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@sadache
sadache / AA.md
Created July 8, 2012 21:29
Is socket.push(bytes) all you need to program Realtime Web apps?

Is socket.push(bytes) all you need to program Realtime Web apps?

One of the goals of Play2 architecture is to provide a programming model for what is called Realtime Web Applications.

Realtime Web Applications

Realtime Web Applications are applications that make use of Websockets, Server Sent Events, Comet or other protocols offering/simulating an open socket between the browser and the server for continuous communication. Basically, these applications let users work with information as it is published - without having to periodically ping the service.

There are quite a few web frameworks that target the development of this type of application: but usually the solution is to simply provide an API that allows developers to push/receive messages from/to an open channel, something like:

@nlinker
nlinker / SCombinator.scala
Created October 12, 2012 03:44 — forked from folone/SCombinator.scala
Y-Combinator in Scala
/**
* <b>Fixed Point Combinator is:</b>
* Y = λf.(λx.f (x x)) (λx.f (x x))
*
* <b>Proof of correctness:</b>
* Y g = (λf . (λx . f (x x)) (λx . f (x x))) g (by definition of Y)
* = (λx . g (x x)) (λx . g (x x)) (β-reduction of λf: applied main function to g)
* = (λy . g (y y)) (λx . g (x x)) (α-conversion: renamed bound variable)
* = g ((λx . g (x x)) (λx . g (x x))) (β-reduction of λy: applied left function to right function)
* = g (Y g) (by second equality) [1]
@Fristi
Fristi / Aggregate.hs
Last active November 6, 2022 20:50
DDD/Event Sourcing in Haskell. Implemented an aggregate as a type class and type families to couple event, command and error types specific to the aggregate. Errors are returned by using Either (Error e) (Event e). Applying Applicative Functors fits here well to sequentially check if the command suffice.
{-# LANGUAGE TypeFamilies #-}
import Data.Function (on)
import Control.Applicative
data EventData e = EventData {
eventId :: Int,
body :: Event e
}
@andrejbauer
andrejbauer / Bijection.md
Last active November 29, 2023 22:05
A bijection between numbers and pairs of numbers.

A bijection between numbers and pairs of numbers

Every once in a while I am faced with someone who denies that the rational numbers (or fractions, or pairs of integers) can be put into a bijective correspondence with natural numbers. To deal with the situation, I coded up the bijection. So now I can just say: "Really? Interesting. Please provide a pair of numbers (i,j) which is not enumerated by f, as defined in my gist." I am still waiting for a valid counter-example.

Anyhow, here is a demo of f and g at work. I am using the Python version, but a Haskell variant is included as well.

The 100-th pair is:

>>> f(100)

(10, 4)

@staltz
staltz / introrx.md
Last active November 19, 2024 13:42
The introduction to Reactive Programming you've been missing
@nlinker
nlinker / gist:45b33c2584f9cb6ddd7b072878aa412a
Created May 13, 2016 22:46 — forked from pcapriotti/gist:1761136
Composition of free monads
{-# LANGUAGE MultiParamTypeClasses, FunctionalDependencies #-}
import Control.Monad
import Control.Monad.Free
class (Functor f, Functor g, Functor h) => Compose f g h | f g -> h where
compose :: f x -> g y -> Free h (Free f x, Free g y)
compose f g = return (liftF f, liftF g)
composeF :: Compose f g h => Free f x -> Free g x -> Free h x
@Fristi
Fristi / Tagless.scala
Last active August 31, 2016 07:38
Introspectable Json algebra. Allows you to derive codecs and documentation. Category + Monoid lifted into the algebra GADT. Composing algebra's is possible via the heterogenous polymorphic tail. Port of JsonGrammar2 by Martijn van Steenbergen en Sjoerd Visscher
package net.vectos
import cats.arrow.Category
import io.circe.Json
import shapeless._
import scala.language.higherKinds
object algebra {
sealed trait JsonGrammarF[F[-_,+_]] extends Category[F] {
@hereismari
hereismari / msi-gtx1060-ubuntu-18.04-deeplearning.md
Last active July 1, 2024 14:42
Setting up a MSI laptop with GPU (gtx1060), Installing Ubuntu 18.04, CUDA, CDNN, Pytorch and TensorFlow