Jason Jobe wildthink

Using Swift Package Manager with iOS

Step 1:

File > New > Project...

Step 2:

Create a Package.swift file in your root project directory, add dependencies, then run swift package fetch on the command line in the same directory. We’re not going to run swift build because it will just complain.

A composable pattern for pure state machines with effects

State machines are everywhere in interactive systems, but they're rarely defined clearly and explicitly. Given some big blob of code including implicit state machines, which transitions are possible and under what conditions? What effects take place on what transitions?

There are existing design patterns for state machines, but all the patterns I've seen complect side effects with the structure of the state machine itself. Instances of these patterns are difficult to test without mocking, and they end up with more dependencies. Worse, the classic patterns compose poorly: hierarchical state machines are typically not straightforward extensions. The functional programming world has solutions, but they don't transpose neatly enough to be broadly usable in mainstream languages.

Here I present a composable pattern for pure state machiness with effects,

Key-Value Memory Networks for Directly Reading Documents

Introduction

Knowledge Bases (KBs) are effective tools for Question Answering (QA) but are often too restrictive (due to fixed schema) and too sparse (due to limitations of Information Extraction (IE) systems).
The paper proposes Key-Value Memory Networks, a neural network architecture based on Memory Networks that can leverage both KBs and raw data for QA.
The paper also introduces MOVIEQA, a new QA dataset that can be answered by a perfect KB, by Wikipedia pages and by an imperfect KB obtained using IE techniques thereby allowing a comparison between systems using any of the three sources.
Link to the paper.

	#!/usr/bin/python3
	# By Steve Hanov, 2011. Released to the public domain.
	# Updated 2014 to use DAWG as a mapping.
	import sys
	import time

	DICTIONARY = "/usr/share/dict/words"
	QUERY = sys.argv[1:]

	# This class represents a node in the directed acyclic word graph (DAWG). It

	#!/usr/bin/env xcrun swift -O

	/*
	gen.swift is a direct port of cfdrake's helloevolve.py from Python 2.7 to Swift 3
	-------------------- https://gist.github.com/cfdrake/973505 ---------------------
	gen.swift implements a genetic algorithm that starts with a base
	population of randomly generated strings, iterates over a certain number of
	generations while implementing 'natural selection', and prints out the most fit
	string.
	The parameters of the simulation can be changed by modifying one of the many

	/*
	Copyright (C) 2016 Apple Inc. All Rights Reserved.
	See LICENSE.txt for this sample’s licensing information

	Abstract:
	`DirectoryMonitor` is used to monitor the contents of the provided directory by using a GCD dispatch source.
	*/

	import Foundation

	// Paste me into a playground!

	import Cocoa

	//: # Basic Setup

	protocol FancyProtocol {
	associatedtype Thing
	func holdPinkyUp(x: Thing)
	}

	//
	// Activity.swift
	//
	// Created by Zachary Waldowski on 8/21/16.
	// Copyright © 2016 Zachary Waldowski. Licensed under MIT.
	//

	import os.activity

	private final class LegacyActivityContext {

	extension NSMutableAttributedString {

	func add(attribute: Attribute, range: NSRange) throws {
	guard (range.location + range.length) <= length else {
	throw AttributedStringError.InvalidRange(range: range)
	}
	addAttribute(attribute.name, value: attribute.value, range: range)
	}
	}