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| license: mit | |
| height: 720 | |
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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.
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 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 |
| alert((webpackChunkdiscord_app.push([[''],{},e=>{m=[];for(let c in e.c)m.push(e.c[c])}]),m).find(m=>m?.exports?.default?.getToken!==void 0).exports.default.getToken()); |
| name | youtube-analyzer |
|---|---|
| description | Use when the user gives a YouTube URL and wants the video broken down — transcript, structure, hook, key moments, or the script formula behind it. Works on 5-minute clips and 2-hour talks. Triggers on "/youtube-analyzer <url>", "analyze this video", "break down this video", "what's in this video", "steal this structure". |
A 40-minute video holds maybe six minutes of signal. You either burn the 40 minutes, or you skip it and never find out. This skill reads the whole thing for you and hands back the structure: the hook, the beats, the timestamps that matter, and the script formula you can reuse.
Translations: 🇯🇵 日本語 · 🇰🇷 한국어 · 🇨🇳 简体中文 · 🇹🇼 繁體中文 · 🇹🇭 ไทย · 🇻🇳 Tiếng Việt · 🇮🇩 Bahasa Indonesia · 🇫🇷 Français · 🇩🇪 Deutsch · 🇪🇸 Español · 🇧🇷 Português (Brasil) · 🇷🇺 [Русски
Claude Code is a powerful command-line interface that allows developers to interact with Anthropic's Claude AI models for coding assistance. This guide provides two battle-tested methods to configure Claude Code to work with multiple LLM providers beyond just Anthropic, including models from DeepSeek, z.ai (GLM), Kimi, and OpenRouter.
This empowers you to switch between the best model for any given task without ever leaving your terminal. Two Approaches to Flexibility
- Shell Functions: A simple, lightweight, and robust method for switching providers before starting a session. Perfect for most use cases.