madrobby

<h1>Demo code for "Extreme JavaScript Performance" @ JSConf.eu</h1>

<script>
var benchmark = function(m,i){ 
  var d1 = new Date, d2, r;
  while(i--) r = m();
  d2 = new Date;
  console.log(d2.getTime()-d1.getTime()); 
  // if(r) console.log(r);
}

makinde

!function() {
  var doc     = document,
      htm     = doc.documentElement,
      lct     = null,   // last click target                                              
      nearest = function(elm, tag) {
        while (elm && elm.nodeName != tag) {
          elm = elm.parentNode;
        }
        return elm;
  };

hiroprotagonist

// force certain pages to be refreshed every time.  mark such pages with
// 'data-cache="never"'
//
    jQuery('div').live('pagehide', function(event, ui){
      var page = jQuery(event.target);

      if(page.attr('data-cache') == 'never'){
        page.remove();
      };
    });

jrust

This is a fork of a fork of Bootstrap Typeahead that adds minimal but powerful extensions.

• Ability to disable autoselect of first matched element.
• Ability to automatically set the width of the dropdown to that of the text input.
• Ability to fetch source element via AJAX
• Ability to have a comma separated list of tags.

For the proper source, and other examples, please see the original gist.

Example showing off all the above features

axefrog

using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Text;

namespace SuffixTreeAlgorithm
{
	public class SuffixTree
	{

johnkpaul

//in your application, rather than using window.location to get the current url
App.getLocation = function(){
    return window.location.protocol + '//' + window.location.host 
              + '/' + Backbone.history.options.root + Backbone.history.getFragment()
}

bokmann

# Thee will be more information here when I share the entire problem space I'm working on, but
# in short, this is preview material for my second talk in a series called "What Computer Scientists Know".
# The first talk is on recursion, and goes through several examples., leading up to a problem based
# on a simple puzzle that initial estimates based on performance of a previous puzzle would take years
# to solve on modern computers with the techniques shown in Ruby. That sets the stage for improving the
# performance of that problem with threading, concurrency, and related tuning.
#
# The second talk is on threading and concurrency, touching on algorithmic performance as well.
# Using some knowledge of the problem (board symmetry, illegal moves, etc), we reduce the problem space
# to about .5% of what we initially thought it was.  Still, the initial single threaded solution took more