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1:16:17ENFaster, But for What? Nicole Forsgren on Measuring Developer Productivity in the AI EraInvitado: Nicole Forsgren · Presenta: Lenny RachitskyThe creator of DORA and SPACE explains why most productivity metrics mislead, how AI changes flow and trust, and how to build a developer experience program that proves its value.
1:36:57ENDo the Dumb Thing First: A CTO's Playbook for Building an AI Coding AssistantPresenta: Jacob EffronSourcegraph CTO Beyang Liu on which AI coding features actually earn daily use, how new models get shipped and evaluated, and why retrieval is still a search problem.
1:24:01ENFrom Sandwich Math to Expert Data: Scale AI's CEO on What Actually Makes Models SmarterPresenta: Lenny RachitskyJason Droege explains what really happened with Meta's investment in Scale AI, how data labeling turned into expert work, and what Uber Eats taught him about building businesses.
57:21ENGemini 2.0 Flash Goes to Production: Pricing, Model Lineup and What Developers Should Build NextPresenta: Nathan LabenzGoogle DeepMind's Logan Kilpatrick walks through launch-day Gemini 2.0 releases, rate limits, model naming, and where he sees startup opportunities.
1:25:34ENShipping in Days: How Claude Code's Head of Product Runs a Team at AI SpeedPresenta: Lenny RachitskyCat Wu, Head of Product for Claude Code and Cowork at Anthropic, explains how the PM role, hiring, tooling and culture change when features go from idea to users in a week or less.
58:57ENWhen the Model Learns to Look Around: Apollo Research on Testing o1 for SchemingPresenta: Nathan LabenzApollo Research founder Marius Hobbhahn explains how his team probed OpenAI's o1 for deception, what it found, and why the trajectory worries him more than the model itself.
1:46:33ENFrom Chaos to a Failure Map: A Hands-On Primer on AI EvalsInvitado: Hamel Husain, Shreya Shankar · Presenta: Lenny RachitskyHamel Husain and Shreya Shankar walk Lenny Rachitsky through a real error-analysis workflow, from reading traces to building a trustworthy LLM judge.









