y047aka

Elm Meetup Planning

アンドレイさんが来日する予定なので、東京滞在に合わせてイベントを開催する。
Elmでのグラフィック表現をテーマとして扱う。

Event:1

アンドレイさんの東京滞在最終日に合わせて開催する。（8日朝に東京を離れるとのこと）
Elm以外のコミュニティの人にも来てもらえるようにしたい。

aratama

"use strict";

// -- stdlib --
var pure = a=>_=>a                        // pure :: a -> IO a
var bind = m=>f=>_=>f(m())()              // bind :: IO a -> (a -> IO b) -> IO b
var exec = m=>m()                         // exec :: IO a -> a
var wrap = f=>a=>_=>f(a)                  // wrap :: (a -> b) -> (a -> IO b)
var put = wrap(console.log.bind(console)) // put :: a -> IO ()
var get = wrap(url=>{                     // get :: string -> IO string
    var xhr = new XMLHttpRequest()

JEG2

defmodule TypeSpecs do
  def for_module(module) do
    {:ok, {^module, [{:abstract_code, {:raw_abstract_v1, attributes}}]}} =
      module |> :code.which |> :beam_lib.chunks([:abstract_code])
    attributes
    |> Enum.filter_map(fn
      {:attribute, _, :spec, _function_and_types} -> true
      _attribute -> false
    end, fn {:attribute, _, :spec, function_and_types} ->
      function_and_types

voluntas

WebRTC コトハジメ

更新

2023-10-04

作者

@voluntas

バージョン

2023.2

URL

https://voluntas.github.io/

gakuzzzz

Scala の省略ルール早覚え

このルールさえ押さえておけば、読んでいるコードが省略記法を使っていてもほぼ読めるようになります。

メソッド定義

def concatAsString(a: Int, b: Int): String = {
  val a_ = a.toString();
  val b_ = b.toString();

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

chitchcock

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real

schmmd

--langdef=scala
--langmap=scala:.scala
--regex-scala=/^[ \t]*((abstract|final|sealed|implicit|lazy)[ \t]*)*(private|protected)?[ \t]*class[ \t]+([a-zA-Z0-9_]+)/\4/c,classes/
--regex-scala=/^[ \t]*((abstract|final|sealed|implicit|lazy)[ \t]*)*(private|protected)?[ \t]*object[ \t]+([a-zA-Z0-9_]+)/\4/c,objects/
--regex-scala=/^[ \t]*((abstract|final|sealed|implicit|lazy)[ \t]*)*(private|protected)?[ \t]*case class[ \t]+([a-zA-Z0-9_]+)/\4/c,case classes/
--regex-scala=/^[ \t]*((abstract|final|sealed|implicit|lazy)[ \t]*)*(private|protected)?[ \t]*case object[ \t]+([a-zA-Z0-9_]+)/\4/c,case objects/
--regex-scala=/^[ \t]*((abstract|final|sealed|implicit|lazy)[ \t]*)*(private|protected)?[ \t]*trait[ \t]+([a-zA-Z0-9_]+)/\4/t,traits/
--regex-scala=/^[ \t]*type[ \t]+([a-zA-Z0-9_]+)/\1/T,types/
--regex-scala=/^[ \t]*((abstract|final|sealed|implicit|lazy)[ \t]*)*def[ \t]+([a-zA-Z0-9_]+)/\3/m,methods/
--regex-scala=/^[ \t]*((abstract|final|sealed|implicit|lazy)[ \t]*)*val[ \t]+([a-zA-Z0-9_]+)/\3/l,constants/