arief nur andono goldalworming

Option 1: Command-line download extension as zip and extract

extension_id=jifpbeccnghkjeaalbbjmodiffmgedin   # change this ID
curl -L -o "$extension_id.zip" "https://clients2.google.com/service/update2/crx?response=redirect&os=mac&arch=x86-64&nacl_arch=x86-64&prod=chromecrx&prodchannel=stable&prodversion=44.0.2403.130&x=id%3D$extension_id%26uc" 
unzip -d "$extension_id-source" "$extension_id.zip"

Thx to crxviewer for the magic download URL.

Golang Workers / Job Queue

A running example of the code from:

This gist creates a working example from blog post, and a alternate example using simple worker pool.

TLDR: if you want simple and controlled concurrency use a worker pool.

API workthough

Open a browser

# start an instance of firefox with selenium-webdriver
driver = Selenium::WebDriver.for :firefox
# :chrome -> chrome
# :ie     -> iexplore

Go to a specified URL

	import speech_recognition
	import pyttsx

	speech_engine = pyttsx.init('sapi5') # see http://pyttsx.readthedocs.org/en/latest/engine.html#pyttsx.init
	speech_engine.setProperty('rate', 150)

	def speak(text):
	speech_engine.say(text)
	speech_engine.runAndWait()

	/*
	*
	* This is a CasperJS script.
	*
	* It try to authenticate so the session cookie can be stored in current profile
	* and then BackstopJS+CasperJS+SlimerJS can use the same cookie during tests.
	*
	* Scenario
	* ========
	*

	# author:
	# @Bartosz Kosarzycki
	#

	sudo apt-get install npm
	sudo npm install -g react-native-cli
	sudo ln -s /usr/bin/nodejs /usr/bin/node
	cd /home/user/your/project/path
	react-native init AwesomeProject
	cd AwesomeProject

	// this is the background code...

	// listen for our browerAction to be clicked
	chrome.browserAction.onClicked.addListener(function (tab) {
	// for the current tab, inject the "inject.js" file & execute it
	chrome.tabs.executeScript(tab.ib, {
	file: 'inject.js'
	});
	});

	import (
	"crypto/md5"
	"encoding/hex"
	)

	func GetMD5Hash(text string) string {
	hasher := md5.New()
	hasher.Write([]byte(text))
	return hex.EncodeToString(hasher.Sum(nil))
	}

	#!/usr/bin/env python
	"""Split large file into multiple pieces for upload to S3.

	S3 only supports 5Gb files for uploading directly, so for larger CloudBioLinux
	box images we need to use boto's multipart file support.

	This parallelizes the task over available cores using multiprocessing.

	Usage:
	s3_multipart_upload.py <file_to_transfer> <bucket_name> [<s3_key_name>]

	#
	# Test of experimental Tornado feature for a streaming request body handler, see
	# https://github.com/nephics/tornado/commit/1bd964488926aac9ef6b52170d5bec76b36df8a6
	#

	#
	# Client sending file to server
	#

	import tornado.httpclient as httpclient