, give it a look. And OpenCUA 32B (Qwen-based) is a specialized computer-usage agent with strong scores; if you’re building automations that need native OS control, it’s worth benchmarking.Also, if you’re running 4-bit: the Intel quantization work is excellent right now. Their 4-bit quants have been extremely high precision in my testing, especially for large coders and reasoners like DeepSeek V3.1. It’s an easy win if you’re trying to squeeze a 30B+ onto a workstation without hemorrhaging quality.Big-co updates and platform shiftsSonic appears in Cursor and ClineIf you open Cursor or fire up Cline, you may see a new “Sonic” model toggle. It’s labeled as a reasoning model and, when you poke the function-calling internals, the call paths include “xai/…” strings. Folks report it’s fast and solid for coding. No official docs yet, but I’d be surprised if this isn’t Grok Code in pre-release clothes.Agents.md: one file to rule your agentsAgentic dev stacks have multiplied config files like gremlins: Cursor’s rules.json, Windsurf’s prompts, MCP server manifests, tool schemas, install scripts… and every tool wants a different filename and format. OpenAI’s Agents.md is a strong attempt at standardization. It’s just Markdown at repo root that says, “here’s how to set up, build, test, and run this project,” plus any agent-specific caveats. Tools then auto-detect and follow your instructions instead of guessing.It’s already supported by OpenAI Codex, Amp, Jules, Cursor, RooCode, and more, with tens of thousands of public repos adopting the pattern. In monorepos, the nearest Agents.md wins, so you can override at the package level. And human chat instructions still override the file’s guidance, which is the right default.GPT‑5 context truncation in the web UI (reports)There’s been a wave of reports that GPT‑5 in the web UI is truncating long prompts even when you’re under the documented context limit. The folks at Repo Prompt reproduced this multiple times and got confirmation from OpenAI that it’s a bug (not a deliberate nerf). If you saw GPT‑5 suddenly forget the bottom half of your carefully structured system prompt in the web app, this likely explains it. The API doesn’t seem affected. Fingers crossed for a quick fix—GPT‑5 is still the best model I’ve used for 300k‑token “read the whole repo and propose a plan” tasks.Image and 3D: Nano Banana and Qwen’s open image editorNano Banana: 3D-consistent scene editing from thin airA stealth model nicknamed “Nano Banana” surfaced in a web demo and started doing the kind of edits you’d normally expect from a 3D suite with a modeler at the controls. Take two photos—your living room and a product shot—and it composites the object into the scene with shockingly consistent lighting and geometry. Ask for a 3D mesh “five inches off the skin,” and the mesh really does offset. Ask for a new camera angle on a single still, and it renders the scene from above with plausible structure. People have been calling it a game-changer and, for once, it doesn’t feel like hyperbole.There’s a strong whiff of 3D world modeling under the hood—some volumetric representation or neural field that enables true view synthesis—and Logan Kilpatrick posted a banana emoji that set speculation on fire. We’ll see where it lands and under what license, but for now the demo has been doing the rounds and it is… wow.If you’re wondering where to try it: LMarena is the currently only way to give it a try, but it's supossedly dropping soon! Qwen Image Edit (20B): fully open and already practical (X, HF)Qwen shipped a 20B image-editing model layered on their existing vision stack, and it’s properly open (permissive license) with strong bilingual text editing in Chinese and English. It handles high-level semantic edits (pose adjustments, rotations, style/IP creation) and low-level touch-ups (add/remove/insert). You can swap objects, expand aspect ratios, keep character identity consistent across panels, and do clean style transfer. It runs locally if you’ve got a decent GPU.What I appreciate here is the precision. Product placement tasks like “put this book in this person’s hand at this angle,” or “make the shoes match the dress” come out with the kind of control that used to require hand masking and a dozen passes. And yes, the capybara mascot is back in the release materials, which made my day! 👏If Nano Banana is the closed-world demo of what’s “beyond SOTA,” Qwen Image Edit is the open tool you can actually ship with today.This week’s buzz from Weights & BiasesTwo quick updates from our side. First, we’re working to bring DeepSeek V3.1 to our inference as fast as we can so you can run serious benchmarks without fussing over serving stacks. Keep an eye on our channels; we’ll shout when it’s live and we’ll have some credits for early feedback.Second, our cofounder Chris Van Pelt released Catnip, a containerized multi‑agent coding workspace that runs multiple Claude Code sessions (or other agents) in isolated sandboxes, each with its own context and notification stream. If you’ve been juggling parallel coding agents that step on each other’s toes, this is catnip indeed.Catnip Github: https://github.com/wandb/catnipClosing thoughtsA year ago, “thinking tokens” weren't even a curiosity; We only got the first whiff of "reasoning" back in September, and now we’re watching hybrid models that do more with less thinking, tool calls woven inside the reasoning loop, and long-context training regimes scaled up by an order of magnitude. The agent stack is maturing fast—desktop RL is finally clearing real tasks; editor ecosystems are converging on a single config file; and even the stealth drops are clearly building toward world-model‑aware editing and control.If you only try two things this week: run DeepSeek V3.1 in both modes (planning with thinking on, execution with thinking off) and throw a complex multi-step tool workflow at it; then take Qwen Image Edit for a spin on a real product-placement or character-consistency task. You’ll feel the future in your hands.I’m off to the desert next week for a bit (no internet where I’m going), but Wolfram and the crew will keep the ship on course. If you’re at Burning Man, DM me—would love to say hi out there. As always, thank you for tuning in and nerding out with us every week.— AlexTL;DR and show notesThursdAI - Aug 21, 2025 - TL;DRThursdAI - Aug 21, 2024 - TL;DRTL;DR of all topics covered:* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases* Co Hosts - @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed* Open Source LLMs // Papers* ByteDance Seed-OSS - 36B open-source LLM family (X, HF, GitHub)* DeepSeek V3.1 - Updated Hybrid model (HF)* Cohere CMD-a Reasoning - (X, Blog, HF)* Zai/Tsinghua ComputerRL - Framework for desktop agents (X, Paper, Benchmark)* IBM & NASA Surya - Solar weather prediction (HF)* NVIDIA Nemotron Nano 9B V2 - (X, Blog, HF, Dataset, Try It) * Alibaba Quark Med* Big CO LLMs + APIs* Sonic Stealth Model - Likely Grok Code* OpenAI agents.md - Unified agent files (agents.md)* GPT-5 Nerf* AI Art & Diffusion & 3D* Nano Banana - Image model (rumored Google)* Qwen-Image-Edit - 20B Image editing (X, HF)* This weeks Buzz* Catnip - Containerized AI agent runner (GitHub) This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey everyone, Alex here 👋Last week, I tried to test GPT-5 and got really surprisingly bad results, but it turns out, as you'll see below, it's partly because they had a bug in the router, and partly because ... well, the router itself! See below for an introduction, written by GPT-5, it's actually not bad?Last week was a whirlwind. We live‑streamed GPT‑5’s “birthday,” ran long, and then promptly spent the next seven days poking every corner of the new router‑driven universe.This week looked quieter on the surface, but it actually delivered a ton: two open‑source world models you can drive in real time, a lean vision‑language model built for edge devices, a 4B local search assistant that tops Perplexity Pro on SimpleQA, a base model “extraction” from GPT‑OSS that reverses alignment, fresh memory features landing across the big labs, and a practical prompting guide to unlock GPT‑5’s reasoning reliably.We also had Alan Dao join to talk about Jan‑v1 and what it takes to train a small model that consistently finds the right answers on the open web—locally.Not bad eh? Much better than last time 👏 Ok let's dive in, a lot to talk about in this "chill" AI week (show notes at the end as always) first open source, and then GPT-5 reactions and then... world models!00:00 Introduction and Welcome00:33 Host Introductions and Health Updates01:26 Recap of Last Week's AI News01:46 Discussion on GPT-5 and Prompt Techniques03:03 World Models and Genie 303:28 Interview with Alan Dow from Jan04:59 Open Source AI Releases06:55 Big Companies and APIs10:14 New Features and Tools14:09 Liquid Vision Language Model26:18 Focusing on the Task at Hand26:18 Reinforcement Learning and Reward Functions26:35 Offline AI and Privacy27:13 Web Retrieval and API Integration30:34 Breaking News: New AI Models30:41 Google's New Model: Gemma 333:53 Meta's Dino E3: Advancements in Computer Vision38:50 Open Source Model Updates45:56 Weights & Biases: New Features and Updates51:32 GPT-5: A Week in Review55:12 Community Outcry Over AI Model Changes56:06 OpenAI's Response to User Feedback56:38 Emotional Attachment to AI Models57:52 GPT-5's Performance in Coding and Writing59:55 Challenges with GPT-5's Custom Instructions01:01:45 New Prompting Techniques for GPT-501:04:10 Evaluating GPT-5's Reasoning Capabilities01:20:01 Open Source World Models and Video Generation01:27:54 Conclusion and Future ExpectationsOpen Source AIWe've had quite a lot of Open Source this week on the show, including a breaking news from the Gemma team!Liquid AI's drops LFM2-VL (X, blog, HF)Let's kick things off with our friends at Liquid AI who released LFM2-VL - their new vision-language models coming in at a tiny 440M and 1.6B parameters.Liquid folks continue to surprise with speedy, mobile device ready models, that run 2X faster vs top VLM peers. With a native 512x512 resolution (which breaks any image size into 512 smart tiles) and an OCRBench of 74, this tiny model beats SmolVLM2 while being half the size. We've chatted with Maxime from liquid about LFM2 back in july, and it's great to see they are making them multimodal as well with the same efficiency gains!Zhipu (z.ai) unleashes GLM-4.5V - 106B VLM (X, Hugging Face)In another "previous good model that now has eyes" release, the fine folks from Zhipu continued training thier recently released (and excelled) GLM 4.5-air with a vision encoder, resulting in probably one of the top vision models in the open source!It's an MoE with only 12B active parameters (106B total) and gets SOTA across 42 public vision-language benches + has a "thinking mode" that reasons about what it sees.Given that GLM-4.5Air is really a strong model, this is de fact the best visual intelligence in the open source, able to rebuild websites from a picture for example and identify statues and locations!Jan V1 - a tiny (4B) local search assistant QwenFinetune (X, Hugging Face)This one release got a lot of attention, as the folks at Menlo Research (Alan Dao who came to chat with us about Jan on the pod today) released an Apache 2 finetune of Qwen3-4B-thinking, that's focused on SimpleQA.They showed that their tiny model is beating Perplexity Pro on SimpleQA.Alan told us on the pod that Jan (the open source Jan app) is born to be an open source alternative to searching with local models!The trick is, you have to enable some source of search data (Exa, Serper, Tavily) via MCP and then enable tools in Jan, and then.. you have a tiny, completely local perplexity clone with a 4B model!Google drops Gemma 3 270M (blog)In some #breakingNews, Google open sourced a tiny (270M) parameters, "good at instruction following" Gemma variant. This joins models like SmolLM and LFM2 in the "smol models" arena, being only 300MB, you can run this.. on a toaster. This one apparently also fine-tunes very well while being very energy efficient!Big Companies (AKA OpenAI corner this past 2 weeks)Ok ok, we're finally here, a week with GPT-5! After watching the live stream and getting access to GPT-5, my first reactions were not great. Apparently, so have other peoples, and many folks outcried and complained about the model, some even yelling "AGI is cancelled".What apparently happened is (and since, been fixed by OpenAI) is that GPT-5 wasn't just a model that launched, it was a "smart" router between a few models, and not only did they have a routing bug, the basic GPT-5 model, the one without thinking, is... not great.But the thinking GPT-5, the one that the router refused to send me to, is really good (as confirmed independently by multiple evals at this point)For one, it's the most accurate function calling model on OpenRouterIt's also one of the best on this new FormulaOne benchmark that was just launchedYou're prompting it wrong!Apparently, not only is GPT-5 more intelligent, it's also significantly "surgical" in instruction following, and so, for many folks, just replacing GPT-5 into their tools or prompts didn't just "work", as this model, more than before, is sensitive to conflicting things in the prompt.OpenAI has released a guide for prompting the model, mostly aimed at developers (as users shouldn't be learning to prompt as models get more intelligent) + they also released a prompt optimizer! Just dump your long and complex prompts in there, and you'll get an updated prompt with explanations of why they changed what they changed!Model Picker (and legacy models) are back!So, OpenAI tried and super quickly reversed course on removing the "model picker". At first, it was only GPT-5 there, but many people complained about the abrupt removal of 4o, their .. favorite models. At first, OpenAI added back the models via a hidden setting, and later, they have added 4o back to everyone by default, while increasing the reasoning quota to 3000 messages per week!Generally, my thoughts are, if you've tried GPT-5 and weren't impressed, give it another go! (especially now that it's connected to Gmail in chats!)Other notable Big Company updatesIn other news, Claude has extended the context window of Sonnet to 1M in the API, and apparently both Claude and Gemini have been adding memory features!Grok video has been catching up and is now free for a while to all usersThis Week's Buzz: Weave DX improvementsQuick update from my day job at Weights & Biases - we've rolled out some quality-of-life improvements to Weave, our LLM observability platform. We now have a unified assets tab where you can manage all your prompts, models, and datasets with full versioning support.Prompts are being version tracked, so if you use that GPT-5 prompt optimizer, we'll store all the previous revisions for ya!The coolest addition? Threads! Perfect for tracking agent executions or grouping related API calls. You just add a thread_id to your traces and Weave handles the rest. If you're building AI applications and not tracking everything, you're flying blind - give Weave a try at wandb.me/weave!World models are getting... open sourced!I still think that Google's Genie-3 release from last week was maybe the more important one, though we didn't really get to play with it yet!And while getting excited by world models, I was thinking that it's goig to take a while for Open Source to catch up. But this week, not 1, but two world models were open sourced, making me think that we'll get to generated worlds quicker than I expected and the race has begun!Skywork's Matrix-Game 2.0 (project, HF)Matrix-game 2 is a auto-regressive diffusion model, that was trained on 1200 hours of Unreal Engine and GTA-5 environments that runs at 25 frames per second!It works by creating an "action injection module" that embeds the mouse/keyboard inputs into the generation, enabling frame-level controls.Hunyuan open-sources GameCraft for real-time, high-dynamic game video generation (X, Hugging Face)Two world-models (well, game models) in the same week? Tencent (who had Hunyuan video before) have trained a game engine on top of their excellent HY-video and have shown the same examples, of building a full world based on a few images.Their pipeline trained on 1M game play clips from AAA titles, and they also map W/A/S/D and mouse signals into continuous camera/action embeddings, allowing for control and angle creation.The cool thing? A quantized 13B version supposedly can run on a RTX 4090!Funnily, they already had Matrix-Game (the one that came out a few days before) benchmarked and beat on the release today!Genie 3 is not messing aroundWhile all the open source is impressive, I was… absolutely blown away by this video from an artist who got the Genie3 team to extend a video of his. Just look at the collision of the plane with the sphere, out of nowhere, Genie3 adds a shadow, and then collision mechanics, the plane bouncing off, and even the jet trails subside and then resume! It really really is crazy to imagine that no prompting was given and the model just.. knew how to do this!Phew, that was a lot! Much more as always on the actual show, despite it being a "quiet" week by summer of 2025 standards, there was a LOT of open source releases and GPT-5 situation shows that even OpenAI can stumble on new tech releases!Keep the feedback coming - find me on Twitter/X at @altryne or email the show. And remember, if you want to catch all the technical details and demos, the video version on YouTube has everything the podcast can't show.Until next week, keep tinkering, keep questioning, and keep pushing the boundaries of what's possible with AI!See you next ThursdAI 🚀TL;DR - All Topics CoveredHosts and Guests* Alex Volkov - AI Evangelist @ Weights & Biases (@altryne)* Co-hosts: @WolframRvnwlf, @yampeleg, @nisten, @ldjconfirmed* Guest: Alan Dao from Jan (@jandotai)Open Source LLMs* Liquid AI LFM2-VL: 440M & 1.6B vision-language models with 2x GPU speedup (Blog, HF)* Jan V1: 4B parameter search assistant beating Perplexity Pro on SimpleQA (X, HF)* GPT-OSS Base: Reverse-engineered base model from Jack Morris (X Thread)* Gemma 3: Google's 270M parameter model with strong instruction following (HF)* Meta Dino v3: Self-supervised vision model for segmentation (Blog)Big Companies & APIs* Mistral Medium 3.1: New model on Mistral platform* Gemini & Claude: Added memory/personalization features* GPT-5 Updates: Router fixes, model selector returned, prompting guide released (Guide, Optimizer)* Claude Sonnet 4: 1M token context window (Announcement)This Week's Buzz* Weave updates: Unified assets tab and threads for agent tracking* New features for LLM observability and evaluationVision & Video* Hunyuan Large Vision: 1B vision encoder + 389B MoE language model (Project)* GLM-4.5V: 106B open source VLM from Zhipu AI (X, HF)World Models* Matrix Game 2.0: Real-time interactive world model, 25fps generation (Project, HF)* Hunyuan GameCraft: Game video generation with physics understanding (X, HF)Tools* Grok: Now includes video generation for all users* Jan Desktop App: Local AI with MCP support and search capabilities This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

Hey folks 👋 Alex here, writing to you, from a makeshift recording studio in an Eastern European hookah bar, where I spent the last 7 hours. Why you ask? Well, when GPT-5 drops, the same week as OpenAI dropping the long awaited OSS models + Google is shipping perfect memory World Models (Genie 3) and tons of other AI drops, well I just couldn't stay away from the stream.Vacation or not, ThursdAI is keeping you up to date (for 32 months straight, which is also the time since the original GPT-4 release which gave this show its name!)So, what did we have today on the stream? Well, we started as usual, talking about the AI releases of the week, as if OpenAI dropping OSS models (apache 2) 120B and 20B is "usual". We then covered incredible releases like Google's World model Genie3 (more on this next week!) and Qwen-image + a few small Qwens.We then were VERY excited to tune in, and watch the (very long) announcement stream from OpenAI, in which they spent an hour to tell us about GPT-5.This was our longest stream by far (3.5 hours, 1hr was just OpenAI live stream) and I'm putting this here mostly unedited, but chapters are up so feel free to skip to the parts that are interesting to you the most.00:00 Introduction and Special Guests00:56 Twitter Space and Live Streaming Plans02:12 Open Source AI Models Overview03:44 Qwen and Other New AI Models08:59 Community Interaction and Comments10:01 Technical Deep Dive into AI Models25:06 OpenAI's New Releases and Benchmarks38:49 Expectations and Use Cases for AI Models40:03 Tool Use vs. Deep Knowledge in AI41:02 Evaluating GPT OSS and OpenAI Critique42:29 Historical and Medical Knowledge in AI51:16 Opus 4.1 and Coding Models55:38 Google's Genie 3: A New World Model01:00:43 Kitten TTS: A Lightweight Text-to-Speech Model01:02:07 11 Labs' Music Generation AI01:08:51 OpenAI's GPT-5 Launch Event01:24:33 Building a French Learning Web App01:26:22 Exploring the Web App Features01:29:19 Introducing Enhanced Voice Features01:30:02 Voice Model Demonstrations01:32:32 Personalizing Chat GPT01:33:23 Memory and Scheduling Features01:35:06 Safety and Training Enhancements01:39:17 Health Applications of GPT-501:45:07 Coding with GPT-501:46:57 Advanced Coding Capabilities01:52:59 Real-World Coding Demonstrations02:10:26 Enterprise Applications of GPT-502:11:49 Amgen's Use of GPT-5 in Drug Design02:12:09 BBVA's Financial Analysis with GPT-502:12:33 Healthcare Applications of GPT-502:12:52 Government Adoption of GPT-502:13:22 Pricing and Availability of GPT-502:13:51 Closing Remarks by Chief Scientist Yakob02:16:03 Live Reactions and Discussions02:16:41 Technical Demonstrations and Comparisons02:33:53 Healthcare and Scientific Advancements with GPT-502:47:09 Final Thoughts and Wrap-Up---My first reactions to GPT-5Look, I gotta keep it real with you, my first gut reaction was, hey, I'm on vacation, I don't have time to edit and write the newsletter (EU timezone) so let's see how ChatGPT-5 handles this task. After all, OpenAI has removed all other models from the dropdown, it's all GPT-5 now. (pricing from the incredible writeup by Simon Willison available here)And to tell you the truth, I was really disappointed! GPT seems to be incredible at coding benchmarks, with 400K tokens and incredible pricing (just $1.25/$10 compared to Opus $15/$75) this model, per the many friends who got to test it early, is a beast at coding! Readily beating opus on affordability per token, switching from thinking to less thinking when it needs to, it definitely seems like a great improvement for coding and agentic tasks.But for my, very much honed prompt of "hey, help me with ThursdAI drafts, here's previous drafts that I wrote myself, mimic my tone" it failed.. spectacularly!Here's just a funny example, after me replying that it did a bad job:It literally wrote "I'm Alex, I build the mind, not the vibe" 🤦‍♂️ What.. the actual...For comparison, here's o3, with the same prompt, with a fairly true to tone draft:High taste testers take on GPT-5But hey, I have tons of previous speakers in our group chats, and many of them who got early access (I didn't... OpenAI, I can be trusted lol) rave about this model. They are saying that this is a huge jump in intelligence.Folks like Dr Derya Unutmaz, who jumped on the live show and described how GPT5 does incredible things with less hallucinations, folks like Swyx from Latent.Space who had early access and even got invited to give first reactions to the OpenAI office, and Pietro Schirano who also showed up in an OpenAI video.So definitely, definitely check out their vibes, as we all try to wrap our heads around this new intelligence king we got!Other GPT5 updatesOpenAI definitely cooked, don't get me wrong, with this model plugging into everything else in their platform like memory, voice (that was upgraded and works in custom GPTs now, yay!), canvas and study mode, this will definitely be an upgrade for many folks using the models.They have now also opened access to GPT-5 to free users, just in time for schools to reopen, including a very interesting Quiz mode (that just showed up for me without asking for it), and connection to Gmail, all those will now work with GPT5.It now has 400K context, way less hallucinations but fewer refusals also, and the developer upgrades like a new verbosity setting and a new "minimal" reasoning setting are all very welcome!OpenAI finally launches gpt-oss (120B / 20B) apache 2 licensed models (model card, HF)It was really funny, on the stream Nisten talked about the open source models OpenAI dropped, and said "when we covered it last week", while it was just two days ago! It really does feel like this world is moving really fast.OpenAI's long promised open source models are here, and they got a fairly mixed bag of reviews from folks. Many folks are celebrating that the western world is now back in the game, releasing incredible local models, with an open license!Though, after the initial excitement, the vibes are split on these models. Folks are saying that maybe these were trained with only synthetic data, because, like Phi, they seem to be very good at benchmarks, and on the specific tasks they were optimized for (code, math) but really bad at creative writing (Sam Paech from EQBench was not impressed), they are also not multilingual, though OpenAI did release a cookbook on finetuning with HuggingFace!Overall, these models are trained for agentic workflows—supporting function calling, web search, Python execution, configurable reasoning effort, and full raw chain-of-thought access, which we will never get from GPT5.I particularly love the new approach, where a reasoning effort can be defined directly via the system prompt, by just adding "reasoning: high" to the system prompt, this model will reason for way longer! Can't wait to get back and bench these and share with you.Overall, the fine-tuning and open source community is split for now, but it's been only a few days, so we'll keep you up to date on how well these models land, regardless, this was a historic week for OpenAI!Speaking of open models, did you have a chance to try our W&B Inference? The team worked hard to bring these new models to you in record time and incredible pricing (just $.05 for 20B and $.15 for 120B!), these models are definitely worth giving a try!Plus, if you comment "OSS Power" on our announcement post, we'll likely give you a few credits to try it out and let us know what you think!World models "holy crap" moment - Google Genie3The other very important release this week was.... not a release at all, but an announcement from Deepmind, with Genie3.This World Model takes a single image or text prompt and creates a fully interactive, controllable 3D environment that runs in real-time at 24fps. An environment you as a user can control, walk (or fly) in, move around the camera view. It's really mindblowing stuff.We've covered world models like Mirage on previous episodes, but what Google released is a MAJOR step up in coherency, temporal consistency and just overall quality!The key breakthrough here is consistency and memory. In one demo, a user could "paint" a virtual wall, turn away, and when they turned back, the paint was still there. This is a massive step towards generalist agents that can train, plan, and reason in entirely simulated worlds, with huge implications for robotics and gaming.We’re hoping to have the Genie 3 team on the show next week to dive even deeper into this incredible technology!!Other AI news this weekThis week, the "other" news could have filled a full show 2 years ago, we got Qwen keeping the third week of releases with 2 new tiny models + a new diffusion model called Qwen-image (Blog, HF)Anthropic decided to pre-empt the GPT5 release, and upgraded Opus 4 and gave us Opus 4.1 with a slight bump in specs.ElevenLabs released a music API called ElevenMusic, which sounds very very good (this on top of last weeks Riffusion + Producer.ai news, that I'm still raving about)Also in voice an audio, a SUPER TINY TTS model called KittenTTS released, with just 15M parameters and a model that's 25MB, it's surprisingly decent at generating voice (X)And to cap it off with breaking news, the Cursor team, who showed up on the OpenAI stream today (marking quite the change in direction from OpenAI + Windsurf previous friendship), dropped their own CLI version of cursor, reminiscent of Claude Code!PHEW, wow ok this was a LOT to process. Not only did we tune in for the full GPT-5 release, we did a live stream when gpt-oss dropped as well.On a personal note, I was very humbled when Sam Altman said it was 32 months since GPT-4 release, because it means this was 32 months of ThursdAI, as many of you know, we started live streaming on March 13, 2023, when GPT-4 was released.I'm very proud of the incredible community we've built (50K views total across all streams this week!), the incredible co-hosts I have, who step up when I'm on vacation and the awesome guests we have on the show, to keep you up to date every week!So, a little favor to ask, if you find our content valuable, entertaining, the best way to support this pod is upgrade to a paid sub, and share ThursdAI with a friend or two! 👏 See you next week 🫡 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

This is a free preview of a paid episode. To hear more, visit sub.thursdai.newsWoohoo, we're almost done with July (my favorite month) and the Open Source AI decided to go out with some fireworks 🎉Hey everyone, Alex here, writing this without my own personal superintelligence (more: later) and this week has been VERY BUSY with many new open source releases.Just 1 hour before the show we already had 4 breaking news releases, a tiny Qwen3-coder, Cohere and StepFun both dropped multimodal SOTAs and our friends from Krea dropped a combined model with BFL called Flux[Krea] 👏 This is on top of a very very busy week, with Runway adding conversation to their video model Alpha, Zucks' superintelligence vision and a new SOTA open video model Wan 2.2. So let's dive straight into this (as always, all show notes and links are in the end) ThursdAI - Recaps of the most high signal AI weekly spaces is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Open Source LLMs & VLMs Tons of new stuff here, I'll try to be brief but each one of these releases deserves a deeper dive for sure. Alibaba is on 🔥 with 3 new Qwen models this weekYes, this is very similar to last week, where they have also dropped 3 new SOTA models in a week, but, these are additional ones. It seems that someone in Alibaba figured out that after splitting away from the hybrid models, they can now release each model separately and get a lot of attention per model! Here's the timeline: * Friday (just after our show): Qwen3-235B-Thinking-2507 drops (235B total, 22B active, HF) * Tuesday: Qwen3-30B-Thinking-2507 (30B total, 3B active, HF)* Today: Qwen3-Coder-Flash-2507 lands (30B total, 3B active for coding, HF)Lets start with the SOTA reasoner, the 235B(A22B)-2507 is absolutely the best reasoner among the open source models.We've put the model on our inference service (at crazy prices $.10/$.10) and it's performing absolutely incredible on reasoning tasks. It also jumped to the top OSS model on Artificial Analysis scores, EQBench, Long Context and more evals. It a really really good reasoning model! Smaller Qwens for local useJust a week ago, we've asked Junyang on our show, about smaller models that folks can run on their devices, and he avoided by saying "we're focusing on the larger models" and this week, they delivered not 1 but 2 smaller versions of the bigger models (perfect for Speculative Decoding if you can host the larger ones that is) The most interesting one is the Qwen3-Coder-flash, which came out today, with very very impressive stats - and the ability to run locally with almost 80 tok/s on a macbook! So for the last two weeks, we now have 3 Qwens (Instruct, Thinking, Coder) and 2 sizes for each (all three have a 30B/A3B version now for local use) 👏Z.ai GLM and StepFun Step3 As we've said previously, Chinese companies completely dominate the open source AI field right now, and this week as saw yet another crazy testament to how stark the difference is! We've seen a rebranded Zhipu (Z.ai previously THUDM) release their new GLM 4.5 - which gives Qwen3-thinking a run for it's money. Not quite at that level, but definitely very close. I personally didn't love the release esthetics, showing a blended eval score, which nobody can replicate feels a bit off. We also talked about how StepFun has stepped in (sorry for the pun) with a new SOTA in multimodality, called Step3. It's a 321B MoE (with a huge 38B active param count) that achieves very significant multi modal scores (The benchmarks look incredible: 74% on MMMU, 64% on MathVision) Big Companies APIs & LLMsWell, we were definitely thinking we'll get GPT-5 or the Open Source AI model from OpenAI this week, but alas, the tea leaves readers were misled (or were being misleading). We 100% know that gpt-5 is coming as multiple screenshots were blurred and then deleted showing companies already testing it. But it looks like August is going to be even hotter than July, with multiple sightings of anonymous testing models on Web Dev arena, like Zenith, Summit, Lobster and a new mystery model on OpenRouter called Zenith - that some claim are the different thinking modes of GPT-5 and the open source model? Zuck shares vision for personalized superintelligence (Meta)In a very "Nat Fridman" like post, Mark Zuckerberg finally shared the vision behind his latest push to assemble the most cracked AI engineers.In his vision, Meta is the right place to provide each one with personalized superintelligence, enhancing individual abilities with user agency according to their own values. (as opposed to a centralized model, which feels like his shot across the bow for the other frontier labs) A few highlights: Zuck leans heavily into the rise of personal devices on top of which humans will interact with this superintelligence, including AR glasses and a departure from a complete "let's open source everything" dogman of the past, now there will be a more deliberate considerations of what to open source. This Week's Buzz: Putting Open Source to Work with W&BWith all these incredible new models, the biggest question is: how can you actually use them? I'm incredibly proud to say that the team at Weights & Biases had all three of the big new Qwen models—Thinking, Instruct, and Coder—live on W&B Inference on day one (link)And our pricing is just unbeatable. Wolfram did a benchmark run that would have cost him $150 using Claude Opus. On W&B Inference with the Qwen3-Thinking model, it cost him 22 cents. That's not a typo. It's a game-changer for developers and researchers.To make it even easier, a listener of the show, Olaf Geibig, posted a fantastic tutorial on how you can use our free credits and W&B Inference to power tools like Claude Code and VS Code using LiteLLM. It takes less than five minutes to set up and gives you access to state-of-the-art models for pennies. All you need to do is add this config to vllm and run claude (or vscode) through it! Give our inference service a try here and follow our main account @weights_biases a follow as we often drop ways to get additional free credits when new models releaseVision & Video modelsWan2.2: Open-Source MoE Video Generation Model Launches (X, HF)This is likely the best open source video model, but definitely the first MoE video model! It came out with text2video, image2video and a combined version. With 5 second 720p videos, that can even be generator at home on a single 4090, this is definitely a step up in the quality of video models that are fully open source. Runway changes the game again - Gen-3 Aleph model for AI video editing / transformation (X, X)Look, there's simply no denying this, AI video has had an incredible year, from open source like Wan, to proprietary models with sounds like VEO3. And it's not surprising that we're seeing this trend, but it's definitely very exciting when we see an approach like Runway has, to editing. This adds a chat to the model, and your ability to edit.. anything in the scene. Remove / Add people and environmental effects, see the same scene from a different angle and a lot more! Expect personalized entertainment very soon! AI Art & Diffusion & 3DFLUX.1 Krea [dev] launches as a state-of-the-art open-weights text-to-image model (X, HuggingFace)Black Forest Labs teamed with Krea AI for Flux.1 Krea [dev], an open-weights text-to-image model ditching the "AI gloss" for natural, distinctive vibes—think DALL-E 2's quirky grain without the saturation. It outperforms open peers and rivals pros in prefs, fully Flux-compatible for LoRAs/tools. Yam and I geeked over the aesthetics frontier; it's a flexible base for fine-tunes, available on Hugging Face with commercial options via FAL/Replicate. If you're tired of cookie-cutter outputs, this breathes fresh life into generations.Ideogram Character launches: one-shot character consistency for everyone (X)Ideogram's Characters feature lets you upload one pic for instant, consistent variants—free for all, with inpainting to swap into memes/art. My tests nailed expressions/scenes (me in cyberpunk? Spot-on), though not always photoreal. Wolfram praised the accuracy; it's a meme-maker's dream! and they give like 10 free ones so give it a goTencent Hunyuan3D World Model 1.0 launches as the first open-source, explorable 3D world generator (X, HF)Tencent's Hunyuan3D World Model 1.0 is the first open-source generator of explorable 3D worlds from text/image—360° immersive, exportable meshes for games/modeling. ~33GB VRAM on complex scenes, but Wolfram called it a metaverse step; I wandered a demo scene, loving the potential despite edges. Integrate into CG pipelines? Game-changer for VR/creators.Voice & Audio Look I wasn't even mentioning this on the show, but it came across my feed just as I was about to wrap up ThursdAI, and it's really something. Riffusion joined forces producer and using FUZZ-2 they now have a fully Chatable studio producer, you can ask for.. anything you would ask in a studio! Here's my first reaction, and it's really fun, I think they still are open with the invite code 'STUDIO'... I'm not afiliated with them at all! Tools Ok I promised some folks we'll add this in, Nisten went super viral last week with him using a new open source tool called Crush from CharmBracelet, which is an open version of VSCode and it looks awesome! He gave a demo live on the show, including how to set it up to work, with subagents etc. If you're into vibe coding, and using the open source models, def. give Crush a try it's really flying and looks cool! Phew, ok, we somehow were able to cover ALLL these releases this week, and we didn’t even have an interview! Here’s the TL;DR and links to the folks who subscribed (I’m trying a new thing to promote subs on this newsletter) and see you in two weeks (next week is Wolframs turn again as I’m somewhere in Europe!) ThursdAI - July 31st, 2025 - TL;DR* Hosts and Guests* Alex Volkov - AI Evangelist & Weights & Biases (@altryne)* Co Hosts - @WolframRvnwlf @yampeleg @nisten @ldj* Open Source LLMs* Zhipu drops GLM-4.5 355B (A32B) AI model (X, HF)* ARCEE AFM‑4.5B and AFM‑4.5B‑Base weights released (X, HF)* Qwen is on 🔥 - 3 new models: