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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.

The Autonomous Agent Prompting Framework

This repository contains a disciplined, evidence-first prompting framework designed to elevate an Agentic AI from a simple command executor to an Autonomous Principal Engineer.

The philosophy is simple: Autonomy through discipline. Trust through verification.

This framework is not just a collection of prompts; it is a complete operational system for managing AI agents. It enforces a rigorous workflow of reconnaissance, planning, safe execution, and self-improvement, ensuring every action the agent takes is deliberate, verifiable, and aligned with senior engineering best practices.

I also have Claude Code prompting for your reference: https://gist.github.com/aashari/1c38e8c7766b5ba81c3a0d4d124a2f58

Common Bugs when Learning React

The following list tries to summarize some of the things that a developer may encounter while learning React. The list focuses on scenarios that result in actual bugs (things that don't work) or things that cause warnings in the console.

using class prop instead of className (for/htmlFor, etc...)
trying to set the style prop with a string
not having a parent element or fragment
not binding (at all or incorrectly)
using the wrong lifecycle hook
misspelling componentWillReceiveProps

(Unofficial) Discord server rules suggestions list

Author's Note

I'll start off with letting you know this is a fork from someone else. However, for some bizarre reason, this is the one everyone finds, so I better get round to updating this. Credit to Cristiano#2233 for the original idea.

Also, I've had a lot of people saying the rules are to strict. If you pick all the rules here, you're right, it would be very strict. However the rules below are guidelines! They are there for you to pick the ones you desire, you can ignore ones you don't want. Hopefully they might help with rules you wouldn't have thought of otherwise.

Generating Procedural Game Worlds with Wave Function Collapse

Wave Function Collapse (WFC) by @exutumno is a new algorithm that can generate procedural patterns from a sample image. It's especially exciting for game designers, letting us draw our ideas instead of hand coding them. We'll take a look at the kinds of output WFC can produce and the meaning of the algorithm's parameters. Then we'll walk through setting up WFC in javascript and the Unity game engine.

The traditional approach to this sort of output is to hand code algorithms that generate features, and combine them to alter your game map. For example you could sprinkle some trees at random coordinates, draw roads with a brownian motion, and add rooms with a Binary Space Partition. This is powerful but time consuming, and your original vision can someti

Advanced JavaScript Learning Resources

This is a list of advanced JavaScript learning resources from people who responded to this [Tweet][13] and this [Tweet][20].

[You Don't Know JS][3]
[Frontend Masters courses by Kyle Simpson][12]
[@mpjme][6]'s [YouTube videos][5]

Example usage

const express = require('express');
const List = require('../models/list');
const Crud = require('./crud');

const list = new Crud(List, { queryKey: 'publicId' });
const router = express.Router();
module.exports = router;

	"""
	The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp

	:toc: macro
	:toc-title: Sommaire
	:toclevels: 3
	:numbered:

	ifndef::env-github[:icons: font]
	ifdef::env-github[]
	:status:
	:outfilesuffix: .adoc
	:caution-caption: :fire:

	#!/bin/bash

	# Setup a Spotify Connect client
	#
	# Hardware:
	# - RaspberryPi3
	# - Creative Sound Blaster Play2
	#
	# Resources:
	# https://github.com/plietar/librespot