Sangwoo Joh sangwoo-joh

아래 메일은 2017-02-12 21:43(이하 한국 표준시)에 한겨레 기사에 대한 의견으로서 기사에 제시된 김재섭 기자의 메일로 보낸 내용이다. 메일에서 사실 관계 등의 오류가 있다면 모두 나의 실수이다.

2017-02-13 14:53에 덧붙임: 더 이상 gist를 비공개로 할 이유가 없어졌으므로 공개로 전환. 이 메일에 대한 답변은 받았으나 공개할 만큼 중요한 반론이 들어 있진 않으며 공개 여부도 묻지 않았으므로 공개하지 않는다. 아래 글 자체에도 다양한 비문과 오자가 있으나 본래 보낸 내용을 살리기 위해 전혀 수정을 하지 않기로 했음을 양해 바람.

2017-02-13 19:00에 덧붙임: 이 기사의 후속으로 구글코리아 측의 기자간담회가 올라갔다. 새 기사에 대해서는 특이한 게 없으므로 노코멘트. 또한 위의 기사 링크를 미디어다음에서 한겨레 웹사이트로 가도록 수정.

원문

안녕하십니까, 귀하께서 작성하신 (물론 저는 그 진위를 알 수 없습니다만, 적어도 그렇게 나와 있는) 기사에 대한 의견을 제기하고자 메일을 씁니다. 이 메일은 저의 개인 의견이며 저를 고용하고 있는 회사나 단체 등의 의견을 전혀 대변하지 않음을 혹시나 싶지만 미리 밝혀 둡니다.

In this tutorial we're going to build a set of parser combinators.

What is a parser combinator?

We'll answer the above question in 2 steps.

What is a parser?
and, what is a parser combinator?

So first question: What is parser?

Program Analysis Resources

(draft; work in progress)

CUSTOM GAME ENGINES: A Small Study

WARNING: Article moved to separate repo to allow users contributions: https://github.com/raysan5/custom_game_engines

A couple of weeks ago I played (and finished) A Plague Tale, a game by Asobo Studio. I was really captivated by the game, not only by the beautiful graphics but also by the story and the locations in the game. I decided to investigate a bit about the game tech and I was surprised to see it was developed with a custom engine by a relatively small studio. I know there are some companies using custom engines but it's very difficult to find a detailed market study with that kind of information curated and updated. So this article.

Nowadays lots of companies choose engines like [Unreal](https:

Use pre-built gccemacs

Fortunatly we could use pre-built gccemacs right now.

Those two repos did the greate job for us.

https://github.com/twlz0ne/nix-gccemacs-darwin

https://github.com/twlz0ne/nix-gccemacs-sierra

Here is the tutorial:

Getting started with Linux kernel development

Prerequisites

The Linux kernel is written in C, so you should have at least a basic understanding of C before diving into kernel work. You don't need expert level C knowledge, since you can always pick some things up underway, but it certainly helps to know the language and to have written some userspace C programs already.

It will also help to be a Linux user. If you have never used Linux before, it's probably a good idea to download a distro and get comfortable with it before you start doing kernel work.

Lastly, knowing git is not actually required, but can really help you (since you can dig through changelogs and search for information you'll need). At a minimum you should probably be able to clone the git repository to a local directory.

	#! /usr/bin/env python3

	"""
	This utility will execute the given command (by default, your shell)
	in a subshell, with an ssh-agent process running and your
	private key added to it. When the subshell exits, the ssh-agent
	process is killed.
	"""

	# This code was written by Atul Varma in February 2017. It requires

	# This code doesn't work, and isn't intended to.
	# The goal of this code is to explain how attention mechansisms work, in code.
	# It is deliberately not vectorized to make it clearer.

	def attention(self, X_in:List[Tensor]):
	# For every token transform previous layer's out
	for i in range(self.sequence_length):
	query[i] = self.Q * X_in[i]
	key[i] = self.K * X_in[i]
	value[i] = self.V * X_in[i]

	#lang rosette/safe


	(require rosette/lib/angelic ; provides `choose*`
	rosette/lib/destruct) ; provides `destruct`
	; Tell Rosette we really do want to use integers.
	(current-bitwidth #f)

	{
	"attention_probs_dropout_prob": 0.1,
	"directionality": "bidi",
	"hidden_act": "gelu",
	"hidden_dropout_prob": 0.1,
	"hidden_size": 1024,
	"initializer_range": 0.02,
	"intermediate_size": 4096,
	"max_position_embeddings": 512,
	"num_attention_heads": 16,