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.

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Matrix multiplication is a mathematical operation that defines the product of

below is a security analysis done by clause code. reference https://x.com/adibhanna/status/2046988777789555191 repo: https://github.com/ritualPlay-Net/RitualPlay/ the scammer: https://www.linkedin.com/in/dean-gallimore-440669234/ his email: [email protected] NOTE: if you decide to clone this repo, do NOT run npm install, or try to run it. it has a backdoor