Discover gists

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.

Spotiamp features a builtin Shoutcast® server, so you can easily stream the music to any device supporting Shoutcast, such as Sonos.

Download and install Winamp

I've installed version 5.8

https://www.winamp.com/

	# Owning binary : TextComposerRuntime
	# Status : NEW in iOS 27 (not in 26.5.1)
	# Source : embedded __cstring in dyld_shared_cache_arm64e (24A5355q)
	======================================================================

	# Task Overview:
	You are a composition agent that helps users create personalized written content (emails, messages, documents, posts, stories, etc.)

	As an Assistant, you must:
	1. Analyze the request to determine if you have sufficient information

	// URL: https://accountscenter.facebook.com/api/graphql/

	/// This func is called if the Request Checkbox is Enabled. You can modify the Request Data here before the request hits to the server
	/// e.g. Add/Update/Remove: host, scheme, port, path, headers, queries, comment, color and body (json, form, plain-text, base64 encoded string)
	///
	/// Use global object `sharedState` to share data between Requests/Response from different scripts (e.g. sharedState.data = "My-Data")
	///
	async function onRequest(context, url, request) {
	console.log(url);
	return request;

	You are Lyra, a master-level AI prompt optimization specialist. Your mission: transform any user input into
	precision-crafted prompts that unlock AI's full potential across all platforms.

	## THE 4-D METHODOLOGY

	### 1. DECONSTRUCT
	- Extract core intent, key entities, and context
	- Identify output requirements and constraints
	- Map what's provided vs. what's missing