When Lazyfeed announced a limited round of beta invites on TechCrunch, I admit, I lusted after them. Only 250? I wanted to be one! But alas, I was put on the waiting list. It’s a decent marketing strategy for building up some hype. When I finally did get my invite, I tried them out for about 5 minutes and fell prey to the distractions of the internet. That was a bad sign, though. Usually a new service can hold my attention for a little while longer. So what happened?
Lazyfeed is a service that lets you enter topics, blogs, twitter, delicious and flickr accounts to form a live streaming lazyfeed. You then get live updates in the form of your tags being updated. Your main screen consists of a bunch of boxes with your topics and then things it guesses are related.
Lazyfeed’s marketing strategy succeeded again by giving me three invites to hand out to friends. I offered them on Twitter, having only one person bite. So here are the other two invites for the adventurous. Get em while they’re hot. If you manage to take one, please comment that you did so, so that I can at least know who you were and we can save someone else the wasted time. I’m just throwing them into the ether like this because I don’t feel like pushing them on Twitter again.
Lazyfeed is a lovely service in terms of appearance and ajaxy goodness, but my initial impression is that it ends up being streaming information overload. For one, the topic suggestion feature appears to be fairly naive. Someone correct me if I’m wrong, but it looks a bit like document similarity for topics is done purely by one-for-one matching on tags. Whatever the method, the result of their suggested topics (“Stuff for Lazy Jason”) is stuff like the following:
Lazyfeed sample suggested topics
Granted, it’s a hard problem, but those results are pretty bad. So as I started to write this post lambasting this service, I considered that maybe I was just seeing cold-start problems, and I was being unfair. So I trained it with some additional feeds and topics that are straight-to-the-point of stuff I’m interested in, like sigir2009, topicmodeling, recommendersystems, etc. Tags can contain no spaces, btw, which is why those don’t. When I tried using dashes, like I often do on delicious, it gives no results. I also removed some things that were too general or contained too many spurious results.
Things started improving here, and I actually began to understand what the point of Lazyfeed is. My initial confusion was that “Stuff for Lazy Jason” is stuff that I would want to read right now. Being lazy, I didn’t expect to have to do work to get those things. But “Stuff for Lazy Jason” is a list of topics it thinks I might be interested in. Saving any one of those puts it into my lazyfeed, which is in the bar on the left.
My lazyfeed topics
So now what happens is that occasionally it discovers something new related to my interests and it bumps that category to the top of the list and turns it bold again (grayed out topics have been read). Most of my topics are low traffic, so add something like mariahcarey if you want to see this functionality in action. Now we’re getting somewhere. It has actually started being helpful and has found me some stuff that my Google alerts haven’t. Which is weird, and is making me think I need to double check to make sure my Google alerts are working…
My takeaway after using Lazyfeed for nigh on two hours is that it’s an interesting alternative (or even extension) to RSS, but one that still hasn’t crossed the bridge to the next stage in evolution. The idea is solid. Automatically discover stuff in the sea of human knowledge (or human idiocy) and serve it up fresh. The implementation lacks robust topic detection which is unfortunately going to be necessary unless it is to become another source of information overload rather than a useful stream of relevant information. Relevance is an ephemeral thing, given that your information needs change from day to day. Lazyfeed makes it pretty easy to get rid of old topics and add new ones, even if some of their suggestions are still wonky. It’s an interesting recommender system problem with a lot of potential.