linus

// Takes a board generated by the bingo() function below, and returns
// a HTML table.
function boardToHTML(board) {
  return `
    <table>
      ${chunk(board, 3).map(card => `<tbody>
        ${card.map(row => `<tr>
          ${row.map(cell => `<td>
            ${cell ? cell.toString().padStart(2, '0') : ''}
          </td>`).join('')}

linus

class Video extends Inline {
  get attrs() {
    return {
      url: new Attribute,
      id: new Attribute({compute: function (attrs) {console.log(attrs);return videoRE.exec(attrs.url)[2]}}),
      type: new Attribute({compute: function (attrs) {console.log(attrs);return videoRE.exec(attrs.url)[1]}}),
      width: new Attribute({default: "640"}),
      height: new Attribute({default: "480"})
    }
  }

linus

Keybase proof

I hereby claim:

• I am linus on github.
• I am yesbabyyes (https://keybase.io/yesbabyyes) on keybase.
• I have a public key whose fingerprint is DC4B 6A42 0A58 45E0 8F42 6943 30E7 9CC0 DEF0 C03E

To claim this, I am signing this object:

linus

var foo = 1, bar = 2;

var foo = 1
  , bar = 2;

var foo = 1,
    bar = 2;

var foo = 1;
var bar = 2;

linus

linus@Newton:~$ redis-cli
redis 127.0.0.1:6379> publish /updates.foo "hello there!"
(integer) 1
redis 127.0.0.1:6379> 

linus

linus@Newton:~/development/projects/turkey$ ab -n 10000 http://localhost:3000/A012B490
This is ApacheBench, Version 2.3 <$Revision: 655654 $>
Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/
Licensed to The Apache Software Foundation, http://www.apache.org/

Benchmarking localhost (be patient)
Completed 1000 requests
Completed 2000 requests
Completed 3000 requests
Completed 4000 requests

linus

var http = require("http");

// Responses for various urls
var responses = {
  "/createUser": "foo",
  "/deleteUser": "bar"
};

var server = http.createServer(function(req, res) {
  // Listen for data on request

linus

# Main function. Takes the original image and a list of
# profile pictures, and returns the finished, improved, mosaic
def create_better_mosaic(original_image, profile_pictures):
  # Calculate the width and height of the scaled image, where
  # each profile picture maps to a pixel in the original image
  width, height = image_ratio(original_image, len(profile_pictures))
  # Get the pixel values of the scaled image, "unfolded" in
  # one dimension
  pixels = get_pixels_one_dimensional(original_image, width, height)
  # Get a list of tuples with (image, color) for each profile picture

linus

# Return the Euclidean distance between color_a and color_b.
# We optimize a little and don't calculate the square root of the distance.
# The sum of the squares of deltas is good enough.
def color_distance(color_a, color_b):
  delta_red = color_a[0] - color_b[0]
  delta_green = color_a[1] - color_b[1]
  delta_blue = color_a[2] - color_b[2]

  return delta_red * delta_red + delta_green * delta_green + delta_blue * delta_blue

linus

# Complex class from http://docstore.mik.ua/orelly/webprog/jscript/ch08_05.htm#jscript4-CHP-8-EX-6
class Complex
    constructor: (@x, @y) ->

    magnitude: ->
        Math.sqrt @x * @x + @y * @y

    negative: ->
        new Complex -@x, -@y