PODCAST · technology
Vanishing Gradients
by Hugo Bowne-Anderson
A podcast for people who build with AI. Long-format conversations with people shaping the field about agents, evals, multimodal systems, data infrastructure, and the tools behind them. Guests include Jeremy Howard (fast.ai), Hamel Husain (Parlance Labs), Shreya Shankar (UC Berkeley), Wes McKinney (creator of pandas), Samuel Colvin (Pydantic) and more. hugobowne.substack.com
Episodes
-
The Science and Engineering of Agent Work
Hilary Mason, CEO of Hidden Door, works with agents across software development and creative projects. Drawing on Richard Hamming’s distinction between science and engineering, she describes agents as “bringing the science back into the engineering and the engineering back into the science.” When...
-
The State of Agentic Data Science
Agents can write the code, run the analysis, and produce a convincing explanation. How do you check that they’ve answered the right question, used the data appropriately, and reached a conclusion you can act on? I joined Gaël Varoquaux (scikit-learn, Inria, and Probabl), Shipra Arora (Bain &...
-
How to Build a Coding Agent with Nico (Amp Code)
“It’s an LLM, a loop, and enough tokens.” —Nicolay Gerold, Amp Code So why does a coding agent forget your instructions, keep reading tiny pieces of a file, or struggle with an edit another model handles easily? Nicolay Gerold (Amp) joins Hugo to take that loop apart and explain the harness…
more -
The Rise of the AI Scientist
“If you feel that a product is hard to eval, or you feel like, ‘I don’t even know how to eval this,’ it’s a strong smell that your product isn’t good.” — Hamel Husain, on AI product design An AI data agent tells you last quarter’s net revenue. It doesn’t show the metric definition, source tables,...
-
If Developers Build on Chinese Open-Weight Models, Who Leads AI?
“It would be sad if local models were not an option and there were only proprietary models. It’s good to have alternatives. Competition is good for business.” — Sebastian Raschka, on open-weight AI Kimi K3’s weights landed about an hour before Hugo Bowne-Anderson and Sebastian Raschka went live....
-
Four Months Inside a Production AI Agent: What Real Users Changed
When ML/AI Engineer William Horton last joined me, Maven Assistant had reached its first external users the day before. The healthcare AI agent was available to 20 percent of Maven Clinic’s users, and the team had deliberately withheld answers about benefits. A wrong response could shape a…
more -
Building an Enterprise AI Agent for Healthcare
Every capability in an agent needs its own evidence and release bar. A model-provider slip, an incorrect tool call, and a wrong fertility-benefits answer should not be held to the same pass rate. William Horton, Staff AI Engineer at Maven Clinic, joined us the day after Maven Assistant reached…
more -
What Claude Fable Means for Coding Agents
Nicolay Gerold works all day and night on AMP, one of the most interesting coding-agent harnesses out there. If you’re building with coding agents, this conversation will help you understand: * when to trust the model, * when to build harnesses around it, * which model is worth paying for, *…
more -
The Future of Agentic Data Science
So I think we’re really at a historical moment, and the opportunity is massive. Almost 15 years ago, we were promised that data science was going to be this incredible thing and create all this value for people. And I think nowadays it’s mostly viewed as a cost center in most companies. I think…
more -
Agent-Harness.ipynb*
One thing that I don’t like about Claude is that you get into this weird mental state: oh, I think I trust the model. Let’s do the slot machine. Hit click, which puts you in an inactive mode of thinking. Maybe it’s better to use a worse model…. Vincent Warmerdam, senior data professional and...
-
Agentic Engineering and the Lost Art of Verification
> I almost don’t read code now. My approach with Roborev is it’s like my code reader. The mantra is: Roborev reads every line of code that is generated. It gets read multiple times. And so, whenever I push up a pull request, the branch gets re-reviewed. And so by the time I’m merging a pull…
more -
Next Level AI Evals for 2026
There are a lot of reasons why we should do AI evals. For many companies doing AI evals is the way to build the feedback loop into the product development lifecycle. So it is like your compass. We’re using AI evals as a compass to guide product development and also product iteration. And also…
more -
Privacy Theater Is Not Privacy Engineering: What It Actually Takes to Ship Safe AI
Katharine Jarmul, Privacy in ML/AI Expert & Author of Practical Data Privacy, joins Hugo to unpack why most AI privacy advice is theater: and what technical privacy actually looks like when you’re shipping LLMs, agents, and multimodal systems into the real world. In this episode, we dig into how…
more -
LLM Architecture in 2026: What You Need to Know with Sebastian Raschka
If you take a model release as an anchor point, let’s say Nemotron 3 or Qwen 3.5, you can go in both directions: You can either plug them into an agent and play around with that, or you can look, okay, what does the model look like under the hood? What are the ingredients? What type of attention...
-
Episode 72: Why Agents Solve the Wrong Problem (and What Data Scientists Do Instead)
I often see what I would consider to be b******t evals, especially in data, like write this dumb SQL. Almost every one of these dumb SQL questions that I’ve seen for benchmarks are just so either obviously easy or overwhelmingly adversarial. They just, they don’t feel valuable as a data…
more -
Episode 71: Durable Agents - How to Build AI Systems That Survive a Crash with Samuel Colvin
Our thesis is that AI is still just engineering… those people who tell us for fun and profit, that somehow AI is so, so profound, so new, so different from anything that’s gone before that it somehow eclipses the need for good engineering practice are wrong. We need that good engineering practice...
-
Episode 70: 1,400 Production AI Deployments
There’s a company who spent almost $50,000 because an agent went into an infinite loop and they forgot about it for a month. It had no failures and I guess no one was monitoring these costs. It’s nice that people do write about that in the database as well. After it happened, they said: watch out...
-
Episode 69: Python is Dead. Long Live Python! With the Creators of pandas & Parquet
> It’s the agent writing the code. And it’s the development loop of writing the code, building testing, write the code, build test and iterating. And so I do think we’ll see for many types of software, a shift away from Python towards other programming languages. I think Go is probably the best...
-
Episode 68: A Builder’s Guide to Agentic Search & Retrieval with Doug Turnbull & John Berryman
The best way to build a horrible search product? Don’t ever measure anything against what a user wants. Search veterans Doug Turnbull (Led Search at Reddit + Shopify; Wrote Relevant Search + AI Powered Search) and John Berryman (Early Engineer on Github Copilot; Author of Relevant Search + Prompt...
-
Episode 67: Saving Hundreds of Hours of Dev Time with AI Agents That Learn
This is continual learning, right? Everyone has been talking about continual learning as the next challenge in AI. Actually, it’s solved. Just tell it to keep some notes somewhere. Sure, it’s not, it’s not machine learning, but in some ways it is because when it will load this text file again, it...
-
Episode 66: The Agent Paradox - Why Moderna's Most Productive AI Systems Aren't Agents
Surprise. We don’t have agents. I actually went in and did an audit of all the LLM applications that we’ve developed internally. And if you were to take Anthropic’s definition of workflow versus agent, we don’t have agents. I would not classify any of our applications as agents. x Eric Ma, who...
-
Episode 65: The Rise of Agentic Search
We’re really moving from a world where humans are authoring search queries and humans are executing those queries and humans are digesting the results to a world where AI is doing that for us. Jeff Huber, CEO and co-founder of Chroma, joins Hugo to talk about how agentic search and retrieval are...
-
Episode 64: Data Science Meets Agentic AI with Michael Kennedy (Talk Python)
We have been sold a story of complexity. Michael Kennedy (Talk Python) argues we can escape this by relentlessly focusing on the problem at hand, reducing costs by orders of magnitude in software, data, and AI. In this episode, Michael joins Hugo to dig into the practical side of running Python...
-
Episode 63: Why Gemini 3 Will Change How You Build AI Agents with Ravin Kumar (Google DeepMind)
Gemini 3 is a few days old and the massive leap in performance and model reasoning has big implications for builders: as models begin to self-heal, builders are literally tearing out the functionality they built just months ago... ripping out the defensive coding and reshipping their agent...
-
Episode 62: Practical AI at Work: How Execs and Developers Can Actually Use LLMs
Many leaders are trapped between chasing ambitious, ill-defined AI projects and the paralysis of not knowing where to start. Dr. Randall Olson argues that the real opportunity isn't in moonshots, but in the "trillions of dollars of business value" available right now. As co-founder of Wyrd…
more -
Episode 61: The AI Agent Reliability Cliff: What Happens When Tools Fail in Production
Most AI teams find their multi-agent systems devolving into chaos, but ML Engineer Alex Strick van Linschoten argues they are ignoring the production reality. In this episode, he draws on insights from the LLM Ops Database (750+ real-world deployments then; now nearly 1,000!) to systematically...
-
Episode 60: 10 Things I Hate About AI Evals with Hamel Husain
Most AI teams find "evals" frustrating, but ML Engineer Hamel Husain argues they’re just using the wrong playbook. In this episode, he lays out a data-centric approach to systematically measure and improve AI, turning unreliable prototypes into robust, production-ready systems. Drawing from his...
-
Episode 59: Patterns and Anti-Patterns For Building with AI
John Berryman (Arcturus Labs; early GitHub Copilot engineer; co-author of Relevant Search and Prompt Engineering for LLMs) has spent years figuring out what makes AI applications actually work in production. In this episode, he shares the “seven deadly sins” of LLM development — and the practical...
-
Episode 58: Building GenAI Systems That Make Business Decisions with Thomas Wiecki (PyMC Labs)
While most conversations about generative AI focus on chatbots, Thomas Wiecki (PyMC Labs, PyMC) has been building systems that help companies make actual business decisions. In this episode, he shares how Bayesian modeling and synthetic consumers can be combined with LLMs to simulate customer...
-
Episode 57: AI Agents and LLM Judges at Scale: Processing Millions of Documents (Without Breaking the Bank)
While many people talk about “agents,” Shreya Shankar (UC Berkeley) has been building the systems that make them reliable. In this episode, she shares how AI agents and LLM judges can be used to process millions of documents accurately and cheaply. Drawing from work on projects ranging from...
-
Episode 56: DeepMind Just Dropped Gemma 270M... And Here’s Why It Matters
While much of the AI world chases ever-larger models, Ravin Kumar (Google DeepMind) and his team build across the size spectrum, from billions of parameters down to this week’s release: Gemma 270M, the smallest member yet of the Gemma 3 open-weight family. At just 270 million parameters, a…
more -
Episode 55: From Frittatas to Production LLMs: Breakfast at SciPy
Traditional software expects 100% passing tests. In LLM-powered systems, that’s not just unrealistic — it’s a feature, not a bug. Eric Ma leads research data science in Moderna’s data science and AI group, and over breakfast at SciPy we explored why AI products break the old rules, what skills...
-
Episode 54: Scaling AI: From Colab to Clusters — A Practitioner’s Guide to Distributed Training and Inference
Colab is cozy. But production won’t fit on a single GPU. Zach Mueller leads Accelerate at Hugging Face and spends his days helping people go from solo scripts to scalable systems. In this episode, he joins me to demystify distributed training and inference — not just for research labs, but for…
more -
Episode 53: Human-Seeded Evals & Self-Tuning Agents: Samuel Colvin on Shipping Reliable LLMs
Demos are easy; durability is hard. Samuel Colvin has spent a decade building guardrails in Python (first with Pydantic, now with Logfire), and he’s convinced most LLM failures have nothing to do with the model itself. They appear where the data is fuzzy, the prompts drift, or no one bothered to...
-
Episode 52: Why Most LLM Products Break at Retrieval (And How to Fix Them)
Most LLM-powered features do not break at the model. They break at the context. So how do you retrieve the right information to get useful results, even under vague or messy user queries? In this episode, we hear from Eric Ma, who leads data science research in the Data Science and AI group at...
-
Episode 51: Why We Built an MCP Server and What Broke First
What does it take to actually ship LLM-powered features, and what breaks when you connect them to real production data? In this episode, we hear from Philip Carter — then a Principal PM at Honeycomb and now a Product Management Director at Salesforce. In early 2023, he helped build one of the…
more -
Episode 50: A Field Guide to Rapidly Improving AI Products -- With Hamel Husain
If we want AI systems that actually work, we need to get much better at evaluating them, not just building more pipelines, agents, and frameworks. In this episode, Hugo talks with Hamel Hussain (ex-Airbnb, GitHub, DataRobot) about how teams can improve AI products by focusing on error analysis,...
-
Episode 49: Why Data and AI Still Break at Scale (and What to Do About It)
If we want AI systems that actually work in production, we need better infrastructure—not just better models. In this episode, Hugo talks with Akshay Agrawal (Marimo, ex-Google Brain, Netflix, Stanford) about why data and AI pipelines still break down at scale, and how we can fix the…
more -
Episode 48: How to Benchmark AGI with Greg Kamradt (ARC-AGI)
If we want to make progress toward AGI, we need a clear definition of intelligence—and a way to measure it. In this episode, Hugo talks with Greg Kamradt, President of the ARC Prize Foundation, about ARC-AGI: a benchmark built on Francois Chollet’s definition of intelligence as “the efficiency at...
-
Episode 47: The Great Pacific Garbage Patch of Code Slop with Joe Reis
What if the cost of writing code dropped to zero — but the cost of understanding it skyrocketed? In this episode, Hugo sits down with Joe Reis to unpack how AI tooling is reshaping the software development lifecycle — from experimentation and prototyping to deployment, maintainability, and...
-
Episode 46: Software Composition Is the New Vibe Coding
What if building software felt more like composing than coding? In this episode, Hugo and Greg explore how LLMs are reshaping the way we think about software development—from deterministic programming to a more flexible, prompt-driven, and collaborative style of building. It’s not just hype or...
-
Episode 45: Your AI application is broken. Here’s what to do about it.
Too many teams are building AI applications without truly understanding why their models fail. Instead of jumping straight to LLM evaluations, dashboards, or vibe checks, how do you actually fix a broken AI app? In this episode, Hugo speaks with Hamel Husain, longtime ML engineer, open-source...
-
Episode 44: The Future of AI Coding Assistants: Who’s Really in Control?
AI coding assistants are reshaping how developers write, debug, and maintain code—but who’s really in control? In this episode, Hugo speaks with Tyler Dunn, CEO and co-founder of Continue, an open-source AI-powered code assistant that gives developers more customization and flexibility in their...
-
Episode 43: Tales from 400+ LLM Deployments: Building Reliable AI Agents in Production
Hugo speaks with Alex Strick van Linschoten, Machine Learning Engineer at ZenML and creator of a comprehensive LLMOps database documenting over 400 deployments. Alex's extensive research into real-world LLM implementations gives him unique insight into what actually works—and what doesn't—when...
-
Episode 42: Learning, Teaching, and Building in the Age of AI
In this episode of Vanishing Gradients, the tables turn as Hugo sits down with Alex Andorra, host of Learning Bayesian Statistics. Hugo shares his journey from mathematics to AI, reflecting on how Bayesian inference shapes his approach to data science, teaching, and building AI-powered...
-
Episode 41: Beyond Prompt Engineering: Can AI Learn to Set Its Own Goals?
Hugo Bowne-Anderson hosts a panel discussion from the MLOps World and Generative AI Summit in Austin, exploring the long-term growth of AI by distinguishing real problem-solving from trend-based solutions. If you're navigating the evolving landscape of generative AI, productionizing models, or...
-
Episode 40: What Every LLM Developer Needs to Know About GPUs
Hugo speaks with Charles Frye, Developer Advocate at Modal and someone who really knows GPUs inside and out. If you’re a data scientist, machine learning engineer, AI researcher, or just someone trying to make sense of hardware for LLMs and AI workflows, this episode is for you. Charles and Hugo...
-
Episode 39: From Models to Products: Bridging Research and Practice in Generative AI at Google Labs
Hugo speaks with Ravin Kumar,*Senior Research Data Scientist at Google Labs. Ravin’s career has taken him from building rockets at SpaceX to driving data science and technology at Sweetgreen, and now to advancing generative AI research and applications at Google Labs and DeepMind. His...
-
Episode 38: The Art of Freelance AI Consulting and Products: Data, Dollars, and Deliverables
Hugo speaks with Jason Liu, an independent AI consultant with experience at Meta and Stitch Fix. At Stitch Fix, Jason developed impactful AI systems, like a $50 million product similarity search and the widely adopted Flight recommendation framework. Now, he helps startups and enterprises design...
-
Episode 37: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 2
Hugo speaks with three leading figures from the world of AI research: Sander Schulhoff, a recent University of Maryland graduate and lead contributor to the Learn Prompting initiative; Philip Resnik, professor at the University of Maryland, known for his pioneering work in computational...
We're indexing this podcast's transcripts for the first time — this can take a minute or two. We'll show results as soon as they're ready.
No matches for "" in this podcast's transcripts.
No topics indexed yet for this podcast.
Loading reviews...
ABOUT THIS SHOW
A podcast for people who build with AI. Long-format conversations with people shaping the field about agents, evals, multimodal systems, data infrastructure, and the tools behind them. Guests include Jeremy Howard (fast.ai), Hamel Husain (Parlance Labs), Shreya Shankar (UC Berkeley), Wes McKinney (creator of pandas), Samuel Colvin (Pydantic) and more.
hugobowne.substack.com
HOSTED BY
Hugo Bowne-Anderson
CATEGORIES
Loading similar podcasts...
Frequently Asked Questions
How many episodes does Vanishing Gradients have?
Vanishing Gradients currently has 86 episodes available on PodParley. New episodes are automatically indexed when they're published to the podcast feed.
What is Vanishing Gradients about?
A podcast for people who build with AI. Long-format conversations with people shaping the field about agents, evals, multimodal systems, data infrastructure, and the tools behind them. Guests include Jeremy Howard (fast.ai), Hamel Husain (Parlance Labs), Shreya Shankar (UC Berkeley), Wes McKinney...
How often does Vanishing Gradients release new episodes?
Vanishing Gradients has 86 episodes. Check the episode list to see recent publication dates and frequency.
Where can I listen to Vanishing Gradients?
You can listen to Vanishing Gradients on PodParley by clicking any episode. We provide an embedded audio player for direct listening, and you can also subscribe via your preferred podcast app using the RSS feed.
Who hosts Vanishing Gradients?
Vanishing Gradients is created and hosted by Hugo Bowne-Anderson.
URL copied to clipboard!