The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Claude models is not affected. We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible. Read our full statement: anthropic.com/news/fable-myt…

阿里为了请无招回来，花了一年多。而让他离开，只用了几天 无招去年 3 月回钉钉以来，陈航已经在社交网络引起三轮广泛讨论： 第一次是 2025 年 4 月初。陈航开始猛抓考勤，要求团队早 9 点打卡、开早晚例会、午休 13:14 必须就位，并召集钉钉 P7 级别以上的产品经理一对一约谈，批评公司失去了早期创业的拼搏文化。 第二次是去年 8 月，钉钉即将召开他回归后的首次发布会。陈航在 0 点后巡楼查岗，当场质问 “为什么 11 点就走”，并带 HR 深夜清点工位。 前两次虽然在社交媒体被热烈讨论，阿里内部都没有针对性动作。这一次完全不一样：6 月 4 日，钉钉员工幽素在阿里内网发布 7.5 万字离职长文《置身钉内》，迅速传播；6 天后，阿里合伙人委员会在内网回应，直指钉钉的管理方式 “不是阿里文化该有的样子”，重申 “视人为人、有情有义”“人是阿里最宝贵的财富”。 这是阿里 “合伙人委员会” 首次回应员工。合伙人委员会是阿里的最高决策集体，集团 CTO 吴泽明（花名：范禹）上月刚成为第 5 名成员。 回应发出后不到 24 小时，陈航卸任钉钉 CEO——他成为阿里成立以来罕见的、因管理文化被直接换掉的元老。 陈航在 2014 年创立钉钉。他逢人便推荐，对产品要求极为严苛，快速做大了这款面向企业的社交产品。 他的管理风格一向以 “高压” 著称，招人标准是 “够不够疯狂”。内部创业早期，他经常从早 8 点干到凌晨一两点，全年无休。2018 年，他在员工动员大会上的言论 “不知道你们 10 点前回家做什么” 就曾被外界批评。 但这些批评在当时没那么重要，因为阿里几轮做社交失败后，终于有一个钉钉，在企业办公市场正面赢过企业微信。钉钉也是阿里除电商相关业务外，少数几个从 0 到 1 做成的产品——对一个大集团来说，有人能做出新东西、进入新领域，太难得了。 当时的高压管理，多数员工还能忍受：在上升周期里，产品有结果，结果带来晋升，可以在杭州换更大的房子。当时的大厂员工普遍接受这样的交换。 陈航做了六年钉钉。2020 年阿里推行 “云钉一体” 战略后，他离开阿里创业，创立两氢一氧，做了四五年宠物智能硬件和日本跨境电商平台。2025 年 3 月底，他回归阿里，重新带队钉钉。与陈航一同离开钉钉创业的核心骨干任卿（原钉钉副总裁）、朱鸿（原钉钉首席架构师、CTO）也一起回归。 一位两氢一氧的前员工认为无招是 “被叫回去的”：2025 年 3 月中旬——无招正式宣布回归钉钉的一周多前——他刚在东京下高井户买下一栋房子，打算用作两氢一氧的日本办公室。 回归后的陈航没有变，管理风格变本加厉。 他回归三天后，就有钉钉员工在社交平台发帖：上班时间提前到 9 点，午休缩短半小时，技术员工全员学 Python；工作时间不能打开微信、小红书，也不能私下加微信，对外沟通统一说 “不好意思，我只有钉钉”。 赶上今年初 OpenClaw 走红，钉钉紧急要上线面向企业的 AI Agent 工作平台“悟空”。员工的上班时间进一步拉长：大年初四全员返工，到三月底没有休过一天。 一位钉钉员工告诉我们，陈航回归后要求每个部门开早晚例会。每天早例会 9 点开始，要打开 AI 听记录音，员工要聚在各自业务的进度表大白板前拍照，AI 数人头、按声纹判断几人参会。 晚例会不得早于晚上 9 点开始，同样要录音、拍照。有人实在有事先走，不同部门会互相 “借人” 凑数——系统只数人头，不识别人脸。“我们互相借 ‘人头’ 这件事，无招不知道。” 一位长期与阿里合作的猎头告诉我们，钉钉部分岗位在四五轮面试后，还要求候选人动员二三十位亲友注册钉钉，并完成 “族谱上钉” 的作业——把家族成员拉进钉钉，建立一个 6 人以上的族谱组织，让家人真实使用、给出产品反馈。 员工在试用期还要在钉钉的服务池中选一个低分企业，或拉一个新公司 “上钉”，把服务做到 1000 分以上——而钉钉上 1000 分的满分企业不足 2%。销售岗位还要把一个飞书客户拉来钉钉。 “钉钉要找服从性高、热爱工作且一天能上班十四个小时的员工。” 这位猎头说，他几乎无法招到合适人选。 我们在钉钉上主动联系了陈航，截至发稿，消息一直是 “未读” 状态。 多位与陈航共事多年的同事，以及两氢一氧时期的创业伙伴认为他一心想做成大事，极度看重客户价值。一位陈航过去的下属说他单纯、专注，待人对人真诚——对别人高要求，对自己也是如此。 前几年一位创业者见到陈航，得到的建议是：创业要像爬山，不要抬头看山顶有多远，否则容易畏难，得专注脚下的每一步。 钉钉早年能做起来，是因为产品坚定站在老板视角，上线之初连会议来电都伪装成老板本人来电。陈航的管理方法也是钉钉产品逻辑的体现。 一位曾经的创业伙伴评价陈航：“他知道时代变了，但可能没关注到人也变了，社会也变了。个体价值和追求需要被尊重，关键人才的创造力需要空间。”

哈哈，严重赞同，去设定一堆角色来聊天没什么价值，纯浪费 Token。就跟早年想给人装上翅膀飞上天一样。 人类之所以这么分工是因为能力有限，无法精通所有工种，不代表 AI 也要这么做。 也不能说完全没用，还是能收获情绪价值，整个三省六部给自己汇报工作圆个帝王梦。

实话说我有点悲观，我不知道自己还能在 harness 上做些什么，当你想控制的马车几乎能自动驾驶去任何地方，控制它这件事本身就缺乏意义。做实时音频模型的创业公司也是一样，ChatGPT 只需要把自家的实时模型接入到 codex in ChatGPT 就分分钟能完成使用几乎双工的语音通道控制你家里 mac/windows/远程 SSH linux 主机上的 codex 从而控制任何计算资源。我觉得虽然今年还没走到一半，但对 harness startup 他们的故事已经结束了…

Now, we see you. 👀

The DeepSeek-V4-Pro discount has been extended until May 31, 2026, 15:59 UTC!

How Anthropic’s product team moves faster than anyone else I sat down with @_catwu, Head of Product for Claude Code at @AnthropicAI, to get a peek into their unprecedented shipping pace, how AI is changing the PM role, and how to be the right amount of AGI-pilled. We discuss: 🔸 How Anthropic’s shipping cadence went from months to weeks to days 🔸 The emerging skills PMs need to develop right now 🔸 Why you should build products that don't work yet—then wait for the model to catch up 🔸 Why a 95% automation isn't really an automation 🔸 Cat’s most underrated AI skill (introspection) 🔸 What Cat actually looks for when hiring PMs now (hint: it's not traditional PM skills) Listen now 👇 youtu.be/PplmzlgE0kg

Introducing Claude Managed Agents: everything you need to build and deploy agents at scale. It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days. Now in public beta on the Claude Platform.

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

The CCP’s response to the @ManusAI deal sends a clear message: there is no such thing as a truly “private” tech company in China. Even when firms seek to globalize or exit the Chinese market, the CCP retains leverage, through regulatory review, ownership structures, and direct intervention. As AI becomes a foundational technology, the United States must approach systems originating in China with clear-eyed scrutiny. wsj.com/tech/leaders-of-ai-f…