全部 AI 动态

昨天我老婆问我 App Store 怎么付费，看了一下才知道她要给儿子买一个打字 App，收费 128 块。。 这。。小孩和女人钱这么好赚？？ 我说等我半个小时，打开 Codex，把打字游戏的需求输入进去，一个 Goal 跑起来，20 分钟就做好部署到 ChatGPT Sites 了。花了几分钟看了一遍，还不错，顺手再加了一个实时拍摄人像，让头像作为游戏主角的功能，30 分钟准时交活儿。 效果怎么样呢？我第一版做了 36 关，儿子一连玩了 18 关 😁😁😁，我边看他玩，边看有什么问题，边让 Codex 改，顺便增加了几十关，和无限模式。。 以后儿子再有什么想玩但花钱的游戏和应用，自己做就行了，大家感兴趣可以看这: https://keyboard-island-doudou.groovy-river-7567.chatgpt.site

Quotes, positions, open orders—too many tabs? Build an investment assistant for Hong Kong and US stocks with WorkBuddy + Futu MCP + Hy4 preview. The walkthrough covers setup, permissions, account checks, and tracking orders, with prompts you can reuse. 👇 https://x.com/i/article/2112200966141575169

https://x.com/i/article/2112152891670896640

added Fish Audio’s Drama-3 to speech-to-speech 🎭 cloned my voice. now I can ask it to whisper, laugh, sigh or get worried, just by talking. cheers to @andimarafioti for the demo + sampling tips! PR + setup: https://github.com/huggingface/speech-to-speech/pull/725

Another fantastic use case for personal agents: study prep I'm preparing for a technical certification right now, and I've trained my @Muse agent on all of the prep exams / materials. Then, I have it sending me random pop questions throughout the day, tracking my progress. After weeks of not being able to find hours to sit down and study, this method has worked very well for me: about a question or two each hour of the day, outside of the meetings that I am in (it knows my calendar). Highly recommend!

Give your agent a way to verify its work. This can dramatically improve the quality of output. With this process in place, Claude Code was able to write 90%+ of its code itself. When generating software at a faster rate than you can review it, the bottleneck shifts to proving that it works. @bcherny and team invested enough in verification, we've been able to move fast while maintaining stability. Code reviews aren't going away, but you need to think of it differently. One human reading through line-by-line is no longer going to work as a gate, it needs to evolve into a multi-layered verification system, built with fast deterministic feedback loops for your agents. This is where e2e tests, feature maps (as @poteto has been advocating), property tests and multi-agent adversarial reviews come in. These all provide opportunities to catch errors early and fix them before things get out of hand. You don't have enough eyeballs for the amount of code being written so your focus needs to be on product intent, risk assessment and evaluating evidence rather than reviewing code. Delegate the code checking work to your automated tests, scale your approvals based on risk, and turn your test failures into living evaluations. This approach helps maintain speed without losing reliability. Let the agents do what they do best and let you focus on what makes you irreplaceable: judgement, direction, and ownership. We tried to capture how we handle code review and verification in this writeup. Hopefully it's helpful.

Stanford、CMU 和 MIT 三所顶级大学的 5 门 AI 课程之外，再分享两门更偏理论和基础的课程。 CS336: Language Modeling from Scratch Stanford AI 课程链的顶点实现课，由 Percy Liang 与 Tatsu Hashimoto 联合执教，从零亲手构建一个完整的语言模型。 CS221: Artificial Intelligence: Principles and Techniques Stanford AI 的本科主干入门课，由 Percy Liang 执教，用统一的数学框架（学习、搜索、MDP、博弈、贝叶斯网络、逻辑）把整个经典 AI 领域走一遍。 这两门课程都来自 Stanford 大学，讲师都有 @percyliang (@stanfordnlp 教授，@togethercompute 联合创始人）。 CS336 课程地址 https://cs336.stanford.edu/ https://www.youtube.com/playlist?list=PLoROMvodv4rMqXOcazWaTUHhq-yembLCV CS221 课程地址 https://stanford-cs221.github.io/autumn2025/ https://www.youtube.com/playlist?list=PLoROMvodv4rMeDqwS1yFl3j3sR_-MQNEN

If we only use AI to make the things that we already make, just faster, then we're definitely missing out on the most interesting part. From my talk at @lennysan’s Summit, here are four ideas for raising the ceiling, not just the floor: - Have a point of view [5:38] - Encode your standards into the machine [9:07] - Don't confuse done with good [12:21] - Unleash creativity and artistry [16:19] Full talk here: https://youtu.be/GLvFTMtw4Jk?si=-5TsYAjoFtgJBUS4

Claude 编写了 Claude Code 约 90% 的代码。 从六月到九月，合并的 PR 数量翻了一倍多，而回退的占比下降了 40% 以上。 我们撰文介绍了我们如何改变代码审查来实现这一目标，以及如何采用它： https://claude.dev/blog/solving-code-review-at-anthropic/

北京上海杭州深圳的AI岗位在哪? 我花了5天时间, 用AI爬取了融资规模1000万美元以上的AI公司, 上市互联网公司, 量化公司和一些neo labs的AI岗位, 并按照城市地区进行聚类. 正值秋招季, 欢迎大家体验使用, 完全免费! 网址: https://jobs.dinq.me 如果有任何想法和需求欢迎联系我!

Claude 编写了 Claude Code 约 90% 的代码。 从六月到九月，合并的 PR 数量翻了一倍多，而回退的占比下降了 40% 以上。 我们撰文介绍了我们如何改变代码审查来实现这一目标，以及如何采用它： https://claude.dev/blog/solving-code-review-at-anthropic/

how do you trust your agents' work more? copy this post/link and give it to your @bot or agent! 1. codebase improvements: this is just good software engineering - better architecture, stronger types and invariants, clear boundaries. agents love to copy what exists and take the shortest path, so make the easy thing the right thing. categorically eliminate problems through code and you won't need to think about it again. 2. static analysis: lint rules, tests, CI, verification (eg TLA+). very token efficient ways to catch issues ahead of time. 3. prose: comments, rules, AGENTS.md, skills - anything that can't be enforced and requires agent judgment. use it minimally, but do use /create-verification-skill. 4. human review: the hardest to scale. at the rate agents land code, it's pretty much impossible to thoroughly review every PR, so spend that effort on the highest leverage point, designing an environment where agents do the right thing by default. this forms an inverse pyramid: you want most of your effort at the top, least at the bottom. codebase improvements are deterministic, making it more token efficient, and lets you sleep more soundly at night. install https://github.com/cursor/plugins/tree/main/pstack and create a new Project: 1. look through my slack/chat messages and code review comments where i've given feedback on PRs, code, architecture 2. use /recall on my past transcripts to learn how i steer agents 3. /poteto-mode use /correct and /architect on past commits to find the most common pitfalls agents fall into, then propose a plan to rearchitect the code i own so it's more agent friendly. answer open questions with prototyping rather than defer to me, and back the plan with real data. 4. before you begin, use /sharpen + /in-your-own-words and confirm this prompt with me

Set you Grok bot voice to Ara on 1.25x and thank me later 😄

Voice calls with Grok Bot are faster, more reliable, and better at remembering past conversations.

Software maintenance is changing. A few months ago, I had to tell Codex exactly where to look for dead code. Now it can find candidates in Slack, check them against production telemetry, and draft cleanup PRs. Here’s how we do this at OpenAI. https://developers.openai.com/blog/maintaining-software-with-the-gpt-6-family

Claude 编写了 Claude Code 约 90% 的代码。 从六月到九月，合并的 PR 数量翻了一倍多，而回退的占比下降了 40% 以上。 我们撰文介绍了我们如何改变代码审查来实现这一目标，以及如何采用它： https://claude.dev/blog/solving-code-review-at-anthropic/