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18:27EN
AI Engineer· Oct 26, 2026Daksh Gupta on Building a Better AI Code Review AgentDaksh Gupta · GreptileGreptile co-founder Daksh Gupta explains why a webhook plus a coding agent is not enough for reliable pull-request review—and how targeted prompts, incident data, model inversion, codebase context, browser testing, and task-specific model routing can help.
34:11EN
GOTO Conferences· Oct 23, 2026Anatoliy Bilenko and Alberto Carretero: Making Database Protocols Reliable with Formal Methods and AIAnatoliy Bilenko, Alberto Carretero · CanonicalCanonical’s database engineers connect executable specifications, model-based tests, failure exploration, and reconstructed execution histories—and explain why none of these techniques proves a real system correct on its own.
59:41EN
20VC with Harry Stebbings· Oct 22, 2026Podcast · 3 speakers20VC: Venture Fund Size, AI Valuations and the Changing Software MarketGuest Jason Lemkin, Rory O’Driscoll · Host Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O'Driscoll debate venture concentration, infrastructure risk, code review and the assumptions behind fast-growing AI companies.
5:15:51EN
Lex Fridman· Aug 26, 2026Podcast · 2 speakersDHH Did Not Write a Line of Omarchy QuattroGuest DHH · Host Lex FridmanThirteen months after a skeptical conversation with Lex Fridman, the Rails creator describes how Opus 4.5, a swarm of coding agents, and a one-minute Linux installer ended his era of hand-chiseled Ruby.
1:34:36ENNate Foster: Treating the Network as a ProgramYaron Minsky, Nate Foster · Jane StreetFrom programmable switches to BGP, a programming-languages researcher explains how higher-level policies and formal models can make networks easier to change.
1:48:21ENWill Wilson on Deterministic Simulation and the Hard Problems of Software TestingYaron Minsky, Will Wilson · Jane StreetThe Antithesis co-founder explains how replayable simulations explore complex software, why properties matter and where testing meets its limits.
1:34:29EN
Cognitive Revolution "How AI Changes Everything"· Feb 18, 2026PodcastThree IMO Golds Missed the Same Problem. Harmonic Wants Math to CompileHost Nathan LabenzVlad Tenev and Tudor Achim built Aristotle to emit Lean proofs a kernel can check—and they argue that is how mathematical superintelligence stays trustworthy.
1:41:34ENCognitive Revolution "How AI Changes Everything"· Dec 24, 2025PodcastThey Owned the Camera Partition—and Still Couldn't Crash the HelicopterHost Nathan LabenzKathleen Fisher and Byron Cook tell Nathan Labenz how formal methods turn some software bugs into closed cases, from a DARPA helicopter to AWS policies checked a billion times a second.
37:45EN
Sequoia Capital· Jun 10, 2025PodcastCodex Is a Thought Experiment: What If the Agent Worked on Its Own Computer?OpenAI’s Codex team on training o3 to write mergeable PRs, running four copies of a 1 a.m. bug, and why the product only clicks once you stop pairing and start delegating.
2:29:04ENLex Fridman· Oct 6, 2024Podcast · 2 speakersKeep the Programmer in the Driver's Seat: Cursor's Founders on Tab, Agents, and Zero-Entropy EditingGuest Cursor Team · Host 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.
1:25:10ENCognitive Revolution "How AI Changes Everything"· Mar 12, 2024PodcastWriting Code Is Only 30% of the Job. CodiumAI Wants the Other 70%Host 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.
32:48EN
No Priors: AI, Machine Learning, Tech, & Startups· Nov 16, 2023Podcast · 2 speakersFourteen People, $200 Million of Compute, and the Reliability Bottleneck for AgentsHost Sarah Guo, Elad GilImbue co-founders Kanjun Qiu and Josh Albrecht on why agents fail, why language models cannot learn general algorithms, and why they are spending a giant round on GPUs rather than headcount.
5:49ENIan on Software Engineering at Jane StreetIan · Jane StreetA London desk developer explains code review, OCaml training, intern projects and the feedback that comes from sitting near users.
3:48ENVal on Software Engineering and Collaboration at Jane StreetVal · Jane StreetA London developer explains how colleagues teach new hires, review production code, and work across teams.
56:06ENHow Jane Street Reviews Code with IronJane Street's internal review tool tracks what each reviewer has actually read, keeps feedback in the code, and enforces review obligations before release.
1:13:25ENNate Foster: Verifying Programmable Network Data PlanesNate Foster, Ron · Cornell UniversityHow p4v applies program verification to packet-processing code, and why its conclusions depend on control-plane assumptions.
59:14ENArjun Guha on Verifying System ConfigurationsArjun Guha · UMass AmherstPuppet manifests can be declarative yet nondeterministic or non-idempotent. A verification tool models resource interactions to find those problems.
1:09:24ENYaron Minsky on Effective ML: Writing OCaml for Code ReadersYaron Minsky · Jane StreetPractical lessons from Jane Street on using types, interfaces and abstraction to keep a large OCaml codebase understandable and correct.
23:58ENEffective ML: Errors, Abstraction and Managing ComplexityA Jane Street engineer discusses explicit failure, boilerplate, type-system restraint and the practical role of side effects.
51:35ENYaron Minsky on Effective ML: Interfaces, Invariants and Code ReadersYaron Minsky · Jane StreetIn the first part of a 2011 Harvard lecture, Jane Street’s Yaron Minsky shows how OCaml interfaces and types can make long-lived code easier to understand and change.
1:25:13EN
Lenny's PodcastPodcastCodex Is Not Just a Coding Tool: OpenAI's Plan for an Agent That Behaves Like a TeammateHost 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.
1:16:17ENLenny's PodcastPodcast · 2 speakersFaster, But for What? Nicole Forsgren on Measuring Developer Productivity in the AI EraGuest Nicole Forsgren · Host 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.
1:07:48ENLenny's PodcastPodcastMost Productivity Metrics Are a Lie: Nicole Forsgren on Measuring Developer Experience in the AI EraHost 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.
1:15:13ENLenny's PodcastPodcast · 2 speakersShipping Real Apps Without Writing Code: A Non-Technical PM's Cursor and Claude Code WorkflowGuest Zevi Arnovitz · Host Lenny RachitskyZevi Arnovitz, a product manager with no technical background, walks Lenny Rachitsky through the slash-command workflow he uses to build and ship products with AI.