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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.

Advanced SQL techniques for beginners

image by author

On a scale from 1 to 10 how good are your data warehousing skills? Want to go above 7/10? This article is for you then.

How good is your SQL? Want to get ready for a job interview asap? This blog post explains the most intricate data warehouse SQL techniques in high detail. I will use BigQuery standard SQL dialect to scribble down a few thoughts on this.

1. Incremental tables and MERGE

@nyancodeid
nyancodeid / README.md
Last active May 22, 2025 21:02
Make RESTful API with Google Apps Script and SpreadSheet

Google Script CRUD

By Ryan Aunur Rassyid

Simply create RESTful API with Google Script and store it to Google SpreadSheet like a Pro.