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129개정렬
2:12:00ENGradient Descent at Test Time: Titans Pushes LLM Context to 10 Million Tokens진행자 Nathan LabenzA Cornell PhD student's neural memory module holds about 70% accuracy where GPT-4's scores fall off a cliff.
1:14:36ENAndrew Feldman on AI Chips, Inference and Competing With Nvidia게스트 Andrew Feldman · 진행자 Harry StebbingsThe Cerebras CEO explains wafer-scale computing, argues inference demand will grow and discusses the limits of his industry’s infrastructure.
12:10ENMercury Coder and Diffusion Language Models: Speed, Demos and LimitsMatthew Berman · Forward FutureMatthew Berman explores Inception’s 2025 coding model, comparing iterative text refinement with autoregressive generation and examining speed claims.
45:06ENMike Schroepfer: AI’s Bottleneck Is Electricity, and One Tanker of Fusion Fuel Could Power the U.S. for a Year진행자 Jacob EffronMeta’s former CTO tells Redpoint why hyperscaler demand is the climate story, why Llama was an access play, and why tomorrow’s CTO still lives or dies on a priority queue.
1:18:09ENSteeve Morin on AI Inference, Chips and the Cost of Switching Hardware게스트 Steeve Morin · 진행자 Harry StebbingsThe ZML founder explains the difference between training and production inference and offers a competitive view of the AI hardware market.
22:32ENThe $5.5 Million Was Only the Last Mile진행자 Sarah Guo, Elad GilOn No Priors' 100th episode, Sarah Guo and Elad Gil separate DeepSeek's paper cost from the compute behind it, then walk through OpenAI's Deep Research, Stargate, and their bets for 2025.
10:01ENDeepSeek R1 Code Optimization and a $3 Visual Counting ExperimentMatthew Berman · Forward FutureMatthew Berman examines a human-guided coding improvement and a small reinforcement-learning experiment, then considers their implications for AI research.
2:49:35ZH潘嘉怡解读 R1、o1 与 Kimi K1.5:强化学习如何教模型学会推理게스트 潘嘉怡 · 진행자 张小珺伯克利博士生潘嘉怡带读 DeepSeek、OpenAI 与 Kimi 的推理模型报告,拆解长思维链、GRPO、奖励设计与蒸馏。R1-Zero 的“顿悟时刻”展示了推理策略如何在强化学习中涌现。
1:00:02ENJonathan Ross on DeepSeek, Open Models and AI Infrastructure게스트 Jonathan Ross · 진행자 Harry StebbingsThe Groq CEO discusses reported training costs, inference demand, competitive strategy and the security concerns surrounding AI.
1:08:12ENGeorge Sivulka on Hebbia, Enterprise AI and the Future of Work게스트 George Sivulka · 진행자 Harry StebbingsThe Hebbia founder discusses document analysis, inference scaling, enterprise adoption and his forecasts for models and employment.
1:24:57ENA 50,000-GPU cap and a 1,700-chip loophole진행자 Jacob EffronSemiAnalysis founder Dylan Patel walks through the AI diffusion rule, China’s compute squeeze, and how xAI stood up a 100,000-GPU cluster in a closed Memphis appliance factory.
23:37ENA Hundred GPUs in Seconds: Why Modal Threw Out Docker and Kubernetes진행자 Sarah Guo, Elad GilErik Bernhardsson tells Elad Gil that AI inference is too bursty for year-long GPU contracts, and that the cloud should feel as fast as a laptop.
30:29ENHow Researchers Think o1-Style Reasoning Models WorkMatthew Berman · Forward FutureMatthew Berman explains a research survey of policy initialization, rewards, search and reinforcement learning, and the open questions behind reasoning models.
55:50ENThere will not be one model. Fireworks is betting on hundreds of small experts진행자 Jacob EffronPyTorch veteran Lin Qiao argues that inference is a compound system hyperscalers cannot buy their way through, and that the products with real traction still keep a human in the loop.
42:21ENThey Cooled 5,000 Watts per Square Centimeter in Space. Now Diamond Goes on AI Chips진행자 Sarah Guo, Elad GilAkash Systems founders Felix Ejeckam and Ty Mitchell tell Sarah Guo why heat is the real limiter on AI, and how lab-grown diamond bonded to semiconductors is their answer.
44:16ENCohere's Aidan Gomez: The Model Curve Is Flattening, and $20 Million Now Buys GPT-4진행자 Sarah Guo, Elad GilThe Attention Is All You Need co-author tells No Priors why enterprises should stop waiting for the next model and start putting today's models into production.
36:49ENJensen Huang: NVIDIA Is No Longer Building Computers. It Is Building AI Factories진행자 Sarah Guo, Elad GilOn No Priors, NVIDIA’s CEO walks through hyper Moore’s law, NVLink, a 100,000-GPU cluster stood up in weeks, and why tokens are the new commodity.
10:39ENZamba2-7B: A Hybrid Mamba–Transformer Model Tested on Coding and ReasoningMatthew Berman · Forward FutureMatthew Berman compares Zyphra’s efficiency claims with his own informal tests, including Python games, spatial puzzles and simple word tasks.
9:38ENLiquid Foundation Models: Company Benchmarks and a Hands-On TestMatthew Berman · Forward FutureMatthew Berman reviews Liquid AI’s 2024 model and memory claims, then tries the 40B preview on coding, logic, and instruction-following prompts.
2:29:04ENKeep the Programmer in the Driver's Seat: Cursor's Founders on Tab, Agents, and Zero-Entropy Editing게스트 Cursor Team · 진행자 Lex FridmanMichael Truell, Aman Sanger, Sualeh Asif, and Arvid Lunnemark tell Lex Fridman why they forked VS Code, how Tab and Apply actually work, and why most programming still needs a human iterating in real time.
0:54ENOpenAI’s o1 Preview: A First Look at Longer ReasoningMatthew Berman · Forward FutureMatthew Berman introduces the o1-preview and o1-mini models and tries a Python game prompt.
37:47ENWorking Backwards from AGI: Magic's Five-Million-Token Bet진행자 Sarah Guo, Elad GilEric Steinberger wants a coding colleague you trust like a teammate — and a world that is still a good place thirty years after AGI.
18:37ENGameNGen Simulates Doom as AI Coding, Safety and Inference News AccelerateMatthew Berman · Forward FutureMatthew Berman reviews a 2024 game-simulation paper, Cursor, robotics, AI safety debates and new enterprise tools.
0:44ENNVIDIA Adds Multi-LoRA Serving to Its RTX AI ToolkitMatthew Berman · Forward FutureIn an NVIDIA-partnered video, Matthew Berman describes using multiple adapters with one base model and a vendor-reported throughput gain.
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