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Vanishing Gradients

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
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  • 24 episodes
  • weekly
  • Avg 1 hr 12 min
  • English
Counted on this page — what you have heard stays on this device, so it is not something the list can be paged by.
  • January 8 · 42 min

    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 leads Research Data Science in the Data Science and AI group at Moderna, joins Hugo on moving past the hype of autonomous agents to build reliable, high-value workflows. We discuss: * Reliable Workflows: Prioritize rigid workflows over dynamic AI agents to ensure reliability and minimize stochasticity in production environments; * Permission Mapping: The true challenge in regulated environments is security, specifically mapping permissions across source documents, vector stores, and model weights; * Trace Log Risk: LLM execution traces pose a regulatory risk, inadvertently leaking restricted data like trade secrets or personal information; * High-Value Data Work: LLMs excel at transforming archived documents and freeform forms into required formats, offloading significant “janitorial” work from scientists; * “Non-LLM” First: Solve problems with simpler tools like Python or ML models before LLMs to ensure robustness and eliminate generative AI stochasticity; * Contextual Evaluation: Tailor evaluation rigor to consequences; low-stakes tools can be “vibe-checked,” while patient safety outputs demand exhaustive error characterization; * Serverless Biotech Backbone: Serverless infrastructure like Modal and reactive notebooks such as Marimo empowers biotech data scientists for rapid deployment without heavy infrastructure overhead. You can also find the full episode on Spotify, Apple Podcasts, and YouTube. You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments! 👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort is in Q1, 2206. Here is a 35% discount code for readers. 👈 https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgch 👉 Eric & Hugo have a free upcoming livestream workshop: Building Tools for Thinking with AI (register to join live or get the recording afterwards) 👈 Show notes * Eric’s website * Eric Ma on LinkedIn * Eric’s blog * Eric’s data science newsletter * Building Effective AI Agents by the Anthropic team * Wow, Marimo from Eric’s blog * Wow, Modal from Eric’s blog * Upcoming Events on Luma * Watch the podcast video on YouTube * Join the final cohort of our Building AI Applications course in Q1, 2026 (35% off for listeners) Timestamps 00:00 Defining Agents and Workflows 02:04 Challenges in Regulated Environments 04:24 Eric Ma's Role at Moderna, Leading Research Data Science in the Data Science and AI Group 12:37 Document Reformatting and Automation 15:42 Data Security and Permission Mapping 20:05 Choosing the Right Model for Production 20:41 Evaluating Model Changes with Benchmarks 23:10 Vibe-Based Evaluation vs. Formal Testing 27:22 Security and Fine-Tuning in LLMs 28:45 Challenges and Future of Fine-Tuning 34:00 Security Layers and Information Leakage 37:48 Wrap-Up and Final Remarks 👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort is in Q1, 2026. Here is a 35% discount code for readers. 👈 https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgch Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe

  • Dec 19, 2025 · 51 min

    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 changing the very nature of search and software for builders and users alike. We Discuss: * “Context engineering”, the strategic design and engineering of what context gets fed to the LLM (data, tools, memory, and more), which is now essential for building reliable, agentic AI systems; * Why simply stuffing large context windows is no longer feasible due to “context rot” as AI applications become more goal-oriented and capable of multi-step tasks * A framework for precisely curating and providing only the most relevant, high-precision information to ensure accurate and dependable AI systems; * The “agent harness”, the collection of tools and capabilities an agent can access, and how to construct these advanced systems; * Emerging best practices for builders, including hybrid search as a robust default, creating “golden datasets” for evaluation, and leveraging sub-agents to break down complex tasks * The major unsolved challenge of agent evaluation, emphasizing a shift towards iterative, data-centric approaches. You can also find the full episode on Spotify, Apple Podcasts, and YouTube. You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments! 👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort is in Q1, 2206. Here is a 35% discount code for readers. 👈 Oh! One more thing: we’ve just announced a Vanishing Gradients livestream for January 21 that you may dig: * A Builder’s Guide to Agentic Search & Retrieval with Doug Turnbull and John Berryman (register to join live or get the recording afterwards. Show notes * Jeff Huber on Twitter * Jeff Huber on LinkedIn * Try Chroma! * Context Rot: How Increasing Input Tokens Impacts LLM Performance by The Chroma Team * AI Agent Harness, 3 Principles for Context Engineering, and the Bitter Lesson Revisited * From Context Engineering to AI Agent Harnesses: The New Software Discipline * Generative Benchmarking by The Chroma Team * Effective context engineering for AI agents by The Anthropic Team * Making Sense of Millions of Conversations for AI Agents by Ivan Leo (Manus) and Hugo * How we built our multi-agent research system by The Anthropic Team * Upcoming Events on Luma * Watch the podcast video on YouTube 👉 Want to learn more about Building AI-Powered Software? Check out our Building AI Applications course. It’s a live cohort with hands on exercises and office hours. Our final cohort is in Q1, 2206. Here is a 35% discount code for readers. 👈 https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgch Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe

  • Dec 3, 2025 · 1 hr 2 min

    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 systems at scale. They connect these ideas to the data science workflow, exploring which software engineering practices allow AI teams to ship faster and with more confidence. They also detail how to dep

  • Nov 22, 2025 · 1 hr

    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 their built just months ago: ripping out the defensive coding and reshipping their agent harnesses entirely. Ravin Kumar (Google DeepMind) joins Hugo to breaks down exactly why the rapid evolution of models like Gemini 3 is changing how we build software. They detail the shift from simple tool cal

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