Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

I have compiled all the resources that helped me in my GATE preparation, and I hope they help you too! 🚀

Every standard we write is governed by a small fixed number of principles: 3 to 5. Not "roughly a handful", not "as many as it needs". If a standard wants a sixth principle, one of the existing five has to be merged, generalized, orcut.

The idea comes from Gerard Holzmann's "The Power of Ten" (NASA/JPL https://spinroot.com/gerard/pdf/P10.pdf). His observation: most coding guidelines run past a hundred rules, each new document longer than the last, many rules born from personal preference or one bad day years ago. The result is that the guidelines have little effect on what developers actually do. Nobody can follow a rulebook they cannot remember.

His answer for safety-critical code was ten strict rules, small enough to hold in your head, each carrying its own rationale. Ours does not need to be that strict, but the reasoning holds at any scale:

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.