필터 결과
24개정렬
35:19EN
AI Engineer· 2026년 10월 25일SallyAnn DeLucia: How to Write Your First AI EvalSallyAnn DeLucia · Arize AITurn an agent’s real traces into concrete pass/fail criteria, then build an LLM judge you can test, align with people, and run in production.
2:10:42EN
Cognitive Revolution "How AI Changes Everything"· 2026년 8월 28일팟캐스트The 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.
36:19EN
No Priors: AI, Machine Learning, Tech, & Startups· 2026년 6월 26일팟캐스트 · 2명The 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.
48:00EN
Unsupervised Learning: With Jacob Effron· 2025년 12월 15일팟캐스트A 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.
32:59ENNo Priors: AI, Machine Learning, Tech, & Startups· 2025년 7월 24일팟캐스트 · 2명A 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.
47:07EN
Sequoia Capital· 2025년 7월 8일팟캐스트Goodfire’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.
40:33ENSequoia Capital· 2025년 6월 24일팟캐스트Fifty 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.
54:53ENUnsupervised Learning: With Jacob Effron· 2025년 6월 17일팟캐스트A 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.
44:21ENUnsupervised Learning: With Jacob Effron· 2025년 6월 4일팟캐스트Mercor'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.
57:11ENUnsupervised Learning: With Jacob Effron· 2025년 1월 30일팟캐스트A 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.
1:46:41ENCognitive Revolution "How AI Changes Everything"· 2024년 12월 21일팟캐스트Frontier 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.
1:23:37ENCognitive Revolution "How AI Changes Everything"· 2024년 10월 23일팟캐스트Nine 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.
38:28ENNo Priors: AI, Machine Learning, Tech, & Startups· 2024년 10월 8일팟캐스트 · 2명Half 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.
1:54:17ENCognitive Revolution "How AI Changes Everything"· 2024년 8월 28일팟캐스트Metaculus 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.
1:56:03ENCognitive Revolution "How AI Changes Everything"· 2024년 7월 13일팟캐스트Humans 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.
55:13ENSequoia Capital· 2024년 7월 2일팟캐스트The 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.
43:45ENNo Priors: AI, Machine Learning, Tech, & Startups· 2023년 8월 3일팟캐스트 · 2명Eight 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.
44:19ENNo Priors: AI, Machine Learning, Tech, & Startups· 2023년 6월 15일팟캐스트 · 2명AI 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.
53:37ENNo Priors: AI, Machine Learning, Tech, & Startups· 2023년 3월 9일팟캐스트 · 2명30 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.
2:34:20EN
Lex Fridman· 2020년 8월 30일팟캐스트 · 2명Intelligence 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.
1:07:08EN
Lenny's Podcast팟캐스트 · 2명If 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.
1:16:17ENLenny's Podcast팟캐스트 · 2명Faster, 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.
1:07:48ENLenny's Podcast팟캐스트Most 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.
1:46:33ENLenny's Podcast팟캐스트 · 3명From 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.