trevmex

/*
 * File: bst.js
 *
 * A pure JavaScript implementation of a binary search tree.
 *
 */

/*
 * Class: BST
 *

ded

function r(f){/in/(document.readyState)?setTimeout(r,9,f):f()}

jlong

var parser = document.createElement('a');
parser.href = "http://example.com:3000/pathname/?search=test#hash";

parser.protocol; // => "http:"
parser.hostname; // => "example.com"
parser.port;     // => "3000"
parser.pathname; // => "/pathname/"
parser.search;   // => "?search=test"
parser.hash;     // => "#hash"
parser.host;     // => "example.com:3000"

3on

var system = require('system');

var page = require('webpage').create();

var  url = "http://www.boston.com/bigpicture/";

var scrap = function(){
	var r = {};
  r.imgCount = $('img.bpImage').length;
  r.imgURL = [];

wmiller

using System.Collections;
using System.Collections.Generic;

public class GameEvent
{
}

public class Events
{
	static Events instanceInternal = null;

dypsilon

Frontend Development

Attention: the list was moved to

https://github.com/dypsilon/frontend-dev-bookmarks

This page is not maintained anymore, please update your bookmarks.

---

adityamenon

server {
    root /path/to/app/public;
    index index.php;

    server_name test.dev;
    set $cors_headers "whatever-custom-headers,you-would-like";

    # redirection to index.php
    location / {
        try_files $uri $uri/ /index.php?$query_string;

bendangelo

/*
* Copyright 2017 Ben D'Angelo
*
* MIT License
*
* Permission is hereby granted, free of charge, to any person obtaining a copy of this
* software and associated documentation files (the "Software"), to deal in the Software
* without restriction, including without limitation the rights to use, copy, modify, merge,
* publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons
* to whom the Software is furnished to do so, subject to the following conditions:

craffel

import matplotlib.pyplot as plt

def draw_neural_net(ax, left, right, bottom, top, layer_sizes):
    '''
    Draw a neural network cartoon using matplotilb.
    
    :usage:
        >>> fig = plt.figure(figsize=(12, 12))
        >>> draw_neural_net(fig.gca(), .1, .9, .1, .9, [4, 7, 2])
    

vasanthk

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?