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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.

@smallnest
smallnest / multi-agent-config-instruction.md
Created March 3, 2026 03:33 — forked from 1va7/multi-agent-config-instruction.md
OpenClaw 多 Agent 架构配置指南 - 把这份文档发给你的 OpenClaw,它会引导你选择最适合的多 Agent 架构配置

OpenClaw 多 Agent 架构配置指南

把这份指南发给你的 OpenClaw,它会引导你选择最适合的多 Agent 架构。


使用说明

当用户把这份文档发给你时,请按照以下流程引导他们选择合适的多 Agent 架构配置。

@padeoe
padeoe / README_hfd.md
Last active June 10, 2026 16:10
CLI-Tool for download Huggingface models and datasets with aria2/wget: hfd

🤗Huggingface Model Downloader

Note

(2025-01-08) Add feature for 🏷️Tag(Revision) Selection, contributed by @Bamboo-D.
(2024-12-17) Add feature for ⚡Quick Startup and ⏭️Fast Resume, enabling skipping of downloaded files, while removing the git clone dependency to accelerate file list retrieval.

Considering the lack of multi-threaded download support in the official huggingface-cli, and the inadequate error handling in hf_transfer, This command-line tool leverages curl and aria2c for fast and robust downloading of models and datasets.

Features

  • ⏯️ Resume from breakpoint: You can re-run it or Ctrl+C anytime.
@y0ngb1n
y0ngb1n / docker-registry-mirrors.md
Last active June 8, 2026 14:28
国内的 Docker Hub 镜像加速器,由国内教育机构与各大云服务商提供的镜像加速服务 | Dockerized 实践 https://github.com/y0ngb1n/dockerized