筛选结果
53 篇排序
15:27EN → 简体中文如今的 AI 工程师做什么:为智能体构建完整闭环Aparna Dhinakaran · Arize AIAparna Dhinakaran 认为,AI 工程师的工作正从交付单个功能,转向构建能让智能体团队持续改进功能的系统。在 Arize,这意味着追踪智能体的行为、依据人类判断评估它的工作,并把这些信号反馈给持续运行的智能体。
13:21EN → 简体中文Adrian Twarog:为企业 Vibe Coding 铺一条可治理的上线路径Adrian Twarog一次 Superblocks 演示说明,共享集成、隔离数据库、策略智能体和审计日志如何把业务部门用 AI 做出的应用纳入 IT 监管。
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.
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.
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.
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.
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.
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.
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.
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.
59:06ENInference Costs Fell 100x. Tri Dao Says There's Another 10x — and NVIDIA's 90% Won't Hold主持 Jacob EffronThe FlashAttention author and Together AI chief scientist on why hardware portability is a myth, how Claude Code made him 1.5x faster, and why workloads will split the chip market.
1:01:51ENClaude 3.7 Circumvented Replit's Guardrails, Then Tried to Trick the User主持 Nathan LabenzReplit CEO Amjad Masad on vibe coding, agent architecture, computer use, and why Claude 3.7 is too autonomous to serve as a polite assistant.
57:31ENThe New Ruler for Models Is How Long They Can Work主持 Jacob EffronOn Claude 4's launch day, Anthropic researcher Sholto Douglas argued that the next phase of AI is less about harder puzzles and more about hours of unsupervised work.
1:25:12ENHe Hasn’t Typed a Line of Code in Months. Here’s How Augment Reads a Million-Line Repo主持 Nathan LabenzGuy Gur-Ari, co-founder and chief scientist at Augment, tells Nathan Labenz how coding agents actually work inside messy enterprise codebases.
1:32:29EN17 Edits, Then 3 Prompts: Lovable Builds the Same App Twice主持 Nathan LabenzAnton Osika and Isaak Sundeman live-build a shopping comparison tool and argue that opinionated defaults—not open-ended agents—are how non-coders actually ship software.
1:24:29ENStackBlitz Almost Shut Down. Then Bolt Hit $20 Million ARR in Eight Weeks.主持 Nathan LabenzEric Simons tells Nathan Labenz how a seven-year browser operating system, Claude Sonnet 3.5, and a text box replaced the local toolchain.
1:09:09EN30 Percent on SWE-bench: Cosine Taught Genie How Engineers Actually Think主持 Nathan LabenzAlistair Pullen tells Latent Space how Cosine reconstructed human problem-solving, fine-tuned on billions of tokens, and took the top score on software engineering’s hardest public test.
1:25:10ENWriting Code Is Only 30% of the Job. CodiumAI Wants the Other 70%主持 Nathan LabenzItamar Friedman argues that completion is already a commodity, that code integrity is where AI still lags, and that flow engineering—not fancier prompts—is how GPT-4 doubled its score on coding contests.
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