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@m3y54m
m3y54m / terminal-proxy-settings.md
Last active June 29, 2026 14:08
Set Proxy in Terminal (Bash, CMD, PowerShell, etc.)
@iscle
iscle / gist:66e946553e74a883b4494d3b6df0ee82
Last active November 19, 2025 16:59
Install python2.7 on Ubuntu 23.04 as "python"
wget https://www.python.org/ftp/python/2.7.18/Python-2.7.18.tgz
tar xzf Python-2.7.18.tgz
cd Python-2.7.18
sudo ./configure --enable-optimizations
sudo make altinstall
sudo ln -s "/usr/local/bin/python2.7" "/usr/bin/python"
@tjumyk
tjumyk / Ubuntu_24.04_Install_Sogou_Pinyin.md
Last active July 14, 2026 03:16
Ubuntu 24.04 Install Sogou Pinyin
  • Remove ibus
sudo apt purge ibus
sudo apt autoremove
  • Install Sogou Pinyin and dependencies
# Download deb installer from https://shurufa.sogou.com/linux
sudo dpkg -i <sogou_xxx.deb>
sudo apt install -f

LLM Wiki

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.

The core idea

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.