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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.

@dosdude1
dosdude1 / patchgb.sh
Created April 23, 2026 01:49
Geekbench 6 macOS engineering sample detection patch
#!/bin/sh
if [ $# -eq 0 ]; then
echo "Usage: patchgb.sh <path to Geekbench 6.app>"
exit 1
fi
gbPath="$1"
if [ ! -d "$gbPath" ]; then
@GGPrompts
GGPrompts / claude-code-mcp-cli-experimental.md
Created December 9, 2025 15:18
Claude Code MCP-CLI Experimental Mode - 80% Token Savings

Claude Code MCP-CLI Experimental Mode

Announced December 8, 2025 by Anthropic engineer @catherinewu

The Problem

MCP servers load full tool definitions into the system prompt at session start. Power users with multiple MCPs (supabase, tabz, shadcn, docker-mcp, etc.) can burn 40-50k tokens before typing anything.

The Solution