banksean

// Ported from Stefan Gustavson's java implementation
// http://staffwww.itn.liu.se/~stegu/simplexnoise/simplexnoise.pdf
// Read Stefan's excellent paper for details on how this code works.
//
// Sean McCullough [email protected]

/**
 * You can pass in a random number generator object if you like.
 * It is assumed to have a random() method.
 */

padolsey

// ----------------------------------------------------------
// A short snippet for detecting versions of IE in JavaScript
// without resorting to user-agent sniffing
// ----------------------------------------------------------
// If you're not in IE (or IE version is less than 5) then:
//     ie === undefined
// If you're in IE (>=5) then you can determine which version:
//     ie === 7; // IE7
// Thus, to detect IE:
//     if (ie) {}

sr3d

#!/usr/bin/env ruby
require 'ftools'
require 'fileutils'
require 'rubygems'
require 'RMagick'
include Magick

require 'open3'

def merge( files = [] )

RobKohr

// Ported from Stefan Gustavson's java implementation
// http://staffwww.itn.liu.se/~stegu/simplexnoise/simplexnoise.pdf
// Read Stefan's excellent paper for details on how this code works.
//
// Sean McCullough [email protected]

/**
 * You can pass in a random number generator object if you like.
 * It is assumed to have a random() method.
 */

Flexi23

/*
These are the helper functions to store and to restore a 2D vector with a custom 16 floating point precision in a texture.
The 16 bit are used as follows: 1 bit is for the sign, 4 bits are used for the exponent, the remaining 11 bit are for the mantissa.

The exponent bias is asymmetric so that the maximum representable number is 2047 (and bigger numbers will be cut)
the accuracy from 1024 - 2047 is one integer
512-1023 it's 1/2 int
256-511 it's 1/4 int and so forth...
between 0 and 1/16 the accuracy is the highest with 1/2048 (which makes 1/32768 the minimum representable number)

mrdoob

// firefox

document.addEventListener( 'visibilitychange', function ( event ) {

	if ( document.hidden === false ) {

		lastTime = performance.now();

	}

Protonk

// A simple Linear Congruential Generator

// Establish the parameters of the generator
var m = 25,
    // a - 1 should be divisible by m's prime factors
    a = 11,
    // c and m should be co-prime
    c = 17;
// Setting the seed
var z = 3;

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

LeaVerou

(function(){
var log = console.log;

console.log = function(str) {
	var css = 'background: linear-gradient(to right, red, yellow, lime, aqua, blue, fuchsia, red); color: white; font-weight: bold;';
	var args = Array.prototype.slice.call(arguments);
	args[0] = '%c' + args[0];
	args.splice(1,0,css);
	return log.apply(console, args);
}

p01

<style>
#b {
  position: fixed;
  top: 0;
  left: 0;
  width: 100%;
  height: 100%;
  background: radial-gradient(circle, #000, #123);
}