Kuririn kuririn1

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.

description

Interview me about the plan

argument-hint

plan

model

opus

Read this plan file $1 and interview me in detail using the AskUserQuestionTool about literally anything: technical implementation, UI & UX, concerns, tradeoffs, etc. but make sure the questions are not obvious.

Delete unused or obsolete files when your changes make them irrelevant (refactors, feature removals, etc.), and revert files only when the change is yours or explicitly requested. If a git operation leaves you unsure about other agents' in-flight work, stop and coordinate instead of deleting.
Before attempting to delete a file to resolve a local type/lint failure, stop and ask the user. Other agents are often editing adjacent files; deleting their work to silence an error is never acceptable without explicit approval.
NEVER edit .env or any environment variable files—only the user may change them.
Coordinate with other agents before removing their in-progress edits—don't revert or delete work you didn't author unless everyone agrees.
Moving/renaming and restoring files is allowed.
ABSOLUTELY NEVER run destructive git operations (e.g., git reset --hard, rm, git checkout/git restore to an older commit) unless the user gives an explicit, written instruction in this conversation. Treat t

	source https://twitter.com/thatroblennon/status/1717312851281654173

	NASA open-sourced a monster of a megaprompt.

	I deconstructed it to see what I could learn.

	7 lessons from a genius-level AI prompt:

	The prompt (which I give in full at the bottom of this post) is designed to guide a user through the biomimicry design process.

	/**
	* Cloudflare Worker for Server-Sent Events (SSE)
	*
	* This worker demonstrates how to set up an SSE endpoint
	* that sends an initial message and then periodic updates.
	*/

	// Listen for incoming requests
	addEventListener('fetch', event => {
	event.respondWith(handleRequest(event.request));

	/*
	This svelte store is a reference to make super quick live and interactive pieces of code.
	See its announcement at https://www.youtube.com/watch?v=A8jkJTWacow&t=18579s
	*/

	type Options<T> = {
	validator: (data: T) => void;
	};

	export function live<T>(path: string, initialValue: T, options?: Options<T>) {