@vista8

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Building apps has never been easier. With Sites, Codex can turn your work, ideas, and plans into an interactive website or app your team can explore, use, and share with a URL. Rolling out to Business and Enterprise plans, before expanding more broadly. Video

We just released the Codex Python SDK 🔥 You can now embed Codex directly into your Python apps and workflows! > Start threads > Run turns > Stream progress > Resume sessions > Pass images > Control sandbox access All whilst reusing your existing Codex auth. pip install openai-codex Go build with it!!

结合最近写skill的一些心得，总结了一个Skill设计五步法： 1、定义结果 创建skill之前，想清楚到底想要啥，包括结果的标准是什么？ 2、对齐标准 为了想清楚这个标准，会和AI做不少交流和探讨 3、深度研究 知道对于结果的标准后，会让GPT 5.5 Pro帮忙做一份深度调研，包括理论原理、相关案例、设计思路等 如果是大量的内部资料，也会通过AI做一轮系统性的梳理，同时再调研外部资料 4、消化成方法论 学习和理解这份报告，理解背后的逻辑、原则、流程、核心方法等内容 5、用meta-skill固化为Skill 把这个作为参考资料，再通过yao-meta-skill来进行创建，将skill的相关补充说明给到codex或cc进行创建 如此完成的第一版Skill，质量就会高很多，然后在这个基础上，持续迭代 写Skill，很像是一种高强度的“以教促学” 是一种很不错的快速学习的过程，在这个过程中，需要自己去深度理解目标和过程原理、流程和方法 当Skill完成后，对这个事情的理解，也就差不多七七八八了

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我的新书《AI 高质量论文写作法》上市。刚拿到崭新样书，很兴奋。相对于3年前《学术写作五步法》，补充了我近几年尝试将 AI 深度融入知识生产工作流的心得和经验，希望对你的研究推进和论文撰写能有实际帮助。感谢老麦和储殷老师的推荐，谢谢大家的支持😄🤝

So I spent some time studying the new Twitter/X algorithm today since the latest version was published about a week ago on Github (github.com/xai-org/x-algorit…). My goal was to answer why so many people have seemingly seen such a dramatic drop in their posts' reach. The first answer, which is actually somewhat unrelated to the ranking algorithm on Github, is the auto-translate feature, rolled out worldwide on April 7, 2026 (nitter.net/nikitabier/status/2041…). Before that date, if you wrote in English about, say, the Trump-Xi Beijing summit, you were competing for attention with maybe 5,000 other English-language accounts writing on geopolitics. After that date, your post is competing for attention with other posts on the same topic IN EVERY LANGUAGE ON EARTH. For some topics that do command global attention like geopolitics, that's a very brutal multiplier: you used to be one of 5,000, you're suddenly one of 50,000 (something of that order): MUCH more difficult to stand out. Secondly, the number of followers you have matters far less than it used to: each post now has to earn its audience reader by reader, on the predicted engagement of the post, and how its topic matches what each reader has recently been engaging with. Here is how the algorithm works, in simple terms: when you, as a reader, open your feed, the algorithm doesn't load "posts from accounts you follow." Instead it runs a 2-stage prediction of what posts you're likely to engage with in that very moment. The first stage is the retrieval stage. The system narrows billions of posts on X/Twitter that day down to roughly 1,500 candidates by matching the semantic content of each post - what it's about - against what you as a reader have recently engaged with. Some candidate posts come from accounts you follow; others are pulled from across the platform by pure topic similarity to your recent interests. You can test this retrieval stage easily: start disproportionally engaging with - say - Brad Pitt videos and you'll bit by bit see your timeline flooded with Brad Pitt content, most of it from accounts you've never followed and never heard of. Then there's the ranking stage. Each of these candidate posts for your feed is fed through a Grok-based model that tries to understand if you'll engage with the post. It looks at 15 engagement metrics: 1) P(favorite) — the reader likes the post 2) P(reply) — the reader replies to it 3) P(repost) — the reader reposts it 4) P(quote) — the reader quote-tweets it 5) P(click) — the reader clicks a link in it 6) P(profile_click) — the reader taps through to your profile 7) P(video_view) — the reader watches the video 8) P(photo_expand) — the reader expands an image 9) P(share) — the reader shares it (DM, off-platform, etc.) 10) P(dwell) — the reader stops scrolling and lingers on the post 11) P(follow_author) — the reader follows you after seeing it 12) P(not_interested) — the reader marks "not interested" 13) P(block_author) — the reader blocks you 14) P(mute_author) — the reader mutes you 15) P(report) — the reader reports the post Fifteen predicted actions, each multiplied by a weight, summed: that sum is the score that determines in which priority a post will be seen among other candidates. Please note that posting something with a video or an image can give your post an advantage as 2 actions are specifically for these: video_view and photo_expand. No video or photo and you don't get a score for these. Also, naturally, having a video maximizes the chance that a user will "dwell" on your post to watch it. Also note that 4 of these actions carry negative weights (not_interested, block_author, mute_author and report): meaning that if the model expects a post to generate a lot of negativity, it'll get de-boosted quite dramatically. But note, first and foremost, what's NOT in there: none of the things that, naively, one might think a serious information platform would weigh. There is no P(this post is true and well-sourced). No P(the author actually knows what they're talking about). No P(this person has spent a decade building a body of work that has held up). No P(this account has earned the right to be taken seriously on this topic). No P(the author has a large following from credible people). The model does not seem to care - at all - about any of that. Every post starts from zero. You could have ten years of rigorous, well-sourced analysis behind you - or you could be just an uneducated rando who registered yesterday. To this algorithm, you're both just a bag of engagement probabilities. Now, sure, to be fair, there is a "brand" effect that's not covered by the algorithm: someone who has in fact built a brand will naturally have better engagement metrics because people recognize their account. But that's an indirect, second-order effect. And crucially, it's legacy: those "brands" were built under earlier versions of the algorithm that gave followers and reputation more weight. Lastly, several other features of the new algorithm compound the dilution, none of them visible from outside but all consequential. The May 15 update added an "impression bloom filter," tightening the rule that once a reader has been served a post, the system won't serve it to them again. Before, a strong post could marinate in someone's feed across multiple refreshes and accumulate engagement on the second or third pass. Now it basically gets one shot. Also, your own posts compete with each other. An "Author Diversity Scorer" inside the ranking stage attenuates the score of every subsequent post of yours that ends up in a reader's candidate pool. In plain terms: if multiple of your posts land in a reader's candidate pool, the system shows one at full strength and dampens the others. So don't post several times consecutively on the same topic. And, last but not least, another huge impact on reach is that, in the old algorithm, when someone reposted or quote-tweeted you, your post was broadcast to their followers' timelines - a repost from an account with 100,000 followers was a huge boost. In the new algorithm, that mechanism is vastly demoted: reposts - like every post - need to go through the retrieval and ranking stage mentioned above, so a repost from a big account is a long way from the boost it used to be. This is especially brutal for low-effort quote tweets, which used to function as cheap amplification: now they often can't even clear the retrieval stage - they simply don't contain enough novel semantic content for the system to match them to anyone's interests. So, putting it all together, the reach collapse comes from many forces stacking at once: - Auto-translate makes your posts compete for attention against an order of magnitude more content - The retrieval stage matches posts by topic, not by who follows you - The ranking stage scores purely on predicted engagement with no weight for credibility, expertise, or track record - The bloom filter narrows every post's window to one strong shot - The diversity scorer penalizes prolific posting - Reposts no longer carry much distribution power Each of these alone would dent your reach. Combined, they amount to a complete reset: your audience that you built painstakingly over years basically doesn't matter much anymore, and it's much - much - harder to stand out even if you're a big account. People structurally rewarded by this algorithm are folks who: - Post visually (videos/images) - Post on globally popular topics because they clear the retrieval stage easily - Provoke strong emotional reactions - likes, replies, reposts - Don't care about accuracy or seriousness because the algorithm doesn't measure it - Don't care about their existing audience because every post is judged in isolation anyway In short this new algorithm, like so many on social media, is all about maximizing whether people will engage with something - not about whether they should.

今晚八点，会通过WaytoAGI做第一场GEO直播分享，会把GEO的底层逻辑、方法、系统原理及理念做一轮讲解，相关的一些资料和系统如下，分享给大家： 1、GEOFlow，今晚主讲的系统和背后的GEO原理 github.com/yaojingang/GEOFlo… 2、元Skill，创建Skil的Skill github.com/yaojingang/yao-me… 3、17套GEO Skill合集 github.com/yaojingang/yao-ge… 4、41篇最新GEO/AI搜索相关论文 github.com/yaojingang/geo-ci… 5、相关文章及文档 《GEO到底是什么》 mp.weixin.qq.com/s/GXuu0Hku-… 《从SEO到GEO，从流量到Agent，真正的变化才刚刚开始》 mp.weixin.qq.com/s/2P_zSjJky… 《GEO白皮书》 yaojingang.feishu.cn/docx/Jv… 《GEO红皮书》 yaojingang.feishu.cn/wiki/Ot… 《GEO蓝皮书》 yaojingang.feishu.cn/wiki/Mw… 《AI营销：从SEO到GEO》提示词合集 yaojingang.feishu.cn/wiki/Yb…