Yossi Elkrief MaTriXy

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This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.

How to Use Codex from Claude Code

Codex is an AI-powered CLI tool that can help with complex debugging, code analysis, and technical questions. When you encounter difficult problems that would benefit from a second perspective or deep analysis, use Codex.

When to Use Codex

Debugging subtle bugs (e.g., bitstream alignment issues, off-by-one errors)
Analyzing complex algorithms against specifications
Getting a detailed code review with specific bug identification
Understanding obscure file formats or protocols

name	description
orchestrating-swarms	Master multi-agent orchestration using Claude Code's TeammateTool and Task system. Use when coordinating multiple agents, running parallel code reviews, creating pipeline workflows with dependencies, building self-organizing task queues, or any task benefiting from divide-and-conquer patterns.

Claude Code Swarm Orchestration

Master multi-agent orchestration using Claude Code's TeammateTool and Task system.

Claude Code TeammateTool - Source Code Analysis

This is not a proposal. This documents existing but hidden functionality found in Claude Code v2.1.19 binary, plus speculation on how it could be used.

Executive Summary

TeammateTool already exists in Claude Code. We extracted this from the compiled binary at ~/.local/share/claude/versions/2.1.19 using strings analysis. The feature is fully implemented but gated behind feature flags (I9() && qFB()).

	#include <metal_stdlib>
	#include <SwiftUI/SwiftUI.h>
	using namespace metal;

	[[ stitchable ]] half4 simpledistort(float2 pos, SwiftUI::Layer l, float4 boundingRect, float progress, float distortion) {

	float2 size = boundingRect.zw;
	float2 uv = pos / size;
	float2 center = float2(0.5, 0.5);
	float2 delta = uv - center;

	"""
	The most atomic way to train and inference 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.

	// Lines
	float hash( float n ) {
	return fract(sin(n)*753.5453123);
	}

	// Slight modification of iq's noise function.
	float noise(vector_float2 x )
	{
	vector_float2 p = floor(x);
	vector_float2 f = fract(x);

	#!/usr/bin/env bash
	set -euo pipefail

	source_dirs=(
	"/Users/someone/code/some-skills"
	"/Users/someone/code/sentry_skills"
	"/Users/someone/code/my-skills"
	)

	codex_dest="$HOME/.codex/skills/public"