A PowerShell script that checks a Windows PC for the "k2f" remote-access agent that was delivered through the NIKKE PC launcher in late 2026, and, only with your confirmation at each step, removes what it leaves behind.
Do not open the NIKKE PC launcher until Level Infinite confirms that all of its modified web files have been fixed. As of the last gist update, one file (
FeedList.feb647c9.js) was still reported as modified.
ARKit face tracking is shrouded in a ton of myth and mystique.
I reverse engineered the internal face tracking API, and this is how it actually works.
This is based on reverse engineering the API of Apple's face detection framework and testing it with my own code. Some of it is educated guesswork based on names and strings. I did not reverse engineer the details of how ARKit/FaceKit works, but given what I've seen, I'm quite confident in the overall big picture.
There aren't many tutorials about this, the only tutorials I've found were about passing through entire PCIe cards to VMs, or refered to old ESXI versions (below 6.5) that used a more comprehensive desktop client instead of the web app. In v6.5, the web app was introduced and the desktop client was deprecated. You used to be able to setup RDMs in the desktop client, but with the introduction of the web console, this is no longer the case. This tutorial shows you how to pass SATA HDDs to the virtual machine on VMWare ESXI 6.5. This tutorial is partially based on VMWare's own KB and the now deprecated Forza IT blog post.
There is now an option while editing your VM's settings to add a New raw disk when you click `Add ha
Caution
As of August 26th 2026, Discord has started suspending quest access of people caught automating quest completion.
Some users have received the following system message:
There isn't much I can do to make the script undetected, so use it at your own risk, as you WILL get flagged by doing so.
| #!/bin/bash | |
| set -e | |
| # Function to print a section header | |
| section() { | |
| echo | |
| echo "🔹 $1" | |
| } |
Latest News: http://site.api.espn.com/apis/site/v2/sports/football/college-football/news
Latest Scores: http://site.api.espn.com/apis/site/v2/sports/football/college-football/scoreboard
| #!/bin/sh | |
| # | |
| # Original: https://github.com/cceckman/homelab/blob/main/helpers/rm2-tailscale.sh | |
| # I am the original author of this document and release it into the public domain. | |
| # Go wild. | |
| # | |
| # This script enables [Tailscale] on a [reMarkable 2] tablet. | |
| # | |
| # Tailscale is run in [userspace networking] mode, so any outbound applications | |
| # will need to be configured to proxy via tailscaled. |
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.
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.