Podcast
Vanishing Gradients
Hosted by Hugo Bowne-Anderson · EN
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
hugobowne.substack.com
32episodes
Episodes
Newest firstAll episodes
Episode 51: Why We Built an MCP Server and What Broke First
Jun 26, 202500:47:41Tap to summarizeWhat 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 first LLM-powered SaaS features to ship to real users. More recently, he and his team built a production-ready MCP server. We cover: • How to evaluate LLM systems using human-aligned judges • The spreadsheet-driven process behind shipping Honeycomb’s first LLM feature • The challenges of tool usage, prompt templates, and flaky model behavior • Where MCP shows promise, and where it breaks in the real world If you’re working on LLMs in production, this one’s for you! LINKS So We Shipped an AI Product: Did it Work? by Philip Carter Vanishing Gradients YouTube Channel Upcoming Events on Luma Hugo's recent newsletter about upcoming events and more! 🎓 Learn more: Hugo's course: Building LLM Applications for Data Scientists and Software Engineers — next cohort starts July 8: https://maven.com/s/course/d56067f338 📺 Watch the video version on YouTube: YouTube link
Transcribe →Episode 50: A Field Guide to Rapidly Improving AI Products -- With Hamel Husain
Jun 17, 2025Tap to summarizeIf 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, data inspection, and systematic iteration. The conversation is based on Hamel’s blog post A Field Guide to Rapidly Improving AI Products, which he joined Hugo’s class to discuss. They cover: 🔍 Why most teams struggle to measure whether their systems are actually improving 📊 How error analysis helps you prioritize what to fix (and when to write evals) 🧮 Why evaluation isn’t just a metric — but a full development process ⚠️ Common mistakes when debugging LLM and agent systems 🛠️ How to think about the tradeoffs in adding more evals vs. fixing obvious issues 👥 Why enabling domain experts — not just engineers — can accelerate iteration If you’ve ever built an AI system and found yourself unsure how to make it better, this conversation is for you. LINKS A Field Guide to Rapidly Improving AI Products by Hamel Husain Vanishing Gradients YouTube Channel Upcoming Events on Luma Hugo's recent newsletter about upcoming events and more! 🎓 Learn more: Hugo's course: Building LLM Applications for Data Scientists and Software Engineers — next cohort starts July 8: https://maven.com/s/course/d56067f338 Hamel & Shreya's course: AI Evals For Engineers & PMs — use code GOHUGORGOHOME for $800 off 📺 Watch the video version on YouTube: YouTube link
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