basuke

#
# python drive.py "origin" ["waypoint" ... ] "destination"
#
# i.e. python drive.py "Union Square, San Francisco" "Ferry Building, San Francisco" 'Bay Bridge' SFO

import sys, json, urllib2, md5, os.path, pprint
from math import radians, sin, cos, atan2, pow, sqrt
from urllib import quote_plus
from xml.sax.saxutils import escape
from optparse import OptionParser

faithfracture

#===============================================================================
# Filename:  boost.sh
# Author:    Pete Goodliffe
# Copyright: (c) Copyright 2009 Pete Goodliffe
# Licence:   Please feel free to use this, with attribution
# Modified version
#===============================================================================
#
# Builds a Boost framework for iOS, iOS Simulator, and OSX.
# Creates a set of universal libraries that can be used on an iOS and in the

addyosmani

{
    "name": "my-app",
    "version": "1.0.0",
    "description": "My test app",
    "main": "src/js/index.js",
    "scripts": {
        "jshint:dist": "jshint src/js/*.js",
        "jshint": "npm run jshint:dist",
        "jscs": "jscs src/*.js",
        "browserify": "browserify -s Validating -o ./dist/js/build.js ./lib/index.js",

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

mikhailov

# Nginx+Unicorn best-practices congifuration guide. Heartbleed fixed.
# We use latest stable nginx with fresh **openssl**, **zlib** and **pcre** dependencies.
# Some extra handy modules to use: --with-http_stub_status_module --with-http_gzip_static_module
#
# Deployment structure
#
# SERVER:
#  /etc/init.d/nginx                             (1. nginx)
#  /home/app/public_html/app_production/current  (Capistrano directory)
#

paulirish

/* bling.js */

window.$ = document.querySelectorAll.bind(document);

Node.prototype.on = window.on = function (name, fn) {
  this.addEventListener(name, fn);
}

NodeList.prototype.__proto__ = Array.prototype;

hellerbarde

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

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?