X：小北

codex is actually insane 🤯 if you thought frontend cloning was impressive, check this out: I just pointed codex at another product, in 30 minutes got back its architecture, data model, prompts with cost estimates. 378-line plan to rebuild it. the craziest part is now I can one shot that entire product with: "/goal implement until your output matches theirs exactly"

Until now, physicians using AI in clinic had to assemble the patient’s context themselves. Allergies, comorbidities, medications, prior procedures, copy-pasted in from the chart. Today we’re announcing a partnership with @CedarsSinai. OpenEvidence now works directly inside Epic, drawing on the patient’s full record and interpreting the medical literature through the lens of that specific patient. Cedars-Sinai is the first academic health system to deploy patient-aware clinical intelligence at enterprise scale. The clinician asks a complex question in natural language. The answer reflects both the best available evidence and the patient in front of them. Patient data is never stored after the clinical session or used for any other purpose.

Great slide from the “How to thrive as an AI-era developer” session at Google I/O today I think this T-shape will apply to not just developers but every job function We need to - go deeper with our domain expertise - go wider with adjacent skills and fields - learn to use AI well on top

x.com/i/article/205724706411…

🚀 We’re hiring! DeepSeek is forming a new Harness team to build Code Harness from the ground up—may be you can call it DeepSeek Code or something like this hhh🤣🤣🤣 📍 Based in Beijing. Two roles open: 🧠 Harness Product Manager → app.mokahr.com/social-recrui… 👨‍💻 Harness R&D Engineer → app.mokahr.com/social-recrui… Research meets product—let's build it together. Hit the links and apply directly! 🔥 #DeepSeek #CodeHarness #AI #Hiring #ProductManager #Engineering #Beijing #Referral

Google 每次都是想象力满分，产品力拉跨。就看看隔壁 Claude 和 Codex，抄都不屑于抄。 这一点倒是跟我很像😂 不管怎么样，还是再期待一次吧，毕竟之前也做过几个惊艳的产品。

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

We raised $3M to build Lucius AI - the Context Layer for Your Organization. Backed by Future Capital Discovery Fund, we’re tackling a problem we kept running into ourselves: Individuals ship 10× faster with AI. Organizations don't. Over 30% of your team's time is spent rebuilding context someone already had. It shows up everywhere a decision was already made but can't be found again - community operations, customer support, pre-sales reception, sales research, project management, internal collaboration. We're building Lucius to close that gap. Video

可以！Google这次是认真了， 结合前一段的goolebook看这个消息，光标停在哪个窗口、哪个界面，它就直接读懂那里在干嘛。 这个交互逻辑如果做顺了， 体验会比”点击分享屏幕”自然一个量级。 再加上Spark模式跑本地Agent， 浮窗快速切模型、一键共享摄像头， 整合Veo 4和Gemini Live…… 说实话，光靠这些功能堆料，Google并不比OA和Anthropic差， Google真正的杀手锏从来不是模型， 是它背后那一套生态——Gmail、Drive、Calendar、Meet、Workspace， 深度打通之后，对重度Google用户来说， 换别家的迁移成本会高到根本懒得换。 桌面端AI的战场开始了，谁先把”操作系统级的感知力”做顺，谁就拿下这一局。

Qwen3.7 Preview By @Alibaba_Qwen lands on Arena for Text and Vision. In Text Arena, Qwen3.7 Max Preview ranks #13 overall. Alibaba is now the #6 lab in this arena. - #7 Math - #9 Expert - #9 Software & IT - #10 Coding In Vision Arena: Qwen3.7 Plus Preview ranks #16 overall, making Alibaba the #5 lab. Congrats to the @Alibaba_Qwen team on the latest progress!

What are best practices for running Claude Code at scale? New blog post on what we've learned from teams running it across multi-million-line monorepos, decades-old legacy systems, and distributed microservices: claude.com/blog/how-claude-c…

CItadel的CEO和创始人肯·格里芬对AI的看法发生了重大转变： “首先，在过去的几个月里，人工智能工具包的生产力发生了飞跃式的变化。它比九个月前强大得多。对我们 Citadel 来说，这使我们能够开发出更广泛的人工智能应用场景。 坦白说，看着人工智能代理在几个小时或几天内就能完成我们通常需要金融硕士和博士花费数周甚至数月才能完成的工作，这真的很有意思。这些不是中层白领工作，而是极其高技能的工作，我姑且用一个词来形容，它们被智能人工智能自动化了。 说实话，有一次周五回家，我其实挺沮丧的，因为你能预见到这将对社会产生多么巨大的影响。当你亲眼目睹这一切，当你看到过去需要数年才能完成的工作在几天或几周内就完成了，你会觉得，哇，这简直太不可思议了。 这是我第一次亲眼见证人工智能在我们公司内部产生的真正影响。” #AI #金融革命 #Citadel Video

You've been asking for this one... Now in preview: Codex in the ChatGPT mobile app. Start new work, review outputs, steer execution, and approve next steps, all from the ChatGPT mobile app. Codex will keep running on your laptop, Mac mini, or devbox. Video

“We did the hardest thing in the history of American health care. We got the majority of American doctors to all voluntarily adopt a single technology platform.” NBC News on how that happened, what U.S. physicians actually do with OpenEvidence, and how partnerships with NEJM, JAMA, NCCN, and Wiley make it possible.

We’re partnering with the Gates Foundation, committing $200 million in grants, Claude credits, and technical support to programs in global health, life sciences, education, agriculture, and economic mobility. Read more: anthropic.com/news/gates-fou…

半年前，我写了10个创作心法，没想到大家反响都特别好。 而这段时间，我给内部写的内容方法论也更新到了2.0。再加上最近我们有新的小伙伴入职，为了帮大家更好地做内容，所以决定给大家做个内部分享。 想了下，也把总结的部分发在这里，希望能对大家有帮助！ 先说一个反共识的：博主不是消耗品。每一个都是IP。 这个时代你想成为一个IP，核心其实就两个东西，内容和影响力。 先聊内容这一块，我总结出来是这三步。 第一步，获取信息 很多人就死在这一步。 热点本质上是个杠杆，是指数级别的杠杆。如果你缺了热点杠杆，传到社会层面的体量就是小。 而掌握了这一点，其实还不够。有的人每天天天刷AI圈动态，也不见得能做好内容。 这里面有个很大的问题，很多人都没注意过，那就是， 做AI内容，你绝对不能只看AI。 xx发表一个演讲一堆人去解读，从严格意义上来说，这不叫做内容，这叫转述，叫翻译。 但做内容本质上就三个字： 讲故事。 而非常非常多讲故事的技巧、所有的节奏，没有一个是来自AI。 所以，我经常让我们内容团队的小伙伴，没事多看看综艺、电影、小说和喜剧。 我个人觉得启发特别大的是，一年一度喜剧大赛。里面sketch十分钟里面可能会连着升三番，每一番都给你很强的情绪波动，看完后还会意犹未尽的。 但你要是去看那种纯AI生成的内容，永远是平的、没节奏的。 但好的节奏是需要刻意编排的，是要跟时代变的。 第二步，找角度 一个好的角度，是有反差的，用八个字总结就是，情理之中，意料之外。 拿情人节举例。 普通媒体在这个日子，多数会去民政局蹲着拍领证的，稍微深一点的去拍"老头配20岁姑娘"这种很反差的。 但我知道的一个做内容的，他们蹲在民政局旁边的垃圾桶一天一夜，把垃圾桶里那些撕碎了的信、卡片拼在一起，组成了一个个故事。 于是，有了《在情人节当天选择离婚的人们》，直接干到几千万阅读。 这才是我心中，找角度的神。 第三步，创作 这步反而最简单。 一般来说，一篇好内容如果能爆，30%归因于第一步获取信息，69%在于角度，而创作只在于1%。 但1%就能决定内容的生死。 这里面两个点，我觉得必须得守住。 一个是节奏。信息第一时间拿到了、好的角度也拿到了，但如果讲不好一个故事，就是创作的节奏出问题了。 第二个是正向价值观。想要长久地做好内容，做好IP，就不要为了流量去碰敏感话题，要守住道德底线。 以上，暂时就这些。 这次分享+Q&A，没想到最后讲了将近三个小时。（商务和其他部门的同学笑称选修课hhhh） 在这个AI时代，希望能用我的这一点点小经验，帮助到大家，哪怕一点点！

Google 刚刚发布了一个新东西：Googlebook 根据Google 自己的表述： 他们想做的已经不再是传统意义上的“操作系统”，而是一个以 Gemini 为核心的 AI Laptop 平台。 Gemini 被塞进了“鼠标指针”： 你晃一下光标，它会主动理解你当前屏幕内容，然后直接给动作建议。 比如： • 指向邮件里的日期 → 自动创建会议 • 选两张图 → 自动生成搭配效果 • 指向内容 → 自动总结 / 改写 / 操作 这其实已经不是传统 OS 思维了。 以前电脑逻辑： 人打开 App → 人操作功能。 现在开始变成： AI 理解上下文 → AI 主动组织操作。 Video

There will be no AI jobpocalypse. The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other technology — does affect jobs, but telling overblown stories of large-scale unemployment is irresponsible and damaging. Let’s put a stop to it. I’ve expressed skepticism about the jobpocalypse in previous posts. I’m glad to see that the popular press is now pushing back on this narrative. The image below features some recent headlines. Software engineering is the sector most affected by AI tools, as coding agents race ahead. Yet hiring of software engineers remains strong! So while there are examples of AI taking away jobs, the trends strongly suggest the net job creation is vastly greater than the job destruction — just like earlier waves of technology. Further, despite all the exciting progress in AI, the U.S. unemployment rate remains a healthy 4.3%. Why is the AI jobpocalypse narrative so popular? For one thing, frontier AI labs have a strong incentive to tell stories that make AI technology sound more powerful. At their most extreme, they promote science-fiction scenarios of AI “taking over” and causing human extinction. If a technology can replace many employees, surely that technology must be very valuable! Also, a lot of SaaS software companies charge around $100-$1000 per user/year. But if an AI company can replace an employee who makes $100,000 — or make them 50% more productive — then charging even $10,000 starts to look reasonable. By anchoring not to typical SaaS prices but to salaries of employees, AI companies can charge a lot more. Additionally, businesses have a strong incentive to talk about layoffs as if they were caused by AI. After all, talking about how they’re using AI to be far more productive with fewer staff makes them look smart. This is a better message than admitting they overhired during the pandemic when capital was abundant due to low interest rates and a massive government financial stimulus. To be clear, I recognize that AI is causing a lot of people’s work to change. This is hard. This is stressful. (And to some, it can be fun.) I empathize with everyone affected. At the same time, this is very different from predicting a collapse of the job market. Societies are capable of telling themselves stories for years that have little basis in reality and lead to poor society-wide decision making. For example, fears over nuclear plant safety led to under-investment in nuclear power. Fears of the “population bomb” in the 1960s led countries to implement harsh policies to reduce their populations. And worries about dietary fat led governments to promote unhealthy high-sugar diets for decades. Now that mainstream media is openly skeptical about the jobpocalypse, I hope these stories will start to lose their teeth (much like fears of AI-driven human extinction have). Contrary to the predictions of an AI jobpocalypse, I predict the opposite: There will be an AI jobapalooza! AI will lead to a lot more good AI engineering jobs, and I’m also optimistic about the future of the overall job market. What AI engineers do will be different from traditional software engineering, and many of these jobs will be in businesses other than traditional large employers of developers. In non-AI roles, too, the skills needed will change because of AI. That makes this a good time to encourage more people to become proficient in AI, and make sure they’re ready for the different but plentiful jobs of the future! [Original text in The Batch newsletter.]

我给每个下属都配了一个专属 Agent，跑在飞书上。现在是他们的 Agent 在跟我的 Agent 对话，我在旁边看着。 带团队这些年，我最大的感受不是累，是碎。 各种项目要跟，各种进展要盯，各种需求要确认。AI 让每个程序员的产出翻了好几倍，活多了，事也多了，人还是那几个。 有一天我看着 Claude Code 自己把一个功能从需求写到上线，然后转头看了眼飞书群，纪要要人整理，进展要人跟，审批堆着等人看。开发层已经 AI 化了，协作层还是原始人。 正好发现飞书 CLI 过去一个月悄悄更新了 100 多条能力，很多上个月还不存在的东西，现在已经可以用了。我就开始动手改。

This works really well btw, at the end of your query ask your LLM to "structure your response as HTML", then view the generated file in your browser. I've also had some success asking the LLM to present its output as slideshows, etc. More generally, imo audio is the human-preferred input to AIs but vision (images/animations/video) is the preferred output from them. Around a ~third of our brains are a massively parallel processor dedicated to vision, it is the 10-lane superhighway of information into brain. As AI improves, I think we'll see a progression that takes advantage: 1) raw text (hard/effortful to read) 2) markdown (bold, italic, headings, tables, a bit easier on the eyes) <-- current default 3) HTML (still procedural with underlying code, but a lot more flexibility on the graphics, layout, even interactivity) <-- early but forming new good default ...4,5,6,... n) interactive neural videos/simulations Imo the extrapolation (though the technology doesn't exist just yet) ends in some kind of interactive videos generated directly by a diffusion neural net. Many open questions as to how exact/procedural "Software 1.0" artifacts (e.g. interactive simulations) may be woven together with neural artifacts (diffusion grids), but generally something in the direction of the recently viral nitter.net/zan2434/status/2046982… There are also improvements necessary and pending at the input. Audio nor text nor video alone are not enough, e.g. I feel a need to point/gesture to things on the screen, similar to all the things you would do with a person physically next to you and your computer screen. TLDR The input/output mind meld between humans and AIs is ongoing and there is a lot of work to do and significant progress to be made, way before jumping all the way into neuralink-esque BCIs and all that. For what's worth exploring at the current stage, hot tip try ask for HTML.

Anthropic 真的惊为天人 直接把金融服务行业的 AI 工作流模板全开源了 投资银行 / 股票研究 / 私募 / 财富管理 / 基金管理 / KYC 风控 七大业务线的参考 agent / 技能包 / 数据连接器 全部公开 这超出了 demo 的范畴，是把「金融行业 AI 落地」的完整 SOP 摆出来 / 让全行业照抄 · 打开仓库 你会看到这些东西 10 个开箱即用的端到端 agent - Pitch Agent / 自动做 pitch deck（comps + 先例 + LBO → 出品牌排版的 deck） - Meeting Prep Agent / 客户会前自动出 briefing pack Market Researcher / 行业或主题 → 行业概览 + 竞争格局 + peer comps + 标的清单 - Earnings Reviewer / 财报会议 + filings → 模型更新 → 研报草稿 - Model Builder / DCF / LBO / 三表 / comps / 直接在 Excel 里建模 - Valuation Reviewer / 私募估值 + LP 报告 - GL Reconciler / 总账核对 + 找差异 + 路由审批 - Month-End Closer / 月末关账 / accruals / 滚动 / 偏差解读 - Statement Auditor / LP statement 审计 - KYC Screener / KYC 文档解析 + 规则引擎跑 + 标记缺口 每个 agent 都是 self-contained 的，带自己用到的全部 skill，clone 下来直接装 · 7 个垂直行业插件 - financial-analysis（核心）：comps / DCF / LBO / 三表 / deck QC / Excel 审计 - investment-banking：CIM / teaser / 流程信 / 买方名单 / 并购模型 / 项目跟踪 - equity-research：财报笔记 / 首次覆盖 / 模型更新 / 投资逻辑跟踪 - private-equity：寻源 / 筛选 / 尽调 / IC memo / 投后监控 - wealth-management：客户复盘 / 财务规划 / 调仓 / 报告 / 税损收割 - fund-admin：GL 核对 / 差异追踪 / NAV 校验 - operations：KYC + 规则引擎 直接用 / 想改也行 / 全是 markdown + json / 没有 build 步骤 · 11 家金融数据商的 MCP 连接器 Daloopa / Morningstar / S&P Global / FactSet / Moody's MT Newswires / Aiera / LSEG / PitchBook / Chronograph / Egnyte 这一行单独值得讲 意味着 Anthropic 已经跟全球最重要的金融数据基础设施都谈完了，你接进 Claude / 这些数据全是开箱即用的，没有这些连接器 / 你自己接每一家的 API / 光人天就要好几个月 LSEG 和 S&P Global 还各自做了 partner-built 的高级插件 一个跑债券相对价值 / 利率曲线 / FX carry / 期权波动率 / 宏观利率监控 一个跑 tear sheet / 财报预览 / 融资 digest · 部署方式两种，一个仓库 方式一：Claude Cowork 插件 装在分析师电脑上 / 个人工作流 方式二：Claude Managed Agents API 跑在公司自己的工作流引擎后面 / 整个公司用 同一份 system prompt / 同一份 skill / 你选在哪儿跑 还附带一个 Microsoft 365 安装工具，让公司 IT admin 把 Claude 部署进 Excel / PowerPoint / Word / Outlook 而且可以走你公司自己的云（Vertex AI / Bedrock / 内部 LLM gateway），不强制走 Anthropic API · 金融是企业 AI 落地最大也最难啃的市场，合规要求高 / 数据敏感 / 流程复杂 Anthropic 这一手 直接把整个行业的 AI 落地 SOP 写明白了 谁照这套搭 / 谁就在 Claude 的轨道上长 谁不照这套搭 / 谁要从零开始踩半年坑 最值得对比的是 OpenAI OpenAI 上周刚上线广告平台 Anthropic 这周直接开源金融行业全套 agent 两家公司的路线分化在这种地方又看得清清楚楚 OpenAI = 大众消费 + 广告 Anthropic = 企业场景 + 开源行业模板 链接 github.com/anthropics/financ…

The human-perceived RGB is image 1 and the Tesla AI photon count reconstruction is image 2. This is why Tesla FSD can see so well at night or through extreme glare.