paulirish

/* bling.js */
window.$ = document.querySelector.bind(document);
window.$$ = document.querySelectorAll.bind(document);
Node.prototype.on = window.on = function(name, fn) { this.addEventListener(name, fn); };
NodeList.prototype.__proto__ = Array.prototype;
NodeList.prototype.on = function(name, fn) { this.forEach((elem) => elem.on(name, fn)); };

chriseidhof

//
//  AppDelegate.swift
//  TypedTableViewControllers
//
//  Created by Chris Eidhof on 23/03/15.
//  Copyright (c) 2015 Unsigned Integer. All rights reserved.
//

import UIKit

krzyzanowskim

// Playground - noun: a place where people can play

import Foundation

typealias Byte = UInt8

protocol GenericIntegerType: IntegerType {
    init(_ v: Int)
    init(_ v: UInt)
    init(_ v: Int8)

AliSoftware

// #!Swift-1.1
import Foundation


// MARK: - (1) classes

// Solution 1:
//  - Use classes instead of struct
// Issue: Violate the concept of moving model to the value layer
//        http://realm.io/news/andy-matuschak-controlling-complexity/

sjoerdvisscher

struct Rect {
    var width = 1
    var height = 1
    
    mutating func scaleBy(value: Int) {
        width *= value
        height *= value
    }
}

mattt

struct Regex {
    let pattern: String
    let options: NSRegularExpressionOptions!

    private var matcher: NSRegularExpression {
        return NSRegularExpression(pattern: self.pattern, options: self.options, error: nil)
    }

    init(pattern: String, options: NSRegularExpressionOptions = nil) {
        self.pattern = pattern

mattt

struct URL {
    var scheme: String?
    var user: String?
    var password: String?
    var host: String?
    var port: Int?
    var path: String?
    var query: String?
    var fragment: String?

Risto-Stevcev

module Main where
main = undefined

patriciogonzalezvivo

VisualSFM by Changchang Wu

Probably the most straight forward way to start generating Point Clouds from a set of pictures.

VisualSFM is a GUI application for 3D reconstruction using structure from motion (SFM). The reconstruction system integrates several of my previous projects: SIFT on GPU(SiftGPU), Multicore Bundle Adjustment, and Towards Linear-time Incremental Structure from Motion. VisualSFM runs fast by exploiting multicore parallelism for feature detection, feature matching, and bundle adjustment.

For dense reconstruction, this program supports Yasutaka Furukawa's PMVS/CMVS tool chain, and can prepare data for Michal Jancosek's CMP-MVS. In addition, the output of VisualSFM is natively supported by Mathias Rothermel and Konrad Wenzel's [SURE]

peeblesjs

operator infix --> {}
func --> (instance: Any, key: String) -> Any? {
    let mirror = reflect(instance)
    
    for index in 0 ..< mirror.count {
        let (childKey, childMirror) = mirror[index]
        if childKey == key {
            return childMirror.value
        }
    }