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Slides0:33EN
Theo - t3․gg· Jul 10, 2026Three Eras of AI Coding Agents: Tool Calls, Long Tasks, and OrchestrationTheo Browne · Ping LabsTheo Browne uses three model releases to explain how he sees coding agents developing.Coding agentsAgent orchestrationModels & training
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Theo - t3․gg· Jul 9, 2026Theo on Why Natural Language Does Not Belong in a TerminalTheo Browne · Ping LabsA brief argument that developers’ attachment to familiar terminals can shape how they imagine new interfaces.AI interface designDeveloper workflows & testingCoding Culture
2:06:41EN
Cognitive Revolution "How AI Changes Everything"· Jul 9, 2026PodcastZero the J-Space and a Model's Hidden Goal Lights Up on Token OneHost Nathan LabenzA weekly Cognitive Revolution cut of Anthropic's global-workspace paper, AI superforecasters, open video world models, and a chip that treats inference as data movement.Models & trainingModel InterpretabilitySafety & governance+3
Slides1:02:09EN
Sources Podcast· Jul 9, 2026Podcast · 3 speakersJon Steinback on Marker, AI and the Thinking Behind WritingGuest Jon Steinback · Host Alex Heath, Ellis HamburgerThe Marker founder argues for an editor that asks questions instead of writing for the user; the hosts examine where AI fits in their own work.Product DesignCreativityAI Product Management+1
6 slides24:43EN
Theo - t3․gg· Jul 9, 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.Coding agentsEvalsAI cost & performance+2
50:57EN
Unsupervised Learning: With Jacob Effron· Jul 9, 2026PodcastA Trillion Dollars of GPUs Will Be Worth $100 Billion in Five Years, Says LSTM's Jürgen SchmidhuberHost 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.Models & trainingAGIAI research+3
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20VC with Harry Stebbings· Jul 9, 2026Podcast · 3 speakers20VC on AI Regulation, Compute Economics and Enterprise AdoptionGuest Jason Lemkin, Rory O’Driscoll · Host Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll debate reported AI policy proposals, infrastructure spending, software deployment and startup liquidity.AI StrategyVenture CapitalEnterprise AI+2
41:05EN
No Priors: AI, Machine Learning, Tech, & Startups· Jul 9, 2026Podcast · 2 speakersThere is no moat: Booking’s CEO on a $1 stock, agentic travel, and why AI still doesn’t move the numbersHost 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.Enterprise & strategyEnterprise AIAI Strategy+3
2 slides1:06EN
Matthew Berman· Jul 9, 2026Two GPT-5.6 Coding Demos: A Block World and a SpreadsheetMatthew Berman · Forward FutureMatthew Berman shows a Minecraft-like game and an Excel-like application built with Codex and GPT-5.6.Coding agentsComputer useDeveloper productivity
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Theo - t3․gg· Jul 8, 2026Theo on Guilt-Merging Pull Requests and Questioning CodeTheo Browne · Ping LabsA short reflection on why developers sometimes accept changes they do not think are right.Code review & verificationSoftware engineeringCoding Culture
8 slides25:34EN
Matthew Berman· Jul 8, 2026Anthropic’s J-Space Research: What Its Experiments Reveal and What Remains UnknownMatthew Berman · Forward FutureMatthew Berman examines experiments on internal representations in Claude, from causal interventions to safety auditing and the limits of consciousness claims.Model InterpretabilityAI researchAI Safety+2
1:53EN
Matthew Berman· Jul 8, 2026What Anthropic’s J-Space Research Suggests About AI ReasoningMatthew Berman · Forward FutureMatthew Berman discusses internal model representations, interventions and the question of consciousness, while the research itself describes important limits.Model InterpretabilityReasoning & test-time computeConsciousness & Intelligence
7 slides28:11EN
Theo - t3․gg· Jul 7, 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.Local inferenceOpen-weight modelsAI cost & performance+2
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Theo - t3․gg· Jul 7, 2026Theo Asks Why Teams Need a Separate System for Environment FilesTheo Browne · Ping LabsA brief prompt to reconsider how development teams share environment configuration.Developer workflows & testingSecurity & safetySoftware engineering
55:19EN
Sequoia Capital· Jul 7, 2026PodcastThe drone is 15% of Zipline: 140 million miles, zero incidentsKeller Rinaudo Cliffton and Eric Watson explain why the aircraft is the small part of an automated logistics system now cheaper than a car.Infrastructure & deploymentRoboticsAutonomous Systems+3
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Matthew Berman· Jul 7, 2026Claude Fable 5 Access Extended Through July 12, 2026Matthew Berman · Forward FutureMatthew Berman reports a short extension for paid Claude subscribers, without a reset of their existing usage quota.Models & training
3 slides18:34EN
Matthew Berman· Jul 7, 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.AI cost & performanceAgent engineeringDeveloper productivity
4:41EN
Jane Street· Jul 6, 2026Inside Jane Street’s Internship: Projects, Mentorship and LearningCody · Jane StreetInterns and employees describe collaborative interviews, learning finance from the basics, and working on projects used by the firm.CareersCompany CultureMachine Learning+1
Slides0:20EN
Theo - t3․gg· Jul 6, 2026Theo on Markdown Files as Instructions for Coding AgentsTheo Browne · Ping LabsA short observation about how agent tools can change the scale of a software idea.Coding agentsProduct StrategyAgent engineering
1:11:01EN
20VC with Harry Stebbings· Jul 6, 2026Podcast · 2 speakersMike Mignano on AI Applications, Open Models and Venture StrategyGuest Mike Mignano · Host Harry StebbingsThe USV partner discusses AI agents, token spending and the opportunities and risks for startups building above the model layer.Venture CapitalAI StrategyAI agents+2
8 slides43:15EN
Theo - t3․gg· Jul 6, 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.Coding agentsAgent orchestrationDeveloper workflows & testing+2
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20VC with Harry Stebbings· Jul 4, 2026Podcast · 2 speakersSierra Co-founder Clay Bavor on Open Models, Enterprise AI and Building TeamsGuest Clay Bavor · Host Harry StebbingsBavor discusses model selection and compute costs, deploying AI agents for enterprises, and how Sierra approaches hiring and company culture.Enterprise AIOpen-weight modelsAI agents+2
1:47:38EN
Cognitive Revolution "How AI Changes Everything"· Jul 4, 2026Podcast19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every ChipHost 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.Models & trainingLocal inferenceScaling Laws+3
7 slides23:24EN
Theo - t3․gg· Jul 4, 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.Coding agentsSafety & governanceAI cost & performance+2
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