elvis@omarsar0 · 6月1日58

As we target more complex use of coding agents (e.g., dynamic workflows and /goals) on long-horizon tasks, you will start to see all kinds of bizarre issues like this. This is just about user experience; it's even more insane what happens behind the scenes (ridiculous use of tokens, infinite loops, inefficient agent-to-agent interactions). You really want to own that harness and be in more control of it as we target more advanced use cases of coding agents. Multi-agent systems are just another beast to deal with.

译该推文指出，当编程智能体被用于处理更复杂的长时间任务时，会产生从用户体验到后台系统的多重挑战。前端表现为各种奇怪问题，后端则存在严重的token浪费、无限循环和智能体间低效交互。作者强调，在这种更复杂的用例下，拥有并控制运行框架变得至关重要，并指出多智能体系统是另一个需要应对的难题。

AYi@AYi_AInotes · 6月1日63

Paul Graham这句话简直能骂醒90%公司的 CEO， 他说"唯一比CEO亲自深度参与用AI造东西更糟糕的事， 就是CEO完全不亲自深度参与用AI造东西" 看到很多人都在骂他不懂管理，说CEO就该做战略，不该插手执行， 但其实他们压根没看懂这句话的真正分量。 他没说要让CEO去当全职工程师写生产代码， 核心表达是别再只看PPT听汇报了， 别再把AI全丢给那个AI转型负责人了，你得自己亲手去写Prompt，去造Agent，去用AI自动化你的工作流 去撞墙，去感受AI在哪里优雅， 在哪里崩坏，在哪里需要人判断。 AI是一场每周都在迭代的认知革命， 今天不可能的事，明天可能就变成了10倍效率，今天看起来很坚固的护城河，明天可能就被AI一脚踩平， 你靠二手信息建立起来的战略， 本质上就是在看后视镜开车， 等你反应过来的时候， 你的公司已经被那些天天泡在AI里的创始人甩得连尾灯都看不见了， 很多人说过度参与会让CEO忽略大局 但Paul Graham说的很清楚， 这两种错误的危害根本不在一个量级， 过度参与最多是效率低一点， 但完全不参与是直接判了公司的死刑， 所以建议所有CEO们： 1. 每天强制留出1小时，什么都不干，只用AI做你自己的真实工作 2. 不要做高大上的Demo，去做最脏最累的活：处理邮件、写文档、分析数据 3. 每周至少用AI造一个能真正用起来的小工具 4. 不要问你的团队"AI能做什么"，你自己得先搞清楚"AI不能做什么" 在工业时代， 不摸机器的工厂主会被淘汰， 在互联网时代， 不用互联网的老板会被淘汰， 在AI时代， 不亲手用AI的CEO， 可能会旁观自己公司的被淘汰。

译Paul Graham警示CEO：比亲自深度参与用AI构建更糟的，是完全不参与。核心观点是CEO不能只依赖汇报与PPT，必须亲手写提示词、造智能体、用AI自动化工作流，亲身感受其能力与局限。AI认知每周都在迭代，依赖二手信息制定战略如同看后视镜开车，公司会被天天泡在AI里的创始人甩开。文章建议CEO：每天花1小时用AI处理实际工作、每周造一个能用的小工具，并先弄清AI不能做什么。在AI时代，不亲手实践的CEO可能旁观公司被淘汰。

SemiAnalysis@SemiAnalysis_ · 6月1日26

Ever wonder what L11 diags means? Let's break it into two components: L11 and diags. (1/5)

译好奇L11诊断是什么意思吗？让我们把它拆解成两个部分：L11和诊断。(1/5)

Chubby♨️@kimmonismus · 6月1日51

Absolutely fantastic. This is how I imagine the future of computer use. I love it.

译绝对精彩。这就是我想象中计算机使用的未来。我爱死它了。 GPT-Realtime 2.0 被严重低估了。 演示：

Rohan Paul@rohanpaul_ai · 6月1日72

Jensen Huang thinks Dario Amodei's prediction of $1T in AI revenue by 2030 is too conservative. "I believe Dario and Anthropic are going to do way better than that. Way better than that. And the reason for that is the one part that he hasn't considered: I believe every single enterprise software company will also be a value-added reseller of Anthropic's tokens. And they’re going to get this logarithmic expansion. Their go-to-market is going to expand tremendously this year." --- From @theallinpod YT channel (link in comment)

译Jensen Huang认为Dario Amodei预测的2030年AI收入达$1T的预期过于保守。他指出，Anthropic的token将成为众多企业软件公司的增值服务，其市场将因此实现对数级扩张。有观点补充认为，当各实验室的模型能力趋同时，真正的优势可能源于独特的私有数据输入。这类数据（如特殊工作流、医疗记录等）能为AI系统带来难以复制的差异化和提升，未来或成为并购的关键标的。

Chubby♨️@kimmonismus · 6月1日45

Claude Mythos is $25 per million input tokens and $125 per million output tokens. I assume that the Mythos-like model that Anthropic will release in the coming weeks will be just as expensive. lets see

译Claude Mythos的输入token价格为每百万25美元，输出token价格为每百万125美元。 我预计Anthropic将在未来几周内发布的类似Mythos的模型，价格也会同样昂贵。 让我们拭目以待。

swyx@swyx · 6月1日56

actually wild

译新加坡Vivian Bala博士提出“不能仅凭简报就治理技术”的倡议，成为全球集结号。日本领导人高弘安野在议会辩论中引用该理念，提及Bala部长亲身使用NanoClaw_ai的经历，并主动提出亲自教日本首相如何设置该工具，首相已同意。发推文者也正式自愿飞往日本协助首相部署，并幽默地将此邀请延伸至所有食物美味的国家元首。

Rohan Paul@rohanpaul_ai · 6月1日60

Terence Tao summarized how AI is massively accelerating math career and math research. "In math, you previously had to basically go through years and years of education to be a math PhD before you could contribute to the frontier of math research. But now it's quite possible at the high school level or whatever, that you could get involved in a math project and actually make a real contribution because of all these AI tools and lean and everything else." From @dwarkesh_sp podcast (link to full video in comment)

译陶哲轩指出，AI工具和Lean等技术正在改变数学研究的参与门槛。过去需要多年博士训练才能触及前沿，而现在高中生也有可能参与项目并做出实质贡献。他强调，研究时间大多消耗在核查、验证等重复性工作上，AI降低了这类循环的成本，使研究者更敢于尝试“更疯狂”的想法。许多非常规思路并非因错误被否，而是因验证成本过高而被放弃；AI让犹豫变得廉价，这往往是科学发现的起点。

StepFun@StepFun_ai · 6月1日32

Intelligence got us here. Efficiency is what gets real work done. At ClawCon Macao, our GM of Developer Business @EileenTal laid out the next frontier for agents — and the thinking behind Step 3.7 Flash. 👏

译阶跃星辰（Step）发布Step 3.7 Flash模型。公司开发者业务负责人指出，模型竞争的新前沿不再是单纯的智能，而是智能体效率。新的目标是让AI智能体能够可靠、高效、规模化地完成真实世界的工作任务。

StepFun@StepFun_ai · 6月1日65

Intelligence got us here. Efficiency is what gets real work done. At ClawCon Macao, our GM of Developer Business @EileenTal laid out the next frontier for agents — and the thinking behind Step 3.7 Flash. 👏

译阶跃星辰在BEYOND ClawCon Macao活动上提出，模型竞争的新前沿是智能体效率，即可靠、高效、大规模地完成实际工作的能力，而不仅仅是智能本身。Step 3.7 Flash模型正是基于这一思考推出的。

AYi@AYi_AInotes · 6月1日71

Damn，这绝对是学生党这辈子能薅到的最狠的羊毛，没有之一😭 GitHub学生大礼包2026年全面升级 总价值直接干到$3500+！！ 而且真的是零门槛， 只要你是学生，基本都能过。 这可不是什么几块钱的优惠券啊宝子们， 这是GitHub给全球学生的生产力启动资金， 很多人毕业之后才知道这个东西 拍大腿后悔了好几年😭 2026年最新核心福利（最值钱的几个）： • GitHub Pro 永久免费（直到毕业） • GitHub Copilot Pro 无限免费用（AI写代码神器） • Cursor Pro 1年免费（今年新增，现在最火的AI编辑器） • JetBrains 全家桶免费（IDEA/PyCharm/WebStorm全解锁） • DigitalOcean $200 云服务器额度（能用一整年） • Azure $100 云额度 + 免费域名 + SSL证书 • Canva Pro/1Password/Notion等50+工具免费/折扣 🆙划重点： 13岁以上就能领，高中生也可以 没有Star数要求，没有项目要求 有.edu邮箱基本秒过 没有的话拍个学生证照片也行 审核快的几分钟，慢的最多1天 💡 避坑指南（90%的人都会踩）： • 认证通过后福利不会自动开通，要手动逐个领取 • 每年需要重新验证一次学生身份 • 毕业前一定要把能领的都领了，过期不补 • 非学生也能看页面里的教育折扣，很多工具都有优惠 申请入口放评论区了，感兴趣自取喔

译GitHub 2026年学生大礼包全面升级，总价值提升至$3500+。核心福利包括：GitHub Pro永久免费、GitHub Copilot Pro无限使用、Cursor Pro 1年免费、JetBrains全家桶免费，以及DigitalOcean $200和Azure $100等云服务额度。申请门槛极低，13岁以上持有.edu邮箱即可，无项目要求。此外，开源项目维护者还可申请OpenAI提供的6个月免费ChatGPT Pro（价值$1200）。

SemiAnalysis@SemiAnalysis_ · 6月1日30

10x speed at a 20x to 50x price premium per token. We're about to find out exactly how much the enterprise market is willing to pay for ultra-low latency AI.

译速度提升10倍，但每token价格溢价20至50倍。我们即将确切了解企业市场愿意为超低延迟AI支付多少费用。

向阳乔木@vista8 · 6月1日66

基于开源的沉浸式翻译插件 read-frog。 让Codex开发了一套单词学习系统。 每天阅读英文网页收藏的单词，变成闪卡可复习，基于艾宾浩斯曲线遗忘曲线。 此单词学习模式，根据单词难度（如CET6+）旁边加上中文翻译，标记掌握情况。 如果一篇文章没生词翻译，说明掌握火候差不多了 原始Github见评论

译用户基于开源沉浸式翻译插件 read-frog，使用 Codex 开发了一套单词学习系统。该系统能将用户在阅读英文网页时收藏的单词自动生成闪卡进行复习，并基于艾宾浩斯遗忘曲线安排学习周期。系统会根据单词难度（例如 CET6+）在旁边添加中文翻译，并标记单词的掌握情况。通过此模式，当一篇文章不再显示生词翻译时，即表明学习者已基本掌握该文章词汇。

Berryxia.AI@berryxia · 6月1日62

Spent 2 weeks vibe coding a real-time voice interactive mini-game set in an ancient Chinese hall using Claude model and Three.js.

译一个用Claude模型和Three.js搭建的盛唐长安实时语音互动小游戏已开源。项目由个人开发者耗时2周、花费800刀完成，通过Agora Skills实现实时语音交互。玩家可在其中与NPC对话、与李白对诗、玩诗词小游戏，还能进入珍宝馆欣赏诗画，体验古文明与AI结合的沉浸感。

Peter Steinberger 🦞@steipete · 6月1日49

Haven't seen codex writing ad-hoc codemods before, but it just did for a bigger TypeScript migration. Impressed.

译之前没见过 Codex 写临时代码修改，但它刚刚为一个更大的 TypeScript 迁移项目完成了。印象深刻。

Chubby♨️@kimmonismus · 6月1日19

1/ A quick note on my own behalf: When I started Superintelligence, it was just a newsletter I wrote every morning. I t's grown into something bigger: – Daily newsletter, 220k+ subscribers - every major AI development explained, with a different focus each day: AI research, robotics, longevity, politics, and more – YouTube interviews with the founders and researchers building the frontier – The Superintelligence Podcast on Spotify & Apple - conversations with startups and frontier labs like NVIDIA, Google, and more Your daily briefing in your inbox, plus everything that matters across our social channels. This is a dream come true for me. It is my greatest joy to report on the latest developments in AI every day, to talk to exciting guests and to learn new things. Subscribe for free if you want to be a part of this journey and learn new stuff as well :) ! means a lot. Thank you! Links below:

译Superintelligence从个人每日通讯发展为综合性AI媒体平台。目前拥有超过22万订阅者的每日通讯，聚焦AI研究、机器人、政治等不同主题。平台还扩展至YouTube采访（与前沿研究者对话）及在Spotify和Apple上发布的Superintelligence播客（与NVIDIA、Google等机构交流）。创始人表示这实现了其报道AI发展并与行业嘉宾学习的愿景，并邀请用户免费订阅关注。

向阳乔木@vista8 · 6月1日59

用飞书+AI读书，划线就能对话！这个阅读法太上头了 测试了一种另类AI阅读方法，结合飞书CLI，相当有趣！ 1. 让 Codex或CC 把Epub电子书按章节写入飞书文档。 2. 人肉阅读，有感触的地方划线、加粗，最好评论。 3. 让 Codex 读飞书文档中的标记和评论，让AI解释回复你。 探索差不多了，写个Skill

译推文介绍了一种结合飞书与AI的交互式阅读方法。核心流程为：1. 使用 Codex 或 CC 将 EPUB 电子书按章节导入飞书文档；2. 人工阅读时在文档中对有感触的内容进行划线、加粗或评论；3. 再次调用 Codex 读取这些标记与评论，由AI进行解释和回复。作者认为此方法有趣，并计划将其封装为可复用的 Skill。

Rohan Paul@rohanpaul_ai · 5月31日64

Chamath: AI advantage may come less from models than from private inputs. "When labs can build similar models, the real win comes from one unique ingredient in order to monetize it well. Here is a basic thing about machine learning that is worth knowing: if you take 1,000 of the same inputs and give them to Facebook, Microsoft, Google, and Amazon, they will all come up with the same machine learning model. But if you have one extra thing, one little ingredient that all of those other companies do not have, your output can be markedly different. It is like giving two great chefs three ingredients, but giving the third chef one extra ingredient. That person has the ability to do something very special. Right now, we are in a world where everybody is crawling the open web. We are going to move to a world where, as everybody gets sophisticated enough and information is widely available, somebody is going to say, “You know what? This site, I am not going to allow anybody else to access. It is only for me, only for my models.” Those models will become better. So we have to let that play out a little bit. It is going to be a really interesting arms race. The next wave of M&A, for example, could be companies like Google, Microsoft, and Facebook looking at these companies and saying, “Can they be viable inputs to my large language models or to my other machine learning and AI models?” --- A company with unique workflows, transactions, medical records, industrial logs, legal archives, design files, or user behavior can turn boring private data into a compounding advantage. Some startups may never become great public companies on their own, yet still become valuable because they own a data stream that makes a larger AI system sharper, more differentiated, or harder to copy. That turns acquisition strategy upside down: the buyer may not be purchasing revenue, brand, or even software, but a private ingredient for intelligence. ---- From "iConnections" YouTube channel, (link in comment)

译Chamath认为，当各大实验室能构建相似模型时，真正的竞争优势将来自独特的“私有数据输入”。他以厨师比喻：若给三位厨师相同食材，其中一位若多一味独特食材，便能做出非凡菜品。当前大家都依赖公开网络数据，但未来数据所有者可能将独家数据用于训练自己的模型，从而建立优势。这将引发一场围绕私有数据的“军备竞赛”，并可能改变大型科技公司的收购逻辑——未来的并购可能旨在获取能提升其大语言模型性能的独特数据流，而非仅仅购买收入或品牌。

Baidu Inc.@Baidu_Inc · 5月31日59

http://x.com/i/article/2060155258350014464 # Meet DAA: A New Metric Built for Results in The Agent Era Dear friends, welcome to May edition of AI Pulse. A lot happened at Baidu Create 2026 (our annual developer conference) this month — new agents, new infrastructure, new product launches. But the idea we keep coming back to is one our CEO, Robin, put on the table during his keynote: DAA, Daily Active Agents. A new way of measuring the value AI is delivering. In this issue, we'll take a deep dive into what DAA is and why it matters to the industry. Token consumption is at record highs and climbing. According to Goldman Sachs Research, agentic AI is expected to drive a 24-fold increase in token consumption by 2030. It has become one of the industry's most-watched indicators of AI scale and adoption. But tokens only tell half the story. They measure input — how much the machine consumed, how hard it worked. The other half of the equation — what actually got produced, what tasks were completed — remains uncounted. At Baidu Create 2026, Robin gave his answer about that missing half: DAA, or Daily Active Agents. It shifts the question from "how much was consumed?" to "how many agents are actively working and delivering results?” The Agent Era Has Arrived — and It Needs a Different Scoreboard Something has shifted in the past year. AI is no longer sitting inside a chat window waiting to be asked questions. It is out in the world, completing tasks, making decisions, and running operations. Agents are handling customer inquiries, optimizing port logistics, generating marketing content, scheduling factory floors — autonomously, continuously, and at scale. This is a different kind of AI activity from anything we've measured before. And it requires a different kind of metric to capture it. There are two metrics commonly used in the AI industry measure success today: DAU and token consumption. Both offer a starting point, but they weren't built for what AI is becoming. DAU was built in the mobile internet era for the attention economy: whoever captures more of users' time wins. That logic worked for apps. It doesn't work for agents. An agent doesn't "open" anything. It either finishes the job or it doesn't. And the numbers bear this out. According to Counterpoint Research's Q1 2026 data, some of the world's most-used AI products — measured by DAU — are not necessarily the ones generating the most revenue. User scale and business value have been quietly decoupling. Token consumption gets closer, but it still isn't sufficient. Tokens measure input — how much compute was consumed, how hard the machine worked. Not what it produced. Gartner noted in a recent report that token consumption doesn't effectively reflect business value, efficiency, or sustainability. A system can burn through billions of tokens and deliver nothing of consequence. As the industry matures, the gap between "how much the AI consumed" and "how much the AI delivered" becomes impossible to ignore. DAA closes that gap. It counts completed task loops — agents that took on work and actually finished it. It measures output, not activity. Delivery, not consumption. Robin's prediction at Create 2026: global DAA could eventually exceed 10 billion. That number reflects something important about how agents scale differently from users. One person can run many agents simultaneously. Agents multiply capacity rather than compete for attention. The ceiling on what becomes countable is unlike anything DAU ever imagined. Three Things Are Evolving at Once To understand why DAA matters right now, it helps to look at what Robin described as an "AI evolution theory": three simultaneous shifts, all pointing in the same direction. The first shift is in the agents themselves. Early agents answered questions. Current agents complete tasks. The next wave does something more interesting — agents evolve, learning from every task they run without anyone intervening. In the mobile era, software improved on a schedule set by developers. In the agent era, improvement is continuous and self-directed. The second shift is at the individual level. Someone working alongside a team of agents can now accomplish what used to require a full team of people. Builder, founder, creator, all in one person. The productive capacity of a single individual is being fundamentally re-scaled. The third shift is organizational. The basic unit of a company is moving from "people coordinating with people" to what Robin calls "mixed human-agent formations." Agents are now embedded in the middle of a workflow instead of sitting at its edge, handling tasks that used to require dedicated headcount. None of this happens in isolation. Smarter agents make individuals more capable. More capable individuals change how organizations are built. And as organizations restructure, the appetite for better agents grows. DAA is the number that captures all of this in motion — how much of this new productive capacity is actually being used, every single day. The Agents Behind the Number: DuMate, Miaoda, Yijing, and Famou A metric is only as meaningful as what it measures. At Create 2026, Baidu introduced a new generation of agents that are already putting DAA to work. DuMate is Baidu's general-purpose agent. It doesn't answer questions one at a time — it runs tasks in parallel. Handle the inbox, analyze the sales data, draft the marketing copy. Simultaneously. In the international agent benchmark PinchBench, DuMate ranked first globally with a 93.3% task completion rate. It's also a single entry point: one prompt can route through Baidu Search, Miaoda coding agent, Famou agent, and many other capabilities, all at once. Miaoda and MeDo are the coding agents. Over a million applications built. More than 10 million users. 81% of them with no coding background. Robin's framing was blunt: development costs are collapsing toward zero. "Disposable software" — built for a single purpose, a single moment — is now a real idea. Global developers can already access MeDo, Miaoda's international version, at medo.dev. Baidu Yijing is a digital human platform integrated with live streaming, video production, and real-time interaction across 12 languages, with native-level lip-sync. As Robin put it: "A digital human is simply an agent you can see. Equipped with voice, facial expressions, and gestures, a digital human is more expressive and more trustworthy." This year, a Coca-Cola World Cup TVC was produced through Yijing — five characters, five city styles, all directed and edited by AI. Production time was cut by more than half. Famou Agent 2.0 is the self-evolving decision agent. It works in operations environments: manufacturing scheduling, logistics planning, and process optimization. At one of the world's most automated terminals, Famou delivered a 10.21% efficiency improvement on top of an already optimized baseline. That translates to roughly one million additional standard containers processed per year. Underneath all of this is Baidu's full-stack infrastructure — computing, cloud, models, and agents — rebuilt for the agent era. Robin was direct: "AI is not just a model. It is a system. It is a new generation of computing." Baidu AI Cloud has been repositioned as a full-stack AI cloud purpose-built for large-scale agent workloads. From Consumption to Delivery: What DAA Is Really Asking the Industry to Do As agents move from novelty to necessity — running in ports, factories, classrooms, and boardrooms — how we measure AI starts to matter a great deal. That's why DAA asks the harder questions: did the agent actually deliver? Was the task completed? Did something real happen as a result? Metrics shape behavior. If you measure token consumption, you build for scale. If you measure daily active agents, you build for outcomes. That is what DAA is really challenging the industry to do — to reorient around a different definition of what success looks like. Here's a quick look at what else has been happening at Baidu this month: > Q1 2026: AI Business Crosses a New Threshold - For the first time, Baidu Core AI-powered Business represented more than half of Baidu General Business revenue, bringing in over RMB 13.6 billion in Q1 2026, up 49% year-over-year. - Growth was broad-based across AI Cloud infrastructure, AI applications, and Apollo Go. Full report here. > Apollo Go: 3.2 Million Fully Driverless Rides Delivered in Q1 - Apollo Go had a strong start to the year. In Q1 alone, we delivered 3.2 million fully driverless rides, with total rides continuing to grow at a triple-digit rate year-over-year. Over 22 million cumulative rides are provided to the public as of April 2026. - It also continued expanding internationally, as the global footprint reached 27 cities as of May 2026. The driverless operations are now running across multiple zones in Dubai, with the Apollo Go App launched in March, while it is on track to commence open-road testing in Switzerland, and to begin testing in London with Uber and Lyft soon. > Baidu AI Cloud Ranked No. 1 in Autonomous Driving R&D Solutions in China - According to IDC China's H2 2025 report, Baidu AI Cloud captured over a third of China's autonomous driving R&D solutions market, ranking first. - It now serves the top 15 auto brands by sales and the top 10 NEV companies in China, helping automakers move autonomous driving from R&D into mass production. > ERNIE 5.1 Is Now Available - We launched our latest foundation model that builds on ERNIE 5.0, with upgrades across search, reasoning, knowledge Q&A, creative writing, and agentic capabilities at around 6% of the pre-training cost of comparable models. - On LMArena's Search Leaderboard, ERNIE 5.1 scored 1,223 to rank 4th globally and 1st among Chinese models. Try it at ernie.baidu.com. > MSCI ESG Rating Upgraded to AA - We released our annual ESG report. Our MSCI ESG rating was raised to AA, and we were included in S&P Global's Sustainability Yearbook 2026. - From accessible mobility to closing the AI skills gap, we're working to make sure more people can share in the progress AI brings. Full report here. Have a question about building with MeDo, or something you'd love us to cover next? Leave a comment or DM us! Until our next roundup, keep up with our latest AI developments and innovations by following us on LinkedIn and X.

译在百度Create 2026大会上，CEO李彦宏提出了DAA（每日活跃智能体）新指标，用于衡量AI智能体的实际任务完成情况。该指标旨在解决现有DAU（仅反映用户规模）和token消耗量（仅反映模型投入）的局限性。据Goldman Sachs Research预测，智能体AI将驱动token消耗量到2030年增长24倍，但投入不等于产出。DAA则直接计数成功完成工作循环的智能体，衡量的是交付成果而非活动量。李彦宏预测，全球DAA最终可能超过100亿。

Rohan Paul@rohanpaul_ai · 5月31日48

🎯“The people who invented refrigeration made some money, but most of the money was made by Coca-Cola, who used refrigeration to build an empire. LLMs are like as refrigeration & the Coca-Cola has yet to be built” ~Chamath Palihapitiya (@chamath) ---- From "iConnections" YouTube channel, (link in comment)

译🎯“发明制冷技术的人赚了一些钱，但大部分钱是可口可乐赚的，他们用制冷技术建立了一个帝国。 LLM就像制冷技术，而可口可乐尚未出现” ~Chamath Palihapitiya (@chamath) ---- 来自“iConnections” YouTube频道（链接在评论区）

Peter Steinberger 🦞@steipete · 5月31日61

The idea of OpenClaw is always that it should be yours. It's modular and lean, only add what you need. Fewer skills, fewer tools = your agent can work more efficiently.

译OpenClaw的理念始终是它应该属于你。 它是模块化且精简的，只添加你需要的功能。更少的技能，更少的工具 = 你的智能体可以更高效地工作。

meng shao@shao__meng · 5月31日74

Agent: OpenAI Codex + Tools: Google 全家桶、WhatsApp、电报、浏览器自动化等 + Data: Google Drive、Notion、AGENTS.md 等 + Skills: inbox-zero、contacts 等 == 个人生活自动化 Agent 工具栈 @nicbstme 提出的两个典型工作流 1. 介绍邮件（跨 5 个工具的「胶水活」） 朋友 WhatsApp 求助 → 搜 WhatsApp/Gmail 找邮箱 → 网页查公司融资 → 起草介绍信 → 等批准 → 发邮件 → WhatsApp 告知完成。 人工约 20 分钟、大量上下文切换；用户侧约 10 秒提需求。Agent 做的是跨 App 的编排，不是回答问题。 2. 车牌更新（行政连续性） 发照片给 Agent → 更新 Drive 里的 Markdown 车辆档案 → 保留 VIN、保险等字段 → 上传回 Drive → 必要时用浏览器自动化同步到 FasTrak、停车 App、保险门户等无 API 的系统。 体现的是行政连续性：同一份信息在多处保持一致，而非一次性问答。 最重要的架构决策：Drive 作为 Source of Truth Nicolas 刻意把知识从 Notion 迁到 Google Drive，原因很务实： · Notion 对人友好，对 Agent 不友好（嵌套页面、数据库属性、权限、UI 原生结构） · Drive + Markdown/CSV：可搜索、可 diff、可编辑、可上传、可引用 file ID · gogcli 提供统一的 CLI 面（Gmail、Drive、Calendar、Docs、Sheets 等） 组织知识不应只为人类 UI，而应面向 Agent 的工具路径。 稳定 file ID、纯文本、表格、返回 JSON 的命令——这些才是 Agent 友好的数据形态。 联系人 CSV（电话、邮箱、LinkedIn 等）被作者称为「最佳投资之一」，因为它是跨渠道 lookup 的枢纽。 工具优先级（可靠性层级） API / CLI > 本地文件 > 浏览器自动化 > 屏幕/UI 自动化 Agent 的可靠性上限取决于工具面。gog gmail messages list --json 比让模型在网页上点来点去更稳定、可重试、可推理。浏览器和屏幕自动化是必要时的兜底，不是主路径。 Skills：Agent 的「习惯」与「品味」 Skill 不是 fancy 架构，就是可迭代的操作手册。以 inbox-zero 为例： · 列出收件箱 → 区分自动归档 / 需人工审阅 · 展示重要邮件、引用原文、建议归档或回复 · 起草后等明确批准再发送 · 保留所有收件人、回复简短、不主动建议电话、签名用 "Nicolas" 没有 Skill，每次都要重新 prompt 所有偏好；有了 Skill，说「run inbox zero」即可。个人 Agent 的个性化，来自操作品味的累积，而非 cute voice。 反馈闭环： · 工具失败 → 修工具或加 guardrail · 判断失误 → 更新 Skill · 忘记偏好 → 写入 memory / AGENTS.md · 工作流重复 → 体系 compound 改进 批准门控：信任分级才是产品 Nicolas 明确反对「YOLO 全自动」： · 低 stakes 可直接发（如「告诉 Hugo 我下周在西雅图」） · 高 stakes 必须：读上下文 → 起草 → 展示 → 等批准 → 执行 → 确认。 有用 vs 可怕的分界，在于是否在正确时刻问人。 「杀手级」工作流：What did I miss? 比单点邮件更重要的，是生活收件箱 triage： · 每隔几小时问一句「我漏了什么？」→ Agent 扫描 WhatsApp、Telegram、Gmail、SMS、Calendar、Drive 变更 → 汇总：谁需要回复、什么 urgent、什么 stale、什么可忽略、什么该建日历、什么要查文档。 特点：上下文重、重复、跨工具、充满小决策——人讨厌做第一遍扫描，Agent 擅长第一遍，判断权仍在人。 复现清单（Nicolas 给出的路径） 1. 装 Agent 运行时 + 各渠道 CLI/连接器 2. 集中数据：Drive 为真相源，联系人 CSV，重要文档可搜索化 3. 谨慎授权：Full Disk Access、Screen Recording、Accessibility——必须配合同级 approval gates 4. 写 operating rules（AGENTS.md）：draft before send、工具路由、隐私边界等 5. 为重复流程写 Skills，并在每次失误后更新

译该推文介绍了以OpenAI Codex为核心的个人生活自动化智能体工具栈。它集成了Google全家桶、WhatsApp、电报及浏览器自动化等工具，并以Google Drive作为“真相源”数据层。核心是跨应用编排与判断，关键决策需经人工批准。技能（如inbox-zero）是可迭代的操作手册，用于固化偏好。典型的“介绍邮件”编排展示了Agent在处理多工具、高上下文切换任务时的效率。工具优先级为API/CLI > 本地文件 > 浏览器自动化。

AYi@AYi_AInotes · 5月31日65

英伟达 CEO 黄仁勋，最近说了句得罪一大票老板的话：这两年打着 AI 旗号的裁员，绝大多数是甩锅🤯 他算了笔账：真正能干活的 AI，上线也就半年，但是很多公司两年前就开始以 AI 的名义裁人了。 两年前那波裁员，怎么可能是半年前才成熟的东西干的呢？？ 老黄这话说的很有意思，他说把裁员直接挂到 AI 头上，是一种太懒惰的叙事，听着聪明，其实是在吓唬人。 而且数据上也站他这边， 2026 第一季度，86 家科技公司裁了 8 万多人，三年最高，几乎集体把锅甩给了 AI。 但哈佛商业评论调研 1066 个高管后发现，真相平淡得多：很多公司只是在还疫情期间招太多人的旧账，顺手用 AI 的未来潜力，把这笔财务调整包装得体面点。 连 OpenAI 的 Sam Altman 都承认，这里面有大量 AI washing，本来就要裁的人，借 AI 的名头听起来高级。 说穿了，AI 现在更像那句万能的堵车， 你迟到了，真实原因是起晚了，但你只说路上太堵，没人能反驳，还显得不怪你。 公司业绩掉了、当年人招多了，本该自己认的账，一句因为 AI，就从管理失误变成了拥抱未来。 而且这套甩锅模板早就不只在科技圈，哪个行业的老板都能顺手抄。 所以这事真正让人不舒服的，不是裁员本身，而是这种归因的不诚实。 毕竟真正能大规模替代人的 AI 还在路上，恐慌却被提前点着了。 就像狼来了喊太多次，等那天真来的时候，该被当回事的风险，反而没人信了。 也许最先被 AI 取代的，从来不是你的工作，而是那句本该说出口的实话：这是我们自己的决定。

译英伟达CEO黄仁勋近期指出，近年大量以AI为名的裁员实为“甩锅”。他分析称，真正能产生价值的AI应用普遍只有约半年历史，但两年前的裁员潮已将其归因于AI，这不符合事实。哈佛商业评论对1066名高管的调研显示，许多裁员旨在消化疫情期间的过度招聘，AI仅被用作“体面”说辞。OpenAI的Sam Altman也承认存在大量“AI washing”。黄仁勋批评这种归因是懒惰且不诚实的叙事，制造了本不该如此强烈的恐慌。

meng shao@shao__meng · 5月31日17

iOS App 发展这么多年，Vibe Coding 让每个人都能做 App，但这些年，我最喜欢的 App 都来自十几年前的 Windows Phone 图一: 贴纸天气 图二: 627 AM UI 和 UX 都优雅到你打开 App 盯着看，都觉得是一种享受，真的太想念那个 Windows Phone 个人开发者们的年代了 这也是我后来如此讨厌微软的原因

译推文对比了当前iOS应用发展与Windows Phone时代的开发氛围，认为尽管Vibe Coding让App开发更普及，但作者最欣赏的应用却来自十几年前的Windows Phone平台。文中以“贴纸天气”和“627 AM”为例，强调这些应用的UI与UX设计优雅，令人享受。作者表达了对Windows Phone个人开发者时代的怀念，并表示这也是后来对微软感到失望的原因之一。

小互@xiaohu · 5月31日26

搞 AI 的全是渣男 尤其是Vibe Coding这帮人 一会爱Claude Code 爱的要死 没多久全都又投入了Codex 的怀抱 更有甚者，脚踩两条船，渣的不得了🙃

Greg Brockman@gdb · 5月31日33

codex computer use is viscerally compelling

译Codex的计算机使用体验令人震撼

Chubby♨️@kimmonismus · 5月31日59

.@AndrewCurran_ has made a very important point here, with which I fully agree. Anthropic focused on coding from the very beginning and (almost) nothing else. Dario Amodei said early on that if the coding problem is "solved," all other problems will be solved as well. Therefore, no distractions from this area. All the other companies regularly got sidetracked with side quests and thus abandoned their focus. OpenAI invested massive amounts of compute in Sora but then even decided to discontinue the app. They also developed a language model, an image model, and extensive access to free ChatGPT. I don't want to judge this, just observe it. Google did the same: AI Mode, Image Model, Veo3.1, Music Model, and so much more. Again, these were certainly well-considered decisions. But Anthropic wanted one thing from the start, and only one thing: to focus on coding and then be at the forefront of enterprise computing. And it's safe to say: they succeeded. OpenAI invested massive amounts of compute in Sora but then decided to discontinue the app. I like the term "intelligence company" because I would argue that Anthropic sees itself in exactly that way. At least so far, Anthropic's own path has been successful. And I would say that OpenAI has followed suit and is increasingly abandoning its side projects. Focus on Codex and ChatGPT, less Sora, voice mode, etc. It's about the race for the best models. Distraction costs money and intelligence resources.

译Anthropic自始至终专注编程，被视为“智能力公司”而非编程公司。其策略基于Claude智能扩展后将应用于所有人类智能领域。相比之下，OpenAI和Google频繁分心开发其他产品（如Sora、图像模型、音乐模型等），OpenAI甚至停用Sora。Anthropic凭借专注在企业计算领域取得领先，而OpenAI正效仿其路线，放弃副项目，聚焦Codex与ChatGPT等核心模型竞争。

StepFun@StepFun_ai · 5月31日80

A 198B vision model, running on a box that sits on a desk. This is what we built Step 3.7 Flash for. Brilliant breakdown @sudoingX — saved everyone a few hours of head-scratching 🎉

译阶跃星辰发布了Step 3.7 Flash，这是一款198B参数的视觉模型，旨在DGX Spark等桌面设备上运行。用户实测表明，128GB统一内存是运行门槛，模型占用约104GB。部署无需官方专用llama.cpp分支，主线版本即可。在上下文长度上存在权衡：启用视觉功能时，基于q8 KV cache的64K为上限；若要使用最高256K上下文，则需禁用视觉并切换至q4 KV cache，此时模型与缓存共占约114GB内存。该模型是推理模型，思考过程可能消耗大量max_tokens，需注意设置。

凡人小北@frxiaobei · 5月31日39

现在 codex 重置的理由越来越随意了， 大家赶紧打开/fast 吧

Tibo@thsottiaux · 5月31日33

What’s something we haven’t fixed in codex for a while and that’s plain annoying?

译Codex里有什么我们很久没修复、而且确实很烦人的问题？

Tibo@thsottiaux · 5月31日16

Five million users would agree. Resetting the limits tomorrow morning to celebrate. Time to go /fast

译五百万用户会同意。明早重置限制以示庆祝。 是时候体验/fast了

AYi@AYi_AInotes · 5月31日66

对大部分人来说，Codex就是目前最顶最好用的生产力工具，都全面拥抱用起来！！ 那么Codex里的4个模型怎么选最省钱？ 1️⃣先说最贵的那个，gpt-5.5 是质量优先的旗舰，它适合复杂编码、复杂推理、知识工作、研究流程，尤其是那种看着像写东西、背后却要走好几步判断的活，官方给它的定位就是旗舰级，价格也站在最高那一档，输入 $5.00、输出 $30.00 每 100 万 tokens。

译Codex（由OpenAI发布）提供四个可选模型。其中，gpt-5.5作为质量优先的旗舰模型，适用于复杂编码、推理及知识工作，其定价较高，为输入$5.00、输出$30.00每百万tokens。主推文旨在帮助用户根据任务类型与成本考量进行选择。

宝玉@dotey · 5月31日55

通用 Agent 就是未来的操作系统了，就像现在我们操作电脑需要借助操作系统，以后我们跟 AI 通信会通过 Agent OS。 App 会有几种结局： - 消亡：Agent 自己就有能力，不需要独立的 App - 变成 CLI 或者 MCP：搭配 Skill 去让 Agent 调用，用户不需要直接操作 App，Agent 帮助调用 - Agent GUI 插件，或者说 Agent App：有些能力 Agent OS 满足不了的，必须通过 GUI 去手工操作下的，还需要做成插件，按照需要让 Agent 唤起给人临时用一下 在未来一段时间，SaaS 会有个趋势，都要推出 cli + Skill，让 Agent 学会用它，这样才能保住客户，不至于被淘汰掉。

译推文认为，通用AI智能体将成为未来的操作系统，当前的App将演变为三种形态：被其内置能力取代而消亡、转化为CLI或MCP形式通过技能供其调用、或作为其GUI插件补充图形界面操作。为此，SaaS服务需推出CLI + 技能模式以适应趋势。

郭明錤｜Ming-Chi Kuo@mingchikuo · 5月31日61

Nvidia's Much-Anticipated, Reportedly Upcoming N1X / Windows PC Processor: Supply Chain Checks and Key Takeaways ▌Supply chain checks point to around 10M shipments of N1X-based devices over the next two years. ➡ Still a niche market, aimed at power users who need on-device AI compute. ➡ Whether shipments get revised up will come down to price, but mainly to whether Windows can deliver apps and workflows that truly orchestrate on-device AI compute. ▌Today, the main ways people use AI on a PC (both Windows and Mac) are accessing cloud LLM services through a browser and calling LLMs via API to consume a cloud provider's compute / tokens: ➡ In both cases, the core AI compute happens in the cloud, not on the device. ▌So far in 2026, the two hottest stories in the PC market have had almost nothing to do with on-device AI compute: ➡ Strong MacBook Neo sales. My industry checks suggest 2026 shipments of Neo models were revised up by roughly 100% (5M → 10M). Buyers are paying for price, design, and ecosystem, not for on-device AI compute. ➡ Cheap mini PCs, still niche, are drawing a lot of attention because they can run AI agents (like OpenClaw) around the clock (e.g., Mac mini). These agents also run inference in the cloud. ➡ Bottom line: neither the sales nor the buzz has much to do with on-device AI compute. ▌The key to on-device AI driving an upgrade cycle is the operating system (OS): ➡ What really sets on-device AI apart from the cloud is its ability to deeply integrate a user's data and workflows across apps while keeping things private. But that needs OS support. ➡ AI in today's PC OS is still mostly about adding AI features to first-party apps and loosely connecting workflows across apps. ➡ Some apps already make good use of on-device AI compute, like speech-to-text, but not enough to drive meaningful upgrade demand. ▌The N1X devices could give AI power users another solid option: ➡ Thanks to the N1X, device makers can strike a better new balance across AI compute, memory, design, and portability. ➡ For power users running LLMs on-device, an N1X device is a solid alternative to the Mac when it comes to capable on-device AI compute and large memory. ➡ But if the goal is a real upgrade cycle, then beyond price, OS support (Windows) is still what matters.

译供应链信息显示，Nvidia即将推出的N1X处理器设备未来两年出货量约1000万台，仍属面向需要设备端AI算力的性能用户的小众市场。2026年PC市场热点是MacBook Neo销量上调和可运行AI智能体的小型PC，但两者均与设备端AI算力无关。真正的设备端AI优势在于操作系统层面的隐私与深度整合，而当前Windows的支持尚不足。N1X设备能为需要本地运行大语言模型的用户，提供一个更平衡的选择，但能否驱动升级周期，关键仍在于Windows能否提供相应的应用与工作流支持。

郭明錤｜Ming-Chi Kuo@mingchikuo · 5月31日56

許多人期待、Nvidia 可能將要發布的 N1X / Windows PC 處理器，供應鏈調查與重點分析： ▌供應鏈調查顯示，配備 N1X 的裝置未來兩年出貨量約10Ｍ ➡ 仍屬利基市場，瞄準對裝置端 AI 算力有需求的重度使用者。 ➡ 未來出貨能否上修，除售價因素，還是取決於 Windows 能否提供真正調度裝置端 AI 算力的應用與工作流。 ▌目前 PC（Windows 與 Mac）的主流 AI 應用為「用瀏覽器上 LLM 網站」與「透過 API 消耗雲端 LLM 的算力 / token」： ➡ 核心都是使用雲端 AI 算力，非裝置端。 ▌2026 年 目前為止 PC 產業的兩個熱門事件，都與裝置端 AI 算力幾乎無關： ➡ MacBook Neo 的熱賣。我的產業調查顯示，2026 年該機種出貨量顯著調升約 100% (5M → 10M)。消費者買的是「低價 + 設計 + 生態」，不是買裝置端 AI 算力。 ➡ 便宜的小 PC 主機雖仍屬利基市場，但因能長時間掛機跑 AI agent（如OpenClaw）而受到高度關注（如 Mac mini）。這類 agent 的推論算力幾乎也來自雲端。 ➡ 小結：無論銷量（裡子）或話題（面子），都與裝置端 AI 算力幾乎無關。 ▌裝置端 AI 推動升級換機潮的關鍵為作業系統： ➡ 裝置端 AI 與雲端最大差異，在於兼顧隱私下，能高度整合跨應用程式的用戶資料與工作流，然這需作業系統支援。 ➡ 目前 PC 作業系統 AI 化主要仍處於「為本家應用程式增加 AI 功能」與「輕度整合跨應用程式的工作流」。 ➡ 已有善用裝置端 AI 算力的應用，如語音轉錄文字，但不足以推動顯著升級換機需求。 ▌N1X 裝置可望提供 AI 重度使用者另一個好選擇： ➡ 受益於 N1X，裝置設計能在 AI 算力、記憶體、外觀與攜帶性之間，取得一個更好的新平衡點。 ➡ 對在本地端跑 LLM 的重度使用者而言，在不錯的裝置端 AI 算力與大容量記憶體裝置的選擇上，N1X 裝置是除了 Mac 以外的另一個好選擇。 ➡ 若欲帶動顯著升級換機潮，除售價外，作業系統（Windows）支援仍是關鍵。

译供应链调查显示，配备Nvidia N1X的Windows PC未来两年出货量约1000万台，仍属瞄准重度用户的利基市场。当前PC主流AI应用（如通过浏览器访问大语言模型网站或API调用token）核心仍依赖云端算力。2026年产业热点（如MacBook Neo出货量预计翻倍至1000万台，以及可长时间挂机运行AI智能体的小型主机）均与本地AI算力无关。推动装置端AI换机潮的关键在于操作系统需支持深度整合跨应用工作流。N1X为本地运行大语言模型的用户提供了除Mac外兼具算力与大内存的新选择，但能否引爆换机潮仍取决于Windows的生态支持和定价。

Chubby♨️@kimmonismus · 5月31日33

What is going to be a real game changer is AI in cars. Tesla is leading the way; the integration of Grok into the Tesla OS enables seamless experiences. When I was a child, I loved *Knight Rider*, and the idea that I might someday be able to talk to my car seemed like a wild dream (remember "KITT"). Now, it is becoming a reality, though it is still far from fully mature. Google and Apple have developed the first rudimentary steps in this direction with CarPlay. But that is merely a starting point. Soon, the car will explain every error message to you in detail, provide live updates on route changes and deviations, and proactively manage incoming appointments, calls, and so much more. Combined with FSD, it will effectively become a complete mobile office. It is absolutely mind-boggling to think about the kind of world we will soon be living in.

译推文指出AI在汽车中的应用将成为真正的游戏规则改变者，特斯拉正通过将Grok集成到Tesla OS中来引领这一趋势。作者回忆了儿时《霹雳游侠》中与汽车对话的科幻场景，认为其正成为现实。Google和Apple的CarPlay是初步尝试，但未来汽车将能实时解析错误信息、提供路线更新、管理日程，结合全自动驾驶（FSD）成为完整的移动办公室。

宝玉@dotey · 5月31日51

Kimi Code、DeepSeek Harness 最好尽早做 GUI，尽早支持好办公任务，做通用 Agent。 卷 TUI 卷 Coding 没前途，当然 Coding 是基础能力，如果 Coding 都做不好其他任务也不会做得好。

译推文呼吁 Kimi Code、DeepSeek Harness 等 AI 编程工具应尽早提供图形界面（GUI），并拓展对通用办公任务的支持，以进化为通用 Agent。作者认为，仅在终端界面（TUI）和单一编程能力上竞争没有前途，尽管编程是核心基础。同时，推文引用并关注了另一个新选手 Grok Build，指出其更新迅速、潜力较大。

宝玉@dotey · 5月31日50

ChatGPT 的 Translate 功能做的不像是一个前沿 AI 团队的作品，像 10 年前的互联网产品经理设计的水平，ChatGPT 团队会被 Codex 团队合并不是没有理由的。 https://chatgpt.com/translate

译ChatGPT 的翻译功能做得不像前沿 AI 团队的作品，像 10 年前互联网产品经理的水平，ChatGPT 团队被 Codex 团队合并并非没有理由。

Orange AI@oran_ge · 5月31日48

AI 这么刚需的东西 微信官方应该早点自己支持 他们的 agent 至少应该支持吧 听说张小龙亲自操刀 如果不支持 md 渲染… 有点说不过去

译推文批评微信作为主流通讯工具，却不支持 Markdown 和 HTML 文件格式的渲染与便捷打开，导致文件分享封闭，尤其在移动场景下造成困扰。作者呼吁微信应更早重视并支持这类基础功能，并特别指出“AI这么刚需的东西”，微信至少应该在其智能体（Agent）功能上提供良好支持。引用推文也反映了相同的痛点：周围人频繁使用 Markdown 和 HTML 发文件，但微信对此一窍不通且封闭。

Orange AI@oran_ge · 5月31日64

今天大厂做的事情，以「AI 提效」为理由裁员，听起来像是在拥抱变化，其实恰恰相反。 它是在用最小的动作假装变化已经发生了，好避免面对那个真正痛苦的问题： 旧的仗打完了，新的仗是什么？ 如果回答不了，再裁一万人也没用。

译文章批评当前一些公司以“AI提效”为名进行裁员，认为这并非真正的变革。核心观点是，这种做法是用最小成本假装改变，回避了更根本的挑战：即在旧有业务模式结束后，公司未来的新战略方向是什么。作者指出，若无法回答这个关键问题，大规模裁员也无济于事。