Budhaditya budhash

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

name	appsec-guardian
description	Expert Application Security Engineer. Prevents insecure code from reaching remote repositories by enforcing OWASP Top 10 and secure SDLC practices. Runs before git push to block vulnerable code.
tools	view, bash_tool, str_replace, create_file, web_search, web_fetch
model	inherit

You are a senior Application Security Engineer with deep expertise in OWASP Top 10, secure SDLC, and security-by-design principles.

Core Mission

How to use:

./wordle.sh

Or try the unlimit mode:

YubiKey 4 series GPG and SSH setup guide

Written for fairly adept technical users, preferably of Debian GNU/Linux, not for absolute beginners.

You'll probably be working with a single smartcard, so you'll want only one primary key (1. Sign & Certify) and two associated subkeys (2. Encrypt, 3. Authenticate). I've published a Bash function which automates this slightly special key generation process.

	"""
	The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp

	# OpenClaw Implementation Prompts

	Each prompt below is a self-contained brief you can hand to an AI coding assistant (or use as a project spec) to build that use case from scratch. Adapt the specific services to whatever you already use — the patterns are what matter.

	---

	## 1) Personal CRM Intelligence

	```
	Build me a personal CRM system that automatically tracks everyone I interact with, with smart filtering so it only adds real people — not newsletters, bots, or cold outreach.

	---
	description: Query OpenAI Codex for root cause analysis (read-only, no edits)
	---

	# Codex Root Cause Investigation

	You are Claude Code, and the user wants to consult OpenAI Codex (via codex CLI) for root cause analysis of a bug or technical issue.

	## Your Task

	#
	# Extract a JSON value in an object or array:
	#
	# name = decode_json_string(get_json_value(json, "author.name"))
	# date = decode_json_string(get_json_value(json, "events.0.date"))
	#
	# Or an entire object:
	#
	# get_json_value(json, "dependencies", deps)
	#

	# - Q* (Q-Star)
	# /\__/\ - q.py
	# ( o.o ) - v0.0.1
	# >^< - by @rUv

	# 01110010 01110101 01110110
	# This is a proof of concept implementation of the Q* (AGI) leak from OpenAi
	# This Python code defines a sophisticated Q-learning agent for reinforcement learning.
	# It includes dynamic exploration, learning from experiences, and checks for convergence.
	# The agent's capabilities are refined iteratively to optimize its decision-making strategy in a given environment.

	//
	// ContentView.swift
	// MadeForYouCard
	//
	// Created by AppleDesignDev on 1/24/22.
	//

	import SwiftUI

	struct ContentView: View {