secondrealm

LLM Local Recall

This is an idea note on extending LLM memory through local retrieval. It captures a system I’ve been using, not a finished product. I’m not a developer, so I’m just trying to describe the system I’ve been hacking together at a level where others can build on it, improve it, extend it, or adapt it.

The problem

I kept running into the same problem: I knew I’d already done the work somewhere, but I couldn’t reliably get back to it.

Not because it was gone. Not because I’d failed to think it through. The problem was that my past work was spread across too many places and too many formats to be consistently useful when I needed it. Some of it was in old LLM chats. Some of it was in notes apps. Some of it lived in PDFs, screenshots, exports from tools I no longer use, half-finished drafts, or folders I hadn’t opened in months. The material existed. The retrieval didn’t.

karpathy

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.