필터 결과
24개정렬
2:10:42ENThe Exam Papers Nobody Audits진행자 Nathan LabenzFrontier labs buy reinforcement-learning environments from a cottage industry of small vendors, and almost nobody checks whether those tests match the real work.
36:19ENThe Benchmark Grid Is Broken: Why Test-Time Compute Has Outrun How We Score Models진행자 Sarah Guo, Elad GilOpenAI researcher Noam Brown argues that static leaderboards hide how long a model is allowed to think — and that safety evals, research taste, and the release cycle have not caught up.
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.
32:59ENA Hundred People, a Billion in Revenue, and Zero Venture Money진행자 Sarah Guo, Elad GilSurge AI founder Edwin Chen on why quality human data is a poetry problem, not a bounding-box problem, and why leaderboards keep training models to write clickbait.
47:07ENGoodfire’s Eric Ho thinks we will decode neural nets by 2028On Sequoia’s Training Data, the interpretability founder argues that sparse autoencoders, model editing, and a third-party lab are how AI stops being grown from data and starts being designed.
40:33ENFifty People in Slack. Four Minutes to the Smoking Gun.Traversal's co-founders on turning DevOps heart attacks into agent-run root cause analysis — and why observability's second-largest software spend still leaves troubleshooting as a whodunit.
54:53ENA Free Voting Board Burned $2 Million. Then It Raised $100 Million.진행자 Jacob EffronDatabricks co-founder Ion Stoica explains why LMArena exists, why China is winning open-source models, and why reliability—not another scaling law—is still the thing holding AI back.
44:21ENMercor's CEO: Knowledge Work's Next Job Is Writing Evals진행자 Jacob EffronBrendan Foody tells Redpoint how Mercor turned AI hiring and expert data into a $100 million round — and why he thinks a huge slice of white-collar work will become creating tests for models.
57:11ENA Million Tokens Cannot Write a Good Joke진행자 Jacob EffronPrinceton's Arvind Narayanan on why agent evals mislead, inference scaling hits a wall, and AI looks more like the internet than an overnight revolution.
1:46:41ENFrontier Models Hit the 10th–40th Percentile on Real AI R&D진행자 Nathan LabenzNeev Parikh of METR walks Nathan Labenz through REBench, a seven-task test of whether agents can do machine learning research—and what happened when one of them decided not to train.
1:23:37ENNine of the top ten research ideas were Claude's진행자 Nathan LabenzStanford PhD student Chenglei Si ran a blind study pitting Claude 3.5 Sonnet against more than a hundred NLP researchers on novel research ideas.
38:28ENHalf of Production AI Is RAG. Fine-Tuning Almost Disappeared.진행자 Sarah Guo, Elad GilBraintrust CEO Ankur Goyal tells No Priors what Notion, Zapier, and Vercel actually ship — and which hyped techniques they quietly dropped.
1:54:17ENMetaculus Flagged Covid in January 2020. Now It Is Putting AI on the Forecasting Desk진행자 Nathan LabenzNathan Labenz talks with Metaculus CEO Deger Turan about epistemic security, a year-long bot tournament, and why a useful forecast is harder than an accurate one.
1:56:03ENHumans Near a Perfect Score. The Best AI Still Gets 39%진행자 Nathan LabenzZapier co-founder Mike Knoop and François Chollet put $1 million on ARC-AGI, a 2019 test of sample-efficient intelligence that language models still cannot crack under a one-cent-per-puzzle budget.
55:13ENThe Best AI Scores 39% on This Test. Zapier’s Co-Founder Put $1 Million on Beating ItFrançois Chollet designed ARC to measure skill-acquisition efficiency; five years later humans still crush it and frontier models do not.
26:00ENWhy a Zapier founder thinks AGI research has been stalled for four years진행자 Sarah Guo, Elad GilMike Knoop and François Chollet put more than a million dollars on beating ARC-AGI, the eval they say actually measures general intelligence.
43:45ENEight Years of No Growth, Then OpenAI and NVIDIA Became His Customers진행자 Sarah Guo, Elad GilWeights & Biases CEO Lukas Biewald tells No Priors how a labeling company with no chasm to cross funded the ML developer tools that trained most of the large language models.
44:19ENAI Will Write Ten Times More Code. Someone Still Has to Keep It Running.진행자 Sarah Guo, Elad GilDatadog co-founder Olivier Pomel on why faster generation moves value from writing software to understanding, securing, and operating it.
53:37EN30 models, 42 scenarios, 7 metrics: Percy Liang's public checkup for language models진행자 Sarah Guo, Elad GilA PubMed-only model once led the U.S. medical licensing exam — until a system roughly 200 times larger overtook it.
2:34:20ENIntelligence Is How Efficiently You Acquire Skills You Were Not Prepared For게스트 François Chollet · 진행자 Lex FridmanFrançois Chollet tells Lex Fridman why skill is not intelligence, why GPT-3 is still pattern matching, and why a grid puzzle called ARC still separates children from machines.
1:07:08ENIf the Model Is the Product, the Eval Is the PRD: Inside Mercor's Rise게스트 Brendan Foody · 진행자 Lenny RachitskyMercor CEO Brendan Foody explains why AI labs will pay almost anything for expert-written evals, how his company grew from $1 million to a $400 million run rate in 16 months, and what that means for jobs and founders.
1:16:17ENFaster, But for What? Nicole Forsgren on Measuring Developer Productivity in the AI Era게스트 Nicole Forsgren · 진행자 Lenny RachitskyThe creator of DORA and SPACE explains why most productivity metrics mislead, how AI changes flow and trust, and how to build a developer experience program that proves its value.
1:07:48ENMost Productivity Metrics Are a Lie: Nicole Forsgren on Measuring Developer Experience in the AI Era진행자 Lenny RachitskyThe creator of DORA and SPACE explains why lines of code and old pipeline metrics fall short with AI, and how to start improving developer experience by listening, surveying and tying results to what leaders care about.
1:46:33ENFrom Chaos to a Failure Map: A Hands-On Primer on AI Evals게스트 Hamel Husain, Shreya Shankar · 진행자 Lenny RachitskyHamel Husain and Shreya Shankar walk Lenny Rachitsky through a real error-analysis workflow, from reading traces to building a trustworthy LLM judge.









