Alaf Azam Khan Alafazam

Update May 2017

Eric Bidelman has documented some of the common workflows possible with headless Chrome over in https://developers.google.com/web/updates/2017/04/headless-chrome.

Update

If you're looking at this in 2016 and beyond, I strongly recommend investigating real headless Chrome: https://chromium.googlesource.com/chromium/src/+/lkgr/headless/README.md

Windows and Mac users might find using Justin Ribeiro's Docker setup useful here while full support for these platforms is being worked out.

Since this is on Hacker News and reddit...

No, I don't distribute my résumé like this. A friend of mine made a joke about me being the kind of person who would do this, so I did (the link on that page was added later). My actual résumé is a good bit crazier.
I apologize for the use of _t in my types. I spend a lot of time at a level where I can do that; "reserved for system libraries? I am the system libraries".
Since people kept complaining, I've fixed the assignments of string literals to non-const char *s.
My use of type * name, however, is entirely intentional.
If you're using an older compiler, you might have trouble with the anonymous unions and the designated initializers - I think gcc 4.4 requires some extra braces to get them working together. Anything reasonably recent should work fine. Clang and gcc (newer than 4.4, at le

Movies Recommendation:

MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

Music Recommendation:

Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real

	#!/bin/bash

	# Script for installing tmux on systems where you don't have root access.
	# tmux will be installed in $HOME/local/bin.
	# It's assumed that wget and a C/C++ compiler are installed.

	# exit on error
	set -e

	TMUX_VERSION=2.3

	from flask import Flask, render_template

	app = Flask(__name__)

	@app.route('/')
	@app.route('/index')
	def index(chartID = 'chart_ID', chart_type = 'bar', chart_height = 350):
	chart = {"renderTo": chartID, "type": chart_type, "height": chart_height,}
	series = [{"name": 'Label1', "data": [1,2,3]}, {"name": 'Label2', "data": [4, 5, 6]}]
	title = {"text": 'My Title'}

	haiku = ->
	adjs = [
	"autumn", "hidden", "bitter", "misty", "silent", "empty", "dry", "dark",
	"summer", "icy", "delicate", "quiet", "white", "cool", "spring", "winter",
	"patient", "twilight", "dawn", "crimson", "wispy", "weathered", "blue",
	"billowing", "broken", "cold", "damp", "falling", "frosty", "green",
	"long", "late", "lingering", "bold", "little", "morning", "muddy", "old",
	"red", "rough", "still", "small", "sparkling", "throbbing", "shy",
	"wandering", "withered", "wild", "black", "young", "holy", "solitary",
	"fragrant", "aged", "snowy", "proud", "floral", "restless", "divine",

	#!python
	#coding= utf-8
	# This script implements the Double Metaphone algorithm (c) 1998, 1999 by Lawrence Philips
	# it was translated to Python from the C source written by Kevin Atkinson (http://aspell.net/metaphone/)
	# By Andrew Collins - January 12, 2007 who claims no rights to this work
	# http://www.atomodo.com/code/double-metaphone
	# Tested with Pyhon 2.4.3
	# Updated Feb 14, 2007 - Found a typo in the 'gh' section
	# Updated Dec 17, 2007 - Bugs fixed in 'S', 'Z', and 'J' sections. Thanks Chris Leong!
	# Updated June 25, 2010 - several bugs fixed thanks to Nils Johnsson for a spectacular