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  • Shanghai Jiao Tong University
  • Shanghai
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@ninehills
ninehills / chatglm-openai-api.ipynb
Last active April 16, 2024 01:15
chatglm-openai-api.ipynb
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@smx-smx
smx-smx / XZ Backdoor Analysis
Last active February 24, 2026 09:30
[WIP] XZ Backdoor Analysis and symbol mapping
XZ Backdoor symbol deobfuscation. Updated as i make progress
@dahaha-365
dahaha-365 / lazy_script.js
Last active June 1, 2026 12:59
Clash Verge Rev 全局扩展脚本(懒人脚本配置)
/***
* Clash Verge Rev 全局扩展脚本(懒人配置)/ Mihomo Party 覆写脚本
* URL: https://gist.github.com/dahaha-365/0b8beb613f8d1ee656fe1f21e1a07959
*/
/**
* 整个脚本的总开关,在Mihomo Party使用的话,请保持为true
* true = 启用
* false = 禁用
*/

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.