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54:26ENTen 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.
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 PipelineModeration: 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 ClickbaitModeration: 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 4.5Moderation: 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 PointModeration: 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.Moderation: 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 PointsModeration: 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.
1:28:17ENAdam Gleave's Three Capability Tiers: Attack Agents in 5–7 Years, Fully AI Companies Around Year 14Moderation: Nathan LabenzFAR.AI and the UK AI Security Institute stacked adversarial attacks on frontier safeguard pipelines and found many breakthroughs were engineering failures, not a verdict against defense in depth.
42:20ENA Name Invented at 2:30 a.m. Put Gemini on Top of the App StoreModeration: Jacob EffronGoogle's Nicole Brichtova and Oliver Wang on Nano Banana, why image models got smart when language models did, and why a prompt still is not a finished product.
1:00:35ENMercor CEO Brendan Foody on Expert Data, AI Evaluations and GrowthGast: Brendan Foody · Moderation: Harry StebbingsFoody explains Mercor’s marketplace, reported revenue run rate and views on model development, margins and company culture.
50:00ENCrosby Bills by the Document, Not the Hour, and Turns Contracts Around in Under an HourThe founders of an AI-native law firm explain why they became lawyers with engineers at the next desk instead of shipping legal software, and how deal velocity became the product.
42:12ENHire the AI First: Andrew Ng on Agentic Workflows and the Scarcity of JudgmentModeration: Sarah Guo, Elad GilOn No Priors, Andrew Ng argues that agentic AI has made software cheap and fast, which moves the bottleneck to product judgment, error analysis, and the instinct to try an AI before adding headcount.
30:05EN100 Signups, 95 Drop-offs: How Gamma Turned AI Slides Into a $50 Million BusinessGamma founder Jon Noronha tells Sequoia's Training Data how a park-bench idea, image models, and relentless A/B testing built a cash-flow-positive visual storytelling platform.
1:48:49EN$500, 2,000 Steps, and a Vending Machine: Grok 4 Profited, Claude Called the FBIModeration: Nathan LabenzAndon Labs put frontier models in charge of simulated and real vending machines to see what breaks when nobody is in the loop.
1:10ENTesting a GPT-5-Generated Rubik’s Cube SimulatorMatthew Berman · Forward FutureMatthew Berman prompts GPT-5 to build a Three.js cube simulator and tries its scramble and solve controls at several sizes.
25:24ENGPT-5 Tested on Coding, Simulations and Multimodal TasksMatthew Berman · Forward FutureMatthew Berman tries browser applications, visual simulations and image prompts, showing both successes and defects in his early hands-on tests.
10:28ENOpenAI’s gpt-oss Models: Open Weights, Benchmarks and SafetyMatthew Berman · Forward FutureMatthew Berman reviews the 120b and 20b models, local deployment claims, selected evaluations and the scope of OpenAI’s safety testing.
3:13ENClaude Opus 4.1: Anthropic’s August 2025 Benchmark UpdateMatthew Berman · Forward FutureMatthew Berman reviews the model’s reported coding, reasoning and tool-use results, including one benchmark that declined.
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