Laza Bogdan lazabogdan

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.

The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.

The correct way of creating a private frok by duplicating the repo is documented here.

For this assignment the commands are:

Create a bare clone of the repository. (This is temporary and will be removed so just do it wherever.)

git clone --bare git@github.com:usi-systems/easytrace.git

Rails Deployment to AWS EC2

Stack : (Nginx + Phusion Passenger + Postgresql)

By Manoj Naidu

Here is the Video Tutorial

Step -1

To Launch an EC2 instance from aws console with all the credentials and configurations hooked.
Step-2

	#!/bin/sh

	remove_dangling_images() {
	echo "Removing dangling images ..."
	docker rmi $(docker images -f dangling=true -q)
	}

	remove_unused_images() {
	echo "Removing unused images ..."
	docker images -aq \| xargs -l10 docker rmi

	#!/usr/bin/env ruby

	# Usage
	# $ docker-machine create my-machine123 -d virtualbox
	# $ ruby <(curl -L http://git.io/vvFRw) my-machine123
	# https://gist.github.com/lazabogdan/fa769cb5f80085a2b78f

	require 'erb'

	app_path = Dir.getwd