rorydbain

#!/usr/bin/env zsh
# Multi-project worktree manager with Claude support
# 
# ASSUMPTIONS & SETUP:
# - Your git projects live in: ~/projects/
# - Worktrees will be created in: ~/projects/worktrees/<project>/<branch>
# - New branches will be named: <your-username>/<feature-name>
#
# DIRECTORY STRUCTURE EXAMPLE:
# ~/projects/

tovacinni

# gtr ─ Git worktree helper
#
# Examples
# --------
#   gtr create feature0          # add ~/code/worktrees/feature0 (branch claude/feature0)
#   gtr create feat1 feat2       # add two worktrees at once
#   gtr rm feature0              # remove the worktree directory
#   gtr cd feature0              # jump into the worktree directory
#   gtr claude feature0          # run `claude` while inside that worktree
#

adibhanna

#!/bin/bash

function go_modernize_interactive() {
    # Check if bat is installed
    if ! command -v bat &>/dev/null; then
        echo "bat is not installed. Please install it first (https://github.com/sharkdp/bat)"
        echo "You can install it with: "
        echo "  - Homebrew: brew install bat"
        echo "  - Ubuntu/Debian: apt install bat"
        echo "  - Fedora: dnf install bat"

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

adibhanna

#--------------------------------------------------------------------------
# Configuration
#--------------------------------------------------------------------------

# Use Vi mode
setw -g mode-keys vi

# Increase scrollback buffer size
set -g history-limit 10000

karpathy

# -----------------------------------------------------------------------------
# AI-powered Git Commit Function
# Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It:
# 1) gets the current staged changed diff
# 2) sends them to an LLM to write the git commit message
# 3) allows you to easily accept, edit, regenerate, cancel
# But - just read and edit the code however you like
# the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/

gcm() {

smx-smx

XZ Backdoor symbol deobfuscation. Updated as i make progress

q3k

0810 b' from '
0678 b' ssh2'
00d8 b'%.48s:%.48s():%d (pid=%ld)\x00'
0708 b'%s'
0108 b'/usr/sbin/sshd\x00'
0870 b'Accepted password for '
01a0 b'Accepted publickey for '
0c40 b'BN_bin2bn\x00'
06d0 b'BN_bn2bin\x00'
0958 b'BN_dup\x00'

0xdevalias

Reverse Engineering on macOS

Some notes, tools, and techniques for reverse engineering macOS binaries.

Table of Contents

• Reverse Engineering Tools
  • Binary Ninja
• Binary Ninja Plugins/Themes

Shubhang

# Author: Shubhang, 2023
# Description: This Python script demonstrates how to visualize audio using NumPy, Matplotlib, and MoviePy. 
# It reads an audio file in WAV format, converts the audio samples to a NumPy array, 
# and creates a video animation from a plot of the audio samples. 
# The resulting video file shows the amplitude of the audio samples over time.



import numpy as np
import matplotlib.pyplot as plt