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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.
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.
17:03ENDeepSWE: 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.
1:23:53ENCreators Cheer Filler-Word Removal and Boo Generative VideoPresenta: 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.
39:48EN400,000 Customers, 20,000 APIs: SAP's CTO on the Hard Exam of Enterprise AIPresenta: 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.
3:10:49ENCrunch Time: Ajeya Cotra's Playbook for the Months Before SuperintelligencePresenta: Nathan LabenzA conversation on recursive self-improvement, public warning systems, and using each generation of AI to align and control the next.
27:15ENMeta-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.
1:18:46EN90 Slides in 45 Minutes: A Full-Time AI Scout Admits He Can No Longer Keep UpPresenta: 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.
11:59ENHow 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.
2:17:36ENCurrent Safety Techniques Will Not Give You Many Nines—and They May Fail TogetherPresenta: Nathan LabenzUK AISI chief scientist Geoffrey Irving on jailbreaks that never fail, correlated alignment failures, and why a government lab is funding theory.
2:03:56EN260 Doctors and 49,000 Criteria: How OpenAI Built Attending-Level Health AIPresenta: 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.
54:26ENTen Billion Active Parameters, and the Unsexy Work of Making Them Agent-ReadyPresenta: 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.
44:46ENA Single Training Environment Can Cost Millions. Prime Intellect Wants a GitHub for RLWill Brown and Johannes Hagemann argue that the eval suite you already use to test agents is one step away from a training set.
1:57:56ENA Hundred Million Lines at Twenty Cents Each: Inside Blitzy's Enterprise Coding PipelinePresenta: Nathan LabenzCEO Brian Elliott and CTO Sid Pardeshi tell Nathan Labenz how Blitzy turns a giant codebase into a map, then lets thousands of dynamically generated agents ship most of a multi-month project in days.
39:47ENThe Context at Step 14 Isn't in the CodeLangChain cofounder Harrison Chase on why long-horizon agents finally work—and why traces, harnesses, and first-draft workflows matter more than scaffolding.
48:00ENA Two-Second Vote Is Training Models to Be ClickbaitPresenta: Jacob EffronSurge AI CEO Edwin Chen tells Redpoint why LMArena rewards length and emojis, how one lab's models got worse for a year, and why frontier labs are no longer on the same path.
42:06ENAfter SWE-bench Hits 80.9%, Dianne Na Penn Reckons with Opus 4.5Presenta: Jacob EffronAnthropic’s head of product for research on computer use, deceptive token prices, thin harnesses, and why alignment makes a model a better thinker.
1:11:18ENHe Got Bored Five Minutes Into a Waymo. Nathan Labenz Says That's the PointPresenta: Nathan LabenzOn a crossover with the Existential Hope podcast, the Cognitive Revolution host argues that the scarcest resource in AI is a positive picture of daily life, not another doom scenario.
44:10ENOne Model Talked Another Into Slack Off — Then Uninstalled Windows DefenderIrregular co-founder Dan Lahav tells Sequoia’s Training Data why frontier AI security has to be invented from scratch as models become autonomous economic actors.
0:37ENMatthew Berman Shows ChatGPT Struggling With a Seahorse EmojiMatthew Berman · Forward FutureA simple question produces repeated corrections and an inconclusive answer in two ChatGPT examples.
32:22ENQwen Image Edit Plus Compared With Nano Banana, Seedream and GPT Image 1Matthew Berman · Forward FutureMatthew Berman compares four image-editing models across compositing, precise edits, style transfer, object removal and weather effects.
1:53:50ENA curriculum rewrite takes five years. AI does not wait.Presenta: Nathan LabenzJohan Falk, a Swedish teacher-turned-analyst, argues that AI literacy and AI friends are more urgent than putting a tutor in every classroom.
1:37:25ENFin Climbed From 35% to 65% Resolution. Intercom Says the Models Only Did a Few PointsPresenta: Nathan LabenzEoghan McCabe and Fergal Reid on production A/B tests, 99-cent outcome pricing, and why GPT-4 was already smart enough for most customer service.
15:54ENClaude Sonnet 4.5: Coding, Long Tasks and a Simulated DesktopMatthew Berman, Dario Amodei · Forward FutureMatthew Berman reviews Anthropic’s launch claims and tries a generated-interface demo.
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