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1:23:09ENThe Right Size of an AI Task Is Whatever You Can CheckPresenta: Nathan LabenzElicit founders Andreas Stuhlmüller and Jungwon Byun walk through how a twelve-person research assistant cut summary hallucinations from 1.5 to 0.5, ships about every week and a half, and treats task decomposition as the way to spend more compute without a black box.
1:28:37ENTwelve Hours After a GPT-4 Demo, He Turned a Decade-Old Legal Startup AroundPresenta: Nathan LabenzPablo Arredondo, co-founder of CaseText and now VP of CoCounsel at Thomson Reuters, traces legal research from 19th-century citators to a retrieval-anchored GPT-4 product that law firms treat more like a colleague than a search box.
42:14ENMeta’s $197 Billion Day Was a Partial Answer to AI’s $200 Billion QuestionPresenta: Sarah Guo, Elad GilSarah Guo and Elad Gil walk through Gemini, Mistral, NVIDIA’s upgrade cycle, Klarna’s support bot, and why the real AI spend still sits with the hyperscalers.
Google Gives You 33 Million Results. Exa Wants 54.Presenta: Nathan LabenzWill Bryk, CEO of Exa, tells Nathan Labenz why search and research are different jobs, and why an AI librarian for the web has to be neural from the first filter.
1:32:19ENHis 2018 Prompting Paper Was Rejected. His Search API Now Beats GooglePresenta: Nathan LabenzRichard Socher traces You.com from a rejected unified NLP model to an LLM-native web index, three assistant modes, and a very low P(doom).
31:28ENBolt Memory onto GPT-4 and Cut Hallucinations by up to 50%Presenta: Sarah Guo, Elad GilPinecone CEO Edo Liberty tells Sarah Guo and Elad Gil how RAG, serverless vectors, and keeping knowledge outside the model make LLMs more reliable.
42:07ENNotion’s Ivan Zhao: RAG Will Make Organizing OptionalPresenta: Sarah Guo, Elad GilThe Notion CEO tells No Priors why he bet the company on GPT-4, why embeddings matter as much as agents, and why software is entering a bundling cycle.
46:36ENAfter a decade of code search, Sourcegraph’s CTO says the coding-AI fight is outside the modelPresenta: Sarah Guo, Elad GilBeyang Liu on Cody, why keyword search still does most of RAG, and why CS fundamentals get more valuable as AI compresses the middleware.
1:53:08ENWhen GPT-4 Writes SQL, Four Tables Is the Breaking PointPresenta: Nathan LabenzJuan Sequeda on why a knowledge graph is the accurate, explainable brain that foundation models do not have.
1:50:57ENTwenty-Eight Days After GPT-4, an AI Designed and Ran a Chemistry ExperimentPresenta: Nathan LabenzGabe Gomes and his Carnegie Mellon students built Coscientist, a language-model agent that plans, codes, and executes real experiments on a remote cloud lab.
1:32:40ENThe Historian Who Taught GPT-3.5 to Beat GPT-4Presenta: Nathan LabenzMark Humphries on a kilometer of unread paper, fur-trade jargon that embeddings cannot see, and why cheap processing will force history to be rewritten.
38:07ENAutoGen Advanced Tutorial: Configuration, Memory, Retrieval and ToolsMatthew Berman · Forward FutureMatthew Berman walks through the 2023 AutoGen interface for multi-agent configuration, caching, retrieval, teachable memory and function calls.
1:26:30ENMost of this data has never been in a database: Chroma's wartime CTO on RAGPresenta: Nathan LabenzAnton Troynikov returns to The Cognitive Revolution to argue that retrieval quality is mostly the embedding, that Chroma should own the RAG loop, and that GPT still fails a basic Game of Life test of reasoning.
32:14ENMemGPT: Managing LLM Memory Beyond a Fixed Context WindowMatthew Berman, Charles Packer, Vivian Fang · Forward FutureMatthew Berman reviews the 2023 research, demonstrates document retrieval and hears from authors Charles Packer and Vivian Fang about their plans.
1:19:52EN1,000 H100s Take 115 Days; 22,000 Take FivePresenta: Nathan LabenzNathan Labenz and Trey Kollmer walk through cluster math that turns GPT-4-scale training into a weekly experiment, then four papers on search-augmented models, recursive code improvers, spatial maps inside Llama 2, and what networks do when they have no idea.
1:23:10ENLuxury Purses Under $500 Beat a Bank's Fraud ModelPresenta: Nathan LabenzArthur.ai CEO Adam Wenchel on boundary attacks, LLM firewalls, and why a model that eats raw text cannot be secured from the inside.
1:40:44ENGive the Model Time, Memory, and Tools: Nathan Labenz’s 2023 Field Guide to Using GPT-4Presenta: Nathan LabenzPart two of the AI Scouting Report walks from H100 clusters and RLHF through chain-of-thought, retrieval, agents, and mixture-of-experts.
54:00ENDouwe Kiela on Data, Retrieval and Enterprise AIInvitado: Douwe Kiela · Presenta: Harry StebbingsThe Contextual AI CEO discusses language model trade-offs, evaluation, open models and the hurdles to enterprise use.
59:36ENPascal Pfeiffer on Retrieval, Open Models, Kaggle and Fine-Tuning with H2O LLM StudioMatthew Berman, Pascal Pfeiffer · Forward FutureIn a 2023 interview, H2O.ai’s Pascal Pfeiffer explains document retrieval and parameter-efficient fine-tuning, reflects on Kaggle competitions and demonstrates LLM Studio. He and Matthew Berman also consider coding assistants and unverified rumors about model architecture.
52:10ENJames Briggs on Vector Databases, Retrieval and AI MemoryMatthew Berman, James Briggs · Forward FuturePinecone’s developer advocacy manager explains embeddings and external knowledge, recounts the surge after ChatGPT and AutoGPT, and considers long context windows.
37:18ENUse Retrieval for a Wi-Fi Password. Use Experts for a Company's Private Language.Presenta: Sarah Guo, Elad GilGoogle Brain's Kelvin Guu on REALM, FLAN, model surgery, and why personalizing large models is still an unsolved interface problem.
1:56:16ENThey Used Humans as Stand-In AIs. Now 250,000 People Run Elicit.Presenta: Nathan LabenzOught's founders tell Nathan Labenz why open-ended reasoning has to be decomposed, supervised, and grounded—not scaled until it merely looks persuasive.
35:36ENFeatured snippets stalled at 7%. A Google veteran used language models to hit 70%Presenta: Sarah Guo, Elad GilNeeva founder Sridhar Ramaswamy tells No Priors why answers always beat links, why privacy never sold the switch, and why most queries do not need the biggest model.
1:44:19ENTen People Versus Google’s $282 Billion Search MachinePresenta: Nathan LabenzPerplexity founder Aravind Srinivas tells Nathan Labenz why citations, speed, and value per minute — not chat bubbles — are how a tiny team competes with Google.
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