FriendFeed takes noisy tracks and makes a best-of collection

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With some services, a strength is also a weakness. Take the micro-messaging service Twitter for example. It’s becoming more and more popular because more and more people are using it. However, because of this popularity, fundamental flaws in its architecture have been exposed and the service is constantly down.

The conversational content site FriendFeed has a little bit of a different problem — but it’s also related to popularity. As more people sign up for the service, the amount of noise being generated it rapidly rising. For some of us, it was getting to the point where if you aren’t watching FriendFeed most of the day, you’re bound to miss something that you probably don’t want to miss. No application should require that much of a time commitment to get the best use out of it.

A few weeks ago FriendFeed launched the “Rooms” feature to try and create a more focused cluster of noise depending on what you were looking for. But now the service has a real solution for those who don’t have time to wade through the noise: Personalized recommendations.

Just as it sounds, FriendFeed now can serve up a of “best of” collection of elements found on the service for a given day, week or month. These collections are completely personalized based on your network of friends. FriendFeed pulls this off by looking at the items those in your network “like” and/or comment on with the greatest frequency.

You can find these new best-of sorting abilities right below the search box in the upper right corner of the page (see screenshot below).

I continue to contend that one of the greatest strengths of FriendFeed is just how responsive and reactive its team is. Not only do they pump out new features left and right, they come up with solutions on the fly based on situations they are seeing on the site. The folks at FriendFeed are on their game.

FriendFeed isn’t going to kill Twitter, but its ability to solve problems and evolve with seeming ease continues to make Twitter look bad.


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