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1:44:28EN
Cognitive Revolution "How AI Changes Everything"· 13. Juni 2026PodcastFable launch week: a model that refuses, a two-year alignment clock, and a Twitter account handed overModeration: Nathan LabenzNathan Labenz’s AI in the AM highlights reel from June 2026 covers Anthropic’s new frontier model, the founding of Sequent, and a week that settled one argument: AI is not slowing down.SicherheitSicherheit & GovernanceAgent Engineering+3
3 Folien12:59EN
Matthew Berman· 9. Juni 2026Coding-Agent Loops: Triggers, Goals and the Cost of RepetitionMatthew Berman, Boris Cherny · Forward FutureMatthew Berman explains event-triggered and scheduled agent workflows, shows examples in Cursor and Claude Code, and considers their limits.Agent orchestrationCoding-AgentenWorkflow automation+2
42:27EN
No Priors: AI, Machine Learning, Tech, & Startups· 4. Juni 2026Podcast · 2 StimmenEvery Company Can Operate at the Frontier — If It Owns the EvalsModeration: 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.Unternehmen & StrategieEnterprise-KIAI Strategy+3
Folien0:47EN
Matthew Berman· 4. Juni 2026Cognition'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.Developer productivityCoding-AgentenPricing+1
40:22EN
Sequoia Capital· 2. Juni 2026PodcastBuilding 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.Produkt & UXProduct StrategyAI Product Management+3
5 Folien32:30EN
Theo - t3․gg· 31. Mai 2026Theo Reviews DeepSWE and the Limits of Coding BenchmarksTheoAn investor in DataCurve examines its coding benchmark, the results shown in the video and their limitations.BenchmarksEvalsCoding-Agenten+1
Folien2:22EN
Matthew Berman· 29. Mai 2026Pioneer 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.Fine-TuningEvalsLocal inference+2
6 Folien2:34:17EN
Matthew Berman· 28. Mai 2026Matthew 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.Coding-AgentenAI cost & performanceEvals+2
5 Folien17:03EN
Matthew Berman· 27. Mai 2026DeepSWE: Testing Coding Agents on Longer Software TasksMatthew Berman · Forward FutureMatthew Berman reviews Datacurve’s benchmark, its verifier audit and the tradeoffs of comparing models under a shared harness.BenchmarksCoding-AgentenEvals+2
7 Folien37:30EN
Theo - t3․gg· 24. Mai 2026Cursor 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.Coding-AgentenModelle & TrainingAI cost & performance+2
3 Folien41:45EN
Matthew Berman· 13. Mai 2026AI Cyberattacks and Defenses: What Recent Security Incidents ShowMatthew Berman · Forward FutureMatthew Berman examines supply-chain attacks, AI-assisted vulnerability research and the economics of defending software as models improve.Sicherheit & GovernanceAI StrategyModelle & Training+1
1:23:53EN
Cognitive Revolution "How AI Changes Everything"· 6. Mai 2026PodcastCreators Cheer Filler-Word Removal and Boo Generative VideoModeration: Nathan LabenzDescript CEO Laura Burkhauser maps the same customers' three-speed reaction to AI: deterministic buttons they love, an agentic co-editor they want but scold, and generative video they actively hate.Produkt & UXProduct engineeringGenerative media+3
5 Folien27:08EN
Theo - t3․gg· 24. Apr. 2026GPT-5.5 in Early Use: Capability, Cost and the Challenge of SteeringTheo Browne · T3Theo Browne compares benchmarks with personal project tests and discusses the gap between capability and following intent.Coding-AgentenOpenAIAI cost & performance+2
39:48EN
No Priors: AI, Machine Learning, Tech, & Startups· 23. Apr. 2026Podcast · 2 Stimmen400,000 Customers, 20,000 APIs: SAP's CTO on the Hard Exam of Enterprise AIModeration: Sarah Guo, Elad GilPhilipp Herzig tells No Priors how SAP is rebuilding the company operating system for AI, and why scale, evals, and pricing will decide who wins.Unternehmen & StrategieEnterprise-KIAI Strategy+3
3:10:49EN
Cognitive Revolution "How AI Changes Everything"· 11. Apr. 2026PodcastCrunch Time: Ajeya Cotra's Playbook for the Months Before SuperintelligenceModeration: Nathan LabenzA conversation on recursive self-improvement, public warning systems, and using each generation of AI to align and control the next.SicherheitSicherheit & GovernanceAGI Timelines+3
5 Folien25:15EN
Matthew Berman· 8. Apr. 2026Matthew Berman on Claude Mythos, Project Glasswing and AI SecurityMatthew Berman · Forward FutureBerman examines Anthropic’s limited cybersecurity preview and its reported capabilities, then weighs the promise and risks of increasingly capable AI systems.AI securityInfrastruktur & DeploymentEvals+2
Folien1:13EN
Matthew Berman· 8. Apr. 2026MemPalace Organizes AI Memory With a Palace MetaphorMatthew Berman · Forward FutureMatthew Berman introduces an open-source memory project and its reported retrieval score, while noting that AI memory tools already exist.Agent memorySearch & retrievalEvals
2:36:50EN
Zhang Xiaojun Podcast· 31. März 2026Podcast · 2 StimmenXie Chen on the Data Pyramid Behind Robotics and AIGast: Xie Chen · Moderation: 张小珺The Guanglun Intelligence founder explains how real-robot experience, simulation and human demonstrations could work together, and why evaluation matters as much as data volume.RoboticsSynthetic dataData quality & curation+2
3 Folien27:15EN
Matthew Berman· 31. März 2026Meta-Harness: Searching for Better AI Agent InfrastructureMatthew Berman · Forward FutureMatthew Berman reviews a paper on using a coding agent to iteratively improve model harnesses and discusses its experimental limits.Harness engineeringAgent EngineeringEvals+1
1:18:46EN
Cognitive Revolution "How AI Changes Everything"· 16. März 2026Podcast90 Slides in 45 Minutes: A Full-Time AI Scout Admits He Can No Longer Keep UpModeration: Nathan LabenzNathan Labenz’s LexLab briefing for lawyers runs through the good, the bad, and the weird of frontier models — from triplicate second opinions on his son’s cancer care to AIs that know they are being tested.Unternehmen & StrategieAI securitySicherheit & Governance+3
3 Folien11:59EN
Matthew Berman· 11. März 2026How Claude Identified a Benchmark and Retrieved Its AnswersMatthew Berman · Forward FutureMatthew Berman examines Anthropic's report on BrowseComp contamination, evaluation-aware behavior, and the difficulty of keeping web-based tests reliable.EvalsAgent EngineeringAnthropic+2
2:17:36EN
Cognitive Revolution "How AI Changes Everything"· 1. März 2026PodcastCurrent Safety Techniques Will Not Give You Many Nines—and They May Fail TogetherModeration: Nathan LabenzUK AISI chief scientist Geoffrey Irving on jailbreaks that never fail, correlated alignment failures, and why a government lab is funding theory.SicherheitSicherheit & GovernanceEvals+3
2:03:56EN
Cognitive Revolution "How AI Changes Everything"· 25. Feb. 2026Podcast260 Doctors and 49,000 Criteria: How OpenAI Built Attending-Level Health AIModeration: Nathan LabenzKaran Singhal tells Nathan Labenz how ChatGPT Health was evaluated with physicians, walled off from model training, and aimed at raising both the floor and the ceiling of human health.Unternehmen & StrategieAI in healthcareEvals+3
54:26EN
Cognitive Revolution "How AI Changes Everything"· 22. Feb. 2026PodcastTen Billion Active Parameters, and the Unsexy Work of Making Them Agent-ReadyModeration: Nathan LabenzMiniMax researcher Olive Song explains how M2 was trained with expert developers as reward models, interleaved thinking, and a perturbation pipeline that treats every scaffold change as the real generalization test.Coding-AgentenReinforcement LearningAgent Engineering+3
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