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1:25:12ENHe Hasn’t Typed a Line of Code in Months. Here’s How Augment Reads a Million-Line RepoModeration: Nathan LabenzGuy Gur-Ari, co-founder and chief scientist at Augment, tells Nathan Labenz how coding agents actually work inside messy enterprise codebases.
5:20:08ENThePrimeagen Would Rather Redesign the Room Than Trust WillpowerGast: ThePrimeagen · Moderation: Lex FridmanLex Fridman talks with Michael Paulson about linked lists, Netflix, Neovim, ADHD, addiction, AI, and the night he says God gave him a conscience.
50:35ENCopilot Already Writes Half the Code. GitHub's CEO Says Agents Still Have Four Gates to ClearModeration: Sarah Guo, Elad GilThomas Dohmke on agent mode, why median developers are not going anywhere, and how AI is repricing the tools that write software.
1:02:41ENMike Krieger on AI Products, Model Competition and the Future of CodingGast: Mike Krieger · Moderation: Harry StebbingsAnthropic’s chief product officer explains how he thinks about product design, model differentiation and the limits of current AI tools.
1:32:29EN17 Edits, Then 3 Prompts: Lovable Builds the Same App TwiceModeration: Nathan LabenzAnton Osika and Isaak Sundeman live-build a shopping comparison tool and argue that opinionated defaults—not open-ended agents—are how non-coders actually ship software.
1:24:29ENStackBlitz Almost Shut Down. Then Bolt Hit $20 Million ARR in Eight Weeks.Moderation: Nathan LabenzEric Simons tells Nathan Labenz how a seven-year browser operating system, Claude Sonnet 3.5, and a text box replaced the local toolchain.
1:32:14ZH李广密:AI 任务引擎、模型竞争与 2025 年的 Agent 预测Gast: 李广密 · Moderation: 张小珺在 2024 年末,李广密与张小珺讨论 AI 如何改变搜索、编程和软件,以及模型公司面临的竞争与数据约束。
52:21ENThe SWE-bench Team on Coding Agents, Evaluation and the Future of ProgrammingMatthew Berman, Kilian Lieret, Ofir Press, Carlos E. Jimenez · Forward FutureKilian Lieret, Ofir Press and Carlos Jimenez explain how real GitHub issues became a benchmark and how interface design shaped SWE-agent.
1:22:09ENTesting ChatGPT o1 Pro Live: Puzzles, Code and the Cost of More ReasoningMatthew Berman · Forward FutureMatthew Berman tries OpenAI’s newly announced $200 monthly plan on audience prompts, encountering slow responses, simple successes and a wrong Sudoku conclusion.
38:05ENFrom Zero to $20M ARR in Two Months: Bolt's Moat Lives in the BrowserModeration: Sarah Guo, Elad GilStackBlitz CEO Eric Simons tells No Priors how a five-year WebContainers bet, an open-sourced prompt, and a community of non-coders turned Bolt.new into an overnight full-stack generator.
48:30ENGuy Podjarny on AI Coding Tools and the Future of Software DevelopmentGast: Guy Podjarny · Moderation: Harry StebbingsThe Tessl CEO examines coding assistants, agentic development, security and the business choices surrounding AI.
1:19:00ENEiso Kant on Poolside, Compute and AI for Software DevelopmentGast: Eiso Kant · Moderation: Harry StebbingsPoolside’s CTO explains code-execution feedback, the cost of model development and his company’s ambitions in the AI race.
2:29:04ENKeep the Programmer in the Driver's Seat: Cursor's Founders on Tab, Agents, and Zero-Entropy EditingGast: Cursor Team · Moderation: Lex FridmanMichael Truell, Aman Sanger, Sualeh Asif, and Arvid Lunnemark tell Lex Fridman why they forked VS Code, how Tab and Apply actually work, and why most programming still needs a human iterating in real time.
1:09:09EN30 Percent on SWE-bench: Cosine Taught Genie How Engineers Actually ThinkModeration: Nathan LabenzAlistair Pullen tells Latent Space how Cosine reconstructed human problem-solving, fine-tuned on billions of tokens, and took the top score on software engineering’s hardest public test.
1:31:56ENTesting OpenAI o1 on Puzzles, Code and Reasoning: A Launch-Day LivestreamMatthew Berman · Forward FutureMatthew Berman tries o1-preview and o1-mini on word counts, Tetris, physical reasoning and a word puzzle, with viewers checking the results.
51:15ENAGI Has One Problem Left: Spend a Million Times More on a TokenMagic founder Eric Steinberger tells Sequoia’s Training Data why he trains his own models, why a software engineer is the MVP of AGI, and why 96% still is not a colleague.
37:47ENWorking Backwards from AGI: Magic's Five-Million-Token BetModeration: Sarah Guo, Elad GilEric Steinberger wants a coding colleague you trust like a teammate — and a world that is still a good place thirty years after AGI.
1:30:24ENDaniel Miessler and Matthew Berman on AI Privacy, Coding, and Generated WorldsMatthew Berman, Daniel Miessler, Melissa, Alex · Forward FutureA 2024 live conversation explores Recall, open models, AI-assisted programming, a Doom simulator and questions about security, energy and employment.
1:02:39ZHKevin 和 Sophia:2023 年硅谷 AI 热潮中的产品、融资与竞争Gast: Kevin Xu, Sophia · Moderation: 张小珺GitHub 从业者与风险投资人回顾 ChatGPT 兴起后的硅谷,讨论模型、Copilot、算力和初创公司。
1:07:34ENGitHub Didn’t Believe Copilot Was Writing 25% of the CodeThomas Dohmke tells Sequoia how an unplanned GPT-3 experiment became Copilot, why GitHub still will not train its own foundation model, and how he wants a billion people writing software by 2030.
59:11ENDon't Train a Model. Factory's Founders Say Go Dig for GoldMatan Grinberg and Eno Reyes tell Sequoia's Training Data why they sell a fleet of job-specific droids on top of other people's models — and how that stack just took SWE-bench from 14% to 19%.
1:25:10ENWriting Code Is Only 30% of the Job. CodiumAI Wants the Other 70%Moderation: 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.
46:36ENAfter a decade of code search, Sourcegraph’s CTO says the coding-AI fight is outside the modelModeration: Sarah Guo, Elad GilBeyang Liu on Cody, why keyword search still does most of RAG, and why CS fundamentals get more valuable as AI compresses the middleware.
1:22:59ENIf Your Agent Is Right 80% of the Time, Ten Steps Leave You With 10%Moderation: Nathan LabenzImbue CTO Josh Albrecht tells Nathan Labenz how a 27-person, pre-product research company is putting $200 million and 10,000 H100s toward agents that can actually finish a long job.
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