kislayverma

By Steve Yegge

Last week I accidentally posted an internal rant about service platforms to my public Google+ account (i.e. this one). It somehow went viral, which is nothing short of stupefying given that it was a massive Wall of Text. The whole thing still feels surreal.

Amazingly, nothing bad happened to me at Google. Everyone just laughed at me a lot, all the way up to the top, for having committed what must be the great-granddaddy of all Reply-All screwups in tech history.

But they also listened, which is super cool. I probably shouldn’t talk much about it, but they’re already figuring out how to deal with some of the issues I raised. I guess I shouldn’t be surprised, though. When I claimed in my internal post that “Google does everything right”, I meant it. When they’re faced with any problem at all, whether it’s technical or organizational or cultural, they set out to solve it in a first-class way.

Anyway, whenever something goes viral, skeptics start wondering if it was faked or staged. My accident

stevenringo

| Title | Description

nikolasburk

GraphQL vs Firebase

With the variety of server-side technologies today, developers have a lot of choices when it comes to deciding what kind of backend to use for their next application.

In this article, we want to explore the differences between GraphQL and Firebase, two very popular server-side technologies.

Overview

Before diving into technical details, let's create some perspective on the two technologies and where they're coming from.

jdegoes

Applied Functional Programming with Scala - Notes

Copyright © 2016-2018 Fantasyland Institute of Learning. All rights reserved.

1. Mastering Functions

A function is a mapping from one set, called a domain, to another set, called the codomain. A function associates every element in the domain with exactly one element in the codomain. In Scala, both domain and codomain are types.

val square : Int => Int = x => x * x

whistler

from csv import DictWriter
from glob import glob
from ofxparse import OfxParser

DATE_FORMAT = "%m/%d/%Y"

def write_csv(statement, out_file):
    print "Writing: " + out_file
    fields = ['date', 'payee', 'debit', 'credit', 'balance']
    with open(out_file, 'w') as f:

djspiewak

Introduction to scalaz-stream

Every application ever written can be viewed as some sort of transformation on data. Data can come from different sources, such as a network or a file or user input or the Large Hadron Collider. It can come from many sources all at once to be merged and aggregated in interesting ways, and it can be produced into many different output sinks, such as a network or files or graphical user interfaces. You might produce your output all at once, as a big data dump at the end of the world (right before your program shuts down), or you might produce it more incrementally. Every application fits into this model.

The scalaz-stream project is an attempt to make it easy to construct, test and scale programs that fit within this model (which is to say, everything). It does this by providing an abstraction around a "stream" of data, which is really just this notion of some number of data being sequentially pulled out of some unspecified data source. On top of this abstraction, sca

negator

    
    
    /**
      The Play (2.3) json combinator library is arguably the best in the scala world. However it doesnt
      work with case classes with greater than 22 fields.
      The following gist leverages the shapeless 'Automatic Typeclass Derivation' facility to work around this
      limitation. Simply stick it in a common location in your code base, and use like so:
      Note: ** Requires Play 2.3 and shapeless 2.0.0  
      
  

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

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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.

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pgaertig

import groovy.sql.Sql
import org.codehaus.groovy.control.CompilerConfiguration

import java.sql.Connection

class FactoryGrill extends Script {

    private Connection connection;
    private Sql sql;

mscharhag

import java.time.*;
import java.time.format.DateTimeFormatter;
import java.time.format.FormatStyle;
import java.time.temporal.ChronoUnit;
import java.time.temporal.TemporalAdjusters;
import java.util.*;

import static java.time.temporal.TemporalAdjusters.*;

public class Java8DateTimeExamples {