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1:25:10ENWriting Code Is Only 30% of the Job. CodiumAI Wants the Other 70%진행자 Nathan LabenzItamar Friedman argues that completion is already a commodity, that code integrity is where AI still lags, and that flow engineering—not fancier prompts—is how GPT-4 doubled its score on coding contests.
1:15:39ENAfter 20 Years of Frontend, v0 Wrote Better Code Than Guillermo Rauch Did진행자 Nathan LabenzThe Vercel CEO tells Nathan Labenz why every product needs ghost text, why Software 2.0 will ossify the old stack, and why betting against open source is still the expensive mistake.
1:32:41ENThere are maybe 5,000 people who can train frontier models. The rest need AI engineers진행자 Nathan LabenzSwyx of Latent Space tells Nathan Labenz how a new job is forming, why OpenAI will Sherlock its developers, and what still does not count as an agent.
50:03ENCopilot Is a Copilot, Not a Pilot: Inbal Shani on the Future of AI in Software Development진행자 Lenny RachitskyGitHub's chief product officer explains what AI changes for developers, what it does not replace, and how teams should adopt it.
1:21:34ENGitLab’s Public-by-Default Playbook: Short Toes, Clear Outcomes, and a Platform Built to Ship진행자 Lenny RachitskyDavid DeSanto explains how GitLab turns radical transparency, remote-first operations, and breadth-then-depth product strategy into a faster DevSecOps platform.
1:07:48ENMost Productivity Metrics Are a Lie: Nicole Forsgren on Measuring Developer Experience in the AI Era진행자 Lenny RachitskyThe creator of DORA and SPACE explains why lines of code and old pipeline metrics fall short with AI, and how to start improving developer experience by listening, surveying and tying results to what leaders care about.
1:19:48ENBigger Models, Cleaner Splits and Human Baselines: Andrew Ng's Practical Recipe for Applied Deep Learning진행자 Lex FridmanIn a 2016 Deep Learning School talk, Andrew Ng distills lessons from leading Baidu's AI team into a workflow for diagnosing models, handling data and building a career.









