Ahmed Morsi eramax

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.

Comparison of Bash, Elvish, NuShell, Murex, es-shell, fish, xonsh, PowerShell, Oils (Oil/OSH/YSH) and Ion shells

(Originally generated by ChatGPT and reviewed/edited by Claude; now corrected/expanded based on reader feedback. YMMV.)

(generated by chatgpt 4o and reviewed/edited by claude 3.5 sonnet, YMMV)

Security Checklist (What can be done in CursorAI & software-level not infrastructure!)

Configuration Security

Detect secrets in code
Identify secrets committed to version control
Flag hardcoded credentials

Authentication & Authorization

Identify missing authentication checks
Detect improper authorization patterns

	#!/usr/bin/env bash

	# Author: Oleh Pshenychnyi
	# Date: 13.02.2021
	#
	# Kill all processes matching a provided pattern.
	#
	# Usage:
	#
	# >> bash killer.sh celery

	user web;

	# One worker process per CPU core.
	worker_processes 8;

	# Also set
	# /etc/security/limits.conf
	# web soft nofile 65535
	# web hard nofile 65535
	# /etc/default/nginx

	# Project Policy

	This policy provides a single, authoritative, and machine-readable source of truth for AI coding agents and humans, ensuring that all work is governed by clear, unambiguous rules and workflows. It aims to eliminate ambiguity, reduce supervision needs, and facilitate automation while maintaining accountability and compliance with best practices.

	# 1. Introduction

	> Rationale: Sets the context, actors, and compliance requirements for the policy, ensuring all participants understand their roles and responsibilities.

	## 1.1 Actors