gregoryyoung

Do you like interesting problems? Do you keep a notepad/laptop next to your bed because you tend to solve problems while sleeping? We might be a good fit.

We are a totally distributed team. Everyone works from home (even if home tends to be in airplanes). Most of the code we write is Open Source and is available on github http://github.com/eventstore/eventstore if you want to look through it. The team completely owns the project.

Perhaps we could be a good fit for you, we are seeking developers who are:

1) Always improving
2) Highly Technical
3) Capable of working in a remote environment
4) Independent

listochkin

HTML, CSS, JS

1. Intro: https://www.youtube.com/playlist?list=PL697D36B35F92E9E4 (1)
2. JS:
   • Single page: https://developer.mozilla.org/en-US/docs/Web/JavaScript/A_re-introduction_to_JavaScript (2)
   • Video: http://video.ch9.ms/ch9/DF01/97ECAC6F-5413-486A-B9D5-9EB60082DF01/MIX11HTM06_high_ch9.mp4 (5)
   • Intermediate: http://ejohn.org/apps/learn (6)
   • Philip Roberts: What the heck is the event loop anyway? https://www.youtube.com/watch?v=8aGhZQkoFbQ

• Book: http://eloquentjavascript.net/

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

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.

---

swlaschin

// ==============================
// This gist is in response to 
// http://programmers.stackexchange.com/questions/228939/how-to-improve-upon-blochs-builder-pattern-to-make-it-more-appropriate-for-use
//
// This is a simple bit of code to do the same thing using the Either monad (e.g. success/failure) and applicatives
// This code is (a) more generic (b) much shorter (c) more type safe
//
// Compare with the Java code at https://gist.github.com/swlaschin/9009343
// ==============================

Xachriel

#Example for the kaggle forums.
library(FNN)
library(stats)

train <- read.csv("data/train.csv", header=TRUE, comment.char="")
test <- read.csv("data/test.csv", header=TRUE, comment.char="")

N <- 30000
set.seed(20140202)
trainingSet <- train[sample(1:nrow(train), N), ]

chaliy

процедура MergeSort (мод a: t) це
		процедура Merge(арг a: t; Size: нат; рез b: t) це
			змін i, j, k, r1, r2: нат;
		поч
			k <- 1;
			поки k<=n повт
			   {визначення границь підмасивів}
			   i <- k; r1 <- i+Size-1;
			   якщо r1>n то r1 <- n кр;
			   j <- r1+1; r2 <- j+Size-1;

palladin

public static class EnumerableEx
{
    public static IEnumerable<R> Select<T1, T2, R>(this IEnumerable<Tuple<T1, T2>> source, Func<T1, T2, R> f)
    {
        return source.Select(t => f(t.Item1, t.Item2));
    }
}

Enumerable.Range(1, 10)
        .Select(x => Tuple.Create(x, x))

chaliy

# You should have pswatch installed (http://psget.net/directory/pswatch/)
# You need small modification to the FSharpKoans/PathToEnlightenment.fs to make sure that you will not need to press any key
#    Comment out
#      printf "Press any key to continue..."
#      System.Console.ReadKey() |> ignore
# 1. Open separate powershell window
# 2. Make sure you are in FSharpKoans solution folder
# 3. Run watch_fsharpkoans.ps1
# 4. Make both your visual studio and powershell window visitble at the same time
# 5. Enjoy!

mausch

"Hopefully the third answer is right; but who knows, maybe I made a mistake; I’m just a human, I can throw exceptions as well."

"I am waving my hands on purpose here, this is very spaghetti like code. And spaghetti is great as food, but not good as code."

"flatMap will allow us to focus on the happy path. flatMap will take care of all the noise. flatMap is the dolby for programmers."

"Great programmers write baby code"

"it's obviously correct"

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