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슬라이드 2장32:20EN
MBMatthew Berman· 2026년 10월 23일Who Is Local AI For? The Costs, Trade-offs and a Hybrid FutureMatthew Berman · Forward FutureMatthew Berman weighs data control and independence against hardware cost, setup complexity and inference speed.Local inferenceOpen-weight modelsAI cost & performance+1
1:04:14EN
Unsupervised Learning: With Jacob Effron· 2026년 7월 31일팟캐스트The Man Who Helped Stand Up Colossus in 120 Days Is Betting on an AI That Lives in Your House진행자 Jacob EffronxAI co-founder Igor Babuschkin left to start River AI, arguing coding agents have crossed a threshold, closed labs are getting squeezed, and the next prize is personal, local intelligence.에이전트AI startups & small teamsCompany Building+3
1:47:38EN
Cognitive Revolution "How AI Changes Everything"· 2026년 7월 4일팟캐스트19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every Chip진행자 Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.모델과 학습Local inferenceScaling Laws+3
1:32:38EN
Cognitive Revolution "How AI Changes Everything"· 2025년 12월 6일팟캐스트Polish Is 1% of Frontier Training Data, So Poland Built Its Own Model진행자 Nathan LabenzMarek Kozlowski of Poland's National Information Processing Institute explains how PLLuM uses language adaptation, organic instructions, and on-prem small models to keep a mid-size country in control of its AI.기업과 전략Open-weight modelsData quality & curation+3
2:24:18EN
Cognitive Revolution "How AI Changes Everything"· 2024년 10월 30일팟캐스트MMLU 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.모델과 학습Local inference평가(Evals)+3
1:16:11EN
Cognitive Revolution "How AI Changes Everything"· 2024년 7월 11일팟캐스트Scooping Food Into Boxes Is 70 Percent of Plant Labor진행자 Nathan LabenzChef Robotics CEO Rajat Bhageria on 20 million plated meals, a foundation model for food, and why the kitchen may leave the house.RoboticsData quality & curationLocal inference+3
34:09EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024년 6월 27일팟캐스트 · 2명Five Independent Teams Found the Same 10-to-1 Mix That Beats Transformers진행자 Sarah Guo, Elad GilThe authors of S4 and Mamba argue that mostly state-space layers plus a dash of attention is the architecture that actually works—and they are shipping it first as real-time speech.모델과 학습Transformers & attention음성 AI+3
29:09EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024년 5월 9일팟캐스트 · 2명The Hyperscalers Are Spending $200 Billion — and the Next Bottleneck Is a Power Plant진행자 Sarah Guo, Elad GilSarah Guo and Elad Gil walk through music models, on-device LLMs, long context, and why AI capex now sits beside oil and broadband.인프라와 배포AI cost & performance컨텍스트 엔지니어링+3
38:33EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024년 3월 21일팟캐스트 · 2명Speed Is the Thing You Can Buy: Baseten’s Bet on Inference진행자 Sarah Guo, Elad GilTuhin Srivastava tells Sarah Guo and Elad Gil why a six-person chatbot company running a billion tokens a day should not be building its own serving stack.인프라와 배포InferenceAI cost & performance+3
1:18:48EN
Lex Fridman팟캐스트Data Movement, Not Math: How to Make Deep Learning Fit on Robots and Phones진행자 Lex FridmanMIT professor Vivienne Sze explains why moving data dominates the energy cost of neural networks and how co-designing hardware and algorithms can cut it by orders of magnitude.인프라와 배포InferenceAI cost & performance+3