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1:44:23EN
Cognitive Revolution "How AI Changes Everything"· 2024년 3월 16일팟캐스트You Can Split Your Vote by Issue — and Hand It to an AI진행자 Nathan LabenzTim Reutemann’s 2018 novel Liquid Reign sketches a 2051 democracy built on Swiss citizen initiatives, liquid delegation, and personal AIs you can mute.
1:01:53ENCognitive Revolution "How AI Changes Everything"· 2024년 2월 13일팟캐스트Companion Apps Open Twice as Often as TikTok. a16z Only Funds Weird but Working진행자 Nathan Labenza16z consumer partners Anish Acharya and Justine Moore walk Nathan Labenz through their Abundance Agenda for AI-native consumer apps.
1:15:39ENCognitive Revolution "How AI Changes Everything"· 2023년 11월 29일팟캐스트After 20 Years of Frontend, v0 Wrote Better Code Than Guillermo Rauch Did진행자 Nathan LabenzThe Vercel CEO tells Nathan Labenz why every product needs ghost text, why Software 2.0 will ossify the old stack, and why betting against open source is still the expensive mistake.
1:25:11ENCognitive Revolution "How AI Changes Everything"· 2023년 10월 26일팟캐스트105 lines of code, a million impressions, and a VC who refused to found the company진행자 Nathan LabenzBabyAGI creator Yohei Nakajima tells Nathan Labenz how a weekend prototype set off the agent wave — and why he went back to writing checks.
55:13ENCognitive Revolution "How AI Changes Everything"· 2023년 9월 7일팟캐스트Two Days to a Prototype: How a16z Turned a Stanford Paper Into AI Town진행자 Nathan LabenzA companion named Alice, a weekend Phaser hack, and Martin Casado's argument that models will never replace formal languages.
1:01:09ENCognitive Revolution "How AI Changes Everything"· 2023년 8월 31일팟캐스트Hugging Face Raised $235M. Community Still Is Not a Moat진행자 Nathan LabenzNathan Labenz and Erik Torenberg unpack Hugging Face's $4.5 billion Series D, and why a thriving open-source hub may still struggle to become a locked-in business.
1:39:53ENCognitive Revolution "How AI Changes Everything"· 2023년 8월 8일팟캐스트Under 100 People, 24 Million Users: Replit's Bet on Artificial Developer Intelligence진행자 Nathan LabenzMichele Catasta, Replit's new VP of AI, lays out a generate-execute-learn loop he will not call AGI, a 2x-to-1000x productivity ladder, and a keystroke-level data advantage.
5:53ENPetals: Distributing Large Language Model Inference Across DevicesMatthew Berman · Forward FutureIn 2023, Matthew Berman explains Petals as a way to use large models through a network of participating computers rather than loading every layer on one device.
1:32:22ENCognitive Revolution "How AI Changes Everything"· 2023년 6월 29일팟캐스트A Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer Away진행자 Nathan LabenzLili Yu of Meta AI explains a multiscale transformer that predicts million-byte sequences, skips tokenization, and aims to model any file as raw bytes.
1:26:20ENCognitive Revolution "How AI Changes Everything"· 2023년 3월 2일팟캐스트Forty Dollars to Index Joe Rogan: Chroma, Shovels, and the Wright Flyer Era of ML진행자 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.
2:54:52EN
Lex Fridman· 2021년 8월 8일팟캐스트The Marvelous Proof Still Lives in the Margin진행자 Lex FridmanLuís and João Batalha, the brothers behind Fermat's Library, talk with Lex Fridman about hidden backstories, a publishing system that buys its own work twice, and how to sit with a paper until it finally clicks.
17:19ENLex Fridman· 2020년 3월 17일팟캐스트Money Is a Game of Points: Vitalik Buterin on Coordination, Public Goods, and Quadratic Funding진행자 Lex FridmanVitalik Buterin explains money as a social point system, why value can survive without physical backing, and how quadratic funding tries to fix public-good underfunding.
1:13:51ENLex Fridman· 2020년 1월 19일팟캐스트Answering Questions Without Seeing the Data: Andrew Trask on the Privacy-Preserving AI Stack진행자 Lex FridmanA tour of remote execution, differential privacy, secure multiparty computation, and the societal consequences of making private data usable.
1:06:39ENWhy Jane Street Uses OCamlA Jane Street talk explains how functional programming and static types support reliable trading software, and examines the costs of choosing a less common language.
1:05:22EN
Unsupervised Learning: With Jacob Effron팟캐스트Build First, Optimize Later: Harrison Chase on LangChain, Evals, and the State-Machine Future of Agents진행자 Jacob EffronHarrison Chase explains how LangChain grew from an orchestration layer into LangSmith and LangGraph, why evaluation datasets force product clarity, and why reliable agents look more like state machines than autonomous loops.
1:11:06ENUnsupervised Learning: With Jacob Effron팟캐스트Replit's Amjad Masad on Building Your Own Model, Latency, and the Next Billion Software Creators진행자 Jacob EffronThe Replit founder explains why AI should be native to the product, why he trained a small in-house model, and why he thinks software engineering is about to split in two.
40:00ENUnsupervised Learning: With Jacob Effron팟캐스트The Winds of Change: How Mistral Plans to Win the Open-Model Era진행자 Jacob EffronArthur Mensch explains Mistral's open-source strategy, enterprise distribution, model research, and views on AI regulation and sovereignty.
1:12:15ENUnsupervised Learning: With Jacob Effron팟캐스트The Open-Model Vibe Check: China, Containment, and the Data Moat진행자 Jacob EffronJacob Effron and guests debate Chinese open-weight models, distillation, containment policy, the OpenAI–Hugging Face incident, and Cursor's data advantage.
1:03:25ENLex Fridman팟캐스트Theano in an Hour: Symbolic Graphs, Automatic Gradients and a Compiler That Rewrites Your Math진행자 Lex FridmanPascal Lamblin of MILA walks through how Theano builds, differentiates, optimizes and runs computation graphs, then trains logistic regression, a convnet and an LSTM.
50:23ENUnsupervised Learning: With Jacob Effron팟캐스트OpenAI’s Platform Bet: Where AI Value Accrues, Why Open Source Trails, and When Superintelligence Arrives진행자 Jacob EffronPeter Welinder explains OpenAI’s platform strategy, the case for proprietary frontier models over open source, the enterprise hallucination problem, and his speculative timeline for AGI and superintelligence.
1:02:06ENLex Fridman팟캐스트Learning TensorFlow Hands-On: From a Mystery Line to Handwritten Digits진행자 Lex FridmanGoogle Brain's Sherry Moore walks a Deep Learning School audience through how TensorFlow works and two core labs, linear regression and MNIST digit recognition.