Farzapedia：用 LLM 把个人笔记变成可浏览的私人百科全书
来源：X/Twitter (Karpathy) 原始链接：https://x.com/karpathy/status/2040572272944324650
English
Farzapedia, personal wikipedia of Farza, good example following my Wiki LLM tweet.
I really like this approach to personalization in a number of ways, compared to "status quo" of an AI that allegedly gets better the more you use it or something:
1. Explicit. The memory artifact is explicit and navigable (the wiki), you can see exactly what the AI does and does not know and you can inspect and manage this artifact, even if you don't do the direct text writing (the LLM does). The knowledge of you is not implicit and unknown, it's explicit and viewable.
2. Yours. Your data is yours, on your local computer, it's not in some particular AI provider's system without the ability to extract it. You're in control of your information.
3. File over app. The memory here is a simple collection of files in universal formats (images, markdown). This means the data is interoperable: you can use a very large collection of tools/CLIs or whatever you want over this information because it's just files. The agents can apply the entire Unix toolkit over them. They can natively read and understand them. Any kind of data can be imported into files as input, and any kind of interface can be used to view them as the output. E.g. you can use Obsidian to view them or vibe code something of your own. Search "File over app" for an article on this philosophy.
4. BYOAI. You can use whatever AI you want to "plug into" this information - Claude, Codex, OpenCode, whatever. You can even think about taking an open source AI and finetuning it on your wiki - in principle, this AI could "know" you in its weights, not just attend over your data.
So this approach to personalization puts *you* in full control. The data is yours. In Universal formats. Explicit and inspectable. Use whatever AI you want over it, keep the AI companies on their toes! :)
Certainly this is not the simplest way to get an AI to know you - it does require you to manage file directories and so on, but agents also make it quite simple and they can help you a lot. I imagine a number of products might come out to make this all easier, but imo "agent proficiency" is a CORE SKILL of the 21st century. These are extremely powerful tools - they speak English and they do all the computer stuff for you. Try this opportunity to play with one.
中文
Farzapedia，Farza 的个人百科全书，是我"维基 LLM"推文的优秀实践案例。
相比"AI 用得越多就越聪明"这种现状，我非常认同这种个性化方案，原因如下：
1. 显式可见。记忆制品是显式且可导航的（就是那个 wiki），你可以精确看到 AI 知道什么、不知道什么，可以检查和管理这个制品，哪怕你并没有亲自动手写文字（LLM 会代劳）。关于你的知识不是隐含的、不可知的，而是显式的、可查阅的。
2. 属于自己。你的数据就在你自己的电脑上，不属于某个 AI 提供商，你随时可以将其导出。你对自己的信息拥有完全的控制权。
3. 文件优于应用。这里的记忆就是一组通用格式文件（图片、markdown）的简单集合。这意味着数据是互通的：你可以用大量工具、CLI 或任何你想要的方式来处理这些信息，因为它们本质就是文件。AI Agent 可以完整运用 Unix 工具链，原生读取和理解这些文件。任何类型的数据都可以导入为文件，任何类型的界面都可以用来查看输出。比如你可以用 Obsidian 来浏览它们，或者凭感觉自己写个工具。搜索"File over app"可以了解这一理念的相关文章。
4. 自备 AI（BYOAI）。你可以用任何你想要的 AI 来"接入"这些信息——Claude、Codex、OpenCode，随便你。你甚至可以想象用开源 AI 在你的 wiki 上做微调——理论上，这个 AI 可以通过权重"认识"你，而不只是查询你的数据。
因此，这种个性化方案让你完全掌控一切。数据是你的，通用格式，显式可查。用你喜欢的 AI 来处理它，让 AI 公司保持警觉！:)
当然，这确实不是让 AI 了解你最简单的方式——确实需要你管理文件目录之类的东西，但 Agent 已经让这件事变得相当简单，而且能帮你大忙。我猜想未来可能会出现不少简化这些流程的产品，但说真的，"Agent 使用能力"是 21 世纪的核心技能。这些工具极其强大——它们听得懂英语，能帮你完成所有电脑操作。抓住这个机会去体验一下吧。