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.

Recupera el control total de tu dispositivo eliminando el software de relleno, bloqueando actualizaciones invasivas y anulando los filtros que restringen la ejecución de aplicaciones de terceros. El objetivo es restaurar la soberanía sobre tu hardware para que decidas qué software instalar y ejecutar sin las restricciones arbitrarias del fabricante, evitando bloqueos de interfaz como el que se muestra en la siguiente imagen:

Status: Proposed design / initial read-only audit completed; cleanup and long-term improvements not yet validated. This is not a performance benchmark, proof of autonomous self-improvement, or recommendation to install a particular collection of plugins.

Purpose: Companion document for a discussion asking coding-agent users what they have actually tried, what improved, what did not, and how they evaluated their results.

This is a shareable consolidation and revision of several prompts, not a byte-for-byte transcript. It deliberately omits account identifiers, local usernames, machine-specific paths, and private configuration details.