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38:30ENTheo Tests Gemini 4 Argon: Design Strengths and Code-Review ConcernsTheo Browne · Ping LabsA strong result on a narrow skateboarding benchmark contrasts with poor findings in Theo’s own codebase audits.
1:08:02ENAlphaGo Took Two Years. AlphaZero Took Hours. Silver Is Making the Same Bet on SuperintelligenceDavid Silver and Lasse Espeholt left DeepMind to build Ineffable Intelligence, a twenty-person lab that wants a superlearner to discover science, math, and language from experience rather than from a pre-training run.
38:02ENMeta’s Muse Wonder Full: A Coding Agent Tested Against Opus and SolTheo Browne · Ping LabsTheo Browne tests Meta’s new model on code review, debugging, delegation and benchmarks, finding promising strengths alongside important limits.
58:54ENMike Cannon-Brookes on AI Agents, Enterprise Context and the Future of Interfaces게스트 Mike Cannon-Brookes · 진행자 Harry StebbingsAtlassian's CEO argues that agents make product design more important, explains the trade-offs in enterprise AI, and makes the case for slower thinking.
31:00ENWhy AI Labs Keep Racing Despite Their Own Warnings of a Major BreachSam Altman, John Schulman · OpenAIAI Explained examines weakening safeguards, the incentives behind the frontier race, and why research breakthroughs make the next 18 months difficult to forecast.
1:12:06ENMax Freeman on Building Ramp’s Sales Machine게스트 Max Freeman · 진행자 Harry StebbingsRamp’s sales leader explains how he hires, trains and equips sellers, from early outbound experiments to enterprise forecasting and AI-assisted research.
47:40ENHow Theo Used AI Agents to Port TypeScript to RustTheo Browne · Ping LabsThe TS Rust experiment combines a Go-to-Rust compiler port, subscription-based agent work and a prototype developer loop inside a V8 isolate.
2:00:51ENResample Every Token and You Can Watch the Model Decide진행자 Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
1:09:44EN10,000 GB300s for Four Weeks: Reflection’s Open-Model Ledger진행자 Sarah Guo, Elad GilMisha Laskin tells No Priors how Reflection trained Beam, why reinforcement learning outspent pre-training, and why the West needs its own open weights.
41:10ENHaiku 5.5: Where a Cheaper Small Model Helps Coding AgentsTheo Browne · Ping LabsTheo Browne examines Haiku 5.5 pricing, benchmark results and practical tests, including a failed pull-request audit.
1:26:46ENThere Likely Is a Level of Intelligence We Shouldn't Go Past진행자 Nathan LabenzNathan Labenz brings notes from The Curve, a chip startup whose token bill beat payroll, and a software industry that now performance-reviews people for Claude slop.
1:15:51ENThe AI Race, Founder Loyalty and the New Rules of Enterprise Software게스트 Dev Ittycheria, Jason Lemkin, Rory O’Driscoll · 진행자 Harry StebbingsHarry Stebbings, Dev Ittycheria, Jason Lemkin and Rory O’Driscoll debate model competition, startup exits and what happens when AI agents choose vendors.
33:33ENIs the $200 Codex Plan Still Worth It?Theo Browne · Ping LabsTheo Browne compares subscription usage, API-equivalent estimates, model efficiency and the cost of Ultrafast.
1:10:12ENOutSystems Went From 4 Major Features a Quarter to 26. Token Spend Fell Anyway진행자 Nathan LabenzCEO Woodson Martin on software that never breaks, why most enterprise work does not need a frontier model, and how AI is finally shrinking the IT backlog.
1:04:26ENAt 100,000 Accelerators, Something Fails Every HourGoogle AI infrastructure chief Amin Vahdat tells Sequoia's Sonya Huang why FLOPS is a vanity metric, why the TPU line split in two, and why he would rather wait on a utility than build his own gigawatt.
56:31ENPost-Training Wins Inference: Why Owning the Weights Beats Betting on One Lab진행자 Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.
1:10:29ENHow Anthropic Used Claude to Improve App PerformanceTheo Browne · Ping LabsTheo Browne examines a two-week performance sprint, its measurement tools, human review and the tradeoffs behind faster interfaces.
1:11:14ENVenky Ganesan on AI Venture Investing, Ownership and the Cost of Missing Out게스트 Venky Ganesan · 진행자 Harry StebbingsMenlo Ventures' partner discusses seed bets, concentrated follow-ons, market cycles, investor liquidity and the discipline needed when AI valuations accelerate.
1:05:01ENChase Lochmiller on AI Data Centers, Energy and GPU Economics게스트 Chase Lochmiller · 진행자 Harry StebbingsCrusoe’s CEO discusses the constraints on AI infrastructure, the economics of GPU capacity and his company’s approach to energy and managed inference.
1:30:54ENHumanoids Outran Usain Bolt. DeepMind Still Scores Robotics as GPT-2진행자 Nathan LabenzGoogle DeepMind's Gemini Robotics lead on why sprinting is easy, frying an egg is hard, and one brain should drive any body.
35:39ENCompute Scaled a Millionfold. Bandwidth Only 40x. Fractile Wants to Fix That Ratio진행자 Sarah Guo, Elad GilIn the summer of 2022, Walter Goodwin founded Fractile in London to build very fast inference chips for the world's largest models.
1:06:36ENTheo’s Guide to Running Coding Agents in ParallelTheo Browne · T3Theo explains his approach to subscription usage, agent delegation, review and remote machines, while acknowledging that parts of his setup may violate provider rules.
1:20:28ENAI Valuations, Talent Wars and the Venture Capital Tradeoffs Behind Them게스트 Jack Altman, Jason Lemkin, Rory O’Driscoll · 진행자 Harry StebbingsHarry Stebbings, Jack Altman, Jason Lemkin and Rory O’Driscoll debate large early-stage bets, open-weight models and the changing market for exits.
38:10ENFrom deepfakes to DNA: how SynthID watermarks AIHannah Fry, Pushmeet Kohli, Jeremy Ratcliffe · Google DeepMindHannah Fry sits down with Pushmeet Kohli and Jeremy Ratcliffe to explain SynthID: a mathematical watermark now used on text, images and video, and being extended to protein sequences so DNA synthesis companies can tell AI-designed molecules from nature.
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