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@johnvilsack
johnvilsack / get-vmwarefusion.sh
Last active November 4, 2026 07:27
Direct Download VMWare Fusion - Download, Install, and Update VMWare Fusion Automatically.
Direct Download VMWare Fusion - Download, Install, and Update VMWare Fusion Automatically.
#!/usr/bin/env bash
# VMware Fusion Latest Version Downloader, Installer, and Updater
# This script automatically finds, downloads, and optionally installs the highest version available
#
# Inspired by this gist: https://gist.github.com/jetfir3/6b28fd279bbcadbae70980bd711a844f
#
# Usage: $0 [-y] [-i] [-d] [-f] [-t <dmg_path>]
@pudquick
pudquick / framework_constants_via_pyobjc.py
Last active November 4, 2026 07:21
Loading framework variables (aka constants) from pyObjC for arbitrary frameworks
from Foundation import NSBundle
# Load the framework bundle by its identifier
Metadata_bundle = NSBundle.bundleWithIdentifier_("com.apple.Metadata")
# Can also use: bundleWithPath or bundleWithURL
# Load the variable aka constant from the framework into globals
# This says to load whatever "kMDSPreferencesName" is and the type should be "an NSObject" aka "figure it out"
# which ends up giving us a NSString which pyObjC bridges to a python string nicely
@hegocre
hegocre / FW13PRO.json
Created September 25, 2026 18:06
Framework 13 Pro EasyEffects
{
"output": {
"bass_enhancer#0": {
"amount": 4.0,
"blend": 0.0,
"bypass": false,
"floor": 10.0,
"floor-active": true,
"harmonics": 10.0,
"input-gain": 0.0,
@MuhammadSaim
MuhammadSaim / rarreg.key
Last active November 4, 2026 07:11
Step 1: Create a file called rarreg.key Step 2: Paste into the file the raw content of this gist Step 3: Go to Winrar install directory (by default => c:\ProgramFiles\WinRAR\ ) Step 4: Paste the rarreg.key into WinRAR directory Step 5: Enjoy
RAR registration data
WinRAR
Unlimited Company License
UID=4b914fb772c8376bf571
6412212250f5711ad072cf351cfa39e2851192daf8a362681bbb1d
cd48da1d14d995f0bbf960fce6cb5ffde62890079861be57638717
7131ced835ed65cc743d9777f2ea71a8e32c7e593cf66794343565
b41bcf56929486b8bcdac33d50ecf773996052598f1f556defffbd
982fbe71e93df6b6346c37a3890f3c7edc65d7f5455470d13d1190
6e6fb824bcf25f155547b5fc41901ad58c0992f570be1cf5608ba9
@MotherSphere
MotherSphere / nikke-pc-launcher-compromise.md
Last active November 4, 2026 07:19
NIKKE PC launcher compromise (Nov 2026): Level Infinite CDN files modified, remote-control agent, indicators

NIKKE PC launcher compromise (November 2026)

한국어 번역 (Korean translation): nikke-pc-launcher-compromise_ko.md

Caution

From 2026-09-27, four official JavaScript files on Level Infinite's CDN contained attacker code. The NIKKE PC launcher loads these files. From Oct 23 at the latest, opening the launcher could install a remote-control agent: while the launcher was open, the attacker could run any command on your machine, read any file and take screenshots.

Update, 2026-11-03 22:40 GMT: three of the files were replaced on Nov 3 at 17:04, 17:13 and 17:21 GMT. The fourth, FeedList.feb647c9.js, was cleaned at the origin on Oct 28, but two CDN servers still delivered the modified copy at 22:26 GMT. The attacker's server no longer answered at 18:31 GMT. None of this undoes anything on machines that were already infected, and the old files may still be in your launcher's cache. **Do not open the NIKKE PC launcher until Level Infinite confirms the fix,

@hoshinolina
hoshinolina / arkit.md
Last active November 4, 2026 07:08
ARKit/FaceKit notes

Introduction

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.

Disclaimer

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.

@miguelmota
miguelmota / remove_tuxera.sh
Last active November 4, 2026 06:59
Completely uninstall and remove Tuxera NTFS on MacOS (resets trial version)
sudo rm -rf /Applications/Tuxera\ Disk\ Manager.app
sudo rm -rf /Library/Application\ Support/Tuxera\ NTFS
sudo rm -rf /Library/Filesystems/fusefs_txantfs.fs
@l-arkadiy-l
l-arkadiy-l / loss_landscape_from_data_model_loss.ipynb
Last active November 4, 2026 06:56
loss_landscape_from_data_model_loss.ipynb
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LLM Wiki

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.

The core idea

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.