筛选结果
89 篇排序
34:11EN → 简体中文Anatoliy Bilenko 和 Alberto Carretero:用形式化方法和 AI 让数据库协议可靠Anatoliy Bilenko, Alberto Carretero · CanonicalCanonical 的数据库工程师把可执行规格、基于模型的测试、故障探索和重建的执行历史串起来,并说明为什么这些方法单独用,都无法证明真实系统正确。
1:26:46ENThere Likely Is a Level of Intelligence We Shouldn't Go Past主持 Nathan LabenzNathan Labenz brings notes from The Curve, a chip startup whose token bill beat payroll, and a software industry that now performance-reviews people for Claude slop.
1:10:12ENOutSystems Went From 4 Major Features a Quarter to 26. Token Spend Fell Anyway主持 Nathan LabenzCEO Woodson Martin on software that never breaks, why most enterprise work does not need a frontier model, and how AI is finally shrinking the IT backlog.
1:01:33EN → 简体中文用编程 Agent 做规格驱动开发:先写规格,再让 Agent 去建Andrew Ng, Paul Everitt · DeepLearning.AIJetBrains 开发者布道师 Paul Everitt 走完一整套规格驱动流程:一份项目章程,规划—实现—验收的功能循环,以及能跨工具带走的 agent 技能。氛围编程的替代方案,是一份 agent 忘不掉的 Markdown 契约。
2:10:42ENThe Exam Papers Nobody Audits主持 Nathan LabenzFrontier labs buy reinforcement-learning environments from a cottage industry of small vendors, and almost nobody checks whether those tests match the real work.
5:15:51EN → 简体中文DHH 没有亲手写一行 Omarchy Quattro 的代码嘉宾 DHH · 主持 Lex Fridman与莱克斯·弗里德曼一次充满怀疑的对话过去十三个月后,Rails 的创造者讲述了 Opus 4.5、一群编码智能体(Agent)和一个一分钟的 Linux 安装器,如何结束了他一凿一凿手写 Ruby 的时代。
1:04:14ENThe Man Who Helped Stand Up Colossus in 120 Days Is Betting on an AI That Lives in Your House主持 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.
51:31ENThey Refunded Nearly $2 Million. Then Factory’s CEO Explained the Dark FactoryMatan Grinberg on being two years early, why a model-agnostic harness beats lab co-design, and why 90 percent of coding tokens will soon run with the lights off.
51:09ENBuild like the model will eat your harness in a yearLogan Kilpatrick, who leads Google AI Studio and the Gemini API, tells Sequoia's Sonya Huang why agent scaffolding is a 12-month asset, why coding already feels like narrow superintelligence, and why Google is optimizing for outcomes instead of eyeballs.
1:13:33ENA Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still Miss主持 Jacob EffronThe Attention Is All You Need co-author tells Unsupervised Learning that transformers will probably keep winning — and that they still do not learn the way people do.
41:09ENAfter Agents Leak Tokens and Wipe Databases, Onyx Trains Small Models to Watch the Big Ones主持 Sarah Guo, Elad GilTwo years ago enterprise security buyers told Maxim Bar Kogan he was too early. Now Fortune 10 companies are calling a sub-100-person Israeli startup because they cannot stop agent adoption — only reduce the chance the next action is illegitimate.
45:33ENCursor Ships 1TB of Composer Weights Across Oceans in Under a MinuteFederico Cassano and Fireworks' Dima walk through the globally split RL stack that trained Composer 2 on a sparse trillion-parameter MoE.
2:12:51ENSteve Newman’s fifteen-app stack exists so he only has to look at one screen主持 Nathan LabenzThe Writely founder walks Nathan Labenz through a vibe-coded attention firewall, a conservative security stance, and why astonishing models still leave him far from AGI.
1:06:31ENAndrej Karpathy Hasn't Typed a Line of Code Since December主持 Sarah Guo, Elad GilOn No Priors, the OpenAI cofounder and former Tesla AI director describes the loopy era of coding agents, AutoResearch, and why the bottleneck is now the human.
54:26ENTen Billion Active Parameters, and the Unsexy Work of Making Them Agent-Ready主持 Nathan LabenzMiniMax researcher Olive Song explains how M2 was trained with expert developers as reward models, interleaved thinking, and a perturbation pipeline that treats every scaffold change as the real generalization test.
1:57:56ENA Hundred Million Lines at Twenty Cents Each: Inside Blitzy's Enterprise Coding Pipeline主持 Nathan LabenzCEO Brian Elliott and CTO Sid Pardeshi tell Nathan Labenz how Blitzy turns a giant codebase into a map, then lets thousands of dynamically generated agents ship most of a multi-month project in days.
48:03ENCoding Will Be Solved. The Bottleneck Is Saying What You WantWarp founder Zach Lloyd tells Sequoia's Sonya Huang why a rebuilt terminal became the lucky form factor for coding agents, and why he stopped subsidizing tokens.
2:12:31ENMost Caffeine users have no idea they are on a blockchain主持 Nathan LabenzDFINITY's Dominic Williams tells Nathan Labenz why a tamper-proof, unstoppable cloud is the right backend for AI that writes software from a chat box.
2:29:42ENTwelve hooks and thirty files: Daniel Miessler’s personal OS on Claude Code主持 Nathan LabenzThe PAI framework treats scaffolding, not the model, as the product — a goal-centered agent stack that Miessler thinks will decide both knowledge-work automation and the next cybersecurity race.
1:54:30ENFrom One in Ten Cancer Cells to One in a Million主持 Nathan LabenzNathan Labenz's solo AMA updates his son's lymphoma treatment, calls Claude Opus 4.5 software AGI, and ranks the live players of early 2026.
1:43:19ENA $100 Million Super PAC Named Him Public Enemy Number One. Donations Went Up.主持 Nathan LabenzIn the second half of Cognitive Revolution’s year-end live show, Assemblymember Alex Bores, former White House adviser Dean Ball, and forecaster Peter Wildeford argue over state AI laws, chip sales to China, and what 2026 will actually feel like.
57:37ENThe hot-swap era for models is over, OpenAI's enterprise product chief says主持 Jacob EffronOlivier Godement on GPT-5.1, Codex, enterprise harnesses, and the three practices that actually make AI land inside large companies.
40:13ENAs long as humans still need to read code, the IDE is not dyingZed founder Nathan Sobo argues that source code is a language for people, that terminal agents still need a visual editor, and that conversations should be anchored to every edit.
42:06ENAfter SWE-bench Hits 80.9%, Dianne Na Penn Reckons with Opus 4.5主持 Jacob EffronAnthropic’s head of product for research on computer use, deceptive token prices, thin harnesses, and why alignment makes a model a better thinker.
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