jdegoes

package spartan.training 

object TheMonadProblem {
  sealed trait Parser[+A] { self =>
    def map[B](f: A => B): Parser[B] = self.flatMap(a => Parser.succeed(f(a)))
    
    def flatMap[B](f: A => Parser[B]): Parser[B] = Parser.FlatMap(self, f)
  }
  object Parser {
    def succeed[A](a: A): Parser[A] = Succeed(a)

onlurking

Programming as Theory Building

Peter Naur

Peter Naur's classic 1985 essay "Programming as Theory Building" argues that a program is not its source code. A program is a shared mental construct (he uses the word theory) that lives in the minds of the people who work on it. If you lose the people, you lose the program. The code is merely a written representation of the program, and it's lossy, so you can't reconstruct

jdegoes

package fpmax

import scala.util.Try
import scala.io.StdIn.readLine

object App0 {
  def main: Unit = {
    println("What is your name?")

    val name = readLine()

OlivierBlanvillain

Revisiting Tagless Final Interpreters

Tageless Final interpreters are an alternative to the traditional Algebraic Data Type (and generalized ADT) based implementation of the interpreter pattern. This document presents the Tageless Final approach with Scala, and shows how Dotty with it's recently added implicits functions makes the approach even more appealing. All examples are direct translations of their Haskell version presented in the Typed Tagless Final Interpreters: Lecture Notes (section 2).

The interpreter pattern has recently received a lot of attention in the Scala community. A lot of efforts have been invested in trying to address the biggest shortcomings of ADT/GADT based solutions: extensibility. One can first look at cats' Inject typeclass for an implementation of [Data Type à la Carte](http://www.cs.ru.nl/~W.Swierstra/Publications/DataTypesA

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

etorreborre

scala> import cats.implicits._
import cats.implicits._

scala> (1 -> 2) === (1 -> 3)
res0: Boolean = true

scala> import scala.reflect.runtime.universe._
import scala.reflect.runtime.universe._

scala> showCode(reify { (1 -> 2) === (1 -> 3) }.tree)

hfreire

# Install QEMU OSX port with ARM support
sudo port install qemu +target_arm
export QEMU=$(which qemu-system-arm)

# Dowload kernel and export location
curl -OL \
https://github.com/dhruvvyas90/qemu-rpi-kernel/blob/master/kernel-qemu-4.1.7-jessie
export RPI_KERNEL=./kernel-qemu-4.1.7-jessie

# Download filesystem and export location

cb372

Category theory jargon cheat sheet

A primer/refresher on the category theory concepts that most commonly crop up in conversations about Scala or FP. (Because it's embarassing when I forget this stuff!)

I'll be assuming Scalaz imports in code samples, and some of the code may be pseudo-Scala.

Functor

A functor is something that supports map.

ohanhi

Learning FP the hard way: Experiences on the Elm language

by Ossi Hanhinen, @ohanhi

with the support of Futurice 💚.

Licensed under CC BY 4.0.

Editorial note

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