絞り込み結果
10 件並び順
1:50:48EN
Y Combinator· 2026年10月24日Why Inference Matters 12× More Than TrainingFrançois Chaubard, Dan Fu, Jeff Morgan, Joe Fioti, Liangsheng Yin · Y CombinatorYC Paper Club walks the inference stack from an API request down to GPU kernels. Four talks from Together AI, Ollama, Luminal, and SGLang argue that serving quality, not just training FLOPs, is where most of the compute and the money now sit.
34:35EN
SemiAnalysis· 2026年10月23日ポッドキャスト · 3 人SemiAnalysis on ASML's Hybrid-Bonding Patents, HBM Economics and the Next Tool Ecosystemゲスト Sherry Zhang, Howie · ホスト Jordan NanosJordan Nanos, Sherry Zhang and Howie examine a track-based bonding design that brings the wafer to the die, then explain why adoption depends on precision, cleanliness, economics and a changing supply chain.
1:00:39EN
a16z· 2026年10月23日ポッドキャスト · 3 人Amazon CEO Andy Jassy on the $220 Billion AI Buildoutゲスト Andy Jassy · ホスト Ben Horowitz, Erik TorenbergEven at roughly $220 billion of 2026 CapEx, Jassy says Amazon is still turning away AI demand. He tells Ben Horowitz and Erik Torenberg why AWS could become a trillion-dollar run-rate business, and why inference cost, custom chips, and smaller teams matter more than squeezing old customers.
1:09:00EN
Dwarkesh Patel· 2026年10月22日ポッドキャスト · 9 人Dwarkesh Patel: What will computer chips look like in 2045?ゲスト Chris Auth, Kevin Fischer, Jack Kavalieros, Tyler Osborn, Pushkar Ranade, Gary Dickerson, Peter Wennink, Elon Musk · ホスト Dwarkesh PatelAfter two days inside Intel’s most advanced fab, Dwarkesh Patel walks through gate-all-around, backside power, High-NA EUV, ruthenium, CFET, and hybrid bonding — and asks Intel’s leadership how the industry can possibly meet AI’s 3×-a-year compute demand when the world’s chipmaking tools cannot.
56:49EN
Stanford AI Club· 2026年10月15日Alfred Lin on Building in the AI Era: The Diffusion Gap, Founder Conviction, and Fire and IceAlfred Lin, Jason Zhang, Tanvir Bhathal · Sequoia CapitalAt Stanford AI Club, Sequoia’s Alfred Lin explains where he sees opportunity in AI and why learning, human understanding, and operating discipline still shape enduring companies.
31:00EN
AI Explained· 2026年10月12日Why AI Labs Keep Racing Despite Their Own Warnings of a Major BreachSam Altman, John Schulman · OpenAIAI Explained examines weakening safeguards, the incentives behind the frontier race, and why research breakthroughs make the next 18 months difficult to forecast.
38:10EN
Google DeepMind· 2026年10月1日From deepfakes to DNA: how SynthID watermarks AIHannah Fry, Pushmeet Kohli, Jeremy Ratcliffe · Google DeepMindHannah Fry sits down with Pushmeet Kohli and Jeremy Ratcliffe to explain SynthID: a mathematical watermark now used on text, images and video, and being extended to protein sequences so DNA synthesis companies can tell AI-designed molecules from nature.
1:35:39EN
Lenny's Podcastポッドキャスト · 3 人Make Time: Pick a Daily Highlight and Build Barriers Against Distractionゲスト Jake Knapp, John Zeratsky · ホスト Lenny RachitskyJake Knapp and John Zeratsky walk through their four-step Make Time framework (highlight, laser, energize, reflect) and close with a short look at the design sprint.
1:46:33ENLenny's Podcastポッドキャスト · 3 人From Chaos to a Failure Map: A Hands-On Primer on AI Evalsゲスト Hamel Husain, Shreya Shankar · ホスト Lenny RachitskyHamel Husain and Shreya Shankar walk Lenny Rachitsky through a real error-analysis workflow, from reading traces to building a trustworthy LLM judge.
1:47:58ENLenny's Podcastポッドキャスト · 3 人Bridging the Divide: How Community Notes Built a Trusted Fact-Checking System on Disagreementゲスト Keith Coleman, Jay Baxter · ホスト Lenny RachitskyKeith Coleman and Jay Baxter, the team behind Community Notes, explain how the algorithm works, how a tiny team built it, and why it survived four leaders at Twitter and X.