Alejandro Rivero arivero

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

	<p>Hello

	<script>
	var xhr = new XMLHttpRequest();
	xhr.open('GET', '/stream');
	xhr.seenBytes = 0;

	xhr.onreadystatechange = function() {
	console.log("state change.. state: "+ xhr.readyState);

	System process daemons that are system-wide provided by mac os x are described by launchd preference files that can be showed with the command:
	$ sudo ls -all /System/Library/LaunchDaemons/

	Third party process daemons that are system-wide provided by the administrator are described by preference files that can be showed with the command:
	$ sudo ls -all /Library/LaunchDaemons/

	Launch Agents that are per-user provided by mac os x usually loaded when the user logs in. Those provided by the system can be found with:
	$ sudo ls -all /System/Library/LaunchAgents/

	Launch Agents that are per-user provided by the administrator and usually loaded when the user logs in. Those provided by the system can be found with:

	function snotebook ()
	{
	#Spark path (based on your computer)
	SPARK_PATH=~/spark-2.0.0-bin-hadoop2.7

	export PYSPARK_DRIVER_PYTHON="jupyter"
	export PYSPARK_DRIVER_PYTHON_OPTS="notebook"

	# For python 3 users, you have to add the line below or you will get an error
	#export PYSPARK_PYTHON=python3

	"""
	Pekka Aalto 2017

	This snippet tries to explain by example what deepmind means
	in https://arxiv.org/abs/1708.04782
	about embedding on channel axis being equivalent to
	one-hot-encoding followed by 1x1 conv.

	They write:

	PkMn BaseStamina BaseAttack BaseDefense Type1 Type2 BaseCaptureRate BaseFleeRate CollisionRadiusM CollisionHeightM CollisionHeadRadiusM MovementType MovementTimerS JumpTimeS AttackTimerS QuickMoves CinematicMoves AnimTime Evolution EvolutionPips PokemonClass PokedexHeightM PokedexWeightKg HeightStdDev WeightStdDev FamilyId CandyToEvolve
	1 90 126 126 HoloPokemonType.POKEMON_TYPE_GRASS HoloPokemonType.POKEMON_TYPE_POISON 0.1599999964237213 0.10000000149011612 0.3815000057220459 0.6539999842643738 0.27250000834465027 HoloPokemonMovementType.POKEMON_ENC_MOVEMENT_JUMP 10.0 1.149999976158142 29.0 d601dd01 5a3b76 6d56d53fdaac2a3f6d56d53f93a9ea3f0000000036ab0a403333b33fbfbbbb3f 02 HoloPokemonClass.POKEMON_CLASS_NORMAL 1 0.699999988079071 6.900000095367432 0.08749999850988388 0.862500011920929 HoloPokemonFamilyId.V0001_FAMILY_BULBASAUR 25
	2 120 156 158 HoloPokemonType.POKEMON_TYPE_GRASS HoloPokemonType.POKEMON_TYPE_POISON 0.07999999821186066 0.07000000029802322 0.3187499940395355 0.637499988079071 0.2549999952316284 H

	license: mit
	height: 800