PedramNavid

 This uses llm.datasette.io and OpenAI.
 
 I use `git commit --template` to provide the output from the LLM to Git. This way, if you do not like the results, you
 can quit your editor and no commit will be made. 
 
 # Shell function for generating a diff and editing it in your default editor:
 
gcllm() {
  GIT_DIR="$(git rev-parse --git-dir)"
  TEMPLATE="$GIT_DIR/COMMIT_EDITMSG_TEMPLATE"

mpilquist

/* Example of encoding Functor/Applicative/Monad from cats with Dotty 0.15 features.
 * Derived in part from Cats -- see https://github.com/typelevel/cats/blob/master/COPYING for full license & copyright.
 */
package structures

import scala.annotation._

trait Functor[F[_]] {
  def (fa: F[A]) map[A, B](f: A => B): F[B]
  def (fa: F[A]) as[A, B](b: B): F[B] =

non

package demo

import cats.{Applicative, Functor, Id, Semigroupal, Traverse}
import cats.arrow.FunctionK

/**
 * Type-lifting operation to replace the wildcard type (i.e. _).
 *
 * In some cases we end up with code like: List[Option[_]]. This is
 * fine unless you later need to write code in terms of a particular

Icelandjack

import Data.Functor.Yoneda
import Data.Char
import Data.Kind

infixr 5
  ·

type  List :: (Type -> Type) -> Constraint
class List f where

kiambogo

import fs2.concurrent.Queue
import cats.implicits._
import cats.effect.Concurrent
import cats.effect.concurrent.Ref
 
def groupBy[F[_], A, K](selector: A => F[K])(implicit F: Concurrent[F]): Pipe[F, A, (K, Stream[F, A])] = {
  in =>
  Stream.eval(Ref.of[F, Map[K, Queue[F, Option[A]]]](Map.empty)).flatMap { st =>
    val cleanup = {
      import alleycats.std.all._

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()

raulraja

import arrow.Kind
import arrow.core.Option
import arrow.core.Try
import arrow.core.functor
import arrow.effects.IO
import arrow.effects.fix
import arrow.effects.functor
import arrow.typeclasses.Functor

/* algebras */

johnynek

import scala.util.Try

object PathSerializer {

  trait SerDe[A] {
    // By using a path dependent type, we can be sure can deserialize without wrapping in Try
    type Serialized
    def ser(a: A): Serialized
    def deser(s: Serialized): A
    // If we convert to a generic type, in this case String, we forget if we can really deserialize

djspiewak

Quick Tips for Fast Code on the JVM

I was talking to a coworker recently about general techniques that almost always form the core of any effort to write very fast, down-to-the-metal hot path code on the JVM, and they pointed out that there really isn't a particularly good place to go for this information. It occurred to me that, really, I had more or less picked up all of it by word of mouth and experience, and there just aren't any good reference sources on the topic. So… here's my word of mouth.

This is by no means a comprehensive gist. It's also important to understand that the techniques that I outline in here are not 100% absolute either. Performance on the JVM is an incredibly complicated subject, and while there are rules that almost always hold true, the "almost" remains very salient. Also, for many or even most applications, there will be other techniques that I'm not mentioning which will have a greater impact. JMH, Java Flight Recorder, and a good profiler are your very best friend! Mea

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