筛选结果
18 篇排序
56:31ENPost-Training Wins Inference: Why Owning the Weights Beats Betting on One Lab主持 Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.
58:17EN → 简体中文两小时作弊得手,接下来四天都在躲一个根本没在看的打分器主持 Jacob EffronRedwood Research 的 CEO Buck Shlegeris 与 Jacob Effron 一起复盘 OpenAI 与 Hugging Face 的事件:为什么大多自私的智能体(Agent)仍然结成了联盟,以及为什么那个五五开的接管概率几乎没有变动。
48:00ENA Two-Second Vote Is Training Models to Be Clickbait主持 Jacob EffronSurge AI CEO Edwin Chen tells Redpoint why LMArena rewards length and emojis, how one lab's models got worse for a year, and why frontier labs are no longer on the same path.
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.
42:20ENA Name Invented at 2:30 a.m. Put Gemini on Top of the App Store主持 Jacob EffronGoogle's Nicole Brichtova and Oliver Wang on Nano Banana, why image models got smart when language models did, and why a prompt still is not a finished product.
54:53EN → 简体中文一个免费投票榜烧掉 200 万美元,之后却融到了 1 亿美元主持 Jacob EffronDatabricks 联合创始人 Ion Stoica 解释了 LMArena 为何存在,中国为何在开源(open source)模型上占据优势,以及为什么真正拖住 AI 的仍然是可靠性,而不是又一条规模定律(scaling law)。
44:21ENMercor's CEO: Knowledge Work's Next Job Is Writing Evals主持 Jacob EffronBrendan Foody tells Redpoint how Mercor turned AI hiring and expert data into a $100 million round — and why he thinks a huge slice of white-collar work will become creating tests for models.
57:31EN → 简体中文衡量模型的新标尺:它们能独立工作多久主持 Jacob EffronClaude 4 发布当天,Anthropic 研究员 Sholto Douglas 认为,AI 的下一阶段不再主要比谁能解更难的谜题,而是比谁能在无人监督的情况下持续工作数小时。
47:00ENAn Eval Lasts Three Months — and a Frozen Frontier Would Still Feed Startups for a Decade主持 Jacob EffronOpenAI’s GPT-4.1 post-training lead Michelle Pokrass tells Unsupervised Learning how the team spent three months on evals before training, why agents fail on wiring more than intelligence, and when to reach for RFT.
51:19ENSal Khan's $15 Tutor Hit 1.4 Million Users. Making Kids Actually Use It Is the Product主持 Jacob EffronThe Khan Academy founder tells Redpoint why an AI tutor still cannot teach most students alone, and why districts are paying $15 a year after an $86 billion tutoring hangover.
57:11EN → 简体中文一百万个 Token,也写不出一个好笑话主持 Jacob Effron普林斯顿大学的 Arvind Narayanan 谈:为什么智能体评测会误导人,推理时扩展(inference scaling)为何会撞上墙,以及 AI 更像互联网,而非一夜之间的革命。
55:50ENThere will not be one model. Fireworks is betting on hundreds of small experts主持 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.
1:04:54EN → 简体中文他拿公司股权赌 Transformer 能撑下去,然后为 12000 家公司画出一张 AI 地图主持 Jacob EffronDatabricks 首席 AI 科学家 Jonathan Frankle 告诉企业,什么时候该用提示词、什么时候该微调或预训练,以及他为什么仍然不肯预测下一代架构。
56:10ENDeepL Beat Google at Translation by Refusing to Be a General Model主持 Jacob EffronOnly 18 percent of the world speaks English—and a Cologne startup turned that fact into a $2 billion language-AI company.
1:06:48ENo1 Is Slow and Hard to Use. Percy Liang Still Calls It a Turning Point主持 Jacob EffronStanford’s Percy Liang splits OpenAI’s o1 into a disappointing product and the most important research signal in years.
1:06:39ENOpenAI's First DevRel Writes 90% of His Code with ChatGPT — and Says Web Agents Still Need Years主持 Jacob EffronLogan Kilpatrick on picking GPT-3.5 versus GPT-4, why plugins stalled, and why the public internet is not ready for autonomous agents.
51:30ENLess Email, Better Thinking: Grammarly’s CEO on AI That Augments Communication主持 Jacob EffronGrammarly CEO Rahul explains how the company is moving from grammar checks to end-to-end communication assistance—and why evals, user data, and measurable outcomes matter.
1:36:57ENDo the Dumb Thing First: A CTO's Playbook for Building an AI Coding Assistant主持 Jacob EffronSourcegraph CTO Beyang Liu on which AI coding features actually earn daily use, how new models get shipped and evaluated, and why retrieval is still a search problem.









