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1:13:48EN
Unsupervised Learning: With Jacob Effron· 16 juil. 2026PodcastCarriers Spend $200 Billion a Year. The Money Went Up the StackAnime : Jacob EffronBenedict Evans tells Jacob Effron why analogizing AI to past platforms is a dead end, and why looking at how those platforms actually made money is not.
1:26:12EN
20VC with Harry Stebbings· 16 juil. 2026Podcast · 3 voix20VC Roundtable on AI Competition, Enterprise Spending and Venture CapitalInvité : Jason Lemkin, Rory O’Driscoll · Anime : Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll discuss a reported Apple lawsuit, AI pricing, memory markets and venture financing.
1:00:47EN
Matthew Berman· 15 juil. 2026AI News Live: A Codex Keyboard, Open Models and Agent Safety ResearchMatthew Berman, Alex, Brian · Forward FutureMatthew Berman, Alex and Brian discuss new tools, model costs and a research report, separating initial impressions from verified results.
25:58EN
Theo - t3․gg· 14 juil. 2026Theo on Codex Ultra: Reasoning Effort, Sub-Agents and UsageTheo Browne · Ping LabsTheo examines the Ultra mode he tested, its effect on multi-agent work, and why he prefers explicit workflow stages.
30:09ENTheo - t3․gg· 13 juil. 2026Theo on Managing GPT-5.6 Sol Usage and Agent WorkTheo Browne · Ping LabsA practical review of reasoning effort, long runs, delegation and stopping points, grounded in the limits and products available in July 2026.
36:00ENTheo - t3․gg· 12 juil. 2026GPT-5.6 Reviewed: Strengths, Costs, and Coding-Agent TradeoffsTheo Browne · Ping LabsTheo Browne assesses Sol, Terra and Luna through benchmark results and hands-on coding experience.
1:00:08EN20VC with Harry Stebbings· 11 juil. 2026Podcast · 2 voixArvind Jain on Enterprise AI, Open Models and the Future of WorkInvité : Arvind Jain · Anime : Harry StebbingsThe Glean founder debates model competition, enterprise AI returns and whether productivity gains will shrink teams or raise the bar for everyone.
26:09ENTheo - t3․gg· 10 juil. 2026What Theo Built with GPT-5.6 During Six Weeks of Early AccessTheo Browne · Ping LabsFrom mobile rewrites to a compiler prototype and remote machine repair, Theo Browne shares unusually large-scale experiments and their limits.
24:43ENTheo - t3․gg· 9 juil. 2026Grok 4.5 for Coding: Theo’s Benchmarks and Early ExperienceTheo Browne · Ping LabsTheo Browne examines Grok 4.5’s cost, benchmark caveats, coding work and 3D experiments after early access in Cursor.
50:57ENUnsupervised Learning: With Jacob Effron· 9 juil. 2026PodcastA Trillion Dollars of GPUs Will Be Worth $100 Billion in Five Years, Says LSTM's Jürgen SchmidhuberAnime : Jacob EffronThe AI pioneer tells Unsupervised Learning's Jacob Effron why he is wildly optimistic about the technology and deeply pessimistic about the model companies buying the chips.
41:05EN
No Priors: AI, Machine Learning, Tech, & Startups· 9 juil. 2026Podcast · 2 voixThere is no moat: Booking’s CEO on a $1 stock, agentic travel, and why AI still doesn’t move the numbersAnime : Sarah Guo, Elad GilGlenn Fogel cashed his 1999 bonus, joined Priceline in February 2000, and watched the Nasdaq peak a week later — then spent 25 years turning a near-delisting into a thousand-fold ride he now wants to spend on AI travel agents.
28:11ENTheo - t3․gg· 7 juil. 2026The Tradeoffs of Running Open-Weight AI Models LocallyTheo Browne · Ping LabsTheo Browne separates the value of open weights from the hardware, concurrency and cost constraints of local inference.
18:34ENMatthew Berman· 7 juil. 2026Model Routing for AI Coding: A Worked Cost ExampleMatthew Berman · Forward FutureMatthew Berman separates planning, implementation and review to explain how different models can fit different tasks.
43:15ENTheo - t3․gg· 6 juil. 2026Theo’s Fable 5 Workflow: Model Routing, PR Review, and VerificationTheo Browne · Ping LabsA practical walkthrough of delegating tasks across models and turning a stalled backlog into reviewed work, with cost and deployment boundaries.
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Cognitive Revolution "How AI Changes Everything"· 4 juil. 2026Podcast19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every ChipAnime : Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.
23:24ENTheo - t3․gg· 4 juil. 2026Fable 5: Theo’s Take on Fallbacks, Subscription Limits, and CostTheo Browne · Ping LabsTheo Browne examines performance claims and shares the cost-saving workflow he used during a limited subscription window.
1:18:07EN20VC with Harry Stebbings· 2 juil. 2026Podcast · 3 voix20VC Roundtable: AI Spending, Model Competition and Software AcquisitionsInvité : Jason Lemkin, Rory O’Driscoll · Anime : Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll debate enterprise AI returns, prediction markets, SaaS roll-ups and the cost of raising capital.
1:01:27ENNo Priors: AI, Machine Learning, Tech, & Startups· 2 juil. 2026Podcast · 2 voixTwo Years and Four Months to a Live Reactor: Valar Atomics Wants Energy Ten Times CheaperAnime : Sarah Guo, Elad GilIsaiah Taylor walks Sarah Guo through Ward 250 in Utah and argues that nuclear is a hardware-iteration problem, not a paper-design one.
28:37ENTheo - t3․gg· 1 juil. 2026Theo Tests Sonnet 5 and Explains Fable’s ReturnTheoTheo compares model benchmarks and his own coding tests, finding promise in Sonnet’s subagent orchestration but questioning its task costs.
2:19ENTheo - t3․gg· 1 juil. 2026Theo on Sonnet 5: Agent Orchestration and Token CostsTheoTheo welcomes Sonnet 5's ability to coordinate subagents while questioning its token efficiency. He also discusses the introductory pricing announced at release.
31:30ENTheo - t3․gg· 30 juin 2026Why Some AI Models Use Fewer Tokens to Solve a TaskTheoTheo explains input, output, caching and reasoning tokens, then explores a hypothesis about terse internal reasoning.
1:10:15EN
Sequoia Capital· 30 juin 2026PodcastThe 100x lives between the layers: Dylan Patel on co-design and a 60x yearSemiAnalysis’s founder argues that matching models, kernels, and silicon is what turns a 2x here and a 2x there into 100x, while equivalent-quality inference has gotten about 60 times cheaper in a year.
22:43ENTheo - t3․gg· 29 juin 2026Mac Prices, Unified Memory and Local AI: Theo’s Buying Trade-offsTheoTheo explains why high-memory Macs appealed for local inference and how rising component prices changed his comparison with PCs.
30:08ENTheo - t3․gg· 27 juin 2026GPT-5.6’s Limited Preview: Benchmarks, Agent Risks and AccessTheoTheo reviews the launch-era announcement, published evaluations and reported agent incidents, while questioning the balance between safety and broad access.Charger plus24 sur 333 affichés