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Record and Compress Screen Videos on Windows

This guide explains how to record your screen using built-in Windows tools and compress large recordings with FFmpeg.

1. Record the screen

Option A: Xbox Game Bar

Xbox Game Bar is convenient for recording an application or game window.

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<?php

declare(strict_types=1);


class SomeClass
{
    public function __construct(
        public readonly string $status
    ) {

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1. Create a file in C:\bin\toast.ps1 from source https://gist.github.com/dend/5ae8a70678e3a35d02ecd39c12f99110:

   curl https://gist.githubusercontent.com/dend/5ae8a70678e3a35d02ecd39c12f99110/raw -o /mnt/c/bin/toast.ps1

2. Create ~/.local/bin/notify-send from the following source:

   powershell.exe "& { . C:\\bin\\toast.ps1; Show-Notification -ToastTitle \"$1\" -ToastText \"$2\" }"

   like this:

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# See https://support.google.com/analytics/answer/9267735?hl=en

^(add_payment_info|add_shipping_info|add_to_cart|add_to_wishlist|begin_checkout|generate_lead|purchase|refund|remove_from_cart|select_item|select_promotion|view_cart|view_item|view_item_list|view_promotion)$

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# Make the video shorter to perform an experiment quicker
docker run --rm -it -v $(pwd):/config linuxserver/ffmpeg:latest -ss 840 -i /config/source.mp4 -c copy -t 120 /config/source-short.mp4

# See https://superuser.com/a/1643035/913241
docker run --rm -it -v $(pwd):/config linuxserver/ffmpeg:latest -i /config/source-short.mp4 -filter:a "speechnorm=e=25:r=0.0001:l=1,highpass=200,lowpass=3000,afftdn" /config/source-short-louder-wo-noise.mp4

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from math import sqrt
from typing import Tuple
import numpy as np
import time
import numba
from scipy.stats import ttest_ind


@numba.njit(parallel=True, fastmath=True, nogil=True)
def compare_mean(z: np.ndarray, y: np.ndarray, n_samples: int = 10_000) -> Tuple[np.ndarray, float, float]:

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from typing import Tuple

import numpy as np
import time
import numba
from scipy.stats import mannwhitneyu


@numba.njit(parallel=True, fastmath=True, nogil=True)
def compare_dist(z: np.ndarray, y: np.ndarray, n_samples: int = 10_000) -> Tuple[np.ndarray, float, float]:

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import scikits.bootstrap as bootstrap
import numpy as np
import time
import numba

@numba.njit(parallel=True, fastmath=True)
def _calculate_jackknife_mean_stat(data: np.ndarray) -> np.ndarray:
    n = data.shape[0]
    jstat = np.zeros(n)
    sum = data.sum()

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import scikits.bootstrap as bootstrap
import numpy as np
import time
import numba

@numba.njit(parallel=True, fastmath=True)
def _calculate_boostrap_mean_stat(data: np.ndarray, n_samples: int) -> np.ndarray:
    n = data.shape[0]
    stat = np.zeros(n_samples)
    for i in numba.prange(n_samples):

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#!/bin/bash
# file: ttfb.sh
# curl command to check the time to first byte
# ** usage **
# 1. ./ttfb.sh "https://google.com"
# 2. seq 10 | xargs -Iz ./ttfb.sh "https://google.com"

curl -o /dev/null \
     -H 'Cache-Control: no-cache' \
     -s \