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Google dork cheatsheet

Search filters

Filter	Description	Example
allintext	Searches for occurrences of all the keywords given.	`allintext:"keyword"`
intext	Searches for the occurrences of keywords all at once or one at a time.	`intext:"keyword"`
inurl	Searches for a URL matching one of the keywords.	`inurl:"keyword"`
allinurl	Searches for a URL matching all the keywords in the query.	`allinurl:"keyword"`
intitle	Searches for occurrences of keywords in title all or one.	`intitle:"keyword"`

Docker Compose Cheat Sheet

Docker Compose is a tool for defining and running multi-container Docker applications. With a docker-compose.yml file, you can configure your application’s services, networks, and volumes. Below is a comprehensive list of commonly used keys/sections along with a brief explanation and example.

Docker Compose File Structure

Key/Section	Description	Example

ANSI Escape Sequences

Standard escape codes are prefixed with Escape:

Ctrl-Key: ^[
Octal: \033
Unicode: \u001b
Hexadecimal: \x1B
Decimal: 27

macOS SQLite database paths and schemas

Path inventory: 2026-01-21. Table inventories: 2026-06-10. macOS build was not recorded in the original inventory.

~ denotes the user's home directory. Paths and schemas vary by macOS version, account configuration, and library location. Listed columns are schema examples; their presence does not establish populated values or retention periods.

Knowledge

~/Library/Application Support/Knowledge/knowledgeC.db

AI-Powered Algorithmic Trading with Python

ODSC London 2023 Half-Day Training

Dr. Yves J. Hilpisch
CEO The Python Quants | The AI Machine
Adjunct Professor of Computational Finance

Raspberry Pi 5 Media Server — Commands

Immich (photos) + Jellyfin (movies/TV) + Tailscale (access from anywhere). Commands only, in the order they're run in the video.

Tested August 2026 on:


Hardware	Raspberry Pi 5, 8GB — CanaKit Starter Kit PRO

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.

	Create a production-ready, visually stunning front-end web app.

	GOAL
	Build a single-page application (plus detail routes) for a fictional company:
	"SILICON EXCHANGE" — a marketplace where people rent out idle GPUs and AI
	accelerators by the hour. Renters browse listings, inspect live utilization
	charts, and reserve time blocks.

	FRONT END ONLY. No backend, no database, no auth server, no API keys. All data
	is mock data defined in code. But the app must behave like the real thing — the

	using UnityEngine;
	using System.Collections.Generic;

	/// <summary>
	/// Get total area of each triangle.
	/// Find a random point within that total area.
	/// Lookup which triangle that point relates to
	/// Find a randiom point which point that triangle
	/// This works for all mesh types, and gives fully distributed results.
	/// Gist: https://gist.github.com/danieldownes/b1c9bab09cce013cc30a4198bfeda0aa