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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.

@EtienneR
EtienneR / user.js
Created January 7, 2016 23:39
XMLHttpRequest RESTful (GET, POST, PUT, DELETE)
// Get all users
var url = "http://localhost:8080/api/v1/users";
var xhr = new XMLHttpRequest()
xhr.open('GET', url, true)
xhr.onload = function () {
var users = JSON.parse(xhr.responseText);
if (xhr.readyState == 4 && xhr.status == "200") {
console.table(users);
} else {
console.error(users);
@bartholomej
bartholomej / css-media-queries-cheat-sheet.css
Last active May 7, 2026 22:37
CSS Media Query Cheat Sheet (with Foundation)
/*------------------------------------------
Responsive Grid Media Queries - 1280, 1024, 768, 480
1280-1024 - desktop (default grid)
1024-768 - tablet landscape
768-480 - tablet
480-less - phone landscape & smaller
--------------------------------------------*/
@media all and (min-width: 1024px) and (max-width: 1280px) { }
@media all and (min-width: 768px) and (max-width: 1024px) { }