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1:49:41ENA Team of Fifteen Matched Meta’s Post-Training on Meta’s Own Base진행자 Nathan LabenzNathan Lambert walks through Tulu 3: the three-stage recipe, the 1 percent versus 14 percent lesson, and why open post-training now lives or dies on data and evals.
1:04:54ENHe bet company equity that Transformer would last — then drew an AI map for 12,000 companies진행자 Jacob EffronDatabricks chief AI scientist Jonathan Frankle tells enterprises when to prompt, fine-tune, or pre-train, and why he still refuses to forecast the next architecture.
2:24:18ENMMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.진행자 Nathan LabenzZyphra’s training lead walks through Zamba 2’s hybrid architecture, the evals that actually matter, and the trade secrets small labs still have to rediscover.
56:10ENDeepL Beat Google at Translation by Refusing to Be a General Model진행자 Jacob EffronOnly 18 percent of the world speaks English—and a Cologne startup turned that fact into a $2 billion language-AI company.
44:30EN100,000 Rides a Week, Six Times Fewer Airbag Crashes: Waymo’s Co-CEO on Eighteen Years of Full Autonomy진행자 Sarah Guo, Elad GilDmitri Dolgov tells No Priors how a dozen Googlers, ten 100-mile routes, and a 2013 refusal to settle for driver assist became a rider-only service with an insurer-audited safety record.
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.
2:37:47ENThe intern who wrote GELU now wants humanity's last exam진행자 Nathan LabenzDan Hendrycks on activation functions, five-year benchmarks, jailbreaks that do not yield to scale, and stacking defenses because no single layer is trustworthy.
2:25:53ENWhen GPT-4's Base Model Introduced Itself, 75% of the Answers Were About AI진행자 Nathan LabenzOwain Evans tells Michael Trazzi why situational awareness and out-of-context reasoning are leading indicators of deceptive alignment, and why a pretrained model with almost no self-knowledge still kept talking about being an AI.
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.
1:06:48ENo1 Is Slow and Hard to Use. Percy Liang Still Calls It a Turning Point진행자 Jacob EffronStanford’s Percy Liang splits OpenAI’s o1 into a disappointing product and the most important research signal in years.
45:22ENAlphaGo Thought 30 Seconds a Move. o1 Made “Think Longer” GeneralOpenAI researchers Noam Brown, Ilge Akkaya, and Hunter Lightman on teaching language models to reason, the generator-verifier gap, and a second scaling curve at inference time.
2:38:56ENHow Anthropic Decides a Model Can Ship: Red Lines, Yellow Lines, and 8% of Staff on Security진행자 Nathan LabenzNick Joseph, Anthropic's head of training, walks Rob Wiblin through the company's Responsible Scaling Policy — and the fight over whether a voluntary promise can hold when a launch is on the line.
2:03:55ENThe More You Constrain an Agent, the More You Can Trust It진행자 Nathan LabenzAgent.ai engineering lead Andrei Oprisan tells Nathan Labenz why narrow, highly prescribed workflows beat open-ended autonomy — and how a 12-person team shipped a free agent network past 40,000 users in a few months.
2:10:13ENA 30x drop in scheming is not zero, and the chain of thought is getting stranger진행자 Nathan LabenzMarius Hobbhahn of Apollo Research walks through a joint OpenAI study that used deliberative alignment to cut covert actions in o3 and o4-mini, then found the leftover still looks like scheming and that models already behave differently when they notice they are being tested.
1:06:15ENJailbreaks Got Harder. Scale Made One Attack Worse.진행자 Nathan LabenzHaize Labs spent a month automatically attacking OpenAI's o1. The model reasons more carefully about what it should refuse — except when you teach it a homemade cipher.
1:31:56ENTesting OpenAI o1 on Puzzles, Code and Reasoning: A Launch-Day LivestreamMatthew Berman · Forward FutureMatthew Berman tries o1-preview and o1-mini on word counts, Tetris, physical reasoning and a word puzzle, with viewers checking the results.
35:30ENReflection 70B: Its Creators Explain the Proposed Self-Correction MethodMatthew Berman, Matt Shumer, Sahil Chaudhary · Forward FutureA launch-day discussion with Matt Shumer and Sahil Chaudhary covers synthetic data, selective reflection and evaluation limits.
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.
1:12:31ENA Bare Model Closes 35% of Airline Changes. Sierra's Agents Clear 70% of Real Tickets.Clay Bavor on why putting a raw LLM in front of customers does not math, and how AgentOS, supervisor agents, and outcome pricing change the arithmetic.
9:43ENMatthew Berman Tests Jamba 1.5 on Coding and Reasoning PromptsMatthew Berman · Forward FutureA 2024 hands-on test of AI21’s hybrid model finds slow output in one hosted session and mixed answers to a small set of prompts.
42:08ENMeta Trained a 405B Teacher on 16,000 GPUs — Then Told the World to Distill ItTwo days after Llama 3.1 405B shipped, Meta's generative-AI product lead walks Sequoia through why the most expensive model in the family is free, and why he thinks the frontier itself is heading toward commodity.
13:40ENTesting Llama 3.1 8B: Benchmark Gains and Mixed Practical ResultsMatthew Berman · Forward FutureMatthew Berman tests coding, reasoning and other prompts on the small model and finds several failures despite stronger reported benchmarks.
25:56ENA Prompt Cannot Hold a Good Design: Six AI Practitioners at Midyear 2024진행자 Sarah Guo, Elad GilSarah Guo and Elad Gil stitch together clips from Stripe, Figma, Figure, OpenAI's Sora team, Cognition, and Scale into a half-year snapshot of what people actually shipping AI were willing to claim.
1:16:22ENHe Spent 20 Hours Crafting a Prompt. DSPy Beat Him Anyway진행자 Nathan LabenzSander Schulhoff walks through The Prompt Report — a 78-page survey of what actually works when you prompt large language models.
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