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.

在预备课程当中，我们已经初步介绍过如何使用 Docker 启动 Tensorflow 环境，并把 Docker 内部的 jupyter notebook 端口暴露给本机。其中介绍的主要命令包括

docker run -it -p 8888:8888 tensorflow/tensorflow

用于以某个 image 为模板，启动一个 container （注意区分 image 和 container 的概念）

docker ps, docker ps -a

分别用于查看正在运行的 container，以及所有的 container（包括已经停止的）

docker start -i container_id

其中 container_id 是通过 docker ps -a 命令看到的，用于启动一个已经停止的 container

docker stop container_id

停止一个正在运行的 container

大家在使用 docker 的时候，可以把 image 理解成一张『操作系统』的光盘，container 就是一台用这张光盘装好系统的『虚拟计算机』。

It's a common confusion about terminal colours... Actually we have this:

• plain ascii
• ansi escape codes (16 colour codes with bold/italic and background)
• 256 colour palette (216 colours + 16 ansi + 24 gray) (colors are 24bit)
• 24bit true colour ("888" colours (aka 16 milion))

printf "\x1b[${bg};2;${red};${green};${blue}m\n"