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69 件並び順
2:46:57ENProgramming Languages Are Bicycles for the Mindゲスト Chris Lattner · ホスト Lex FridmanChris Lattner talks with Lex Fridman about Swift, LLVM, RISC-V, and why the next leap in computing is as much a language problem as a silicon one.
59:33ENLaurent Mazare on Python, OCaml and Machine LearningRon Minsky, Laurent Mazare · Jane StreetA researcher compares exploratory notebooks with robust production systems, and explains how language interoperability and resource management connect the two.
1:23:49ENSteve Lee on PowerShell 7: Compatibility, New Features and the RoadmapSteve Lee · MicrosoftA historical look at the move from Windows PowerShell to cross-platform PowerShell, including demonstrations of the version 7 preview and its compatibility trade-offs.
1:10:58ENThe Hadoop Lesson That Made Google Give TensorFlow Awayゲスト Rajat Monga · ホスト Lex FridmanIn November 2015, Google put its core internal machine-learning system on GitHub. Rajat Monga explains why a company of that size would hand over the hard-to-copy part.
1:03:45ENJane Street and the OCaml CompilerRon Minsky · Jane StreetRon Minsky traces an accidental language adoption into a long-term commitment to compiler engineering and a shared open-source ecosystem.
1:14:23ENCristina Rosu: Building Dynamic OCaml Web Apps with Incr_domCristina Rosu · Jane StreetA practical introduction to the Elm architecture, virtual DOM and incremental computation, followed by a live counter-app demonstration.
1:06:39ENWhy Jane Street Uses OCamlA Jane Street talk explains how functional programming and static types support reliable trading software, and examines the costs of choosing a less common language.
1:04:56ENYaron Minsky on Incremental: From Research Idea to Production LibraryYaron Minsky · Jane StreetA history of the algorithms, memory problems and performance tradeoffs behind Jane Street’s OCaml library for self-adjusting computations.
1:25:13ENCodex Is Not Just a Coding Tool: OpenAI's Plan for an Agent That Behaves Like a Teammateホスト Lenny RachitskyCodex product lead Alexander Embiricos explains the 20x growth, why every useful agent may need to write code, and why human review speed is the real bottleneck.
1:39:51ENAfter the Inflection Point: Simon Willison on Agentic Engineering, Dark Factories and the Coming AI Challenger Disasterホスト Lenny RachitskyDjango co-creator Simon Willison explains what changed in November 2025, how he works with coding agents, and why prompt injection remains the field's biggest unsolved risk.
1:11:06ENReplit's Amjad Masad on Building Your Own Model, Latency, and the Next Billion Software Creatorsホスト Jacob EffronThe Replit founder explains why AI should be native to the product, why he trained a small in-house model, and why he thinks software engineering is about to split in two.
1:04:25ENInside GitHub's Copilot Bet: Agents, Open Source, and Why the Developer Job Enduresホスト Jacob EffronGitHub CEO Thomas explains how Copilot went from a research bet to 1.3 million developers, and what agents, fine-tuning and a multi-model world mean for the next five years of software.
1:23:15ENCamille Fournier on Rewrites, One-on-Ones and Building Platform Teamsゲスト Camille Fournier · ホスト Lenny RachitskyThe author of The Manager's Path explains what annoys engineers about PMs, why rewrites are a trap, how to move into management, and what makes platform teams work.
1:32:32ENDevin's Scott Wu on Building an AI Software Engineer and Turning Coders into Architectsゲスト Scott Wu · ホスト Lenny RachitskyThe Cognition co-founder explains how his 15-person team uses fleets of Devins to build Devin, what the product can and can't do, and why he expects more engineers, not fewer.
50:03ENCopilot Is a Copilot, Not a Pilot: Inbal Shani on the Future of AI in Software Developmentホスト Lenny RachitskyGitHub's chief product officer explains what AI changes for developers, what it does not replace, and how teams should adopt it.
1:16:17ENFaster, But for What? Nicole Forsgren on Measuring Developer Productivity in the AI Eraゲスト Nicole Forsgren · ホスト 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.
1:05:00ENFrom Arctic Code Vault to Autocomplete: How GitHub Copilot Went From Moonshot to Productゲスト Ryan J. Salva · ホスト Lenny RachitskyGitHub VP of product Ryan J. Salva explains how a small research team's experiment became Copilot, and what it took to scale it responsibly.
1:16:53ENEngineering Leadership After ZIRP: Strategy, Systems and Accountabilityゲスト Will Larson · ホスト Lenny RachitskyCarta CTO Will Larson on how engineering management changed after the zero-interest-rate era, and the habits he relies on now: systems models, written strategy, shared incentives and honest values.
1:38:45ENWhen Coding Stops Being the Bottleneck: Inside Anthropic's Claude Code and Cowork Teamsホスト Lenny RachitskyFiona Fung, who leads the teams behind Claude Code and Cowork, describes how an engineering org that ships roughly eight times more code manages quality, hiring, metrics and culture.
1:36:57ENDo the Dumb Thing First: A CTO's Playbook for Building an AI Coding Assistantホスト 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.
1:27:45ENCoding Is Solved, Says the Creator of Claude Code: Boris Cherny on What Comes Nextホスト Lenny RachitskyBoris Cherny, head of Claude Code at Anthropic, on the tool's origin, the principle of latent demand, and how work changes when engineers stop writing code by hand.
1:07:48ENMost Productivity Metrics Are a Lie: Nicole Forsgren on Measuring Developer Experience in the AI Eraホスト 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.
1:41:26ENThe AI Scouting Report, Part 3: What AI Can Already Do, Who Will Win, and Why Nathan Labenz Wants Caution on Scalingホスト Nathan LabenzNathan Labenz closes his AP-course tour of AI with capabilities, market moats, jobs, law, safety plans and a stance on what to do next.
1:09:56ENWhen Anyone Can Build Anything, Taste Becomes the Product Jobホスト Lenny RachitskyCodex desktop lead Andrew Ambrosino on how AI has inverted product development, why roles are blurring but shouldn't vanish, and where Codex and ChatGPT are headed.
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