trashhalo

Ghostty Keyboard Shortcuts

Default keyboard shortcuts for Ghostty terminal emulator. Platform-specific differences are noted where applicable.

Window Management

Action	Windows/Linux	macOS
New window	Ctrl+Shift+N	Cmd+N
Close window	Alt+F4	Cmd+Shift+W

fvariable

AMD

• Size: 1.35GB
• Magnet link:

magnet:?xt=urn:btih:651570d629b83e95353c47f9e1184dfc16023898

Ashley Madison

In July 2015, a group calling itself “The Impact Team” stole the user data of Ashley Madison, a commercial website billed as enabling extramarital affairs. The group copied personal information about the website’s user base and threatened to release users’ names and personally identifying information if Ashley Madison would not immediately shut down.

• Size: 28.7GB

diego3g

import React, { Component } from 'react';

import { View, Text, StyleSheet, ScrollView } from 'react-native';

import 'config/ReactotronConfig';
import 'config/DevToolsConfig';

import Post from 'components/Post';

const textos = [

karpathy

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.

alesha-pro

Building Claude Code from Source

Step-by-step guide for building the Claude Code CLI from the alesha-pro/claude-code repository — leaked Anthropic Claude Code source code.

Requirements

• Linux (Ubuntu 22.04+) or macOS
• 4GB RAM, 4 CPU cores, 30GB disk
• Bun >= 1.3
• Git

uknj

Sub-Industry Code,Sub-Industry,Definition,Industry Code,Industry,Industry Group Code,Industry Group,Sector Code,Sector
10101010,Oil & Gas Drilling,Drilling contractors or owners of drilling rigs that contract their services for drilling wells.,101010,Energy Equipment & Services,1010,Energy,10,Energy
10101020,Oil & Gas Equipment & Services,"Manufacturers of equipment, including drilling rigs and equipment, and providers of supplies such as fractured silica and services to companies involved in the drilling, evaluation and completion of oil and gas wells.

This Sub-Industry includes companies that provide information and data services such as seismic data collection primarily to the oil & gas industry and distributors of oil & gas equipment products.

This Sub-Industry excludes oil spill services companies classified in the Environmental & Facilities Services Sub-Industry.",101010,Energy Equipment & Services,1010,Energy,10,Energy
10102010,Integrated Oil & Gas,"Integrated oil companies engaged in the exploration

aashari

# Principal Software Engineer Operating Guidelines

**Version**: 5.2
**Last Updated**: 2025-11-15

You're operating as a principal engineer with full access to this machine. Think of yourself as someone who's been trusted with root access and the autonomy to get things done efficiently and correctly.

**Principal Engineer Mindset:**
- **Deep Context Gathering** - Curious about everything. Gather comprehensive context before acting. Understand the full system, not just your immediate task.
- **Architectural Thinking** - Design systems that scale. Make decisions considering long-term implications, maintainability, and system-wide impact.

aashari

The Autonomous Agent Prompting Framework

This repository contains a disciplined, evidence-first prompting framework designed to elevate an Agentic AI from a simple command executor to an Autonomous Principal Engineer.

The philosophy is simple: Autonomy through discipline. Trust through verification.

This framework is not just a collection of prompts; it is a complete operational system for managing AI agents. It enforces a rigorous workflow of reconnaissance, planning, safe execution, and self-improvement, ensuring every action the agent takes is deliberate, verifiable, and aligned with senior engineering best practices.

I also have Claude Code prompting for your reference: https://gist.github.com/aashari/1c38e8c7766b5ba81c3a0d4d124a2f58