I started sketching out a system very similar to this a while ago - I wouldn't be surprised if plenty of Twitter-using hackers have had the same thought, TBH. T

I started sketching out a system very similar to this a while ago - I wouldn't be surprised if plenty of Twitter-using hackers have had the same thought, TBH. The problem is, as you've correctly identified, time! As well as keyword filtering and friend bonus there are a ton of things you can pull into the mix. I was actually working on two ideas, one to tell you whether you should follow someone based on their twitter fingerprint and your preferences, and one to pull out interesting stuff from the stream. As I replied further down this thread, interesting is just damn hard. Even trying to concretely identify what I found interesting such that it could be expressed as a grammar or decision tree or set of regexps or whatever... yeah, never really got round to it. I'd keep realising new properties of tweets that made them interesting to me. It's a fascinating problem, though, and very closely related to the "Oh dear gods I have ten thousand unread RSS items" one - that I still haven't solved, nor seen an acceptable solution for! We're drilling down into sentiment right now, but that's not a solution in and of itself; it's an interesting filter though, in terms of only showing stuff with extreme sentiment or stuff that deviates from an individual's normal. It's almost a boringness anti-filter. Kinda works, but is in no fit state to release - again, due to time!