gladiopeace

Running Windows 10/11 Guest with GPU Passthrough using laptop running Fedora

Abstract

This is a full guide for people who wanted to set up Windows 10/11 VM with QEMU/KVM hypervisor enhancements for a laptop that is configured with hybrid graphics card like Intel/AMD + NVIDIA. This process will take about 1 to 2 hours, depending on your system's performance and your patience =)

There is another comprehensive guide you can follow here (shoutout to asus-linux team who made supergfxctl which is a very important tool for this guide). It is more up-to-date than mine. I would probably incorporate those information into my guide, but you are welcome to use this one as a reference!

Before we proceed:

This guide is exclusively for Fedora users because this distro is quite different to set up than other distro such as Arch. I would say Arch is easier to setup than Fedora, but sometimes you like to use Fedora than Arc

Tornado Cash Community Build

TL;DR

ipfs://bafybeia7cu2axyyxsarmaemvlpdpofa4q23lzpltbl4jbrnfixdn573h4y

If you don't yet have an IPFS enabled browser then you can use one of the following instead (not recommended): https://bafybeia7cu2axyyxsarmaemvlpdpofa4q23lzpltbl4jbrnfixdn573h4y.ipfs.tornadocash.is https://bafybeia7cu2axyyxsarmaemvlpdpofa4q23lzpltbl4jbrnfixdn573h4y.ipfs.cf-ipfs.com

How To: IPFS Enabled Browser

Android Bypass Certificate Pinning and MitM Attack Setup

This is a gist used in the following blog posts:

Do need DAM if...	Do NOT need DAM if...
Have 1000+ assets	Have less tahn 1000+ assets
Collaborate with team on your assets	Work alone
Have licensed assets	Have short asset lifecycle
Search assets across your library	Do not develop assets

	import pyarrow.flight as fl
	import json

	client = fl.FlightClient("grpc://127.0.0.1:8815")
	print(
	client.do_get(
	fl.Ticket(json.dumps({"contract": "0x", "event": "0x"}))
	).read_pandas()
	)

	// Name: Midjourney Prompt
	// Description: Generate a Random Midjourney Prompt
	// Author: John Lindquist
	// Twitter: @johnlindquist

	import "@johnlindquist/kit"

	let count = parseInt(
	await arg({
	placeholder: "How many prompts to generate and paste?",

	{
	"embeddings": [
	{
	"tensorName": "JD skills(Deepwalk)",
	"tensorShape": [
	23221,
	64
	],
	"tensorPath": "https://gist.githubusercontent.com/YoungXu06/fe9dc4d9bdf60b60bdcbe915ee8d8d54/raw/ebff657496190cd4790dddc044c625bfda4e9998/skill_dw_embs.tsv",
	"metadataPath": "https://gist.githubusercontent.com/YoungXu06/fe9dc4d9bdf60b60bdcbe915ee8d8d54/raw/ebff657496190cd4790dddc044c625bfda4e9998/skill_dw_dict.tsv"

	# _____ .___ __
	# / _ \ ____ __\| _/____ ____ \| \| __ ___________
	# / /_\ \ / \ / __ \|/ _ \_/ ___\\| \|/ // __ \_ __ \
	# / \| \ \| \/ /_/ ( <_> ) \___\| <\ ___/\| \| \/
	# \____\|__ /___\| /\____ \|\____/ \___ >__\|_ \\___ >__\|
	# \/ \/ \/ \/ \/ \/
	#
	# Unbloat version of budtmo/docker-android-x86-11.0
	# Thanks to @benjamin.botto for 3D hardware acceleration inside docker
	#

	for %%f in ("_RAW_GRIDS_WEBP/*.webp") do (
	ffmpeg.exe -n -i "_RAW_GRIDS_WEBP/%%f" -vf "crop=iw/2:ih/2:0:0" "_SPLIT_GRIDS\%%~nf-01.png"
	ffmpeg.exe -n -i "_RAW_GRIDS_WEBP/%%f" -vf "crop=iw/2:ih/2:iw/2:0" "_SPLIT_GRIDS\%%~nf-02.png"
	ffmpeg.exe -n -i "_RAW_GRIDS_WEBP/%%f" -vf "crop=iw/2:ih/2:0:ih/2" "_SPLIT_GRIDS\%%~nf-03.png"
	ffmpeg.exe -n -i "_RAW_GRIDS_WEBP/%%f" -vf "crop=iw/2:ih/2:iw/2:ih/2" "_SPLIT_GRIDS\%%~nf-04.png"
	)
	pause

	# give Python access to Blender's functionality
	import bpy


	def set_end_frame(frame_count):
	"""set the end frame"""
	bpy.context.scene.frame_end = frame_count


	def set_fps(fps):