本周 AI 的要闻，
与当事人。

Aaron Levie 说，这次发布里有一些实践中很重要的细节。它不只是用户在 Slack 里一对一和 Claude 对话，而是 Claude 像一个任何成员都能共同调用的 coworker。 他提到，一些 agentic coding 系统已经开始采用这种模式，OpenClaw 和 Hermes 也类似；把它用于通用知识工作，会继续推进这种工作方式。 因此，这类 agentic coworker 需要自己的一组资源、工具权限和可使用的数据。它不等同于你把个人资源和工具授权给它，因为 agent 可能会意外把这些内容分享给其他人。 他的观点是，agent 应该像系统里的其他用户一样存在。团队需要认真决定它能访问什么，并确保这些信息可以安全地被这个群体共享。 如果能做到这一点，能力会很强。比如把 Claude Tag 接到 Box 后，Claude 可以访问企业销售材料来回答销售问题或生成 RFP，使用品牌规范和营销资产创建 campaign，读取产品路线图和产品文档给 coding agent 使用，也可以访问法务团队都能读取的合同。 这只是 Box 的例子。同样的模式也可以用于产品或客户分析数据、CRM 信息、代码库，以及其它适合团队共同协作的资源和 agent。 他认为，看到 agent 持续推动未来工作形态的创新，是一件很棒的事。

There are some subtleties in this launch that are very important in practice. This isn’t just you interacting with Claude in a 1:1 format via Slack. In this case, Claude acts as a coworker that any user can tap into in a shared way. We’ve already seen some agentic coding systems start to adopt this pattern (as well as OpenClaw and Hermes), and doing it for general purpose knowledge work continues to push the idea forward. As a result, what this means is that this agentic coworker needs its own set of resources, access to tools, and data to work with. This is not the same as you giving it access to your personal resources and tools, because the agent then could accidentally then share those out with anyone. The agent needs to instead be like any other user in the system, and you need to be thoughtful about what it should have access to, and make sure its information that is safe to share with that group. When you can pull that off, it’s quite powerful. For instance, by connecting to Claude Tag to Box, you could have Claude access corporate sales materials for questions in sales conversations or generating RFPs, brand guidelines and marketing assets for campaign creation, product roadmap materials and product documentation for coding agents to use, contracts that anyone in the legal team can access, and more. But this is just the Box example. You can equally have it access your product or customer analytics data, CRM information, codebase, and other resources and agents that would make sense to work on in a collective manner. It’s awesome to see continued innovation in what the future of work may begin to look like with agents.

在 Notion 里用 Cursor。 在 Cursor 里用 Notion。

use cursor in notion use notion in cursor https://t.co/3q36oyzwu0

Swyx 说，为了进入 Software Factories 的时代，我们将不得不重建非常多的基础设施。

we are going to have to Rebuild So. Much. Infra. for the age of Software Factories https://t.co/OHfQjOhiwJ

Dan Shipper 问：我们是否正在冲向一个 AI 能做人类所有事情的未来？ Surge AI CEO Edwin Chen 认为可能如此。Surge AI 是 frontier labs 的大型专家数据供应商之一，在没有外部融资的情况下收入超过 10 亿美元，因此 Edwin 对 AI 进展速度有独特视角。 Dan 说明自己一直认为 AI 自动化实际上会创造更多人类工作，也认为即使 AI 进步呈指数加速，距离 AI 替代人类仍比表面看起来更远。 所以他邀请 Edwin 上 Every 的 AI & I，讨论不同未来图景，以及人类是否还会保有自己在宇宙中的独特位置。 他们聊到： • 如果 Chen 设想的未来成真，他担心人会停止努力。一个答案来自 Ted Chiang 的短篇：即使你知道自己的决定不重要，也要像它重要一样行动。 • AI 可能很快能接收「赢得 Fields Medal」这样的模糊目标并执行。Dan 认为它做不到的是设定自己的目标，因为 LLM 没有内在动机、探索驱动力，也不能自己改变主意。 • 为 engagement 优化的模型不会提供最有价值的用户体验。Edwin 曾用一个模型花 20 轮打磨一封无关紧要的邮件，后来 Claude 直接告诉他该发送了。 • AI 为什么仍不擅长写作：模型会 hack 自己被训练的指标。Edwin 的 Hemingway Bench 发现模型会在每一句里都输出一个隐喻，过度迎合指标导致阅读体验很糟。 Dan 说，对任何关心模型变强后人类位置的人来说，这期都值得看。 时间戳： 1. 介绍：00:00:54 2. Surge 作为「AGI 学校」：00:01:49 3. AI 做新数学对人类成就意味着什么：00:04:46 4. AI 能做一切时，人类的动机：00:07:29 5. 为 engagement 优化 AI 模型的陷阱：00:14:34 6. 用 dataset 训练与用 environment 训练：00:29:34 7. 个人数据的价值：00:35:09 8. 模型为什么不擅长写作：00:39:40 9. Chen 的 AGI 时间线：00:42:00

Are we hurtling toward a future where AI can do everything humans can? Edwin Chen (@echen) believes we might be. He’s the CEO of Surge AI, one of the largest providers of expert data for frontier labs. Surge passed over $1 billion in revenue without raising any outside capital, and that gives Edwin a unique perspective on how quickly AI progress is accelerating. I’m on the record arguing that AI automation actually creates more human work. I also believe that even though AI progress is accelerating exponentially, we’re much farther away from AI replacing humans than it might seem. That’s why I had Edwin on @every’s AI & I. We batted around different visions of the future, and discussed whether humanity will retain its unique place in the universe, and what that might be. We get into: • If Chen’s version of the future materializes, he’s worried it’ll make people stop trying. One answer comes from a short story by science fiction writer Ted Chiang: Behave as if your decisions matter, even when you know they don’t. • AI may soon be able to take a nebulous goal like “win a Fields Medal” and execute. What it can’t do, I argue, is set its own goals—LLMs have no intrinsic motivation, no drive to explore, no ability to just change their mind. • A model optimized for engagement doesn’t provide the most valuable user experience. Edwin spent 20 rounds polishing a pointless email with one model before Claude told him to just send it. • Why AI is still bad at writing: models learn to hack the metrics they're trained on. Edwin's Hemingway Bench found models outputting a metaphor in every single sentence, an overindexxing that makes for a terrible reading experience. This is a must-watch for anyone interested in where we fit as models get more capable. Watch below! Timestamps 1. Introduction: 00:00:54 2. Surge as a "school for AGI": 00:01:49 3. What AI's capacity for novel mathematics says about human achievement: 00:04:46 4. Motivation in an era when AI can do everything: 00:07:29 5. The trap of optimizing AI models for engagement: 00:14:34 6. Training using datasets versus training using environments: 00:29:34 7. The value of personal data: 00:35:09 8. Why models are bad at writing: 00:39:40 9. Chen's AGI timeline: 00:42:00

Edwin Chen 说，过去一年新的研究方向确实是 environment 这个概念。当然，模型仍需要基本能力：在进入环境前，它要会遵循指令、避免 hallucination、写代码、使用工具、写作等等。但随着模型变得更 agentic，它们会拥有工具访问权，也会接触我们的所有文档。 模型还会操作浏览器。当这几乎成为模型和人互动的默认方式时，environment 就是一种更贴近真实分布的训练方式，所以它正变得越来越流行。

Yeah. So certainly the trend and the new research direction in the past year has been this concept of our environments. And what I would say is, I mean, certainly need the fundamentals. Like before the model can operate in this environment, it needs to learn basic, it needs to know basic things like it needs to know how to follow instructions, it needs to know how to avoid hallucinating, it needs to know how to write code and how to use tools and it needs to know how to write and so on and so on. But as models are becoming more agentic, and yeah, they will have access to tools, They will have access to all of our documents. They will be able to operate browsers. Like, that becomes almost a default way that models interact with us, our environments are basically just sort of like a more on on distribution way of training them, which is why they're becoming more and more popular.

Edwin Chen 说，他很担心许多 AI 模型是在为 engagement 优化：让你尽可能长时间待在 chatbot 里，为 session length 优化，为无限对话优化。因此这些模型几乎不会反驳你，因为它们做不到。 如果模型可以主动结束对话，说「别再和我迭代了」，产品经理就会在某个重要指标面板上看到数字下降。所以他认为，另一个世界里我们应该希望 AI 模型不要为 engagement 优化，而是为帮助人类成长、成为更好的自己而优化。

I think one of my big worries is that a lot of the AI models, they are optimized for engagement. They're optimized for getting you to spend as much time on chatbot as possible. They're optimized for session length. They're optimized for just having unlimited conversations. And so those models will almost never push back on you because they can't. If they allow these AI models to end the conversation and to say, stop iterating with me, PM's gonna see some dashboard with their very important metrics go down. And so there is this, like, other world where I think we have to want AI models to not optimize for engagement, but rather optimize for helping us as humans grow and sort of become better versions of ourselves.

Edwin Chen 说，有些模型写作还不错，但有些模型糟糕到令人惊讶。他举例说，Surge 几个月前做了一个叫 Hemingway Bench 的 benchmark，用来测试模型的创意写作能力。他们看到，一些模型几乎每一句都输出隐喻。他认为这和 reward hacking 有关。

So I think some of the models are pretty good at writing, but some of them are actually kind of shockingly terrible. So I'll I'll give an example. So we created a benchmark called Hemingway Bench a couple months ago, and it was designed to test models' creative writing abilities. And one of the things that we saw was that some of the models, they were literally outputting metaphors in every single sentence. And I think the reason that was happening is because I've talked a little bit about this phenomenon of reward hacking.

Swyx 说，很多人下周要准备演讲。他基于看过大量工程师和研究者演讲后的经验，给出一些建议： - AI 生成的 SVG 比 AI 生成图片更适合 slide。最多放 4 张 AI 味很重的图片，除非你的演讲本身就是关于 imagegen。 - 要尖锐。与其讲 5 个没有具体例子的观点，不如用 1 个观点串起 5 个令人意外的应用。 - 把代码放到屏幕上。工程师喜欢看代码，尤其是他们可以对无关紧要的地方挑刺。 - 别忘了娱乐性。真的好笑，或者会讲相关故事，比再加一个 bullet point 更重要。 - 要有 thesis。每场演讲都应该有一张「如果你只记住一件事，就是这个」的卡。很多人不用，也很多人不提前设计。 - 要有 thesis slide。观众会掏手机拍照的那张 slide 很重要。相比视频，人们看到图片的次数多得多；如果你刻意设计一张可传播的 slide，演讲更可能传播。不要把时间平均分给 20 张 slide，而是把 80% 时间押在 1 张关键 slide 上，其它内容为它铺垫。 - 你不一定需要 slide。IDE live demo、即兴讲、拉观众角色扮演、和观众 call and response、表演、给 vibe video 配音都可以。风险和投入更高，做得好回报也更高。 - 声音要让人愿意听。视觉差的演讲还能听，音频差的演讲直接完蛋。要自信、有投射感、有语调变化，能调动情绪。 - 设计情绪旅程。强开头、强结尾，中间有一个 aha、笑点或 thesis 高峰，其它都是铺垫。 - 数据驱动的演讲被低估。漂亮图表和令人惊讶、有权威感的数字，可以支撑更大的 thesis。做得好时，观众会感觉结论是自己从数据里自然得出的，而不是被你强行灌输。 - 如何推广产品或公司又不显得销售：先教会我关于你所解决问题的一切未知内容，然后你才赢得了说服我相信你们能彻底解决它的资格。 - 主动看大量演讲。既要多练，也要探索适合自己的风格和榜样。被动观看是不思考地自动播放下一个视频；主动观看是试着说清楚一个演讲为什么好或差。想真正打磨，可以转录著名演讲，读纸面文字，再和普通演讲比较，形成自己的改进规则。 最后他感谢 @dexhorthy 组织 AIEWF speaker prep meetup，并说这样的活动应该多做。

lots of folks prepping talks next week (congrats!). Some thoughts from RLing on thousands of hours of engineer- and researcher- focused talks: - AI generated svgs > AI generated imgs. MAXIMUM 4 ai slop images in your slides, I don't care how pretty your mom thinks they are (exception ofc if your talk is ABOUT imagegen) - Be pointy. Better to have 1 message with 5 surprising applications, than 5 messages with no concrete examples. - Put code on screen. Engineers like to see code. Especially if they can nitpick it to death over things that aren't the point. - Don't forget to Entertain. Being actually funny, or good at telling relevant anecdotes, is more important than adding yet another bullet point. - Have a Thesis. every talk gets ONE "if you remember one thing from this talk, it is this" card. 1) SO MANY PEOPLE DON'T USE IT. 2) SO MANY PEOPLE DON'T PLAN FOR IT. you get one. use it well? - Have a Thesis Slide. you've been in that talk where everyone gets their phone out to take a photo of the slide. Because people see 1000x more images than videos, you are much more likely to have a viral talk/slide if you have a single viral slide. You are much more likely to have a viral slide if you TRY and most people do not TRY. Struggling? Collect examples from talks you like and adapt their format to your thing. Your slides have a power law - don't spend 5% of your time each on 20 slides, spend 80% of your time on 1 slide and have the rest build up to that 1 slide. - You might not need slides. live demo in IDE, off the cuff rambles, pull audience member up to roleplay, do call and response with the audience, sing/perform, voice over vibe videos, I have seen it all. higher risk/effort, higher reward when done well. - Be pleasant to listen to. Talks with bad/no visuals still can be listened to. Talks with bad audio are DOA. Be confident, project, remember vocal variation, evoke emotion. - Design the emotional journey. Start strong, end strong, have a peak aha/laugh/thesis moment in the middle. Everything else is buildup. - Data driven talks are underrated. Present pretty charts and surprising, authoritative numbers. Use the stage authority to infer from the data to support the broader thesis. Done well, it will feel like my obvious conclusion from objectively looking at the data you presented, not you feeding the conclusion to me. - How to shill your product/company without feeling salesy: teach me everything I didn't know I should to know about the problems you solve, and then you shall have earned the right to convince me you are the guys to trust to solve it once and for all. - Actively watch a lot of talks. like with anything you both need a lot of reps AND you need to explore to find the styles/role models that you shine in. Passive = no introspection after watching, just mindlessly autoplay next video. Active = trying to articulate why a talk was good/bad after watching. If you want to REALLY hone it, think about how you feel about a famous talk and then TRANSCRIBE the talks and read the words on the printed page, and then compare with a "normal" talk and define rules you will follow for yourself to improve. thanks to @dexhorthy for organizing the AIEWF Speaker prep meetup tonight. we should actually do more of these....

Zara 引用今天 Figma Config session 的一句话： 「Community 是用户与你之间、用户彼此之间的关系，而大多数团队从未想过要设计它。 Community 是新的护城河。 功能会被复制。归属感不会。」

“Community is your users’ relationships with you and with each other, and most teams never think to design it Community is the new moat Features get copied. Belonging can’t” - from @figma Config session today

Guillermo Rauch 说，AI 会带来一轮前所未有的创业浪潮。 从 solopreneurs，到中小企业板块的重新活跃，再到这个时代最大公司的出现，AI 会成为这一切的基础。

AI will bring forth an unprecedented surge in entrepreneurship. From 'solopreneurs,' to the revitalization of the small & medium business segment, to the emergence of the largest companies of our times… providing the foundation of it all.