djspiewak

Greasing the Skids: Building Remote Teams

In the wake of the virus that-must-not-be-named (which most people misname anyway), it seems like everyone and their cat has posted some sort of opinion or how-to on making remote work, work. This is a good thing! Working remotely, particularly full-time, is hard! I've done it for my entire career (aside from an odd 14 month office period in the middle that we shall not speak of), but more relevantly, for the past two years I've been responsible for building, managing, and enabling an entirely remote team, distributed across nine timezones. Remote teams don't just happen by installing Slack and telling everyone to work on their couch: they require a lot of effort to do correctly and efficiently. But, done right, it can be a massive multiplier on your team efficiency and flexibility.

Here's how we do it. I'm going to attempt to structure this post more towards management than engineering, and so I apologize in advance if I assume terminology or knowledge which

mtsokol

import zio._
import zio.random._
import zio.console._
import zio.duration._

object Main extends App {

  override def run(args: List[String]) = program.exitCode

  sealed trait Diagnostic

jdegoes

object test {
  import scalaz.zio._

  type UserID = String
  case class UserProfile(name: String)

  // The database module:
  trait Database {
    val database: Database.Service

ubourdon

package service.utils

import scalaz.{-\/, \/, \/-}
import scalaz.zio.{DefaultRuntime, IO, ZIO}
import play.api.libs.concurrent.Execution.Implicits
import scalaz.concurrent.{Task => ZTask}

import scala.concurrent.{ExecutionContext, Future}

package object zio {

igor-ramazanov

package io.github.themirrortruth

object KVStore {
  def main(args: Array[String]): Unit = {
    import cats.free.Free
    import cats.{Id, ~>}
    
    //Strict language for working with abstract key-value stores defined as Algebraic Data Type (ADT)
    sealed trait KVStoreApi[Key, Value, Result]
        extends Product

djspiewak

Thread Pools

Thread pools on the JVM should usually be divided into the following three categories:

1. CPU-bound
2. Blocking IO
3. Non-blocking IO polling

Each of these categories has a different optimal configuration and usage pattern.

LnL7

self: super:

{
  # Install overlay:
  # $ mkdir -p ~/.config/nixpkgs/overlays
  # $ curl https://gist.githubusercontent.com/LnL7/570349866bb69467d0caf5cb175faa74/raw/3f3d53fe8e8713ee321ee894ecf76edbcb0b3711/lnl-overlay.nix -o ~/.config/nixpkgs/overlays/lnl.nix
  userPackages = super.userPackages or {} // {

    # Example:
    hello = self.hello;

d1egoaz

If you want to learn Scala, I’d suggest:

• Install IntelliJ + Scala Plugin
• Don’t do the Coursera courses yet.
• Don’t do the “red book” Functional Programming in Scala yet.
• Do: http://underscore.io/books/
  • Essential Scala
  • Essential Play
  • Essential Slick
• Do Scala for the Impatient: https://www.amazon.com/Scala-Impatient-Cay-S-Horstmann/dp/0321774094
• The Neophyte’s Guide to Scala http://danielwestheide.com/scala/neophytes.html

noopkat

⚠️ this is now stupidly out of date

Computers

• 13" Macbook Pro 3.3 GHz i7 (late 2016)
• Microsoft Surface Book (2016)

Peripherals

• Dell up3216q 32" monitor

jkpl

Enforcing invariants in Scala datatypes

Scala provides many tools to help us build programs with less runtime errors. Instead of relying on nulls, the recommended practice is to use the Option type. Instead of throwing exceptions, Try and Either types are used for representing potential error scenarios. What’s common with these features is that they’re used for capturing runtime features in the type system, thus lifting the runtime scenario handling to the compilation phase: your program doesn’t compile until you’ve explicitly handled nulls, exceptions, and other runtime features in your code.

In his “Strategic Scala Style” blog post series,