一种用自然选择驱动个人知识库新陈代谢的模式。
大多数人的笔记系统是图书馆：你写一篇笔记，它被归档，从此静静躺着。偶尔被翻出来，但更多时候永远沉底。笔记只会被「更新」——有新信息就补上去。它不会自己生长、不会淘汰过时的内容、不会发现两个从未被放在一起的想法之间有惊人的关联。Karpathy 的 LLM Wiki 解决了「编译」问题——让 LLM 帮你维护知识库的一致性和交叉引用。但它本质上还是一座越来越厚的百科全书。没有新陈代谢。
这里的想法不同。笔记不是静态文档，是有机体。整个笔记系统不是图书馆，是生态系统。有机体会繁殖、变异、竞争、死亡。生态系统会自发涌现出你从未预期的新物种。驱动这一切的引擎，是达尔文的三件套：
Audit this entire codebase for materially useful simplifications in its data structures, state representation, control flow, algorithms, and ownership.
This is an audit-only exercise. Do not edit files, run tests, implement recommendations, commit, or push. Read-only inspection commands are allowed.
You are the coordinator. Continue until the complete codebase has been reviewed and the final audit is validated.
Inspect the repository and inventory every identifiable subsystem.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Caution
As of April 7th 2026, Discord has expressed their intent to crack down on automating quest completion.
Some users have received the following system message:
There isn't much I can do to make the script undetected, so use it at your own risk, as you most likely WILL get flagged by doing so.
| Filter | Description | Example |
|---|---|---|
| allintext | Searches for occurrences of all the keywords given. | allintext:"keyword" |
| intext | Searches for the occurrences of keywords all at once or one at a time. | intext:"keyword" |
| inurl | Searches for a URL matching one of the keywords. | inurl:"keyword" |
| allinurl | Searches for a URL matching all the keywords in the query. | allinurl:"keyword" |
| intitle | Searches for occurrences of keywords in title all or one. | intitle:"keyword" |
| Alligator | |
| Anteater | |
| Armadillo | |
| Axolotl | |
| Badger | |
| Bat | |
| Beaver | |
| Buffalo | |
| Camel | |
| Capybara |
| #!/usr/bin/env python3 | |
| # /// script | |
| # requires-python = ">=3.10" | |
| # dependencies = [] | |
| # /// | |
| """Prune uv-managed Python installs, keeping only the newest patch per minor version. | |
| Why: uv installs a fresh patch release whenever one lands and never removes the old | |
| one, so `uv python list` slowly fills with 3.13.12, 3.13.13, 3.13.14, 3.13.15, and so | |
| on. Each of those is a complete standalone copy of Python sitting in |