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Swyx
Latent Space 联合创始人
2026-05-23 06:33
学习变压器的框架
这条推文讨论了学习变压器的有效性及其局限性,提到作者与@ankit2119在今年早些时候撰写的关于对抗性世界模型的必要性,描述了这些模型的某些功能。
co-sign. a very handy mental framework for what kinds of learning transformers do well today, and why it runs into limitations. when @ankit2119 and i wrote about the need for adversarial world models earlier this year, we were describing a couple of the functions of these rungs https://t.co/XK81Dn6GP2
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未来将会很精彩!
这条推文表达了对未来事件的期待,强调将会有激动人心的事情发生。
It’s going to be epic!
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OpenAI与Anthropic的竞争
处理了1400条回复,显示OpenAI正在追赶Anthropic,'Codex'的提及次数超过了'Claude Code',但按模型提及次数来看,A\仍然占据优势。
Processed 1400 replies
◾ OpenAI is catching up to Anthropic
◾ 'Codex' got more mentions than 'Claude Code'
◾ However, by model mentions, A\ is mogging https://t.co/BjtqVGmlUg
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展示你最自豪的AI作品
这条推文邀请用户分享他们使用AI构建的最自豪的作品,要求回复包含有效产品链接及主要使用的模型或代理。
Show me the thing you’ve built with AI you’re most proud of. Reply with a working product URL and what model / agent you primarily used.
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工作不会消失的原因
这篇文章探讨了为什么工作不会像某些人预测的那样消失,强调我们常常混淆任务完成与整个工作的消失,即使我们可以自动化工作中的某些任务,工作的定义依然存在。
This is a fantastic post about why jobs aren’t going away in the way some predict. We are constantly making the mistake of confusing task completion with AI with being able to eliminate the whole job.
Even as we can automate one or many tasks within a job, the definition of the
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工程师永远不会消失
这条推文指出,工程师不会消失的原因在于我们已经大大简化了创建和发现安全问题的过程,因此新的瓶颈在于我们实际审查、响应和修复这些问题的能力。
Here’s a key line in this mythos update. This is precisely an example of why engineers don’t go away, ever.
We’ve made it far easier to create and find security issues, which means the new bottleneck is our ability to actually review, respond to, and fix the issues.
Far from https://t.co/dpJUdu0Uvh
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AI未来的两种工作
Bob McGrew提出的框架认为,在AI的未来中,只有两种工作:孤独的天才和经理。其他一切都会被吸收,孤独的天才是指在电脑前独自工作的人,受到AI的1000倍放大。
Bob McGrew has a framework I keep thinking about: in the AI future there are only two jobs. The Lone Genius and the Manager.
That's it. Everything else gets absorbed.
The Lone Genius is the person sitting alone at a computer, amplified 1000x by AI. One person with taste,
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不可扩展的事情
这条推文认为“做不可扩展的事情”不仅是与早期用户建立关系,还包括在最大密度下产生错误。当手动进行所有操作时,每小时都会出现错误,每个错误都能教会你一些东西。
Everyone thinks "do things that don't scale" is about building relationships with early users.
Yes AND it's about generating mistakes at maximum density.
When you're doing everything manually (onboarding, support, delivery) you hit errors every hour. Each error teaches you
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初创企业面临的挑战
Geoffrey Moore指出,初创企业在沟壑中死亡是因为务实的买家要求“完整产品”,他们不容忍缺口,需要参考和完整解决方案。因为买家不会在没有完美的情况下购买,所以这个沟壑是致命的。
Geoffrey Moore says startups die in the chasm because pragmatist buyers demand a "whole product." These folks won't tolerate gaps. They need references. They need the complete solution. The chasm is lethal because because the buyers won't buy without perfection.
But Moore's
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B2B公司的机会
这条推文提到作者在近一年前写的内容,现看到越来越多的B2B公司意识到这一机会,呼吁不要再等一年,叙事和氛围在众多竞争中极为重要。
Wrote this almost a year ago, and finally starting to see more B2B companies wake up to this opportunity..
Don’t wait another year. Narratives and vibes are extremely important to stand out amongst the slop 🪄
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Codex 生成了一个笑脸?
Codex 似乎生成了一个笑脸表情符号,表达了一种轻松愉快的情绪。
codex... made a smiley? :) https://t.co/UJc4X4A8q1
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Twitter 繁忙时的替代方案
如果 Twitter 太忙,可以尝试这个链接,提供了一个替代的平台。
It Twitter's too busy for you, try https://t.co/eykEElx1Ez https://t.co/dlq25G9hno
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自动分类技能的开发
我为 Codex 开发了一个自动分类技能,包含一套指导方针,并从我的仓库中读取 VISION.md。现在,符合项目愿景、代码推断清晰、修复明确且可进行实时测试的问题和拉取请求都能被自动处理。
I built an autotriage skill for codex that has a set of guidelines + reads VISION.md from my repos, so issues/prs that have a clear way of
- fit vision of the project
- being inferrable in code with high confidence
- clear fix
- can be live tested
Are now worked on autonomously. https://t.co/dTN8BD5P71
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更具意识形态而非技术性
这似乎更倾向于意识形态而不是技术问题。
This seems more ideological than technical.
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cmux 的赞美
我虽然来得晚,但 cmux 非常棒。当前的分工是:Codex Mac 应用用于知识工作、学习和阅读,而 cmux 加 Codex CLI 则用于编码。
I'm late to the party, but cmux is great. https://t.co/8uuStvqwcm
current split:
codex mac app: knowledege work, learning, reading
cmux + codex cli: coding https://t.co/ozQZ8vZQ8k
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AI 发展的反思
你能想象在六个月前(2025 年 11 月),我们主要与大型语言模型(LLMs)聊天并对此感到高兴的世界吗?现在是 2026 年 5 月,这些 LLMs 生成的代码比我们所有人写的代码还要多。
Can you imagine that we lived in a world 6 months (Nov 2025) ago when we would mostly just chat with LLMs and would be so happy about AI??
It's May 2026 and these LLMs now have produced more code than we have written over all time.