Today was the second of a series of hack mornings at the Think Tank Congress coworking space. The results were even more stunning than the first time, and sparked a lot of ideas. Here is a summary of the projects that were completed in 2.5 hours
- John Colton joined us as a C hacker looking to get his feet wet with web development. By the end of the morning he got a python web app running with Flask, got set up with Github, and deployed the app to Heroku.
- Abe Fettig, Alex Launi, and Elliot Murphy worked on a web enabled door latch with a Raspberry Pi, using a Node.js REST API talking to a PiFace digital I/O board. At the end of the morning they demoed pressing a button on a single-page web app served from Heroku and the local door latch buzzed and opened. Next time additional sensors and some real time status updates will be added, and then it's time to think about installing it on a real door.
- Patrick Kenney was exploring building a responsive 3 column single-page web app, rendering some of the areas off-canvas and then using ScrollTo for navigation and pure CSS3 animations. It worked and he published the code!
- Alex Dorsk was working on a web app which aggregates information about all the music events happening in Portland. Since each venue publishes the calendar in a different way, he needed to do screen scraping, and Alex got scrapers written for 7 venues in a single morning using ScraPy.
- Hasan Adil was experimenting with data driven visualization using D3.js. He got an interactive force directed graph running, visualizing node size and connectedness. Shiny!
- Daniel Kopyc added speech recognition to an iOS app, using the OpenEars CMU speech recognition library, and drove his demo by talking to an iPad.
- Hugh Morgenbesser put together a Facebook app using Django. He showed how using 2scoops of Django, Fandjango, and FacePy make it possible to have a running app in a couple of hours.
- Alex Jones is working on digitizing beekeeping, and wanted to see whether he could use computer vision to count the number of mites on an IPM board. Using Processing PYImage and a digital photo of a board with some mites on it, he was able to "see" all the mites using color and blob detection and then calculate the number of mites by estimating the average area of a typical mite.
