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.

這篇文章旨在作為快速參考與展示。要更多完整的資訊，請見 John Gruber 原本的規格與 Github 偏好的 Markdown（Github-flavored Markdown，簡寫為GFM）資訊頁。

如果你正在找 Markdown Here 的小抄（Cheatsheet），這裡也有一篇。你也可以看看更多 Markdown 的工具。

譯註：可以參考這份中文版文件，有更詳盡的 Markdown 語法說明；如果需要可以練習的線上編輯器，可以試試看HackMD。

Headers

ALL INSTALLATIONS ASSUME YES WHEN PROMPTED, that's what -y does

This script can be copy paste to ssh as is. No hands installation. :-)

yum install zsh -y