Build your AI Second Brain using Obsidian and Andrej Karpathy’s LLM Wiki pattern. Learn how to use agentic tools like Claude Code to turn raw notes into a persistent, self-organizing knowledge base

If you read this all the way through, you will end up with a working blueprint for a second brain that compounds over time. The goal is to replace scattered PDFs, browser tabs, notes, and chat threads with a system that keeps getting better as you add material.

That is what makes Andrej Karpathy’s llm-wiki gist worth studying. GitHub shows the gist was first published on April 4, 2026.[1](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f#file-llm-wiki-md) The idea is simple and strong: instead of asking an LLM to rediscover your knowledge from raw files every time, you let it maintain a persistent wiki that keeps improving as new sources come in.[1](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f#file-llm-wiki-md)

This guide turns that pattern into a practical setup for TheToolNerd readers. The stack is straightforward.

Use Obsidian as the local knowledge base, use an agentic coding assistant such as Claude Code to maintain the wiki, and use a simple ingest workflow so every useful article, note, or transcript has a home. If you already work with AI coding tools, this also fits well with my earlier write-ups on Claude Code and Windsurf. For adjacent tools, browse the TheToolNerd directory.

What you will get from this guide

Before you start building, it helps to know what this setup is supposed to give you.

Why this pattern matters

Most people still use LLMs like temporary search layers. They upload documents, ask a question, get an answer, and move on. That works for one-off retrieval, but it does not build memory. Karpathy’s argument is different. He describes a persistent, LLM-maintained wiki that sits between you and your raw documents, so synthesis happens once and then gets updated over time instead of being recomputed from scratch on every question.

“The wiki is a persistent, compounding artifact. The cross-references are already there. The contradictions have already been flagged. The synthesis already reflects everything you’ve read.” - Andrej Karpathy

That is the real shift. You stop treating the model like a temporary answer machine and start using it as a maintainer for a knowledge system.