Alvaro Martinez alvaromashiro

{
  "apps": {
    "http": {
      "servers": {
        "example": {
          "listen": [
            ":80",
            ":443"
          ],

1- Go to: https://www.facebook.com/{your_id_or_username}/allactivity/?category_key=LIKEDPOSTS&filter_hidden=ALL&filter_privacy=NONE You can use Filter to choose year or month.
2- Open the console and run the following JavaScript code and wait:

setInterval(() => {
  for (const Button of document.querySelectorAll('div[aria-label="Action options"]')) {
    Button.click()
    document.querySelector('div[role="menuitem"]').click()
  }
}, 1000)

Getting Started

Install:

jest: npm install --save-dev jest
ts-jest: npm install --save-dev ts-jest @types/jest

Modify package.json

"jest": {
  "transform": {
 "^.+\\.tsx?$": "ts-jest"

	function svg2png(svgContent, width, height, callback) {
	// svgContent should be in base64
	let svgData = svgContent;
	let canvas = document.createElement("canvas");
	let context = canvas.getContext("2d");

	canvas.width = width;
	canvas.height = height;

	let image = new Image();

	<template>
	<VirtualScroller v-slot="{ item }" :item-height="35" :items="myMassiveArray">
	<div :item="item" :key="item.id">{{ item.details.i.guess? }}</div>
	</VirtualScroller>
	</template>

	/*
	Copy this into the console of any web page that is interactive and doesn't
	do hard reloads. You will hear your DOM changes as different pitches of
	audio.

	I have found this interesting for debugging, but also fun to hear web pages
	render like UIs do in movies.
	*/

	const audioCtx = new (window.AudioContext \|\| window.webkitAudioContext)()

	<?php

	use PhpCsFixer\Config;
	use PhpCsFixer\Finder;

	$rules = [
	'array_indentation' => true,
	'array_syntax' => ['syntax' => 'short'],
	'binary_operator_spaces' => [
	'default' => 'single_space',

	var db = mongoose.connect('mongodb://localhost:27017/DB');

	// In middleware
	app.use(function (req, res, next) {

	// action after response
	var afterResponse = function() {
	logger.info({req: req}, "End request");

	// any other clean ups

	{
	"apps": {
	"http": {
	"servers": {
	"example": {
	"listen": ["127.0.0.1:80"],
	"routes": [
	{
	"match":[{"host":["a.myhost.com"]}],
	"handle": [

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats