Stupid Projects From The Stupid Hackathon

This weekend, Amelia Winger-Bearskin and I organized our second Stupid Shit No One Needs & Terrible Ideas Hackathon, a hackathon that asks participants to produce project that have no value whatsoever (PATENT PENDING). It was a resounding and unqualified success. Here is a small sample of the 100,000+ projects that people made over the course of the day. (If you made a project and want me to add it to this list, just get in touch.)

iPad On A Face

iPad On A Face by Cheryl Wu is a telepresence robot, except it’s a human with an iPad on his or her face.

face


Kim Kardashian On A Newton

Perhaps a sister project to iPad On A Face, Kim Kardashian On A Newton is photo of Kim Kardashian on an Apple Newton. Apologies for the lack of credits on this one – I can’t remember who made it.

B2hywHeIQAEb0Nk


Rearview Mirror

Rearview Mirror by Matt Romein and Sam Sadtler is a virtual reality application that shows you what’s behind you.


Golden Bitcoin Pasties

3D printed Bitcoin pasties, by Xuedi Chen and Pedro Galvao.


The Emoji Subtitle Creator

The Emoji Subtitle Creator by Ross Goodwin and Seth Kranzler automatically translates normal subtitles into ascii symbols.


How to say Mitt Romney in different languages

How to say Mitt Romney in different languages by Owen Weeks is an interactive tool that teaches the user to say the word “Mitt Romney” in a variety of languages.

howtosaymittromneyindifferentlanguages_screenshot


Tweet Your Food

Tweet Your Food by Denny George and Matthew Kaney is an important innovation in the sphere of the “quantified self”. It’s a device that tweets every time you eat a bite of food.

IceCreamTweet

Screen Shot 2014-11-17 at 3.39.29 PM


Needybot

Needybot by Jessie Contour, Eric Bichan and Will Acheson is a robot that cries when you aren’t soothing it.


Stupid Font

Stupid Font, by Stupid Hackathon veteran Laura Juo-Hsin Chen is a hand tracing of TEFF Lexicon, the most expensive font in the world.

stupidFont_11


Profanity65

Profanity65 by Nick Doiron adds profanity to encrypted PGP messages.

Screen Shot 2014-11-17 at 4.40.31 PM


Drone Delivery with Drone Doulas

Drone Delivery with Drone Doulas by Amelia Winger-Bearksin. The future of babies.


Focus Tools

Focus Tools by Brian Clifton is a Chrome extension that replaces all YouTube videos with this gif:


Egg Timer

Egg Timer by Pam Liou is an hourglass that counts down to menopause.

Screen-Shot-2014-11-17-at-5.09.51-PM


Intellectual Babes Calendar

Intellectual Babes Calendar by Caitlin Weaver is a calendar of re-touched male intellectuals coming soon for the holiday calendar gifting season. The calendar will feature: Slavoj Žižek, Jorge Luis Borges, Roland Barthes (UNTOUCHED), Jean-Paul Sartre, Plato, Friedrich Nietzsche, Arthur Schopenhauer, Walt Whitman, AND MANY, MANY MORE.

From the project’s creator: “It’s hard to care about the Western intellectual tradition when it was created by a bunch of old ugly white dudes. Why would you ever read some decrepit geezer’s book? The intellectual babes calendar streamlines and beautifies the men responsible for building the pillars of our culture – maybe not all the way to the point of 8 out of 10 / would fuck, but definitely lifting them out of the 10 below zero / def wouldn’t read category.”

Here are Italo Calvino (April) and Vladimir Nabokov (February):

italocalvino_caitlin

nabokov_caitlin


Thank You!

We want to thank Grand Daisy bakery for making us pizza, Sam Agnew and Ordrin for paying for those pizzas, and ITP for sponsoring and hosting the event.

Yelp Prison Review Faxbot

prisonfaxheader

Fletcher Bach and I recently discovered that there are reviews of prisons on Yelp. Some of these reviews are snarky one-liners -for example, one yelper describes Rikers Island as a “great island getaway right in my own backyard”. Other reviews appear to be honest first person accounts. Some people review what it’s like to visit the prison; others describe their experiences as inmates.

The reviews provide valuable first-hand insight into the workings of the prison system, but, we wondered, do prison administrators get the opportunity to read them? Do prison administrators even know their institutions are being reviewed on Yelp?

So, we came up with a helpful automated solution to ensure that prison administrators receive the feedback they may not know they are getting: a Yelp prison review faxbot. It’s a small python program that constantly monitors Yelp for reviews of prisons. Whenever the script finds a new review it automatically sends a fax to that prison with the Yelp star rating and content of the new review. Today we sent out 12 faxes, just to get things started.

Prison Faxes

At the moment we are monitoring the following prison reviews, these being the prisons we’ve found that have both Yelp reviews and fax numbers:

We’ll be growing this list – feel free to get in touch if you find anything else to add in. We’re also thinking of switching to physical mail rather than fax.

The code for the faxbot is here on github.

Some Shit Other Than Guns And Tanks That Police Departments Get From The Pentagon

bouncy

The New York times recently did an interactive piece on the militarization of the police, revealing what equipment police departments across the country receive from the Pentagon. I did a quick analysis of the raw files from the Times (which they kindly posted on GitHub) to see what items were most popular nationwide. The top 10 items police departments received, in total, are:

  • 146,866 cartridge magazines
  • 119,067 electrical wires
  • 61,548 rifles (these are 5.56 millimeter assault rifles)
  • 54,805 cold weather shirts
  • 43,828 socket head cap screws
  • 43,638 field packs
  • 36,787 reflex sights
  • 36,617 ammunition chests
  • 35,290 elastic bandage kits
  • 21,313 industrial goggles

And of course there are plenty of tanks, aircraft, and body armor in there as well, ready to be deployed at a moment’s notice in suburban wherever.

The Pentagon however, supplies much more than military-grade nightmare gear. According to the list they are also distributing the following items:

  • 31 armoires
  • 1 money counter
  • 6 french horns and 1 euphonium
  • 179 assorted lawn mowers
  • 271 assorted treadmills
  • 72 golf carts and 1 order of golf balls
  • 2 pizza ovens
  • 37 kitchen spatulas, 35 laboratory spatulas, and 3 dental spatulas
  • 71 dessert spoons, 82 tea spoons, and 12 picnic spoons
  • 30 shower curtain hooks
  • 1 pair of cotton underwear
  • 1252 laundry bags
  • 1 recumbent exercise bike
  • 39 scooters
  • 5809 wet weather poncho liners
  • 2 food carrying carts
  • 17 dish towels
  • 23 soccer balls
  • 3 fishing boats, 19 kayaks, and 7 canoes
  • 10 men’s pajama trousers
  • 4758 neckerchiefs
  • 1 santa’s uniform
  • 1 simulated suicide bomber vest
  • 4 black rain coats
  • 3 “slaving” accessories (I’m hoping this is a typo)
  • 1 bouncy castle w/ blower (what was the Pentagon doing with a bouncy castle in the first place?)

As the Times notes, “this data does not represent all of the military-style gear that law enforcement agencies have. Agencies also purchase equipment with their own money or with federal grants.” It’s a distinct possibility that there’s more than one police department with a bouncy castle out there, inhabited, I hope, by a santasuit-wearing cop holding an assault rifle in one hand and a dental spatula in the other.

Videogrep: Automatic Supercuts with Python

Videogrep is a python script that searches through dialog in videos and then cuts together a new video based on what it finds. Basically, it’s a command-line “supercut” generator. The code is here on github.

The script searches through a video’s associated subtitle file (which needs to be in the same folder as the video, in standard .srt format), identifies timestamps for the dialog, and then uses the wonderful moviepy library to generate the new final cut.

Here’s one of the results: every instance of a character saying the word “time” in the movie In Time (a film whose dialog appears to consist mostly of clock-related puns).

 

The script also works with multiple video files in the same directory. As an experiment, I mass downloaded press briefings from the Whitehouse youtube channel, then ran a word-level n-gram analysis on the subtitle tracks to find some commonly used phrases. I discovered that the phrase “what I can tell you” is occurs pretty frequently.

So, here is Jay Carney, the former Press Secretary, telling us what he can tell us. Note the necktie transitions.

 

You may have noticed some strange cuts in the above video. The accuracy of the edits is completely reliant on the accuracy of the subtitle tracks.

 

You can also use videogrep to find instances of people employing specific grammatical structures (I do this with the pattern library). For example, by running the following command

I end up with a video of TED speakers saying “[gerund] [determiner] [adjective] [noun]”

 

As I final experiment, I wrote a complimentary script that finds dialog-free sections of videos. Here, for example, is “Total Silence”: all the one to two second silences in the movie Total Recall.

 

Feel free to mess around with the script on github, and let me know if you have any suggestions for source material to run through it.