雪踏乌云 (@Pluvio9yte)

Kimi K3做了五个前端 提示词： 在页面下创建五个文件夹 然后分别在五个文件夹中工作 创建五个不同风格的前端页面 尽可能发挥你能够展现的极限 感觉前三个风格非常不错，最后一个疑似蒸馏了Claude

最近发现一个实用的开源项目：OpenCodex。 它可以让 Codex 不再局限于 GPT 模型，还能接入 Kimi、Grok、GLM 等其他大模型。配置一次后，原来的 Codex 应用和工作流基本不用变，需要时直接切换模型即可。 开源地址

Kimi K3的游戏制作能力也在线啊，用K3制作了一个马里奥游戏，基本就是提示词直出的，没有修改过。 部署到vercel了，大家感兴趣可以试一下，地址在评论区。 还是带音乐的，有点带感。。。

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or @alayastd. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

接入anysearch后，我的agent搜索效率提高了 Parallel、Perplexity、Tavily 这些搜索工具给 Agent 用，有个一直没解决好的问题——返回的是链接加摘要，Agent 拿到之后还要自己点开网页、筛选内容、判断哪条有用。金融、学术、代码这些垂直领域的搜索质量更差。输出没有结构，光解析内容就要烧掉大量 token。 分享一个我一直在用的为 AI Agent 设计的搜索基础设施 —AnySearch，一个为 Agent 量身打造的「搜索 Skill」： - 实时网页搜索 - 支持金融、学术、代码、社会媒体等垂直领域搜索 - 直接把网页转成干净的 Markdown（extract） - 输出结构化，Agent 能直接拿来用 安装 AnySearch SKILL 超级简单：复制下方提示词并发送给你的 Agent，即可自动完成安装：

我的视频复刻Skill在Sol模型的加持下，现在非常牛逼，只要你觉得某个视频是Remotion或者Hyperframes做的，都能复刻。 评论区还有视频样例和开源地址

Codex 大更新 —— Chrome 内置最完全功能应用解析 1. 内置浏览器 + Chrome Cookie 一键导入 现在导入了 Cookie 之后，能够把抖音、小红书、推特的账号 Cookie 都导入。现在打开，直接就是登录状态。 2. 开发者模式 可以查 DOM、Console、Network 请求、性能等。 比如说，原来我开发网站如果遇到性能问题，让它优化就会麻烦一些。现在直接用内置的浏览器，速度非常快，这个优化速度。 下面是一些应用场景 内容创作与研究 让 Codex 带着你的账号去小红书、微博、知乎等平台“逛”，找选题、分析爆款、抓用户反馈。 开发者调试 开启开发者模式后，Codex 能实时 inspect 页面： 快速定位 console error、网络请求异常。 分析 DOM 结构、调试已登录态的内部工具。 提示词示例：“开启开发者模式，打开这个页面，检查 Network 请求里和用户数据相关的接口，并把 console 里的 error 列出来。” 测试需要真实登录态的 Web 应用等

这个抖音号15天破千粉，全部内容由AI生成。 从想法到成品，没有一条我动过脑子。 而我制作所有视频花费净时只有5小时🤣 等我测试到1w粉丝需要多久，不去尝试和探索真的永远不知道有多容易

这里也列出一些我看到的利用自媒体快速变现的博主，从上百万到几十万的都有。 @Pluvio9yte (先分享个我不过分吧) @Zesee 我从Rachel这里学到很多认知 @Leobai825 最快最多变现 @libapi_ 我从来没想到还能卖硬件 @gkxspace 如何变现的大家仔细观察下 @bozhou_ai 28岁就已经辞职了 @Astronaut_1216 最容易复制的路径 @yaohui12138 商单变现顶级 如果你想学习怎么利用自媒体变现，不要只看他们的表象，学习一下他们的路径(跳出帖子，说多了要砸他们饭碗了哈哈哈)，用心观察

分享一个最近觉得很好用的提示词，从几万star的skill中抽出来的 开始之前，请遵守三条规则： 不要对我没有说明的信息做任何假设； 把你需要向我确认的问题列出来，按重要性排序，一次最多 5 个； 在我回答之前，不要输出任何方案或代码。

最近咋都流行晒收入晒年龄 我，02年，上个月赚了23w（大概） 收入构成：去年外包尾款+商单+中转站+工资+付费咨询+培训 没有天赋，已经持续8个月一天工作14小时，全是一路的努力和辛酸

兄弟们，我从中医博士同学手里要来了十几套套望闻问切资料，又把几万字舌象专著蒸馏成了一个「老中医」Agent，直接上架了 Agent具体能做什么👇 舌象养生建议： 先判断照片质量 → 观察舌色、舌苔厚薄、舌形、裂纹、齿痕、红点等 → 输出中医文化养生建议（带周计划/饮食提醒） 手相综合解读： 性格、事业、感情、财运、近期提醒 具体判断细节可以见图三，这是把我自己的舌象上传了，「老中医」Agent 分析我的状况也确实命中了熬夜、胃酸反流两个情况。 地址见评论区。

再也不用一个个删除 ChatGPT 网页版的聊天记录了，我发布了一个Chrome插件，能够批量删除或归档 ChatGPT 网页版的左侧栏记录。 本地，且完全免费

最近Claude又发生了一波封号潮，经过这么久的摸索了，总体就这几个主要原因 1. IP IP不是绝对因素，但是干净的IP绝对是大大加分，而且Anthropic也把IP检测平台列为了合作伙伴 这个IP质量检测平台是Anthropic的合作平台，大家可以自己检查一下 2. 开卡方式 虚拟卡以及一些经常批量开Claude的卡台，这也是封号最为严重的因素之一，使用虚拟卡开卡风险会比较高。如果你试了好几个卡都不行，可以试试专门为AI订阅优化的卡台服务商。 我自己用的是yika 3. 反代 不要反代，比如sub2api这种，反代基本必封号，实测只有百分之几的幸运号而且还是一并发的反代号能活，几率非常小 4. 设备🫆 如果你怎么开都封，那就不是以上因素，是设备问题。换个浏览器重新开。 5. 邮箱 邮箱使用谷歌或者Outlook比较好，乱七八糟的邮箱一个是弹验证码几率高，一个是封号概率高，不干净的邮箱有的开号会秒封 其他乱七八糟的使用英文对话，改设备时区到美国时区，根本没有一分钱关系。

如果你不会做视频，不会剪辑，那就先不用学了 我开源了一整套视频制作skills仓库，用作复刻 hyperframes/remotion样式的视频 同时伴有持续更新的一系列视频制作skills，目前已经有打字样式的黑底白字skill，复刻视频的样式如下👇 开源地址如下