Connect with people using Foursquare and Twitter
We use your Foursquare checkins to introduce you in real-time with people you have common interests using your Twitter graph.
I'd love to hear the concepts behind this, I'm guessing you first do a radial search from current location, yield list twitter handles, fetch last n dozen tweets, extract links, cluster, intersect, introduce.
I don't have a 4[] account to try it but im curious what you used. Obviously in 8 hours there's only limited technology that can be done, but if you're using python or another language with a rich set of libraries it's possible to do quite a lot during that time. Obviously 8 hours means 6 when you factor in HN time :)
Foursquare has a API method that exposes the people who are at the same place than you are.
Then we get their twitter nicknames and find who you have the most followings in common.
We use Rails 3, Memcache, Redis, Resque, Foursquare API (with Push) and Twitter API.
Thanks, that's essentially what I said, but I am curious what you mean by "most followings in common" (you mean people intersect?) I assumed you did some form of semantic analysis of the tweet streams to intersect based on common RT's or link topics?
I'm actually surprised there is a maxima between people intersects, or that it carries some meaning.
I wasn't really asking about the front-end, that seems like any of the boilerplate environments would have been equivalent, and just becomes a matter of most familiar, but rather the 'backend'.
Let's say you can write a UI, you have a lat/lon, you have the tweet streams from those around you, how, in a meaningful way do you find similarity? That's the core of your idea. Because Mike and Brett both follow JetBlue what does that tell you? However, if Mike and Brett both RT'd a link about the speed of limit hard limit, it's reasonable to assume they have some similarity in interest. That's the part I was interested in getting your thoughts (mainly because its something I've both thought about and worked on)
Try to run some computation on top of Twitter using the followings in common and you'll see that it's actually working great.
We only display with whom you have more than 4 followings in common.
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[ 3.3 ms ] story [ 42.4 ms ] threadI don't have a 4[] account to try it but im curious what you used. Obviously in 8 hours there's only limited technology that can be done, but if you're using python or another language with a rich set of libraries it's possible to do quite a lot during that time. Obviously 8 hours means 6 when you factor in HN time :)
We use Rails 3, Memcache, Redis, Resque, Foursquare API (with Push) and Twitter API.
I'm actually surprised there is a maxima between people intersects, or that it carries some meaning.
I wasn't really asking about the front-end, that seems like any of the boilerplate environments would have been equivalent, and just becomes a matter of most familiar, but rather the 'backend'.
Let's say you can write a UI, you have a lat/lon, you have the tweet streams from those around you, how, in a meaningful way do you find similarity? That's the core of your idea. Because Mike and Brett both follow JetBlue what does that tell you? However, if Mike and Brett both RT'd a link about the speed of limit hard limit, it's reasonable to assume they have some similarity in interest. That's the part I was interested in getting your thoughts (mainly because its something I've both thought about and worked on)
I was also at the hackathon. A couple of friends and I hacked together a lists feature: http://foursquare.heroku.com.
(As long as I participate in the app, of course.)