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7 slides35:19EN
AI Engineer· Oct 25, 2026SallyAnn 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.Evals & observabilityEvalsAgent engineering+2
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Cognitive Revolution "How AI Changes Everything"· Aug 28, 2026PodcastThe Exam Papers Nobody AuditsHost 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.Evals & observabilityEvalsReward hacking+3
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No Priors: AI, Machine Learning, Tech, & Startups· Jun 26, 2026Podcast · 2 speakersThe Benchmark Grid Is Broken: Why Test-Time Compute Has Outrun How We Score ModelsHost 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.Evals & observabilityEvalsBenchmarks+3
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Unsupervised Learning: With Jacob Effron· Dec 15, 2025PodcastA Two-Second Vote Is Training Models to Be ClickbaitHost 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.Evals & observabilityRL environments & simulatorsData quality & curation+3
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No Priors: AI, Machine Learning, Tech, & Startups· Jul 24, 2025Podcast · 2 speakersA Hundred People, a Billion in Revenue, and Zero Venture MoneyHost 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.Evals & observabilityData quality & curationHuman-in-the-loop validation+3
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Sequoia Capital· Jul 8, 2025PodcastGoodfire’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.Evals & observabilityModel InterpretabilitySafety & governance+3
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Sequoia Capital· Jun 24, 2025PodcastFifty 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.Evals & observabilityObservability & reliabilityAgent engineering+3
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Unsupervised Learning: With Jacob Effron· Jun 17, 2025PodcastA Free Voting Board Burned $2 Million. Then It Raised $100 Million.Host 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.Evals & observabilityEvalsBenchmarks+3
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Unsupervised Learning: With Jacob Effron· Jun 4, 2025PodcastMercor's CEO: Knowledge Work's Next Job Is Writing EvalsHost 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.Evals & observabilityEvalsData quality & curation+3
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Unsupervised Learning: With Jacob Effron· Jan 30, 2025PodcastA Million Tokens Cannot Write a Good JokeHost 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.Evals & observabilityEvalsBenchmarks+3
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Cognitive Revolution "How AI Changes Everything"· Dec 21, 2024PodcastFrontier Models Hit the 10th–40th Percentile on Real AI R&DHost 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.Evals & observabilityEvalsBenchmarks+3
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Cognitive Revolution "How AI Changes Everything"· Oct 23, 2024PodcastNine of the top ten research ideas were Claude'sHost 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.Evals & observabilityEvalsBenchmarks+3
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No Priors: AI, Machine Learning, Tech, & Startups· Oct 8, 2024Podcast · 2 speakersHalf of Production AI Is RAG. Fine-Tuning Almost Disappeared.Host Sarah Guo, Elad GilBraintrust CEO Ankur Goyal tells No Priors what Notion, Zapier, and Vercel actually ship — and which hyped techniques they quietly dropped.Evals & observabilityEvalsRetrieval-augmented generation+3
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Cognitive Revolution "How AI Changes Everything"· Aug 28, 2024PodcastMetaculus Flagged Covid in January 2020. Now It Is Putting AI on the Forecasting DeskHost 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.Evals & observabilityEvalsBenchmarks+3
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Cognitive Revolution "How AI Changes Everything"· Jul 13, 2024PodcastHumans Near a Perfect Score. The Best AI Still Gets 39%Host 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.Evals & observabilityAGIBenchmarks+3
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Sequoia Capital· Jul 2, 2024PodcastThe 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.Evals & observabilityBenchmarksAGI Timelines+3
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No Priors: AI, Machine Learning, Tech, & Startups· Jun 11, 2024Podcast · 2 speakersWhy a Zapier founder thinks AGI research has been stalled for four yearsHost 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.Evals & observabilityAGIBenchmarks+3
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No Priors: AI, Machine Learning, Tech, & Startups· Aug 3, 2023Podcast · 2 speakersEight Years of No Growth, Then OpenAI and NVIDIA Became His CustomersHost 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.Evals & observabilityObservability & reliabilityDeveloper workflows & testing+3
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No Priors: AI, Machine Learning, Tech, & Startups· Jun 15, 2023Podcast · 2 speakersAI Will Write Ten Times More Code. Someone Still Has to Keep It Running.Host Sarah Guo, Elad GilDatadog co-founder Olivier Pomel on why faster generation moves value from writing software to understanding, securing, and operating it.Evals & observabilityObservability & reliabilityDeveloper productivity+3
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No Priors: AI, Machine Learning, Tech, & Startups· Mar 9, 2023Podcast · 2 speakers30 models, 42 scenarios, 7 metrics: Percy Liang's public checkup for language modelsHost Sarah Guo, Elad GilA PubMed-only model once led the U.S. medical licensing exam — until a system roughly 200 times larger overtook it.Evals & observabilityAI researchFoundation Models+3
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Lex Fridman· Aug 30, 2020Podcast · 2 speakersIntelligence Is How Efficiently You Acquire Skills You Were Not Prepared ForGuest François Chollet · Host 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.Evals & observabilityBenchmarksAI research+3
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Lenny's PodcastPodcast · 2 speakersIf the Model Is the Product, the Eval Is the PRD: Inside Mercor's RiseGuest Brendan Foody · Host 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.Evals & observabilityEvalsData quality & curation+3
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Lenny's PodcastPodcast · 2 speakersFaster, But for What? Nicole Forsgren on Measuring Developer Productivity in the AI EraGuest Nicole Forsgren · Host 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.Evals & observabilityDeveloper productivityEvals+3
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Lenny's PodcastPodcastMost Productivity Metrics Are a Lie: Nicole Forsgren on Measuring Developer Experience in the AI EraHost 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.Evals & observabilityDeveloper productivityDeveloper workflows & testing+3
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