Luis Arias kaaloo

A quick note on how I'm currently handling Blue/Green or A/B deployments with Terraform and AWS EC2 Auto Scaling.

In my particular use case, I want to be able to inspect an AMI deployment manually before disabling the previous deployment.

Hopefully someone finds this useful, and if you have and feedback please leave a comment or email me.

Overview

I build my AMI's using Packer and Ansible.

Comparing ES7 and core.async

ES7	core.async
`async function() {...}`	`(fn [] (go ...))`
`await ...`	`(<! ...)`
`await*` or `Promise.all(...)`	`(doseq [c ...] (<! c))`

Using Mailboxes in Elm

Max Goldstein | July 30, 2015 | Elm 0.15.1

In Elm, signals always have a data source associated with them. Window.dimensions is exactly what you think it is, and you can't send your own events on it. You can derive your own signals from these primitives using map, filter, and merge, but the timing of events is beyond your control.

This becomes a problem when you try to add UI elements. We want to be able to add checkboxes and dropdown menus, and to receive the current state of these elements as a signal. So how do we do that?

The Bad Old Days

303 See Other

Location: https://github.com/wsargent/docker-cheat-sheet

Hi there!

The docker cheat sheet has moved to a Github project under https://github.com/wsargent/docker-cheat-sheet.

Please click on the link above to go to the cheat sheet.

	import logging

	import numpy as np
	import tensorflow as tf
	from tensorflow.contrib import layers


	GO_TOKEN = 0
	END_TOKEN = 1
	UNK_TOKEN = 2

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	module Main where

	import Signal


	type alias Message =
	{ operation : String
	, message : String
	}

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

	import org.mozilla.javascript.Context;
	import org.mozilla.javascript.Function;
	import org.mozilla.javascript.NativeObject;
	import org.mozilla.javascript.Scriptable;
	import org.mozilla.javascript.commonjs.module.Require;
	import org.mozilla.javascript.commonjs.module.RequireBuilder;
	import org.mozilla.javascript.commonjs.module.provider.SoftCachingModuleScriptProvider;
	import org.mozilla.javascript.commonjs.module.provider.UrlModuleSourceProvider;

	import java.io.File;

	#!/bin/bash
	# bash generate random alphanumeric string
	#

	# bash generate random 32 character alphanumeric string (upper and lowercase) and
	NEW_UUID=$(cat /dev/urandom \| tr -dc 'a-zA-Z0-9' \| fold -w 32 \| head -n 1)

	# bash generate random 32 character alphanumeric string (lowercase only)
	cat /dev/urandom \| tr -dc 'a-z0-9' \| fold -w 32 \| head -n 1