- Copy
prissy
to~/bin
(or elsewhere in your$PATH
) chmod +x prissy
gem install awesome_print multi_json
$ some_command_that_outputs_json | prissy
#!/usr/bin/env ruby | |
=begin | |
INSTALL: | |
curl http://github.com/defunkt/gist/raw/master/gist.rb > gist && | |
chmod 755 gist && | |
sudo mv gist /usr/local/bin/gist |
require 'rubygems' | |
require 'rack' | |
class Object | |
def webapp | |
class << self | |
define_method :call do |env| | |
func, *attrs = env['PATH_INFO'].split('/').reject(&:empty?) | |
[200, {}, send(func, *attrs)] | |
end |
" This gets added to your vimperatorrc; makes ':yt' trigger youtube-dl | |
" You can also bind it directly to a keystroke. | |
" Requires youtube-dl: https://github.com/rg3/youtube-dl | |
" On a Mac with Homebrew installed, you can 'brew install youtube-dl' | |
comm! yt 'exe "!" + eval("plugins.youtubeDownload.youtubeDownload()")' |
#!/usr/bin/env ruby -pi.bak | |
# bacon before takes no argument | |
$_.sub!(/before\s*\(?\s*:each\s*\)?\s*/, 'before ') | |
# same with after | |
# No magic annoying spaces after 'should'. | |
# Matchers are methods on the Should object | |
$_.sub!(/\.should\s*/, '.should.') | |
# fix `should_not` -> `should._not` |
#!/usr/bin/env ruby | |
require 'github-campfire' | |
def ask_for_image(phrase) | |
`growlnotify -m "#{phrase}"` | |
GitHub::Campfire.notify("/img me #{phrase}", GitHub::Campfire::HUBOT) | |
end | |
phrases = [ | |
'hubot ignite', # new presentation style |
<html> | |
<head> | |
<title>Marquee Fishtank</title> | |
<style> | |
#fishtank { | |
width: 350px; | |
background-color: aqua; | |
} | |
#fish1 { color: red } | |
#fish2 { color: orange } |
require "json" | |
require "typhoeus" | |
require "pp" | |
# MigrateRepo usage instructions: | |
# | |
# The way this is used at Airbnb for migrating from GitHub to GHE is by | |
# creating a separate organization with no members called "migration", | |
# adding that as the target organization, and then once the migration | |
# has finished, moving it over to the intended location. |
The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.
The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training. The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.