retronym

class A
class A2 extends A
class B

trait M[X]

//
// Upper Type Bound
//
def upperTypeBound[AA <: A](x: AA): A = x

oxbowlakes

/**
 * Part Zero : 10:15 Saturday Night
 *
 * (In which we will see how to let the type system help you handle failure)...
 *
 * First let's define a domain. (All the following requires scala 2.9.x and scalaz 6.0)
 */

import scalaz._
import Scalaz._

tonymorris

object SKI_Applicative {
/*
First, let's talk about the SK combinator calculus and how it contributes to solving your problem.

The SK combinator calculus is made of two functions (aka combinators): S and K. It is sometimes called the SKI combinator calculus, however, the I combinator can be derived from S and K. The key observation of SK is that it is a turing-complete system and therefore, anything that can be expressed as SK is also turing-complete. Here is a demonstration that Scala's type system is turing-complete (and therefore, undecidable) for example[1].

The K combinator is the most trivial of the two. It is sometimes called "const" (as in Haskell). There is also some discussion about its evaluation strategy in Scala and how to best express it[2]. The K function might be paraphrased as, "takes a value and returns a (constant) unary function that always returns that value."
*/
  def k[A, B]: A => B => A =
    a => _ => a

travisbrown

import shapeless._

trait Flatten[I, O <: HList] {
  def apply(i: I): O
}

trait FlattenLow {
  implicit def otherFlatten[I] = new Flatten[I, I :: HNil] {
    def apply(i: I) = i :: HNil
  }

poetix

package com.youdevise.lofty

import org.specs2.mutable._
import scala.language.dynamics

trait State[S, A] {
  val runState: Function[S, (S, A)]
  def flatMap[B](f: A => Function[S, (S, B)]):Function[S, (S, B)] = (state:S) => {
    val (newState, value) = runState(state)
    f(value)(newState)

poetix

package com.youdevise.lofty

import org.specs2.mutable._
import scala.language.dynamics
import scala.language.reflectiveCalls
import scalaz._
import Scalaz._
import scala.reflect.runtime.universe._

trait Builder[T] {

Mortimerp9

/**
 * A monad to abstract dependencies in the code, see https://coderwall.com/p/kh_z5g
 */
object Reader {

  /**
   * an implicit to convert a function A => B in a Reader[A, B]
   */
  implicit def reader[C, R](block: C => R): Reader[C, R] = Reader(block)

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

runarorama

sealed trait Interact[A]

case class Ask(prompt: String)
  extends Interact[String]

case class Tell(msg: String)
  extends Interact[Unit]

trait Monad[M[_]] {
  def pure[A](a: A): M[A]

kevinwright

As compiled by Kevin Wright a.k.a @thecoda

(executive producer of the movie, and I didn't even know it... clever huh?)

please, please, please - If you know of any slides/code/whatever not on here, then ping me on twitter or comment this Gist!

This gist will be updated as and when I find new information. So it's probably best not to fork it, or you'll miss the updates!

Monday June 16th