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27:53ENGrok 4.7: Strong Codebase Investigation, Uneven ValueTheo Browne · T3Theo compares launch claims, token costs, benchmarks and his own codebase and game tests, finding useful review behavior alongside significant tradeoffs.
41:32ENUsing Fable 5.1 for Longer Coding TasksTheo Browne · T3Theo Browne shares how he prompts coding agents, sets end states and uses reviews and tests to support longer-running work.
0:23ENWhy AI Safety Evaluators Need More Than a Black BoxTheo Browne · T3Theo Browne explains a proposal to give outside evaluators employee-like access inside AI companies.
30:29ENJev: A Fast Classifier for Structured DecisionsTheo Browne · T3Theo Browne explores Typesafe AI’s Jev through classification, game and developer-workflow demos, and explains where he thinks a reasoning model is still needed.
0:24ENWhy Theo Worries About AI Harm Before ShutdownTheo Browne · T3Theo Browne argues that harmful effects could persist even after a system is switched off.
27:09ENWhy Theo Values Reliable Coding Agents Over Peak PerformanceTheo Browne · T3Theo Browne argues that a model’s weakest moments matter when a coding task stretches from investigation through testing and review.
0:22ENAI Leaders Express Support for Slowing the FrontierTheo Browne · T3Theo Browne discusses public responses to Dario Amodei’s proposal for independent AI evaluators.
38:21ENHow Theo Works With One Hand: Dictation and Coding AgentsTheo Browne · T3After a hand injury, Theo Browne combines voice input, one-handed navigation and agents to reduce friction in his daily work.
1:02:53ENShopify’s Return to Native: What AI Changes for React NativeTheo Browne · T3Theo Browne weighs Shopify’s move to Swift and Kotlin against over-the-air updates, tooling, platform limitations and his own AI-assisted port.
34:48ENDario Amodei’s Three-Part Proposal to Pace AI DevelopmentTheo Browne · T3Theo Browne examines a proposal for embedded evaluators, coordination among democracies and eventual international agreements.
0:22ENTheo’s Take on GPT-6 Astra: A Next-Generation Model With Rough EdgesTheoTheo praises the model across coding and other tasks while warning that it can still over-engineer and get stuck in loops.
1:16:51ENFable 5.1 vs GPT-6 Astra: Theo on Coding, Computer Use and CostTheoTheo compares the models through coding projects, 3D demos and agent workflows, and explains why he uses each for different jobs.
49:22ENTheo’s AI Agent Workflow: Parallel Investigations, Review Loops and Safer MergesTheoA walkthrough of how Theo uses agents, remote previews and release safety nets to keep coding while running a company.
26:33ENTheo on AI Safety, Jacob Coxon’s Resignation and Model MonitorabilityTheoA researcher’s departure prompts Theo to examine competitive pressure, alignment and what adversarial tests reveal about monitoring model reasoning.
28:36ENTheo on Partial Understanding: Working in Large Codebases With AITheoReading Sean Goedecke on theory-building, Theo explains why engineers need useful mental models without memorizing an entire system.
44:12ENGPT-6 Astra: Theo’s Early Tests of Computer Use, 3D and CodingTheoTheo reviews launch-era pricing and benchmarks, shows projects built with the model, and recounts a frustrating PR-review failure.
55:06ENFable 5.1 in Practice: Theo on Costs, Agent Workflows and PR QualityTheoTheo reviews the launch, prompting guidance and his early coding experiments, including an observational comparison of pull requests.
0:24ENTheo’s Low-Cost Experiment Auditing T3 Code Pull RequestsTheoTheo describes using an inexpensive model to review recently updated pull requests and proposes a recurring readiness check.
34:23ENHow Reward Hacking Changed an Experimental Claude ModelTheo Browne · Ping LabsTheo examines Anthropic’s deliberately trained reward-seeking model, its simulated security tests, and the limits of what the results show.
0:24ENTheo’s Interpretation of OpenAI’s Decision on Cursor and AstraTheoTheo connects the end of the partnership to concerns about model distillation and access to Astra.
15:20ENWhy Claude Code’s Limit Increase Means Less Usage for Current SubscribersTheo Browne · Ping LabsTheo explains the difference between Anthropic’s permanent baseline increase and the temporary allowance users were receiving, and criticizes how the change was communicated.
27:05ENOpenAI and Cursor’s Split: What Changes for DevelopersTheo Browne · Ping LabsTheo explains the proposed end of direct OpenAI model access in Cursor, the dispute over usage figures, and alternatives for developers.
25:27ENThe New Chips Challenging NVIDIA’s AI AdvantageTheo Browne, Jim Cramer, Jensen Huang · Ping LabsTheo compares local inference hardware with Chinese accelerators and OpenAI’s Jalapeño, focusing on memory, software and power efficiency.
0:36ENTheo on Ox Alpha, Revealed as GLM-5.3-FlashTheo Browne · Ping LabsTheo discusses the model’s price, multimodal inputs and Chinese-chip deployment in a short first impression.
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