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SnapTap + JumpThrow Macro for QMK Keyboards

Enhance your CS2 gameplay with the SnapTap + JumpThrow macro designed for QMK keyboards. This macro enables precise movement control and consistent grenade throws, giving you a competitive edge, all while remaining undetectable by CS2 due to its careful imitation of human behavior.

Features:

SnapTap Functionality: Execute rapid direction changes with minimal input delay, allowing for swift and responsive strafing during gameplay.
JumpThrow Macro: Perform perfectly timed grenade throws with a single key press, ensuring consistent and accurate throws every time.
Customizable Delays: Adjust delay settings to fine-tune the responsiveness and behavior of the SnapTap and JumpThrow actions according to your playstyle.
Seamless Integration: Easily incorporate this macro into your existing QMK firmware, with simple configuration options.

Arch Linux installation notes

LUKS2 encrypted BTRFS system partition with Limine/Snapper integration and hybernate to swapfile

macOS Seatbelt/Sandbox Trace Script

macOS sandbox profiles used to be able to include a trace command that would write all the denied operations to a sandbox profile, allowing a profile to be iterativley built up. Apple removed that functionality for reasons explained below.

trace.sh examines the kernel log for the denied operations and creates the relevant allow rules in a sandbox profile, just like the sandbox profile trace command used to.

shrink.sh tries to reduce a sandbox profile to the minimum lines necessary.

It's very rough and ready at the moment (check the sed regex'es in the script to see what I mean) and needs more testing with a wider set of use cases.

The Direct Response Copy Skill

Write copy that converts. Landing pages, emails, sales copy, headlines, CTAs, social posts — anything persuasive.

This is an AI skill file. It turns any AI into a direct response copywriter trained on the frameworks of Schwartz, Hopkins, Ogilvy, Halbert, Caples, Sugarman, and Collier. Instead of getting generic AI copy, you get internet-native writing that sounds like a smart friend explaining something — while quietly deploying every persuasion principle in the book.

How to use this

From Zero to OpenClaw on Proxmox LXC (Debian 13): A Real-World Build Log

I wanted a self-hosted OpenClaw deployment on Proxmox, reachable on my LAN, with:

Gemini API key support
Gemini CLI OAuth (Google AI Pro)
Telegram bot integration
Brave Search API integration
Browser tool support in an LXC
OpenRouter free model option (without Gemini CLI install)

Complete Recent Discord Quest

Note

This does not works in browser for quests which require you to play a game! Use the desktop app to complete those.

How to use this script:

Accept a quest under the Quests tab
Press Ctrl+Shift+I to open DevTools
Go to the Console tab
Paste the following code and hit enter:

Ansible Development Standards

Document Version: 1.0.0
Last Updated: 2025-02-10
Target Audience: Mid-level engineers developing enterprise Ansible automation
Purpose: Core standards for production-grade Ansible roles, playbooks, and custom modules

Performant local mixture-of-experts CPU inference with GPU acceleration in llama.cpp

Introduction

So you want to try one of those fancy huge mixture-of-experts (MoE) models locally? Well, whether you've got a gaming PC or a large multi-GPU workstation, we've got you covered. As long as you've downloaded enough RAM beforehand.

Anatomy of a MoE Model

MoE models are described in terms of their total parameters and active parameters - i.e. DeepSeek V3 671B A37B has 671B total parameters, but we are using only 37B parameters at a time during each forward pass through the model.

	# Chrome OS recovery images manual download
	https://dl.google.com/dl/edgedl/chromeos/recovery/recovery2.json
	https://dl.google.com/dl/edgedl/chromeos/recovery/recovery.json

	# Google Chrome OS Flex images manual download
	https://dl.google.com/dl/edgedl/chromeos/recovery/cloudready_recovery2.json
	https://dl.google.com/dl/edgedl/chromeos/recovery/cloudready_recovery.json

	import os
	import hashlib
	import sys

	def get_sha1(file_path):
	"""Calculate the SHA1 hash of a file in chunks to handle large files."""
	sha1 = hashlib.sha1()
	try:
	with open(file_path, 'rb') as f:
	# Read in 64KB chunks for efficiency