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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.

@livecodelife
livecodelife / roo_workflow.md
Last active April 23, 2026 12:59
Roo Code Setup and Workflow for the best $0 development

Roo Code Workflow: An Advanced LLM-Powered Development Setup

This gist outlines a highly effective and cost-optimized workflow for software development using Roo Code, leveraging a multi-model approach. This setup has been successfully used to build working applications, such as Baccarat game simulations with betting strategy analysis, and my personal portfolio site.


Core Components & Model Allocation

The power of this setup lies in strategically assigning different Large Language Models (LLMs) to specialized "modes" within Roo Code, optimizing for performance, cost, and specific task requirements.

@tathamoddie
tathamoddie / demo2.yaml
Created August 30, 2020 05:23
ESPHome demo config for M5Stack Atom Lite
substitutions:
device_name: demo2
friendly_name: Demo 2
## Boilerplate
esphome:
name: ${device_name}
platform: ESP32
board: m5stack-core-esp32