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1:19:52EN1,000 H100s Take 115 Days; 22,000 Take FiveModeration: 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 ModelModeration: 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-4Moderation: Nathan LabenzPart two of the AI Scouting Report walks from H100 clusters and RLHF through chain-of-thought, retrieval, agents, and mixture-of-experts.
9:55ENBuilding AI Characters With the a16z Companion AppMatthew Berman · Forward FutureMatthew Berman walks through a 2023 starter project for conversational characters, from service configuration and embeddings to editing a companion’s personality.
1:56:16ENThey Used Humans as Stand-In AIs. Now 250,000 People Run Elicit.Moderation: 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.
5:08ENChatting with a PDF in Colab Using LangChain and a Vector IndexMatthew Berman · Forward FutureMatthew Berman demonstrates a 2023 PDF question-answering notebook; its retrieval workflow does not fine-tune model weights and its revenue example is incorrect.
1:44:19ENTen People Versus Google’s $282 Billion Search MachineModeration: 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.
1:26:20ENForty Dollars to Index Joe Rogan: Chroma, Shovels, and the Wright Flyer Era of MLModeration: Nathan LabenzChroma co-founder Anton Troynikov on embeddings as the storage layer for language-model apps, Stable Attribution, and why machine learning is still flying on intuition.
1:36:57ENDo the Dumb Thing First: A CTO's Playbook for Building an AI Coding AssistantModeration: Jacob EffronSourcegraph CTO Beyang Liu on which AI coding features actually earn daily use, how new models get shipped and evaluated, and why retrieval is still a search problem.
57:25ENFrom RAG to Reasoning: Why the Future of AI Is a System, Not a ModelModeration: Jacob EffronDouwe Kiela explains what OpenAI's o1 reveals about test-time compute, why enterprise AI must be specialized, and where post-training and evaluations are heading.
51:55ENThe Search Engine Under AI Apps: Why Bigger Context Windows Aren't EnoughModeration: Jacob EffronTurbopuffer CEO Simon on object-storage databases, vector-search trade-offs, and the AI features that will become SaaS table stakes.
46:48ENSnowflake's AI Stack: Small Models, the Text-to-SQL Iceberg, and Why Governance Comes FirstModeration: Jacob EffronSnowflake head of AI Baris Gekken explains how Arctic and Cortex were built, where enterprise AI actually works today, and why precision, governance and evaluation matter more than raw model capability.
1:06:22ENPerplexity's Bet: Search Becomes Answers, and Answers Become a BusinessModeration: Jacob EffronThe Perplexity CEO explains how a consumer AI search product is built, sequenced, and defended against Google.
1:01:13ENPinecone's Vector Database Bet: Scaling Through the ChatGPT TsunamiModeration: Jacob EffronEdo Liberty explains how Pinecone scaled through the generative AI boom, why vector search is more than a database add-on, and where he sees the next AI infrastructure shifts.









