All Episodes

The Science and Engineering of Agent Work

The State of Agentic Data Science

How to Build a Coding Agent with Nico (Amp Code)

The Rise of the AI Scientist

If Developers Build on Chinese Open-Weight Models, Who Leads AI?

Four Months Inside a Production AI Agent: What Real Users Changed

Building an Enterprise AI Agent for Healthcare

What Claude Fable Means for Coding Agents

The Future of Agentic Data Science

Agent-Harness.ipynb*

Agentic Engineering and the Lost Art of Verification

Next Level AI Evals for 2026

Privacy Theater Is Not Privacy Engineering: What It Actually Takes to Ship Safe AI

LLM Architecture in 2026: What You Need to Know with Sebastian Raschka

Episode 72: Why Agents Solve the Wrong Problem (and What Data Scientists Do Instead)

Episode 71: Durable Agents - How to Build AI Systems That Survive a Crash with Samuel Colvin

Episode 70: 1,400 Production AI Deployments

Episode 69: Python is Dead. Long Live Python! With the Creators of pandas & Parquet

Episode 68: A Builder’s Guide to Agentic Search & Retrieval with Doug Turnbull & John Berryman

Episode 67: Saving Hundreds of Hours of Dev Time with AI Agents That Learn

Episode 66: The Agent Paradox - Why Moderna's Most Productive AI Systems Aren't Agents

Episode 65: The Rise of Agentic Search

Episode 64: Data Science Meets Agentic AI with Michael Kennedy (Talk Python)

Episode 63: Why Gemini 3 Will Change How You Build AI Agents with Ravin Kumar (Google DeepMind)

Episode 62: Practical AI at Work: How Execs and Developers Can Actually Use LLMs

Episode 61: The AI Agent Reliability Cliff: What Happens When Tools Fail in Production

Episode 60: 10 Things I Hate About AI Evals with Hamel Husain

Episode 59: Patterns and Anti-Patterns For Building with AI

Episode 58: Building GenAI Systems That Make Business Decisions with Thomas Wiecki (PyMC Labs)

Episode 57: AI Agents and LLM Judges at Scale: Processing Millions of Documents (Without Breaking the Bank)

Episode 56: DeepMind Just Dropped Gemma 270M... And Here’s Why It Matters

Episode 55: From Frittatas to Production LLMs: Breakfast at SciPy

Episode 54: Scaling AI: From Colab to Clusters — A Practitioner’s Guide to Distributed Training and Inference

Episode 53: Human-Seeded Evals & Self-Tuning Agents: Samuel Colvin on Shipping Reliable LLMs

Episode 52: Why Most LLM Products Break at Retrieval (And How to Fix Them)

Episode 51: Why We Built an MCP Server and What Broke First

Episode 50: A Field Guide to Rapidly Improving AI Products -- With Hamel Husain

Episode 49: Why Data and AI Still Break at Scale (and What to Do About It)

Episode 48: How to Benchmark AGI with Greg Kamradt (ARC-AGI)

Episode 47: The Great Pacific Garbage Patch of Code Slop with Joe Reis

Episode 46: Software Composition Is the New Vibe Coding

Episode 45: Your AI application is broken. Here’s what to do about it.

Episode 44: The Future of AI Coding Assistants: Who’s Really in Control?

Episode 43: Tales from 400+ LLM Deployments: Building Reliable AI Agents in Production

Episode 42: Learning, Teaching, and Building in the Age of AI

Episode 41: Beyond Prompt Engineering: Can AI Learn to Set Its Own Goals?

Episode 40: What Every LLM Developer Needs to Know About GPUs

Episode 39: From Models to Products: Bridging Research and Practice in Generative AI at Google Labs

Episode 38: The Art of Freelance AI Consulting and Products: Data, Dollars, and Deliverables

Episode 37: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 2

Episode 36: Prompt Engineering, Security in Generative AI, and the Future of AI Research Part 1

Episode 35: Open Science at NASA -- Measuring Impact and the Future of AI

Episode 34: The AI Revolution Will Not Be Monopolized

Episode 33: What We Learned Teaching LLMs to 1,000s of Data Scientists

Episode 32: Building Reliable and Robust ML/AI Pipelines

Episode 31: Rethinking Data Science, Machine Learning, and AI

Episode 30: Lessons from a Year of Building with LLMs (Part 2)

Episode 29: Lessons from a Year of Building with LLMs (Part 1)

Episode 28: Beyond Supervised Learning: The Rise of In-Context Learning with LLMs

Episode 27: How to Build Terrible AI Systems

Episode 26: Developing and Training LLMs From Scratch

Episode 25: Fully Reproducible ML & AI Workflows

Episode 24: LLM and GenAI Accessibility

Episode 23: Statistical and Algorithmic Thinking in the AI Age

Episode 22: LLMs, OpenAI, and the Existential Crisis for Machine Learning Engineering

Episode 21: Deploying LLMs in Production: Lessons Learned

Episode 20: Data Science: Past, Present, and Future

Episode 19: Privacy and Security in Data Science and Machine Learning

Episode 18: Research Data Science in Biotech

Episode 17: End-to-End Data Science

Episode 16: Data Science and Decision Making Under Uncertainty

Episode 15: Uncertainty, Risk, and Simulation in Data Science

Episode 14: Decision Science, MLOps, and Machine Learning Everywhere

Episode 13: The Data Science Skills Gap, Economics, and Public Health

Episode 12: Data Science for Social Media: Twitter and Reddit

Episode 11: Data Science: The Great Stagnation

Episode 10: Investing in Machine Learning

9: AutoML, Literate Programming, and Data Tooling Cargo Cults

Episode 8: The Open Source Cybernetic Revolution

Episode 7: The Evolution of Python for Data Science

Episode 6: Bullshit Jobs in Data Science (and what to do about them)

Episode 5: Executive Data Science

Episode 4: Machine Learning at T-Mobile

Episode 3: Language Tech For All

Episode 2: Making Data Science Uncool Again

Episode 1: Introducing Vanishing Gradients