Inference by Turing Post

"The numbers of people who are going to be able to become AI builders is going to explode. It's gonna go from maybe a few hundred thousands or low millions… to maybe tens of millions, fifties of millions, maybe a hundred million at some point." Clément Delangue, co-founder and CEO of Hugging Face, believes we are entering a new phase of AI – one where building models, fine-tuning systems, running local AI, and even experimenting with robotics may no longer be limited to a small technical elite. His passion for open source is very contagious! I enjoyed chatting with him about: Why the next wave of AI builders won't be traditional engineers – and how that could push the field beyond slop toward biology, medicine, and climate What open source actually solves in cybersecurity – and why "safety" is often a cover story for business strategy Why lobbying against open source in the US would be a strategic mistake that could cost the country its AI leadership Why comparing open weights to closed APIs is irrelevant and why benchmarks miss what really matters What Hugging Face is learning as agents become a new kind of user How LeRobot and Reachy Mini are turning AI into something people lile Why training, fine-tuning, and post-training on your own data are becoming the real differentiators as building apps gets trivial What three months of paternity leave taught Clem We also talk about fear-based AI marketing, how public perception shifts the moment people build with AI, what's missing in robotics datasets, and why Clem keeps coming back to Camus' Sisyphus as a metaphor for being a founder right now A conversation about agency, openness, and what it means to democratize AI before it gets locked down. Watch it. *Chapters:* 00:00 AI Builders Are About to Explode 00:35 Why Coding Agents Still Struggle with AI 02:23 100 Million AI Builders 03:30 Non-Technical People Entering AI 05:15 How Building AI Can Change Public Perception 06:22 Who Can Make AI More Open? 08:02 Fear-Based Marketing in AI 09:33 Open Source, Cybersecurity, and Risk 12:31 Why Companies Don’t Open Source 14:30 Lobbying Against Open Source 17:24 What Changed During Paternity Leave 19:11 Making Hugging Face Agent-Native 21:00 Hugging Face Robotics and LeRobot 23:01 Local AI, Open Models, and the Future *Did you like the episode? Do the following:* 📌 Subscribe here and here (https://www.turingpost.com/subscribe) for more conversations with the builders shaping real-world AI. 💬 Leave a comment 👍 Like it 🫶 Thank you for watching and sharing! *Guest:* Clément Delangue, co-founder & CEO of Hugging Face https://x.com/ClementDelangue https://www.linkedin.com/in/clementdelangue https://huggingface.co/clem https://huggingface.co/ *Projects discussed:* ML Intern LeRobot SO-101 / LeRobot docs 📰 Want the transcript and edited version? Subscribe to Turing Post: https://www.turingpost.com/subscribe Turing Post is a newsletter about AI’s past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live. Follow us - Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #HuggingFace #ClemDelangue #OpenSourceAI #LocalAI #AIAgents #RoboticsAI #LeRobot #MLIntern #AIBuilders #FutureOfAI

Can AI actually help children learn better – or are schools still too slow, too scared, and too locked into the old system? Neeru Khosla, co-founder of CK-12 Foundation, believes this moment could become a turning point for education. After nearly two decades building free learning tools for students and teachers, she argues that AI is our chance to finally understand how students think, where they get stuck, and how to help each child learn in a way that works for them. *In this episode of Inference, we get into:* - Why prompting is not cheating, but a real learning skill - Why textbooks alone were never enough for deep understanding - What C means in CK-12 (it’s important!) - What Neeru learned from launching Flexi, CK-12’s AI tutor, now used by millions of students - Why standardized testing misses the most important part of learning - Why teachers need support, visibility, and confidence – not fear - Why AI literacy may become as fundamental as reading, writing, and math - How curiosity, mentorship, and community shape better learning outcomes - Why “attention is all you need” is no longer enough – and what we need now We also talk about public school inertia, philanthropy, core values when raising kids, and why Neeru believes AI should be used as augmented intelligence – not something to fear, but something to help humans grow. This is a conversation about education, equity, curiosity, and what it would really take to build a learning system that works for every child. Watch it. I’m really passionate about this topic and think that everyone should think and talk more about it. *Did you like the episode? You know the drill:* 📌 Subscribe for more conversations with the people rethinking how AI will shape society 💬 Leave a comment if this resonated with you 👍 Like it if you liked it 🫶 Thank you for watching and sharing *Guest:* Neeru Khosla, co-founder and executive director of CK-12 Foundation https://info.ck12.org/neeru-khosla https://www.ck12.org/flexi/ *📰 Want the transcript and edited version?* Subscribe to Turing Post: https://www.turingpost.com/subscribe *Chapters* 0:00 Why Education Is the Greatest Gift to Society 0:27 Meet Neeru Khosla & the Mission of CK-12 1:32 How Technology Changed Learning Over the Years 3:20 Why AI Became a Turning Point in Education 5:52 Flexi: CK-12’s AI Tutor Used by Millions 8:26 Does AI Require Rethinking the Education System? 11:12 What Teachers Need From AI Right Now 12:56 Essential Skills for Kids and Teachers in the AI Era 18:13 From Molecular Biology to Building CK-12 23:27 Why Education Is a Human Right — and What Comes Next Turing Post is a newsletter about AI’s past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live. *Follow us* Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #AIinEducation #EducationAI #NeeruKhosla #CK12 #Flexi #AILiteracy #FutureOfEducation #EdTech #PersonalizedLearning #TuringPost

At NVIDIA GTC, we sat down with Sanja Fidler, VP of AI Research at NVIDIA and one of the leading voices in spatial intelligence and physical AI. We dive into world models, robotics, autonomous driving, and the hard problems AI still hasn’t solved. If you want to understand where AI goes next and what occupies the minds of the best researchers, you need to watch this video. *In this episode:* Why transformers and world models are not competing ideas Why physical AI is still a major frontier The evolution of simulation Why 3D matters for robotics and real-world intelligence What’s still missing in multimodal AI Whether autonomous driving could have a “ChatGPT moment” before robotics does If you enjoy conversations at the edge of AI research, *subscribe to Turing Post* for more interviews with the people building the future https://www.turingpost.com/ *Chapters:* 0:00 Physical AI vs Transformers — The Big Question 0:19 Introduction: NVIDIA & Spatial Intelligence Lab 0:38 Transformers vs World Models — Not a Competition 1:45 World Models as Simulators of Reality 3:20 Are New Architectures Replacing Transformers? 4:17 “Alpa Dreams” — Real-Time Interactive AI Worlds 6:22 The Evolution of Simulation in Self-Driving 7:44 From 3D Reconstruction to True World Modeling 10:26 Multimodal AI: Audio, Radar, and Physical Interaction 13:29 AGI, Robotics & the Future of Physical AI *Did you like the episode? You know the drill:* 📌 Subscribe here and here (https://www.turingpost.com/subscribe) for more conversations with the builders shaping real-world AI. 💬 Leave a comment 👍 Like it 🫶 Thank you for watching and sharing! *Guest:* Sanja Fidler – NVIDIA Research https://research.nvidia.com/person/sanja-fidler University of Toronto https://www.cs.toronto.edu/~fidler/ Spatial Intelligence Lab https://research.nvidia.com/labs/sil/ Google Scholar https://scholar.google.com/citations?user=CUlqK5EAAAAJ&hl=en X https://x.com/FidlerSanja LinkedIn https://ca.linkedin.com/in/sanja-fidler-2846a1a #AI #NVIDIA #SanjaFidler #WorldModels #PhysicalAI #SpatialIntelligence #Robotics #AutonomousDriving #Transformers #GTC

What if the future of self-driving isn’t one perfect robotaxi – but a stack that can turn almost any car into a self-driving car? In this episode of Inference, we ride through San Francisco – as one of the first to do this test drive – and talk about what’s changing in autonomous driving: cheaper hardware, better models, synthetic data, and a whole new approach to building the software behind the wheel. Ali Kani has been at NVIDIA Automotive for almost 8 years – he’s been through all the ups and downs, and he’s eager to share. *We talk about:* Why Level 2 is already possible with a surprisingly cheap sensor setup What is still missing for Level 4 Why next year could matter for Level 4 How NVIDIA combines an end-to-end driving model with a classical safety stack ​​Why open source matters for the future of autonomous driving Why synthetic data and simulation may matter as much as real-world driving data How different cities, laws, and driving cultures change the way autonomous systems behave Why the goal is bigger than one self-driving car – it’s making many cars autonomous by open sourcing the whole stack (it’s HUGE) We also experience live what still makes urban driving hard: construction, cyclists, congestion, weird negotiations at stop signs, and all the messy little moments humans barely notice but cars have to handle perfectly. What I liked about this conversation is that it makes the shift feel very real. *We’re moving from self-driving built inside closed labs to self-driving becoming a shared capability that can spread across the whole car industry.* This is a conversation about a future that starts tomorrow. It’s open and very exciting. Chapters: 0:00 The Future of Self-Driving Starts Now 0:19 Open Autonomous Driving Beyond Tesla and Waymo 1:07 Inside NVIDIA’s Low-Cost Level 2 Self-Driving Stack 1:48 From Level 2 to Level 4: Hyperion, Thor, and Redundancy 2:43 How NVIDIA Combines End-to-End AI with Safety Guardrails 3:56 What Changed in AlphaMaio Since GTC 5:12 The Key Technologies Needed to Solve Self-Driving 7:22 Real Data vs Synthetic Data in Autonomous Driving 9:21 Driving Through Real San Francisco Traffic 18:55 AlphaDream and the Next Generation of Simulation *Follow on*: https://www.turingpost.com/ https://www.turingpost.com/p/av *Did you like the episode? You know the drill:* 📌 Subscribe here and here (https://www.turingpost.com/subscribe) for more conversations with the builders shaping real-world AI. 💬 Leave a comment 👍 Like it 🫶 Thank you for watching and sharing! *Guest:* Ali Kani, Vice President and General Manager of Automotive, NVIDIA https://www.linkedin.com/in/ali-kani-b22198 https://blogs.nvidia.com/blog/author/alikani/ Read more: https://www.turingpost.com/p/selfdriving https://thefocus.ai/posts/the-car-wash-test/

In this second part of my conversation with Michael Bolin, lead for open-source Codex at OpenAI, we move from harness engineering to the human side of the story. What does it mean to be a programmer when you are no longer typing most of the code? Which skills become more important in an agent-driven workflow? Will coding agents eventually take over most software implementation? And if that happens, what is left for the human engineer besides pushing prompts around like a confused project manager with Wi-Fi? All of it and more in this part – watch it. *Follow on*: https://www.turingpost.com/ *Did you like the episode? You know the drill:* 📌 Subscribe for more conversations with the builders shaping real-world AI. 💬 Leave a comment if this resonated. 👍 Like it if you liked it. 🫶 Thank you for watching and sharing! *Guest:* Michael Bolin, tech lead on Codex, OpenAI https://www.linkedin.com/in/michael-bolin-7632712/ https://x.com/bolinfest https://github.com/openai/codex Chapters: 0:00 — Do You Still Need to Learn Coding? 0:18 — From Systems to Humans: The Future of Programming 0:39 — Switching Mindset: Building for Agents vs Developers 1:13 — What Happens When Agents Consume the Web? 1:27 — Programmer Identity in the Age of AI 2:15 — Are Engineers Building More Than Ever? 2:37 — Key Skills for Engineers Working with AI Agents 3:59 — Will Agents Take Over Coding? 4:57 — Engineering Taste vs AI Decisions 5:10 — From Idea to Product Faster Than Ever 6:01 — Risks: Losing Human Judgment Too Early 6:42 — Do We Still Need Humans in the Loop? 8:06 — Book That Shaped a Builder’s Mindset 📰 Transcript:https://www.turingpost.com/p/bolincodex https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se #AI #OpenAI #Codex #MichaelBolin #SoftwareEngineering #Programming #CodingAgents #AIAgents #DeveloperTools #HarnessEngineering #FutureOfWork #Engineering #TuringPost

Regarding the question of what matters most – the model or the harness – Michael Bolin is somewhere in the middle. Stronger models clearly pushed Codex to new heights. But without the right harness around them, those models would not be able to operate reliably, and – most importantly – safely on a real developer’s machine. At least, not yet. In this episode of Inference, I talk with Michael Bolin – lead for open source Codex at OpenAI – about the engineering layer that makes coding agents actually function: the agent loop, sandboxing, tool orchestration, and the design decisions that determine how much freedom an agent should have. In this conversation, we get into: What a harness actually is and why every coding agent needs one Can a model be enough for a reliable coding workflow Why do they build harness as small and tight as possible How Codex handles sandboxing and security across OS Why safety and security are not the same thing in agentic systems How coding agents are changing the daily workflow of developers Why documentation, tests, repo structure, and agents.md suddenly matter more Whether too much context can make an agent worse Why Michael believes the future may involve fewer tools, but more powerful ones If you’re trying to understand where coding agents are actually going, this episode is for you. Subscribe to the channel to be notified about Part 2, where we discuss what becomes of the software engineer in the age of agents. Chapters: 0:00 The New Inner Loop of AI Coding Agents 0:17 Introduction: Michael Bolin and Open Source Codex 1:17 What the “Harness” Is in AI Coding Agents 2:13 Security and Sandboxing for AI Agents 4:33 Codex Launch and Rapid Growth 5:25 The Codex App: A New Interface for Developers 6:36 How Coding Agents Change Developer Workflows 10:04 Writing Codebases and Documentation for AI Agents 12:44 Context Engineering and Prompting for Codex 16:02 Model vs Harness: What Really Matters for Agents 19:23 Multi-Agent Systems, Tools, and the Future of AI Development *Follow on*: https://www.turingpost.com/ *Did you like the episode? You know the drill:* 📌 Subscribe here and here (https://www.turingpost.com/subscribe) for more conversations with the builders shaping real-world AI. 💬 Leave a comment 👍 Like it 🫶 Thank you for watching and sharing! *Guest:*  Michael Bolin, tech lead on Codex, OpenAI https://www.linkedin.com/in/michael-bolin-7632712/ https://x.com/bolinfest https://github.com/openai/codex 📰 Transcript: https://www.turingpost.com/bolin1 *Turing Post* – AI stories from labs the Valley doesn't cover. https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se Tags: #AI #OpenAI #Codex #CodingAgents #DeveloperTools #AgenticAI #SoftwareEngineering #HarnessEngineering #Harness

In this episode, Ioannis Antonoglou, co-founder and CTO @ReflectionAI (ex-DeepMind, AlphaGo/AlphaZero/MuZero) explains what they are building: a frontier open-weight “general agent model” trained end-to-end with pretraining plus reinforcement learning. And I’ll be honest: I left this conversation more skeptical than I expected. They raised $2 billion last year. But where the results? Reflection’s thesis is huge – build the missing Western open base model, then use RL to push it to the frontier. The problem is that this is also the slowest path in the game. “All hands on deck building the model” means no clear wedge product yet, few concrete proof points, and a lot of execution risk while closed labs keep shipping. Am I missing something? Watch the video and leave your opinion in the comments Chapters: 0:00 Building AGI and the Mission Behind Reflection 0:25 From AlphaGo to Today: How AI Progress Really Happens 2:11 Breakthroughs vs. Engineering: What Still Matters Most 3:10 Defining AGI and Why It May Not Need Huge Breakthroughs 3:41 Why Reflection Shifted from Coding Agents to Frontier Models 5:15 The New Focus: Open Frontier Models and General Agents 6:33 Bottlenecks in Building Frontier AI: Team, Compute, and Scale 7:48 AI Tools, Internal Workflows, and Model-First Strategy 8:24 Can Open Models Catch Closed Labs? 10:34 Reinforcement Learning, Research Priorities, and Advice for Young Builders 14:01 Joining DeepMind, Open Science, and the Book That Shaped Him *Follow on*: https://www.turingpost.com/ *Did you like the episode? You know the drill:* 📌 Subscribe for more conversations with the builders shaping real-world AI. 💬 Leave a comment if this resonated. 👍 Like it if you liked it. 🫶 Thank you for watching and sharing! *Guest:* Ioannis Antonoglou, Co-Founder, President & CTO at Reflection AI https://x.com/real_ioannis https://www.linkedin.com/in/ioannis-alexandros-antonoglou-45393253 https://reflection.ai/ 📰 Transcript: https://www.turingpost.com/nathan *Turing Post* – AI stories from labs the Valley doesn't cover. https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se Tags: #reflectionai #opensource #deepmind #ai #openclaw #aisafety

Ioannis Antonoglou helped build AlphaGo, AlphaZero, and MuZero at DeepMind. Now he’s CTO and co-founder of Reflection AI, betting that frontier models should be open weights, not a black box behind an API. In Part 1, we talk about openness as an actual strategy: why open models can move faster, why “sovereignty” matters for enterprises and governments, and why safety might improve when the ecosystem can stress-test the system instead of guessing. We also get into the uncomfortable part: capable open agents can misbehave in public, fast (OpenClaw is the recent reminder). Is that a reason to close everything up, or a reason to make the risks visible and fixable? Topics covered:  – Why a former DeepMind builder chose open weights  – Open models as a commercial engine (and what investors bought)  – Openness, safety, and “more eyes on the system”  – Concentration of AI power in closed labs  – Who open frontier models are really for (research, enterprises, governments) Subscribe for Part 2: how Reflection plans to compete with closed labs and what they’re building under the hood. Chapters: 0:00 — “No One Was Sharing This Information” 0:16 — From DeepMind to Reflection AI 0:52 — Why Move from Closed Labs to Open Weights? 2:20 — Pitching Open Models Before the DeepSeek Moment 3:31 — What Changed in the Past Year 4:43 — Why Openness Accelerates Scientific Progress 6:06 — Open Source vs Safety: The Open Claw Case 7:19 — The Real Concern: Concentration of AI Power 8:23 — The Open Source Paradox 9:11 — The Value Proposition of an Open Frontier Model *Follow on*: https://www.turingpost.com/ *Did you like the episode? You know the drill:* 📌 Subscribe for more conversations with the builders shaping real-world AI. 💬 Leave a comment if this resonated. 👍 Like it if you liked it. 🫶 Thank you for watching and sharing! *Guest:* Ioannis Antonoglou, Co-Founder, President & CTO at Reflection AI https://x.com/real_ioannis https://www.linkedin.com/in/ioannis-alexandros-antonoglou-45393253/ https://reflection.ai/ 📰 Transcript: https://www.turingpost.com/antonoglou_part1 *Turing Post* – AI stories from labs the Valley doesn't cover. https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se Tags: #reflectionai #opensource #deepmind #ai #openclaw #aisafety

Open models are often discussed as if they’re competing head-to-head with frontier systems. Are they catching up? Falling behind? Are they “good enough” yet? Nathan Lambert doesn’t believe open models will ever catch up with closed ones, and he explains clearly why. But he also argues that this is the wrong framing. Nathan is a research scientist at the Allen Institute for AI, the author of the RLHF Book, and the writer behind the Interconnects newsletter. He’s also one of the clearest voices on what open models are for, and just as importantly, what they are not. We talk about how academic AI research lost influence as training scaled up, why open models became the main place where experimentation still happens, and why that role matters even when open models trail frontier systems. We also discuss why China’s open model ecosystem developed so differently from the US one, and what that tells us about incentives, talent, and access to resources. From there, the conversation moves into the mechanics: post-training and reinforcement learning complexity, data availability, coding agents, hybrid architectures, and the very practical reasons most people continue to rely on closed models, even when they support openness in principle. This is a conversation about how AI research actually moves, where open models fit into that picture, and what it means to build systems when the frontier is expensive, fast-moving, and increasingly product-driven. This conversation offers a realistic look at where the open ecosystem stands today. Watch it! *Follow on*: https://www.turingpost.com/ *Did you like the episode? You know the drill:* 📌 Subscribe for more conversations with the builders shaping real-world AI. 💬 Leave a comment if this resonated. 👍 Like it if you liked it. 🫶 Thank you for watching and sharing! *Guest:* Nathan Lambert, Research Scientist at Allen Institute for AI (AI2) https://x.com/natolambert https://www.linkedin.com/in/natolambert/ https://www.interconnects.ai/ (his newsletter on open models + RL + everything important in AI) https://rlhfbook.com/ - The RLHF Book https://allenai.org/ *Links:* State of AI in 2026 (Lex Fridman interview): https://www.youtube.com/watch?v=EV7WhVT270Q&t=10206s NVIDIA’s path to open models https://www.youtube.com/watch?v=Y3Vb6ecvfpU OLMo models: https://allenai.org/olmo NVIDIA Nemotron: https://developer.nvidia.com/nemotron SpaceX + xAI partnership: https://www.spacex.com/updates#xai-joins-spacex Season of the Witch (book): https://www.simonandschuster.com/books/Season-of-the-Witch/David-Talbot/9781439108246 📰 Transcript: https://www.turingpost.com/nathanlambert *Turing Post* – AI stories from labs the Valley doesn't cover. https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se

First Western interview with a senior MiniMax researcher. Olive Song explains how they actually build models that work. When MiniMax's RL training wouldn't converge, they debugged layer by layer until they found it: fp32 precision in the LM head. When their models learned to "hack" during training, exploiting loopholes to maximize rewards, they had to rethink alignment from scratch. When benchmarks said their models were good but production said otherwise, they discovered the problem: environment adaptation. Olive talks about working at a pace where new models drop at midnight and you test them at midnight. How they use an internal AI agent to read every new paper published overnight. Why they sit with developers during experiments to catch dangerous behaviors in real-time. What "ICU in the morning, KTV at night" means when results swing wildly. How problem-solving becomes discovery when you're debugging behaviors no one has seen before. This is how Chinese labs are moving fast: first-principles thinking, engineering discipline, and willingness to work whenever the model in experimentation requires you to. We spoke on Sunday at 9 pm Beijing time. Olive was still waiting for results from new model experiments, so my first question was obvious: does everyone at the company work like this? *Follow on*: https://www.turingpost.com/ *Did you like the episode? You know the drill:* 📌 Subscribe for more conversations with the builders shaping real-world AI. 💬 Leave a comment if this resonated. 👍 Like it if you liked it. 🫶 Thank you for watching and sharing! *Guest:* Olive Song, Senior Researcher at MiniMax MiniMax: https://www.minimaxi.com/ Models: https://huggingface.co/MiniMaxAI *Links:* vLLM: https://github.com/vllm-project/vllm SGLang: https://github.com/sgl-project/sglang 📰 Transcript: https://www.turingpost.com/olive Chapters: 0:00 – Reinforcement Learning and Unexpected Model Behaviors 3:08 – Roleplay, Alignment, and “AI with Everyone” 4:02 – How AI Changes Daily Life and Productivity 4:59 – Inside Miniax: How Researchers and Engineers Work Together 5:32 – Human Alignment and Safety in Open Models 6:16 – Why Engineering Details Matter More Than Algorithms 8:17 – Open Weights: Benefits, Risks, and Responsibility 10:57 – Specialization vs General AI Models 12:07 – Agentic AI and Long-Horizon Tasks 29:50 – AGI, Creativity, and the Future of AI *Turing Post* – AI stories from labs the Valley doesn't cover. https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se #MiniMax #ReinforcementLearning #AIResearch #OpenWeights #ChineseAI #OpensourceAI

In the first episode of Inference’s quarterly series on Open Source AI, we talk to Raffi Krikorian, CTO of Mozilla, about when open source AI stops being aspirational and becomes an operational choice.We explore why stories like Pinterest saving $10 million by moving to open models are real, but often misunderstood, and why timing matters more than ideology. Raffi lays out his view of a missing “LAMP stack for AI” and explains why the hardest problem to solve isn’t models or data, but the connective glue that holds AI systems together. Along the way, he shares how Mozilla is navigating these tradeoffs in practice, why even open-source-first organizations still rely on closed tools during experimentation, and what the browser era taught Mozilla about defaults, user choice, and long-term control. He also shares a few practical recommendations in this episode that apply even if you’re still experimenting. Listen closely. This conversation kicks off our Open Source AI series for 2026, focused on real tradeoffs, real economics, and the decisions companies are making right now. Follow on: https://www.turingpost.com/ *Did you like the episode? You know the drill:* 📌 Subscribe for more conversations with the builders shaping real-world AI. 💬 Leave a comment if this resonated. 👍 Like it if you liked it. 🫶 Thank you for watching and sharing! *Guest:* Raffi Krikorian, CTO at Mozilla LinkedIn: https://www.linkedin.com/in/rkrikorian/ Mozilla AI: https://mozilla.ai/ Mozilla Blog: https://blog.mozilla.org/en/mozilla/mozilla-open-source-ai-strategy/ *Links mentioned:* Raffi's post here about our OSAI strategy: https://blog.mozilla.org/en/mozilla/mozilla-open-source-ai-strategy/ 🌐 #1: Mastering Open Source AI in 2026: Essential Decisions for Builders https://www.turingpost.com/p/opensource1 Mozilla Data Collective: https://data.mozilla.org/ Langchain: https://www.langchain.com/ OpenRouter: https://openrouter.ai/ AI2 (Allen Institute for AI): https://allenai.org/ Flower AI (Federated Learning): https://flower.dev/ Einstein's Dreams by Alan Lightman: https://www.goodreads.com/book/show/14376.Einstein_s_Dreams 📰 The transcript and edited version at https://www.turingpost.com/krikorian *Chapters:* 0:00 Cold Open — Values vs Economics in Open Source AI 0:28 Intro: Why This Season Focuses on Open Source AI 0:54 When Open Source Becomes a Business Decision 1:44 Pinterest Saved $10M + The Shift From Prototyping to Production 2:42 Mozilla’s “Choice Suite” + The Terraform “Exit Door” 5:21 Mozilla’s Mission: Do for AI What Mozilla Did for the Web 7:09 The “LAMP Stack” for AI + Standards Across the Stack 9:52 Small Models, Specialization, and Model Composability 15:45 Data, Privacy, and “I Own My Context” 18:36 “This Is a Fight Worth Having” + The Signal Analogy 21:42 1–2–3 Steps for Companies to Start (Instrument Choice Early) 24:22 Book Pick: Einstein’s Dreams + Closing Turing Post is a newsletter about AI's past, present, and future. Ksenia Se explores how intelligent systems are built – and how they're changing how we think, work, and live. *Follow us →* Turing Post: https://x.com/TheTuringPost Ksenia Se: https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #OpenSourceAI #LAMPStackForAI #AIEconomics #MozillaAI #AIInfrastructure #DataProvenance #FederatedLearning #OpenModels

Is math the ultimate test for AI reasoning? Or is next-token prediction fundamentally incapable of discovering new truths and discovering conjectures?Carina Hong, co-founder and CEO of Axiom Math, argues that to build true reasoning capabilities, we need to move beyond "chatty" models to systems that can verify their own work using formal logic. In this episode of Inference, we get into: Why current LLMs are like secretaries (good at retrieval) but bad at de novo mathematics The three pillars of an AI Mathematician How AlphaGeometry proved that symbolic logic and neural networks must merge The difference between AGI and Superintelligence Why "Theory Building" is harder to benchmark than the International Math Olympiad (IMO) The scarcity of formal math data (Lean) compared to Python code We also discuss the bottlenecks: the "chicken and egg" problem of auto-formalization, why Axiom bets on specific superintelligence over general models, and how AI will serve as the algorithmic pillar for the future of hard science. This is a conversation about the structure of truth, the limits of intuition, and what happens when machines start grading their own homework. Watch it! Did you like the episode? You know the drill: 📌 Subscribe for more conversations with the builders shaping real-world AI. 💬 Leave a comment if this resonated. 👍 Like it if you liked it. 🫶 Thank you for watching and sharing! *Guest:* Carina Hong, co-founder and CEO of Axiom Math https://www.axiom.xyz/ https://x.com/CarinaLHong https://www.linkedin.com/in/carina-hong/ 📰 The transcript and edited version at https://www.turingpost.com/carina/ Chapters: 0:53 Why LLMs Struggle with Basic Math 2:42 Building an AI Mathematician: The 3 Pillars (Prover, Knowledge Base, Conjecturer) 5:50 The Role of Human-AI Collaboration 6:34 Can AI Have Intuition? (Conjectures & AlphaGeometry) 10:16 A Hybrid Approach: LLMs + Formal Verification 11:24 Specialist Science Models vs. Generalist Giants 13:33 The Problem with Current AI Benchmarks 16:34 Practical Applications: Enterprise & Formal Verification 21:24 The Main Bottleneck: Data Scarcity 23:49 AGI vs. Superintelligence: The "Plate" Analogy 26:31 Book Recommendations (Math, Law, and Literature) 30:56 How to Use AI for Math Discovery Today Turing Post is a newsletter about AI's past, present, and future. Ksenia Se explores how intelligent systems are built – and how they're changing how we think, work, and live. Follow us → Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #AI #FutureOfAI #MathAI #FormalVerification #Lean #AxiomMath #Superintelligence #Reasoning

Is security still about patching after the crash? Or do we need to rethink everything when AI can cause failures on its own? Anneka Gupta, Chief Product Officer at Rubrik, argues we're now living in the world before the crash – where autonomous systems can create their own failures. In this episode of Inference, we explore: Why AI agents are "the human problem on steroids" The three pillars of AI resilience: visibility, governance, and reversibility How to log everything an agent does (and why that's harder than it sounds) The mental shift from deterministic code to outcome-driven experimentation Why most large enterprises are stuck in AI prototyping (70-90% never reach production) The tension between letting agents act and keeping them safe What an "undo button" for AGI would actually look like How AGI will accelerate the cat-and-mouse game between attackers and defenders We also discuss why teleportation beats all other sci-fi tech, why Asimov's philosophical approach to robots shaped her thinking, and how the fastest path to AI intuition is just... using it every day. This is a conversation about designing for uncertainty, building guardrails without paralyzing innovation, and what security means when the system can outsmart its own rules. Did you like the episode? You know the drill: 📌 Subscribe for more conversations with the builders shaping real-world AI. 💬 Leave a comment if this resonated. 👍 Like it if you liked it. 🫶 Thank you for watching and sharing! Guest: Anneka Gupta, Chief Product Officer at Rubrik https://www.linkedin.com/in/annekagupta/ https://x.com/annekagupta https://www.rubrik.com/ 📰 Want the transcript and edited version? Subscribe to Turing Post: https://www.turingpost.com/subscribe Chapters: Turing Post is a newsletter about AI's past, present, and future. Ksenia Se explores how intelligent systems are built – and how they're changing how we think, work, and live. Follow us → Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #AI #AIAgents #Cybersecurity #AIGovernance #EnterpriseAI #AIResilience #Rubrik #FutureOfSecurity

When robots move into the real world, speed and safety come from simulation! In his first sit-down interview, Spencer Huang – NVIDIA’s product lead for robotics software – talks about his role at NVIDIA, a flat organization where “you have access to everything.” We discuss how open source shapes NVIDIA’s robotics ecosystem, how robots learn physics through simulation, and why neural simulators and world models may evolve alongside conventional physics. I also ask him what’s harder: working on robotics or being Jensen Huang’s son. Watch to learn a lot about robotics, NVIDIA, and its big plans ahead. It was a real pleasure chatting with Spencer. *We cover:* - NVIDIA’s big picture - The “three computers” of robotics – training, simulation, deployment - Isaac Lab, Arena, and the path to policy evaluation at scale - Physics engines, interop, and why OpenUSD can unify fragmented toolchains - Neural simulators vs conventional simulators – a data flywheel, not a rivalry - Safety as an architecture problem – graceful failure and functional safety - Synthetic data for manipulation – soft bodies, contact forces, distributional realism - Why the biggest bottleneck is robotics data, and how open ecosystems help reach baseline - NVIDIA’s “Mission is Boss” culture – cross-pollinating research into robotics This is a ground-level look at how robots learn to handle the messy world – and why simulation needs both fidelity and diversity to produce robust skills. *Chapters*: 0:22 The future of Physical AI begins here 1:00 Inside NVIDIA’s secret blueprint for teaching robots 3:46 Why safety is the hardest part of robotics 4:11 Simulation: the new classroom for machines 8:55 Can robots really understand physics? 13:55 How NVIDIA builds robot brains without a PhD 16:47 The plan to unify a fragmented robotics world 20:31 Why open source is NVIDIA’s biggest power move 21:21 What’s harder – robotics or being Jensen Huang’s son? 24:31 The one thing holding robotics back 27:56 The sci-fi books that shaped Spencer's mind *Did you like the episode? You know the drill:*  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! *Guest:* Spencer Huang, NVIDIA – a product line manager at NVIDIA leading robotics software product. His work centers on open-source simulation frameworks for robot learning, synthetic data generation methodologies, and advancing robot autonomy – from industrial mobile manipulators to generalist humanoid robots. https://www.linkedin.com/in/spencermhuang/ *📰 Want the transcript and edited version?* Find it here: https://www.turingpost.com/spencer *Turing Post* is a newsletter about AI’s past, present, and future – exploring how intelligent systems are built and how they’re changing how we think, work, and live. 📩 Sign up: https://www.turingpost.com Follow Ksenia Se and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #robotics #simulation #NVIDIA #Omniverse #digitaltwins #worldmodels #physicalAI #reinforcementlearning #syntheticdata

When thousands of AI agents begin to act on our behalf, who builds the system they all run on? Renen Hallak – founder and CEO of VAST Data – believes we’re witnessing the birth of an *AI Operating System*: a foundational layer that connects data, compute, and policy for the agentic era. In this episode of Inference, we talk about how enterprises are moving from sandboxes and proof-of-concepts to full production agents, why *metadata matters more than “big data,”* and how the next infrastructure revolution will quietly define who controls intelligence at scale. *We go deep into:* What “AI OS” really means – and why the old stack can’t handle agentic systems Why enterprises are underestimating the magnitude (but overestimating the speed) of this shift The evolving role of data, metadata, and context in intelligent systems How control, safety, and observability must be baked into infrastructure – not added later Why Renen says the next 10 years will reshape everything – from jobs to the meaning of money The ladder of progress: storage → database → data platform → operating system What first-principles thinking looks like inside a company building for AGI-scale systems This is a conversation about the architecture of the future – and the fine line between control and creativity when intelligence becomes infrastructure. Watch the episode! *Did you like the episode? You know the drill:*  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! *Guest:* Renen Hallak, Founder & CEO, VAST Data https://www.linkedin.com/in/renenh/ https://www.linkedin.com/company/vast-data/ *📰 Want the transcript and edited version?* Find it here: https://www.turingpost.com/p/renen *Chapters:* *Turing Post* is a newsletter about AI’s past, present, and future – exploring how intelligent systems are built and how they’re changing how we think, work, and live. 📩 Sign up: https://www.turingpost.com *Follow us:* Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #agenticOS, #enterpriseAI, #metadata, #AIoperatingsystem, exabyte storage, GPUs, production AI

This episode of Inference is dedicated to Breast Cancer Awareness Month. I’m talking with Akash Parvatikar – AI scientist and product leader in digital pathology and computational biology. He leads PathologyMap™ at HistoWiz, a digital pathology platform that turns whole-slide images into searchable, analyzable data with AI tools – streamlining research and accelerating insights for cancer and precision medicine. Digital pathology is a very new field, but an important one, considering that the US is facing a large shortage of pathologists. *What you’ll learn:* - What “digital pathology” actually is – and why scanning glass slides changes everything - Where AI already helps today and where it’s still just a very promising technology - Why explainability, failure modes, and data standards decide clinical adoption - What is the real bottleneck for using AI in pathology and diagnosis - How agentic workflows might enter the lab in pieces first - A practical timeline for digitization, FDA-type approvals, and hospital rollouts - The human role that stays *Big idea:* Digitize first. Validate carefully. Then scale tools that clinicians trust. Telepathology expands access. Good AI here speaks the pathologist’s language. Remember – AI that can’t explain itself in clinical terms won’t ship. Watch the episode! *Did you like the episode? You know the drill:*  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! *Guest:* Akash Parvatikar, AI Scientist, leading PathologyMap at HistoWiz https://www.linkedin.com/in/akash007/ https://home.histowiz.com/pathology_map/ 📰 Want the transcript and edited version?  Subscribe to Turing Post https://www.turingpost.com/subscribe *Chapters:* 1:22 - The Current State and Future of AI in Cancer Diagnostics 2:27 - Real-World vs. Aspirational AI Breakthroughs in Patient Outcomes 3:36 - Evolution of AI Usage by Clinicians 4:47 - The Technical Challenges of AI in Pathology 7:22 - The Role of Generative AI in Diagnostics 8:42 - The Potential of Agentic AI Workflows in Pathology 9:50 - Key Bottlenecks in AI for Pathology 12:13 - About the Pathology Map Platform 13:49 - Navigating Regulations in AI-Powered Diagnostics 14:40 - The Human Impact of AI in Cancer Diagnostics 16:40 - What is Digital Pathology? 18:21 - Timeline for Mainstream Adoption of AI in Pathology 19:42 - The "Spotify for Precision Medicine" 20:20 - The Future Role of Humans in AI-Assisted Pathology 21:36 - The Economics of AI in Pathology 22:48 - Concerns and Excitations About the Future of AI in Pathology 24:43 - Book Recommendation Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live. *Sign up:* Turing Post: https://www.turingpost.com Follow us: Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #DigitalPathology #AIMedicine #CancerDiagnostics #PrecisionMedicine #BreastCancerAwareness #EarlyDetection #AIForGood

Is compute the main roadblock? Or the models are not big enough for AGI? Ulrik Hansen, president and co-founder of Encord, argues that the true bottleneck is data. In this episode of Inference, we get into: Why models are mostly interchangeable, but data orchestration makes or breaks real-world AI Tesla’s compounding advantage from live human feedback vs. Waymo’s cautious rollout Why robotics lags behind digital AI – and how feedback loops shape both The coming split between “cheap” intelligence (facts and patterns) and “expensive” intelligence (creativity, taste, vision) What is the new connection economy We also discuss the risks: synthetic data eating its own tail, the trust and safety challenges that make brand more valuable than ever, and why Ulrik believes the next 10 years will bring more change than the last 50. This is a conversation about the future of AI systems, the bottlenecks that matter, and what it means when humans and machines start sharing the same workflows. Watch it! Did you like the episode? You know the drill:  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! Guest: Ulrik Hansen, president and co-founder of Encord https://www.linkedin.com/in/ulrik-stig-hansen-2658273b/ https://x.com/ulrikstighansen https://x.com/encord_team https://encord.com/ 📰 Want the transcript and edited version?  Subscribe to Turing Post https://www.turingpost.com/subscribe Chapters Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live. Sign up: Turing Post: https://www.turingpost.com Follow us Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #AI #FutureOfAI #DataBottleneck #SelfDriving #Tesla #Waymo #Robotics

What comes after the IDE? In this episode of Inference, I sit down with Zach Lloyd, founder of Warp, to talk about a new category he’s coining: the Agentic Development Environment (ADE). We explore why coding is shifting from keystrokes to prompts, how Warp positions itself against tools like Cursor and Claude Code, and what it means for developers when your “junior dev” is an AI agent that can already set up projects, fix bugs, and explain code line by line. We also touch on the risks: vibe coding that ships junk to production, the flood of bad software that might follow, and why developers still need to stay in the loop — not as code typists, but as orchestrators, reviewers, and intent-shapers. This is a conversation about the future of developer workbenches, the end of IDE dominance, and whether ADEs will become the default way we build software. Watch it! Did you like the episode? You know the drill:  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! Guest: Zach Lloyd, founder of Warp https://www.linkedin.com/in/zachlloyd/ https://x.com/zachlloydtweets https://x.com/warpdotdev https://www.warp.dev/ 📰 Want the transcript and edited version?  Subscribe to Turing Post https://www.turingpost.com/subscribe Chapters Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live. Sign up: Turing Post: https://www.turingpost.com Follow us Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase #Warp #AgenticAI #AgenticDevelopment #AItools #CodingAgents #SoftwareDevelopment #Cursor #ClaudeCode #IDE #ADE #AgenticWorkflows #FutureOfCoding #AIforDevelopers #TuringPost

What limits AI today isn’t imagination – it’s the cost of running it at scale. In this episode of Inference, Ksenia Se sits down with Lin Qiao, co-founder & CEO of Fireworks AI (an inference-first company) and former head of PyTorch at Meta, where she led the rebuild of Meta’s entire AI infrastructure stack. We talk about: Why product-market fit can be the beginning of bankruptcy in GenAI The iceberg problem of hidden GPU costs Why inference scales with people, not researchers 2025 as the year of AI agents (coding, hiring, SRE, customer service, medical, marketing) Open vs closed models – and why Chinese labs are setting new precedents The coming wave of 100× more efficient AI infrastructure Watch to hear Lin’s vision for inference, alignment, and the future of AI infrastructure. And – at the end – Lin shares her very personal journey to overcome fears. Watch it! Did you like the episode? You know the drill:  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! Guest: Lin Qiao, co-founder & CEO of Fireworks AI and former head of PyTorch at Meta https://www.linkedin.com/in/lin-qiao-22248b4 https://x.com/lqiao https://x.com/FireworksAI_HQ https://fireworks.ai/ 📰 Want the transcript and edited version?  Subscribe to Turing Post https://www.turingpost.com/subscribe Chapters Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live. Sign up: Turing Post: https://www.turingpost.com Follow us Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase

Right now, the biggest leap for AI isn’t a bigger model – it’s giving models and agents a way to act. In this episode of Inference, I sit down with Alex Hancock – Senior Software Engineer at Block, core contributor to Goose (the open-source, multi-purpose AI agent), and a member of the Model Context Protocol (MCP) Steering Committee – to talk about the infrastructure that’s quietly powering the next wave of AI. *We cover:*  – What MCP is – and why it’s exploding in adoption  – How it turns models from “brains in jars” into agents with arms and legs  – The MCP Steering Committee’s push for openness and real governance  – Why SDK parity, registry design, and OAuth 2.1 are make-or-break for developers  – How MCP and A2A fit together – and where they might compete  – Context discovery, context management, and why they’re the hardest problems in agentic AI  – The lessons from Goose on staying model-agnostic in a fast-moving ecosystem  – What this shift means for software development – and for the humans in the loop Alex also shares his view on the next year of protocol development, why he thinks AGI will arrive incrementally, and how a runner’s mindset shapes his approach to building tools that last. If you’re building agents, connecting models to the world, or just trying to understand the emerging “protocol layer” of AI, this conversation will give you a front-row seat. Let’s find out how we’re teaching AI to act – and what’s still missing. *Did you like the episode? You know the drill:*  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! *Guest:* Alex Hancock, Senior Software Engineer at Block, Goose Maintainer & MCP Steering Committee Member https://www.linkedin.com/in/alexjhancock/ https://x.com/alexjhancock https://github.com/block/goose MCP https://github.com/modelcontextprotocol Building to Last: A New Governance Model for MCP https://blog.modelcontextprotocol.io/posts/2025-07-31-governance-for-mcp/ *📰 Want the transcript and edited version?*  Subscribe to Turing Post: https://www.turingpost.com/subscribe Chapters *coming* Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live. *Follow us:* Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase

In this episode of Inference, I sit down with Amr Awadallah – founder & CEO of Vectara, founder of Cloudera, ex-Google Cloud, and the original builder of Yahoo’s data platform – to unpack what’s actually happening with retrieval-augmented generation (RAG) in 2025. We get into why RAG is far from dead, how context windows mislead more than they help, and what it really takes to separate reasoning from memory. Amr breaks down the case for retrieval with access control, the rise of hallucination detection models, and why DIY RAG stacks fall apart in production. We also talk about the roots of RAG, Amr’s take on AGI timelines and what science fiction taught him about the future. If you care about truth in AI, or you're building with (or around) LLMs, this one will reshape how you think about trustworthy systems. Did you like the episode? You know the drill:  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! Guest: Amr Awadallah, Founder and CEO at Vectara https://www.linkedin.com/in/awadallah/ https://x.com/awadallah https://www.vectara.com/ 📰 Want the transcript and edited version? Subscribe to Turing Post: https://www.turingpost.com/subscribe Chapters 00:00 – Intro 00:44 – Why RAG isn’t dead (despite big context windows) 01:59 – Memory vs reasoning: the case for retrieval 02:45 – Retrieval + access control = trusted AI 06:51 – Why DIY RAG stacks fail in production 09:46 – Hallucination detection and guardian agents 13:14 – Open-source strategy behind Vectara 16:08 – Who really invented RAG? 17:30 – Can hallucinations ever go away? 20:27 – What AGI means to Amr 22:09 – Books that shaped his thinking Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live. Sign up (Jensen Huang is already in): https://www.turingpost.com Things mentioned during the interview: Hughes Hallucination Evaluation Model (HHEM) Leaderboard https://huggingface.co/spaces/vectara/leaderboard HHEM 2.1: A Better Hallucination Detection Model and a New Leaderboard https://www.vectara.com/blog/hhem-2-1-a-better-hallucination-detection-model HCMBench: an evaluation toolkit for hallucination correction models https://www.vectara.com/blog/hcmbench-an-evaluation-toolkit-for-hallucination-correction-models Books: Foundation series by Isaac Asimov https://en.wikipedia.org/wiki/Foundation_(novel_series) Sapiens: A Brief History of Humankind Hardcover by Yuval Noah Harari https://www.amazon.com/Sapiens-Humankind-Yuval-Noah-Harari/dp/0062316095 Setting the Record Straight on who invented RAG https://www.linkedin.com/pulse/setting-record-straight-who-invented-rag-amr-awadallah-8cwvc/ Follow us: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase

At some point in the last year, bots became your biggest website visitors. Not people. Not crawlers. Not even APIs. Bots with goals. Agents with plans. Linda Tong, CEO of Webflow, has seen it up close – and she's redesigning the web to meet them. In this episode, we talk about what it means to build agent-first websites: How to talk to bots. How to let them click buttons. And how to create experiences that work for humans and AI – without turning the internet into garbage. We cover:  – When bot traffic started overtaking humans  – Why AEO (agentic engine optimization) is the new SEO  – Why websites need a second language – for LLMs  – What "agent-ready" structure really means  – Hybrid UX: visual for humans, semantic for agents  – Why dynamic, personalized web experiences are overdue  – Leadership, kindness, and Ender’s Game as a design philosophy This one's fast, nerdy, real, and fun. Linda’s not afraid to challenge old assumptions – or to break her own product if it means building what’s next. Did you like the episode? You know the drill:  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! Guest: Linda Tong, CEO @Webflow https://www.linkedin.com/in/lktong/ https://x.com/yaylt https://webflow.com/ 📰 Want the transcript and edited version?  Subscribe to Turing Post *Chapters* 0:00 - Introduction 0:43 - The Rise of Non-Human Traffic 1:54 - When Did the Shift to Bot Traffic Start? 2:24 - Good Bots vs. Bad Bots 3:39 - The Emergence of AEO (AI/Agentic Engine Optimization) 5:18 - Building Websites for Agents 6:43 - What Agents Need from a Website 8:55 - Enabling Agents to Take Action 10:04 - The Future of Websites: Dual Human and Agent Interfaces 12:12 - The Vision for a Conversational Webflow 14:19 - Beyond Creation: The Future of Dynamic Web Experiences 18:42 - Is SEO Dead? The Relationship Between SEO and AEO 22:10 - The Impact of AGI on Web Development 24:19 - The Book That Shaped Linda 27:00 - Final Thoughts: The Need for "Kind AI" Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Se explores how intelligent systems are built – and how they’re changing how we think, work, and live. Sign up (Jensen Huang is already in): Turing Post: https://www.turingpost.com Follow us Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase

At Microsoft Build, I actually sat down with Eric Boyd, Corporate Vice President leading engineering for Microsoft’s AI platform, to talk about what it really means to build AI infrastructure that companies can trust – not just to assist, but to act. We get into the messy reality of enterprise adoption, why trust is still the bottleneck, and what it will take to move from copilots to fully autonomous agents.We cover: - When we'll trust AI to run businesses - What Microsoft learned from early agent deployments - How AI makes life easier - The architecture behind GitHub agents (and why guardrails matter) - Why developer interviews should include AI tools - Agentic Web, NLweb, and the new AI-native internet - Teaching kids (and enterprises) how to use powerful AI safely - Eric’s take on AGI vs “just really useful tools” If you’re serious about deploying agents in production, this conversation is a blueprint. Eric blends product realism, philosophical clarity, and just enough dad humor. I loved this one. Did you like the episode? You know the drill:  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! Guest: Eric Boyd, CVP of AI platform at Microsoft https://www.linkedin.com/in/emboyd/ 📰 Want the transcript and edited version?  Subscribe to Turing Post https://www.turingpost.com/subscribe Chapters 0:00 The big question: When will we trust AI to run our businesses? 1:28 From code-completions to autonomous agents – the developer lens 2:15 Agent acts like a real dev and succeeds 3:25 AI taking over tedious work 3:32 Building trustworthy AI vs. convincing stakeholders to trust it 4:46 Copilot in the enterprise: early lessons and the guard-rail mindset 6:17 What is Agentic Web? 7:55 Parenting in the AI age 9:41 What counts as AGI? 11:32 How developer roles are already shifting with AI 12:33 Timeline forecast for 2-5 years re 13:33 Opportunities and concerns 15:57 Enterprise hurdles: identity, governance, and data-leak safeguards 16:48 Books that shaped the guest Turing Post is a newsletter about AI's past, present, and future. We explore how intelligent systems are built – and how they’re changing how we think, work, and live. Sign up (Jense Huang is already in): Turing Post: https://www.turingpost.com Follow us Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase

In this episode, I sit down with Olga Megorskaya, CEO of Toloka, to explore what true human-AI co-agency looks like in practice. We talk about how the role of humans in AI systems has evolved from simple labeling tasks to expert judgment and co-execution with agents – and why this shift changes everything.We get into: - Why "humans as callable functions" is the wrong metaphor – and what to use instead - What co-agency really means? - Why some data tasks now take days, not seconds – and what that says about modern AI - The biggest bottleneck in human-AI teamwork (and it’s not tech) - The future of benchmarks, the limits of synthetic data, and why it is important to teach humans to distrust AI - Why AI agents need humans to teach them when not to trust the plan If you're building agentic systems or care about scalable human-AI workflows, this conversation is packed with hard-won perspective from someone who’s quietly powering some of the most advanced models in production. Olga brings a systems-level view that few others can – and we even nerd out about Foucault’s Pendulum, the power of text, and the underrated role of human judgment in the age of agents. Did you like the episode? You know the drill:  📌 Subscribe for more conversations with the builders shaping real-world AI.  💬 Leave a comment if this resonated.  👍 Like it if you liked it.  🫶 Thank you for watching and sharing! Guest:  Olga Megorskaya, CEO of Toloka 📰 Want the transcript and edited version?  Subscribe to Turing Post https://www.turingpost.com/subscribe Chapters 0:00 – Intro: Humans as Callable Functions? 0:33 – Evolving with ML: From Crowd Labeling to Experts 3:10 – The Rise of Deep Domain Tasks and Foundational Models 5:46 – The Next Phase: Agentic Systems and Complex Human Tasks 7:16 – What Is True Co-Agency? 9:00 – Task Planning: When AI Guides the Human 10:39 – The Critical Skill: Knowing When Not to Trust the Model 13:25 – Engineering Limitations vs. Judgment Gaps 15:19 – What Changed Post-ChatGPT? 18:04 – Role of Synthetic vs. Human Data 21:01 – Is Co-Agency a Path to AGI? 25:08 – How To Ensure Safe AI Deployment 27:04 – Benchmarks: Internal, Leaky, and Community-Led 28:59 – The Power of Text: Umberto Eco and AI Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Semenova explores how intelligent systems are built – and how they’re changing how we think, work, and live. Sign up: Turing Post: https://www.turingpost.com If you’d like to keep followingOlga and Toloka: https://www.linkedin.com/in/omegorskaya/ https://x.com/TolokaAI Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase

What it actually takes to build models that improve over time. In this episode, I sit down with Devvret Rishi, CEO and co-founder of Predibase, to talk about the shift from static models to continuous learning loops, the rise of reinforcement fine-tuning (RFT), and why the real future of enterprise AI isn’t chatty generalists – it’s focused, specialized agents that get the job done.We cover: The real meaning behind "train once, learn forever" How RFT works (and why it might replace traditional fine-tuning) What makes inference so hard in production Open-source model gaps—and why evaluation is still mostly vibes Dev’s take on agentic workflows, intelligent inference, and the road ahead If you're building with LLMs, this conversation is packed with hard-earned insights from someone who's doing the work – and shipping real systems. Dev is super structural! I really enjoyed this conversation. Did you like the video? You know what to do: 📌 Subscribe for more deep dives with the minds shaping AI. Leave a comment if you have something to say. Like it if you liked it. That’s it. Oh yeap, one more thing: Thank you for watching and sharing this video. We truly appreciate you. Guest: Devvret Rishi, co-founder and CEO at Predibase https://predibase.com/ If you don’t see a transcript, subscribe to receive our edited conversation as a newsletter: https://www.turingpost.com/subscribe Chapters: 00:00 - Intro 00:07 - When Will We Train Once and Learn Forever? 01:04 - Reinforcement Fine-Tuning (RFT): What It Is and Why It Matters 03:37 - Continuous Feedback Loops in Production 04:38 - What's Blocking Companies From Adopting Feedback Loops? 05:40 - Upcoming Features at Predibase 06:11 - Agentic Workflows: Definition and Challenges 08:08 - Lessons From Google Assistant and Agent Design 08:27 - Balancing Product and Research in a Fast-Moving Space 10:18 - Pivoting After the ChatGPT Moment 12:53 - The Rise of Narrow AI Use Cases 14:53 - Strategic Planning in a Shifting Landscape 16:51 - Why Inference Gets Hard at Scale 20:06 - Intelligent Inference: The Next Evolution 20:41 - Gaps in the Open Source AI Stack 22:06 - How Companies Actually Evaluate LLMs 23:48 - Open Source vs. Closed Source Reasoning 25:03 - Dev’s Perspective on AGI 26:55 - Hype vs. Real Value in AI 30:25 - How Startups Are Redefining AI Development 30:39 - Book That Shaped Dev’s Thinking 31:53 - Is Predibase a Happy Organization? 32:25 - Closing Thoughts Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Semenova explores how intelligent systems are built – and how they’re changing how we think, work, and live. Sign up: Turing Post: https://www.turingpost.com FOLLOW US Devvret and Predibase: https://devinthedetail.substack.com/ https://www.linkedin.com/company/predibase/ Ksenia and Turing Post: https://x.com/TheTuringPost https://www.linkedin.com/in/ksenia-se https://huggingface.co/Kseniase

What happens when one of the architects of modern vector search asks whether AI can remember like a seasoned engineer, not a gold‑fish savant? In this episode, Edo Liberty – founder & CEO of Pinecone and one‑time Amazon scientist – joins me to discuss true memory in LLMs. We unpack the gap between raw cognitive skill and workable knowledge, why RAG still feels pre‑ChatGPT, and the breakthroughs needed to move from demo‑ware to dependable memory stacks. Edo explains why a vector database needs to be built from the ground (and then rebuilt many times), that storage – not compute – has become the next hardware frontier, and predicts a near‑term future where ingesting a million documents is table stakes for any serious agent. We also touch the thorny issues of truth, contested data, and whether knowledgeable AI is an inevitable waypoint on the road to AGI. Whether you wrangle embeddings for a living, scout the next infrastructure wave, or simply wonder how machines will keep their facts straight, this conversation will sharpen your view of “memory” in the age of autonomous agents. Let’s find out when tomorrow’s AI will finally remember what matters. (CORRECTION: the opening slide introduces Edo Liberty as a co-founder. We apologize for this error: Edo Liberty is the Founder and CEO of Pinecone.) Did you like the video? You know what to do: Subscribe to the channel. Leave a comment if you have something to say. Like it if you liked it. That’s all. Thanks. Guest: Edo Liberty, CEO and founder at Pinecone Website: https://www.pinecone.io/ Additional Reading: https://www.turingpost.com/ Chapters 00:00 Intro & The Big Question – When will we give AI true memory? 01:20 Defining AI Memory and Knowledge 02:50 The Current State of Memory Systems in AI 04:35 What’s Missing for “True Memory”? 06:00 Hardware and Software Scaling Challenges 07:45 Contextual Models and Memory-Aware Retrieval 08:55 Query Understanding as a Task, Not a String 10:00 Pinecone’s Full Stack Approach 11:00 Commoditization of Vector Databases? 13:00 When Scale Breaks Your Architecture 15:00 The Rise of Multi-Tenant & Micro-Indexing 17:25 Dynamically Choosing the Right Indexing Method 19:05 Infrastructure for Agentic Workflows 20:15 The Hard Questions: What is Knowledge? 21:55 Truth vs Frequency in AI 22:45 What is “Knowledgeable AI”? 23:35 Is Memory a Path to AGI? 24:40 A Book That Shaped a CEO – *Endurance* by Shackleton 26:45 What Excites or Worries You About AI’s Future? 29:10 Final Thoughts: Sea Change is Here In Turing Post we love machine learning and AI so deeply that we cover it extensively from all perspectives: past of it, its present, and our joint-future. We explain what happens the way you will understand. Sign up: Turing Post: https://www.turingpost.com FOLLOW US Edo Liberty: https://www.linkedin.com/in/edo-liberty-4380164/ Pinecone: https://x.com/pinecone Ksenia and Turing Post: Hugging Face: https://huggingface.co/Kseniase Turing Post: https://x.com/TheTuringPost Ksenia: https://x.com/Kseniase_ Linkedin: TuringPost: https://www.linkedin.com/company/theturingpost Ksenia: https://www.linkedin.com/in/ksenia-se

What happens when the biggest advocate for coding literacy starts telling people not to learn to code? In this episode, Amjad Masad, CEO and co-founder at Replit, joins me to talk about his controversial shift in thinking – from teaching millions how to code to building agents that do it for you. Are we entering a post-coding world? What even is programming when you're just texting with a machine?We talk about Replit's evolving vision, how software agents are already powering real businesses, and why the next billion-dollar startups might be solo founders augmented by AI. Amjad also shares what still stands in the way of fully autonomous agents, how AGI fits into his long-term view, and why open source still matters in the age of AI. Whether you're a developer, founder, or just AI-curious, this conversation will make you rethink what it means to “build software” in 2025. Did you like the video? You know what to do: Subscribe to the channel. Leave a comment if you have something to say. Like it if you liked it. That’s all. Thanks. Guest: Amjad Masad, CEO and co-founder at Replit Website: https://replit.com/~ Additional Reading: https://www.turingpost.com/p/amjad Chapters 00:00 Why Amjad changed his mind about coding 00:55 From code to agents: the next abstraction layer 02:05 Cognitive dissonance and the birth of Replit agents 03:38 Agent V3: toward fully autonomous software developers 04:51 Engineering platforms for long-running agents 05:30 Do agents actually work in 2025? 05:48 Real-world examples: Replit agents in action 06:36 Is Replit still a coding platform? 07:43 Why code generation beats no-code platforms 08:22 Can AI agents really create billionaires? 10:59 Every startup is now an AI startup 12:31 Solo founders and the rise of one-person AI companies 14:00 What Amjad thinks AGI really is 17:46 Replit as a habitat for AI 19:50 Open source tools vs internal no-code systems 21:02 Replit's evolving community vision 22:19 MCP vs A2A: who’s winning the protocol game 23:48 The books that shaped Amjad’s thinking about AI 25:47 What excites Amjad most about an AI-powered future Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Semenova explores how intelligent systems are built – and how they’re changing how we think, work, and live. Sign up: Turing Post: https://www.turingpost.com FOLLOW US Amjad: https://x.com/amasad Replit: https://x.com/replit Ksenia and Turing Post: Hugging Face: https://huggingface.co/KseniaseTuring Post: https://x.com/TheTuringPost Ksenia: https://x.com/Kseniase_ Linkedin: TuringPost: https://www.linkedin.com/company/theturingpost Ksenia: https://www.linkedin.com/in/ksenia-se

In the episode 001: the incredible Sharon Zhou, co-founder and CEO of Lamini. She’s a generative AI trailblazer, a Stanford-trained protégé of Andrew Ng – who, along with Andrej Karpathy and others, is also an investor in her company Lamini. From co-creating one of Coursera’s top AI courses to making MIT’s prestigious “35 under 35” list, Sharon turns complex tech into everyday magic.She is also super fun to talk to! We discussed: – How to empower developers to understand and work with AI – Lamini's technical approach to AI hallucinations (it's solvable!) – Why benchmarks ≠ reality – A notable industry use case and the importance of focusing on objective outputs: Subjective goals confuse it! – And one of my favourite moments: Sharon crushes two of the hottest topics – agents and RAG. Turns out researchers don’t understand why there’s all this hype around these two. – We also talked about open-source and its importance. – And last but not least, Sharon (who teaches millions on Coursera) shared how to fight the lack of knowledge about AI. Her recipe: lower the barrier to entry, help people level up – plus memes! Please give this video a watch and tell us what you think! Likes and subscribing to the channel are hugely appreciated. 00:00 Intro & Sharon Zhou’s Early Days in GenAI 01:25 Maternal Instincts for AI Models 02:42 From Classics to Code: Language, Product, and AI 04:30 The Spark Behind Lamini 07:45 Solving Hallucinations at a Technical Level 09:20 Benchmarks That Matter to Enterprises 11:58 Staying Technical as a Founder 13:27 The Agent & RAG Hype: Industry Misconceptions 18:44 Use Cases: From Colgate to Cancer Research 20:07 The Power of Objective Use Cases 22:28 What Comes After Hallucinations? 23:21 Following AI Research (and When It’s Useful) 26:23 Open Source & Model Ownership Philosophy 28:06 Bringing AI Education to Everyone 32:36 AI Natives & Edutainment for the Next Gen 34:18 Outro Lamini Website - https://www.lamini.ai Twitter - https://x.com/laminiai Sharon Zhou LinkedIn - https://www.linkedin.com/in/zhousharon/ Twitter - https://x.com/realSharonZhou/ Turing Post Website - https://www.turingpost.com/ Twitter - https://x.com/TheTuringPost Ksenia Se (publisher) LinkedIn - https://www.linkedin.com/in/ksenia-se Twitter - https://x.com/kseniase_

In this episode of Inference, I sit down with Mati Staniszewski, co-founder and CEO of ElevenLabs, to explore the future of AI voice, real-time multilingual translation, and emotionally rich speech synthesis. We dive into what still makes dubbing hard, how Lex Fridman's podcast was localized, and what it takes to preserve tone, timing, and emotion across languages. Mati shares why speaker detection in noisy rooms is tricky, how fast their models really are (70ms TTS!), and the deeper strategy behind partnering with creators and enterprises to show – not just tell – what the tech can do. What needs to happen for natural, free-flowing multilingual conversations to become reality? Mati says: give it two or three years. Watch to learn more! Guest: Mati Staniszewski, co-founder and CEO at ElevenLabs Website: https://elevenlabs.io/ Additional Reading: https://www.turingpost.com/p/mati Chapters 0:00 Real-time voice translation 0:11 Language barriers and AI 0:29 Why ElevenLabs started 0:36 Dubbing in Poland 0:45 Preserving emotion in translation 1:06 Tech challenges in real-time translation 1:17 Ideal device setup 2:32 Speaker diarization and emotional nuance 3:04 Speech-to-text to LLM to TTS pipeline 5:51 Concrete examples: healthcare & customer support 7:05 Real-time AI dubbing use cases 8:02 Lex Fridman podcast dubbing challenge 13:01 Audio model performance & latency 14:44 Conversational AI & multimodal future 16:57 Product vs research focus at ElevenLabs 20:42 Why ElevenLabs didn't open source (yet) 21:28 Strategy: creators, enterprises & brand building Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Semenova explores how intelligent systems are built—and how they’re changing how we think, work, and live. Sign up: Turing Post: https://www.turingpost.com FOLLOW US ON SOCIAL Twitter (X): Mati: https://x.com/matistanis ElevenLabs: https://x.com/elevenlabsio Turing Post: https://x.com/TheTuringPost Ksenia: https://x.com/Kseniase_ Linkedin: TuringPost: https://www.linkedin.com/company/theturing... Ksenia: https://www.linkedin.com/in/ksenia-se SUBSCRIBE TO OUR CHANNEL, SHARE YOUR FEEDBACK