Zach LastZactionHero

Today I made the decision to shut down King's Cross Coffee, the potentially-popular coffee shop that existed only in the hearts and minds of coffee-drinking Ann Arborites. I decided to shut it down because people were actually going there. It didn't seem fair, and I'd proved my point.

I'd been experimenting with the concept of automatically favoriting tweets from a local audience as a way of driving traffic. I set up a product called LocalFavorite which takes a search term and location range and favorites 3 tweets each hour.

The Experiment

Can a brand with no real-world presence build a legitimate following by Twitter favorites alone?

To control my variables, I created the Twitter account @KingsCrossCafe, a realistic-looking coffee shop in Ann Arbor, MI. I gave it a few photos, a real address, a bit.ly link, and posted a few tweets. Then I began automatically favoriting anyone in the Ann Arbor area that mentions 'coffee' or being 'tired'.

	require 'selenium-webdriver'
	require 'selenium/client'
	#system 'java -jar ~/Downloads/selenium-2.37.0/selenium-server-standalone-2.37.0.jar &'

	browser = Selenium::Client::Driver.new \
	:host => "localhost",
	:port => 4444,
	:browser => "*firefox",
	:url => "http://www.pictage.com",
	:timeout_in_second => 60

	//------------------------------------------------------------------------------
	// Download Langauges
	//
	// VITLanguage: An individual language, name ("English", "Spanish") and code
	// ("en", "es")
	//
	// VITLanguageManager: Download an array of VITLanguages from API
	//
	//------------------------------------------------------------------------------

	### THE SCRIPT

	#!/usr/bin/env ruby
	dump = `ps -ef \| grep zeus`
	processes = dump.split("\n")

	processes.each do \|process\|
	process_id = process.scan(/[0-9]+/)[1]
	`sudo kill -9 #{process_id}`
	end

	import tflearn
	from tflearn.data_preprocessing import ImagePreprocessing
	from tflearn.data_augmentation import ImageAugmentation

	from tflearn.layers.core import input_data, dropout, fully_connected
	from tflearn.layers.conv import conv_1d, max_pool_1d
	from tflearn.layers.estimator import regression

	img_prep = ImagePreprocessing()
	img_prep.add_featurewise_zero_center()

	from sklearn import linear_model

	Y = np.array(Y) # array of training answers, 1 or 0

	X = np.array([]) # array of image brightness
	for filename in filenames:
	# load the images
	image = misc.imread(constants.IMAGE_64_PATH + '/' + filename, mode='L')
	avg_brightness = np.matrix(image).mean()
	X = np.append(X, avg_brightness)

	.NET
	360-degree video
	3D Animation
	3D Design
	3D Model Maker
	3D Modelling
	3D Printing
	3D Rendering
	3ds Max
	4D

	# Git
	# https://gist.github.com/n0n0x/7be453ff18f58020bafe
	source ~/.git_bash_completion

	# Go
	export GOPATH=$HOME/development/go
	export GOSRC=$GOPATH/src/github.com/LastZactionHero
	export GOBIN=$GOPATH/bin
	PATH=$PATH:$GOBIN
	PATH=$PATH:/usr/local/go/bin