I've been toiling away in the laserdeathstehr labs and have come up with another little project. This one is called TwitterBurst. It is a realtime visualization of the activity on twitter. You can see a video of it here: http://www.youtube.com/watch?v=_i3Zg5CEHnc. And the source for the project is at https://hg.laserdeathstehr.com
TwitterBurst visualizes tweets coming in as 'bursts' of particles. Retweets are blue, at replies are red, and the rest of the tweets are green.
The app is powered by a few different technologies. The main part, the visualization, is written in processing.js and is based on the example from: http://processingjs.org/learning/topic/multipleparticlesystems
The server is a very simple app, powered by Sinatra.
The data is supplied by Twitter's streaming API, and I am accessing it
using the python library, TweetStream. Its all linked together using
the key value store, Redis. When a tweet comes in, it is categorized and written
to the Redis store. Then when the visualization makes a request for more
data, the web app looks into the store, grabs the data, and serializes it as json
and passes it on.
Like always, feel free to leave any comments or feedback!