It also means that rather than contacting @tweeteorites on Twitter to have timelines set up for your user, you can just authenticate with OAuth (by clicking on the login link) and timelines will be set up for your user automatically.

We don’t use your OAuth permission for anything other than favoriting tweets and following users (and only at your request), so you don’t need to worry about us using up your API requests. But if you are an OAuth user on the site, your favorites and friends’ favorites will be looked at more often by our software, which will help keep your timelines up to date.

Enjoy!

There are a few distinct “pages” on Tweeteorites. One is the Leaderboard: this is the tweets that have been favorited the most in the last 24 hours by the people we’re tracking. This is a global view, in that it is looking at all tweets the Tweeteorites server has seen.

Then there are the individual views — views limited to one user. For example, if you click on a user picture on the front page, it will take you to that person’s “Favorite Stream” page (example). Other individual views are the “Ego Boost” (who has been favoriting that person’s tweets) and “Should Follow” (a recommendation of who that person should be following based on who their friends are favoriting/following).

For an individual’s Favorite Stream to be built, we need to see what their friends are favoriting. This is pretty easy: we just grab all their friends, then see what their friends favorite. For the Ego Boost, it’s harder. As of this writing, Ashton Kutcher has 2,675,471 followers on Twitter. In order to reasonably expect to find everyone who has favorited one of his tweets, we’d need to scan all 2 million+ of his followers. Our request quota to Twitter is 20,000 requests per hour — hence the problem. We try to be smart by grabbing favorites on a schedule proportionate to the how often a user favorites tweets, but that’s still a whole lot of requests. Plus, we’ve got other requests to make, like updating social networks, etc.

So, our solution is this: we only even attempt to build full “individual” pages (which we call timelines) for users who request it. That is, until we can get some API from Twitter that lets us see all the favorites happening all the time (which would be super awesome).

That’s why the site says that if you want timelines enabled for a user you should ask @tweeteorites, which is our account on Twitter. You can format the message however you like, send us a direct message, or whatever. We may not reply to you, but we will most likely enable timelines for your account.

And, yes, it also means that we get some extra exposure to people who are following you and see you request timelines on Tweeteorites. If that bothers you, DM us or change the formatting of the tweet to start with “@tweeteorites” rather than “Hi”.

So… why was today fun? Today I wrote some software that lets me respond to all of those tweets to @tweeteorites and enable peoples’ timelines. I’ve been working night and day on Tweeteorites for 2 months, and it was nice to respond to so many peoples’ requests individually. The software I wrote was basically a custom Twitter client that lets me see who is messaging @tweeteorites and respond to them with an individualized message and enable their timeline at the same time, saving me a whole lot of work.

Today was also fun because as I worked I had a window open looking at my server logs as people visited the site. It was very rewarding to see so many people visiting the site I’ve spent so much time on recently. I’m especially thankful to those of you who’ve sent me messages saying you like Tweeteorites. I look forward to seeing what everyone thinks as I improve upon it, as there are more interesting things to come!

What is it? If you’re a Twitter user, you know that you can mark individual tweets as favorites. It was always really frustrating to me that I could never see what my friends were “favoriting” though. So, Tweeteorites scans through all of your friends’ tweets, building timelines of what your friends are favoriting.

Another frustration for me was that it was hard to know who to follow on Twitter. So, I built a system that analyzes who you’re following, who your friends are following, and what they’re all favoriting. Then, it recommends the people it thinks you’d be most interested in following. Basically, the people you might get the most enjoyment out of following.

That’s the short description! There’s more exciting stuff coming soon, so I’ve got to get back to work!