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261개정렬
39:48EN400,000 Customers, 20,000 APIs: SAP's CTO on the Hard Exam of Enterprise AI진행자 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 Superintelligence진행자 Nathan LabenzA conversation on recursive self-improvement, public warning systems, and using each generation of AI to align and control the next.
25:15ENMatthew 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.
1:13ENMemPalace 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.
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 Up진행자 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 Together진행자 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 AI진행자 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-Ready진행자 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.
0:25ENGemini 3.1 Pro: Four Benchmark Results from Its 2026 LaunchMatthew Berman · Forward FutureMatthew Berman reviews reported academic, abstract-reasoning and coding scores; the chart distinguishes ARC-AGI-2 from GPQA Diamond.
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.
0:53ENClaude Opus 4.6: Coding Claims, Agent Evaluation, and a Longer ContextMatthew Berman · Forward FutureMatthew Berman discusses Anthropic’s February 2026 model launch and a chart of AI task horizons.
1:57:56ENA Hundred Million Lines at Twenty Cents Each: Inside Blitzy's Enterprise Coding Pipeline진행자 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.
17:44ENRecursive Language Models: Extending Long-Input Work Beyond a Context WindowMatthew Berman · Forward FutureMatthew Berman examines an MIT research approach that lets language models inspect external text and make sub-calls, along with its benchmark and cost tradeoffs.
48:00ENA Two-Second Vote Is Training Models to Be Clickbait진행자 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.
1:54:33ENCreative Agents, AI Evaluations and Enterprise Trust: Forward Future LiveMatthew Berman, Nick Wentz, Fabian Stelzer, Tejal Patwardhan, Christina Cacioppo, Jeremy Epling, Spenser Skates · Forward FutureLeaders from Glif, OpenAI, Vanta and Amplitude discuss creative workflows, knowledge-work benchmarks and practical AI adoption in December 2025.
7:14ENAWS AgentCore Adds Policy Controls, Evaluations and Episodic MemoryMatthew Berman · Forward FutureMatthew Berman examines AWS’s December 2025 agent-platform announcements and the enterprise problems they aim to address.
42:06ENAfter SWE-bench Hits 80.9%, Dianne Na Penn Reckons with Opus 4.5진행자 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 Point진행자 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.
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