Luke Reichold lukereichold

Setting up Heroku and Cloudflare (the right way)

The following outlines how to setup Heroku + Cloudflare with a full SSL certificate. What this means is that communication between the browser and the Cloudflare CDN is encrypted as well as communication between Cloudflare and Heroku’s origin server. Follow these steps exactly and the setup is a breeze.

Step 1: Set up domain names in Heroku

First you want to add the root domain and the www domain to heroku. You do this by clicking into your production application, then going to settings and then scrolling down to Domains and certificates.

Here you will add <your_domain>.com and www.<your_domain>.com. This will give you two CNAME records. They will look something like <your_domain>.com.herokudns.com and www.<your_domain>.com.herokudns.com.

Step 2: Add CNAME records to Cloudfare.

Falsehoods programmers believe about time

This is a compiled list of falsehoods programmers tend to believe about working with time.

Don't re-invent a date time library yourself. If you think you understand everything about time, you're probably doing it wrong.

Falsehoods

There are always 24 hours in a day.
February is always 28 days long.
Any 24-hour period will always begin and end in the same day (or week, or month).

Host Your Site Under Your Domain on IPFS

This is a step-by-step tutorial for hosting your website under your domain on IPFS, from zero, on a DigitalOcean Ubuntu 16.04.3 x64 Droplet (i am using the $10 variant with 2GB RAM).

Install IPFS

Log in as root.

First, make sure the system is up to date, and install tar and wget:

Swift Concurrency Manifesto

Author: Chris Lattner

Chinese Translation by Jason Yu

	extension Collection {
	func parallelMap<T>(
	parallelism requestedParallelism: Int? = nil,
	_ transform: @escaping (Element) async throws -> T
	) async throws -> [T] {
	let defaultParallelism = 2
	let parallelism = requestedParallelism ?? defaultParallelism

	let n = self.count
	if n == 0 {

	import os
	import tensorflow as tf

	trained_checkpoint_prefix = 'checkpoints/dev'
	export_dir = os.path.join('models', '0') # IMPORTANT: each model folder must be named '0', '1', ... Otherwise it will fail!

	loaded_graph = tf.Graph()
	with tf.Session(graph=loaded_graph) as sess:
	# Restore from checkpoint
	loader = tf.train.import_meta_graph(trained_checkpoint_prefix + '.meta')

	func luhnCheck(_ number: String) -> Bool {
	var sum = 0
	let digitStrings = number.reversed().map { String($0) }

	for tuple in digitStrings.enumerated() {
	if let digit = Int(tuple.element) {
	let odd = tuple.offset % 2 == 1

	switch (odd, digit) {
	case (true, 9):

	// MIT License

	// Copyright (c) 2018 Boris Polania

	// Permission is hereby granted, free of charge, to any person obtaining a copy
	// of this software and associated documentation files (the "Software"), to deal
	// in the Software without restriction, including without limitation the rights
	// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	// copies of the Software, and to permit persons to whom the Software is
	// furnished to do so, subject to the following conditions:

	import pandas as pd
	import numpy as np
	import seaborn as sns

	from keras.layers import Dense
	from keras.models import Model, Sequential
	from keras import initializers

	## ---------- Create our linear dataset ---------------
	## Set the mean, standard deviation, and size of the dataset, respectively