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1:06:36ENBills Doubled Overnight. Next, the APIs Might Vanish.Moderation: Jacob EffronSix months after their last roundup, Ari Morcos and Rob Toews tell Jacob Efron why coding agents finally work, why near-frontier open weights may have peaked, and why a compute famine could pull the plug on public APIs.
30:40ENTheo on Agent Workflows, Usage Limits, and Building MoreTheoTheo demonstrates multi-agent PR reviews, ranked work queues and remote development while reflecting on cost, verification and sleep.
32:53ENTheo’s Fable 5 Review: Benchmarks, Real Projects, and LimitsTheoTheo tests a newly released model on coding and creative work, highlighting impressive results alongside failures, safeguards and cost.
1:45:59EN87% of Workers Use AI. Only 13% Say the Company Got Better.Moderation: Nathan LabenzGlean’s Work AI Index names the missing hours: botsitting, botshitting, and a hamster wheel of polished nonsense.
44:57ENTheo on Anthropic’s Case for Preparing for AI Self-ImprovementTheoAn examination of internal productivity evidence, uncertain AI futures and the challenge of coordinating a pause.
30:11ENWhat Cloudflare’s VoidZero Acquisition Could Mean for Vite and AI DeploymentTheoTheo examines the developer experience behind the deal and demonstrates his own unreleased cloud project.
1:22:36ENA Thousand Researchers, a Million AIs: Frontier Labs Start Talking About the BrakesModeration: Nathan LabenzThe first week of AI in the AM follows OpenAI's timeline for an ML-intern model this year and a human-level AI researcher by early 2028 — and the closed-door meeting where labs admitted they might one day need to slow down together.
44:52ENAnthropic’s Recursive Self-Improvement Report: Evidence, Limits and a Debate Over Slowing AIMatthew Berman · Forward FutureMatthew Berman examines Anthropic’s 2026 claims about AI-assisted development, research benchmarks and possible paths to autonomous self-improvement.
1:31:56ENWhen AI Builds Itself: Anthropic’s Recursive Improvement Essay and Its LimitsMatthew Berman · Forward FutureMatthew Berman examines Anthropic’s internal coding and research claims, questions its proposed slowdown, and considers what autonomous AI development could mean.
2:59:44ENTwo Endangered Languages Broke Transformers. Nested Learning Gave Models a Sleep Button.Moderation: Nathan LabenzGoogle researcher Ali Behrouz tells Nathan Labenz how stacking update frequencies — not just layers — and an offline sleep phase could give language models genuine continual learning.
1:13:33ENA Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still MissModeration: Jacob EffronThe Attention Is All You Need co-author tells Unsupervised Learning that transformers will probably keep winning — and that they still do not learn the way people do.
38:50ENCan AI Help Less Experienced Engineers Grow?TheoTheo discusses coding assistants, team coordination and the difference between learning with AI and blindly relaying its output.
56:10ENReid Hoffman Pledged a Billion to Free DeepMind. Hassabis Never Played the CardModeration: Jacob EffronSebastian Mallaby's biography of Demis Hassabis, discussed with Jacob Effron, is a story of a founder who tried to stop the AI race and then became one of its central players.
2:33:35ENA security researcher audits the AI stack that booked a week of guestsModeration: Nathan LabenzNathan Labenz walks Daniel Miessler through a 1GB second brain, two named AI employees, and the blast-radius rules that should govern them.
32:30ENTheo Reviews DeepSWE and the Limits of Coding BenchmarksTheoAn investor in DataCurve examines its coding benchmark, the results shown in the video and their limitations.
2:34:17ENMatthew Berman Tests Claude Opus 4.8 and Dynamic Workflows LiveMatthew Berman · Forward FutureA launch-day walkthrough compares Anthropic’s reported benchmarks with informal coding experiments and a multi-agent website redesign.
47:33ENHow Theo’s AI Coding Workflow ChangedTheoA walkthrough of remote agents, project context, short prompts, verification and pull-request decisions.
1:41:58ENA thousand people on guard, a million in the sprintModeration: Nathan Labenz80,000 Hours founder Ben Todd on how to spend a career in the years when your impact on AI will actually peak.
37:56ENClaude Code, Codex, and Cursor: Three Approaches to AI CodingTheo Browne · T3Theo compares terminal-first coding agents, local verification, cloud execution, and the trade-offs behind their interfaces.
37:30ENCursor Composer 2.5: Speed, Coding Benchmarks, and the Limits of EvaluationTheo Browne · T3Theo examines Cursor’s coding model, its training methods, practical performance, and the difficulty of independent testing.
2:13:11ENThe Models Understand Shutdown. They Just Don't Care.Moderation: Nathan LabenzPalisade Research's Jeffrey Ladish on shutdown resistance, open-weight self-replication, and why all compute looks like food to a strategic agent.
59:41ENThe GPT moment for video hasn’t arrived, but AGI may already be hereModeration: Jacob EffronA day after Google I/O 2026, Gemini co-lead Oriol Vinyals argues that world models, file-system memory, and infinite-complexity RL environments will define the next era of frontier models.
1:01:14ENThe Model Eats the Scaffolding: How Google Plans to Ship Agents EverywhereModeration: Nathan LabenzThree years after the leaked “no moat” memo, DeepMind’s Gemini leads walk through 3.5 Flash, Omni, and the shared agent harness now running through Google’s products.
39:05ENWhy GitHub Copilot Changed Its Billing for Agent WorkflowsTheo Browne · T3Theo explains why one agent request can consume vastly different resources, and what that means for subscription pricing.
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