mmalecki

for (var i = 0; i < 1024 * 1024; i++) {
  process.nextTick(function () { Math.sqrt(i) } )
}

nateware

# Step 1: Set priveleges

$ sudo /System/Library/CoreServices/RemoteManagement/ARDAgent.app/Contents/Resources/kickstart -configure -allowAccessFor -allUsers -privs -all

Starting...
Setting allow all users to YES.
Setting all users privileges to 1073742079.
Done.

# Step 2: Allow VNC clients

kelvinn

# post_loc.txt contains the json you want to post
# -p means to POST it
# -H adds an Auth header (could be Basic or Token)
# -T sets the Content-Type
# -c is concurrent clients
# -n is the number of requests to run in the test

ab -p post_loc.txt -T application/json -H 'Authorization: Token abcd1234' -c 10 -n 2000 http://example.com/api/v1/locations/

syzer

{
  "result": "SUCCESS",
  "interfaceVersion": "1.0.3",
  "requested": "10/17/2013 15:31:20",
  "lastUpdated": "10/16/2013 10:52:39",
  "tasks": [
    {
      "id": 104,
      "complete": false,
      "priority": "high",

syzer

// task #function composiotion
var articles = [
    {
        title: 'Why OO Sucks by Joe Armstrong',
        url: 'http://www.bluetail.com/~joe/vol1/v1_oo.html',
        author: {
            name: 'Joe Armstrong',
            email: 'empty@email.com'
        }
    },

syzer

/**
 * Created by syzer on 12/19/2014.
 * ex shows how to decouple constant error checking from function/class body
 * AKA TypeScript AKA WarnScript :)
 */
var _ = require('lodash');

function doStuffNumberPlusOneTimes(number, fun) {
    "use strict";

rrag

license: MIT
height: 770

starfys

//Based on the fast inverse square root function
// https://en.wikipedia.org/wiki/Fast_inverse_square_root
// Some original comments preserved for humor value
// Designed to try to mimic the original as closely as possible
function Q_rsqrt(number)
{ 
    var i;
    var x2, y;
    const threehalfs = 1.5;
  

arundasan91

Freshly brewed !

With the availability of huge amount of data for research and powerfull machines to run your code on, Machine Learning and Neural Networks is gaining their foot again and impacting us more than ever in our everyday lives. With huge players like Google opensourcing part of their Machine Learning systems like the TensorFlow software library for numerical computation, there are many options for someone interested in starting off with Machine Learning/Neural Nets to choose from. Caffe, a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and its contributors, comes to the play with a fresh cup of coffee.

Installation Instructions (Ubuntu 14 Trusty)

The following section is divided in to two parts. Caffe's documentation suggest

metasyn