Joseph Dung kod3r

The introduction to Reactive Programming you've been missing

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

General Background and Overview

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.
Models and Issues in Data Stream Systems
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
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

Source: http://christonard.com/12-free-data-mining-books/

An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie & Tibshirani – This book is fantastic and has helped me quite a bit. It provides an overview of several methods, along with the R code for how to complete them. 426 Pages.
The Elements of Statistical Learning by Hastie, Tibshirani & Friedman – This is an in-depth overview of methods, complete with theory, derivations & code. I’d definitely consider this a graduate level text. I’d also consider it one of the best books available on the topic of data mining. 745 Pages.
A Programmer’s Guide to Data Mining by Ron Zacharski – This one is an online book, each chapter downloadable as a PDF. It’s also still in progress, with chapters being added a few times each year. Probabilistic Programming & Bayesian Methods for Hackers by Cam Davidson-Pilson – This book is absolutely fantastic. The author explains Bayesian statistics, provides several diverse examples of how to a

Don't use VPN services.

No, seriously, don't. You're probably reading this because you've asked what VPN service to use, and this is the answer.

Note: The content in this post does not apply to using VPN for their intended purpose; that is, as a virtual private (internal) network. It only applies to using it as a glorified proxy, which is what every third-party "VPN provider" does.

A Russian translation of this article can be found here, contributed by Timur Demin.
A Turkish translation can be found here, contributed by agyild.
There's also this article about VPN services, which is honestly better written (and has more cat pictures!) than my article.

Beyond Web 2.0 - Django and Python in the modern web ecosystem

Django is over 10 years old; but the web that it was built for - the world of "Web 2.0" - doesn't really exist any more. Yes, we still need to rapidly develop database-backed websites, AJAX-enabled, but the modern web is faced with new problems, and new challenges. Many of those challenges involve interacting with devices that aren't desktop machines, and platforms that aren't a traditional browser - places where Python isn't a first-class citizen.

Does this mean we have to abandon Django and Python for other frameworks and languages? Can we keep using Python and Django on the server side and interact with independent client side frameworks? Or can we push Python and Django into these new environments?

In this talk, Dr Russell Keith-Magee will explore the problems, and some potential solutions, to these problems.

Detailed abstract

Comprehensions in Python the Jedi way

by Bjørn Friese

Beautiful is better than ugly. Explicit is better than implicit.

-- The Zen of Python

I frequently deal with collections of things in the programs I write. Collections of droids, jedis, planets, lightsabers, starfighters, etc. When programming in Python, these collections of things are usually represented as lists, sets and dictionaries. Oftentimes, what I want to do with collections is to transform them in various ways. Comprehensions is a powerful syntax for doing just that. I use them extensively, and it's one of the things that keep me coming back to Python. Let me show you a few examples of the incredible usefulness of comprehensions.

When setting up (that's immediately when user enables it in settings) Opera VPN sends few API requests to https://api.surfeasy.com to obtain credentials and proxy IPs, see below, also see The Oprah Proxy.

The browser then talks to a proxy de0.opera-proxy.net (when VPN location is set to Germany), it's IP address can only be resolved from within Opera when VPN is on, it's 185.108.219.42 (or similar, see below). It's an HTTP/S proxy which requires auth.

When loading a page with Opera VPN enabled, the browser sends a lot of requests to de0.opera-proxy.net with Proxy-Authorization request header.

The Proxy-Authorization header decoded: CC68FE24C34B5B2414FB1DC116342EADA7D5C46B:9B9BE3FAE674A33D1820315F4CC94372926C8210B6AEC0B662EC7CAD611D86A3 (that's sha1(device_id):device_password, where device_id and device_password come from the POST /v2/register_device API call, please note that this decoded header is from another Opera installation and thus contains

Idiomatic React Testing Patterns

Testing React components seems simple at first. Then you need to test something that isn't a pure interaction and things seem to break down. These 4 patterns should help you use a pattern that is repeatable and readable for the type of test you need.

Setup

I recommend doing all setup in the most functional way possible. If you can avoid it, don't set variables in a beforeEach. This will help ensure tests are isolated and make things a bit easier to reason about. I use a pattern

	#!/usr/bin/python

	import requests, json, pprint, time, socket

	CARBON_SERVER = '0.0.0.0'
	CARBON_PORT = 2003

	def send_msg(message):
	#print 'sending message: %s' % message
	sock = socket.socket()

	const I = x => x
	const K = x => y => x
	const A = f => x => f (x)
	const T = x => f => f (x)
	const W = f => x => f (x) (x)
	const C = f => y => x => f (x) (y)
	const B = f => g => x => f (g (x))
	const S = f => g => x => f (x) (g (x))
	const S_ = f => g => x => f (g (x)) (x)
	const S2 = f => g => h => x => f (g (x)) (h (x))