iamandrewluca

(function bookmarksExportToCsv() {
  /**
   * 1. Export bookmarks from browser (supported any Chromium based browsers and Safari) (chrome://bookmarks)
   * 2. Open exported html file again in the browser
   * 3. Copy paste this entire file in console, and execute it (hit enter)
   * 4. You will be prompted to save a CSV file. Save it.
   * 5. Open Notion. Click Import -> CSV
   * 6. Select saved CSV file. Wait for import
   * 7. You have a new database with all your bookmarks
   */

lats

// ==UserScript==
// @name     Activate all Itch.io Bundle downloads
// @version  1
// @include        https://itch.io/bundle/download/*
// @include        https://*.itch.io/*
// @require https://ajax.googleapis.com/ajax/libs/jquery/3.4.1/jquery.min.js
// @grant    none
// ==/UserScript==

$(document).ready(function() {

Brainiarc7

ffmpeg -ss <start_time> -i video.mp4 -t <duration> -q:v 2 -vf select="eq(pict_type\,PICT_TYPE_I)" -vsync 0 frame%03d.jpg

RomainKurtz

I was able to find a VERY QUICK AND DIRTY way to use the media-autobuild suite to compile my own 64-bit static FFmpeg for Windows with the NDI library.

Download it and extract to a place on your computer, and keep note of the path. I put it in "D:\ndi\media-autobuild_suite-master", so for the sake of these instructions when you see "<autobuild>", you need to substitute whatever path you've put it in.

During the initial setup process, request to use the static build and add whatever else you'd like to have in your ffmpeg, then pause what you're doing when the on-screen prompts tell you the ffmpeg_options file has been written, then go into <autobuild>\build\ffmpeg_options.txt and add somewhere a line with

Code:
--enable-libndi_newtek

Brainiarc7

# first get the PPA repository driver
sudo add-apt-repository ppa:graphics-drivers/ppa

# install nvidai driver 
sudo apt install nvidia-384 nvidia-384-dev

# install other import packages
sudo apt-get install g++ freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libglu1-mesa libglu1-mesa-dev

# CUDA 9 requires gcc 6

Desani

This script utilizes ffmpeg, the same tool Plex uses, to decode the video stream and captures the output for any errors during playback and sends the playback errors to a log file. So essentially it plays the video in the background faster than regular speed. It then checks the error output log file to see if there is anything inside. If ffmpeg was able to cleanly play the file, it counts as a passed file. If there is any error output, an error could be anything from a container issue, a missed frame issue, media corruption or more, it counts the file as failed. So if there would be an issue with playback and a video freezing, it would be caught by this method of checking for errors. Because of the nature of the error log, any errors that show up, even simple ones, will all count as a fail and the output is captured so you can view the error log. Some simple errors are easy to fix so I have included an auto-repair feature which attempts to re-encode the file which is able to correct some issues that would cau

Brainiarc7

How to install NVIDIA Docker 2 package on Ubuntu and Debian:

If you came to this result (from Google or elsewhere) after realizing that Nvidia-docker's entry on this subject does not result in a working installation, here are the basic steps needed to install this package correctly:

For starters, ensure that you've installed the latest Docker Community edition by following the steps below:

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88

sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"

Roliga

#!/bin/bash

useColors=true
usePager=true

usage() {
	echo "\
Usage: $(basename $0) [OPTIONS]
Shows information about IOMMU groups relevant for working with PCI-passthrough

Brainiarc7

Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:

TensorFlow is now distributed under an Apache v2 open source license on GitHub.

On Ubuntu 16.04LTS+:

Step 1. Install NVIDIA CUDA:

To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:

Brainiarc7

Streaming your Linux desktop to Youtube and Twitch via Nvidia's NVENC and VAAPI:

Considerations to take when live streaming:

The following best practice observations apply when using a hardware-based encoder for live streaming to any platform:

1. Set the buffer size (-bufsize:v) equal to the target bitrate (-b:v). You want to ensure that you're encoding in CBR mode.

2. Set up the encoders as shown: