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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.
1:39:00EN20VC on AI Financing, Token Budgets and the Cost of Startup Intensity게스트 Jason Lemkin, Rory O’Driscoll · 진행자 Harry StebbingsHarry Stebbings, Jason Lemkin and Rory O’Driscoll debate reported listings, software valuations, AI spending and demanding work cultures.
18:56ENWhy Programmer Laziness Still Matters in the Age of AITheo, Bryan CantrillTheo reads Bryan Cantrill’s essay on abstraction, code volume and the human responsibility to make software simpler.
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.
42:27ENEvery Company Can Operate at the Frontier — If It Owns the Evals진행자 Sarah Guo, Elad GilAt Microsoft Build, Satya Nadella tells No Priors and Latent Space why AI is an ecosystem game, why private evals beat public benchmarks, and why the Azure networking team now asks for tokens instead of headcount.
0:47ENCognition's AI Productivity Guarantee for DevinMatthew Berman · Forward FutureMatthew Berman discusses a 2026 enterprise offer that compares estimated engineering value with the cost of using Devin.
2:59:44ENTwo Endangered Languages Broke Transformers. Nested Learning Gave Models a Sleep Button.진행자 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.
20:29ENHow Prompts Become Technical Debt in AI CodingTheoTheo examines outdated agent instructions, model-specific behavior and the value of regularly auditing project guidance.
1:13:33ENA Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still Miss진행자 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.
20:12ENAI, Jobs and the Gap Between Automation Hype and Business ValueMatthew Berman · Forward FutureMatthew Berman argues that adoption is slower and more complicated than feared, while discussing layoffs, AI costs and the work humans still do.
1:06ENRunning AI Agents in the Cloud Instead of on a LaptopMatthew Berman · Forward FutureMatthew Berman argues that cloud-hosted agents can keep working when a laptop sleeps or loses connectivity. The video is sponsored by DigitalOcean.
40:22ENBuilding Gets Easier Near AGI. Knowing What to Build Does Not.Listen Labs CEO Alfred Wahlforss on AI interview agents, a 30-million-person audience, and why transcripts beat credit-card data when you need to predict customers.
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.
0:53ENA Coding Benchmark Comparison Raises Questions About SWE-benchMatthew Berman · Forward FutureMatthew Berman contrasts a SWE-bench Pro chart with DeepSWE results and questions whether the first matches users' experience.
56:10ENReid Hoffman Pledged a Billion to Free DeepMind. Hassabis Never Played the Card진행자 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.
1:14:12ENBrendan Foody on Mercor, AI Data and the Future of Work게스트 Brendan Foody · 진행자 Harry StebbingsThe Mercor CEO discusses a security incident, data-provider economics, enterprise agents and the uncertain effects of AI on employment.
1:34:36ENNate Foster: Treating the Network as a ProgramYaron Minsky, Nate Foster · Jane StreetFrom programmable switches to BGP, a programming-languages researcher explains how higher-level policies and formal models can make networks easier to change.
2:33:35ENA security researcher audits the AI stack that booked a week of guests진행자 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.
56:55ENNico Laqua on Corgi’s Work Culture, Fundraising and AI게스트 Nico Laqua · 진행자 Harry StebbingsCorgi’s co-founder describes a seven-day work culture, the company’s cafe and equity practices, and his approach to investors and AI.
28:02ENTheo Tests Claude Opus 4.8 and Claude Code WorkflowsTheoA hands-on look at model behavior, early benchmarks, usage estimates and parallel agent workflows.
2:22ENPioneer Agent and Automated Fine-Tuning of Small ModelsMatthew Berman · Forward FutureIn a Fastino-sponsored video, Matthew Berman describes a system that automates parts of model adaptation and discusses its reported benchmark results.
21:56ENMatthew Berman on Pope Leo XIV’s AI Encyclical and Anthropic’s RoleMatthew Berman, Chris Olah · Forward FutureA close reading of Magnifica humanitas raises questions about human dignity, AI companionship, concentrated power and who should govern AI.
2:53:42ENThe Moon Is Falling. It Just Keeps Missing.게스트 Biggest Mysteries in Physics · 진행자 Lex FridmanFermilab physicist Don Lincoln tells Lex Fridman that the history of physics is a history of unification, and that antimatter, dark energy, and a theory of everything still sit far beyond the experiments we can run.
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