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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.

@rlueder
rlueder / claude-code-pr-workflow.md
Last active March 23, 2026 19:08
Automated PR Reviews with Claude Opus on AWS Bedrock — GitHub Actions workflow

Automated PR Reviews with Claude Opus on AWS Bedrock

A GitHub Actions workflow that uses Claude Opus (via AWS Bedrock) to automatically review pull requests with inline comments.

Features

  • Two-round review system — Round 1 reviews the full diff, Round 2 only reviews changes made after Round 1 (to address feedback). No further reviews after Round 2.
  • Inline PR comments — Feedback is posted as review comments on specific file/line, not just a wall of text.
  • Cost tracking — Each review comment includes token usage and estimated cost.
  • Smart skipping — Docs/config-only PRs are skipped automatically.