Build Your Own AI-Powered Twitter Feed

One of our favorite quick demos of’s capabilities is to show how easy it is to take your own Twitter feed, score each of the Tweets, and surface the most relevant ones. It’s one of the first new agent templates we built for the platform and it’s a lot of fun to try out.

To show how easy it can be, we put together a Ruby on Rails project to help you get started and get more acquainted with You can find that project on GitHub.

Getting Started

Getting your Tweet Relevance agent set up will take just a couple of minutes. Once you’ve signed up for a free account, go to your Dashboard and take note of the API key shown on the top left-hand corner of the page. You’ll need it later.

Next step is to create your Tweet Relevance agent. There are two ways to do this. The easiest way is to use this link and just click on Create.

Alternatively, you can create it manually by logging into your dashboard, clicking on the ADD AN AGENT  button. From there, select the TWEET RELEVANCE template:


That will automatically create a Tweet Relevance agent like the one below:


This initial template starts off with just 3 rules that should be pretty easy to understand:

  • tweets with more likes are more relevant
  • tweets with more shares are more relevant
  • older tweets are less relevant

Take note of your agent ID, which is found just above the TWEET RELEVANCE title on this page. Later in this post we suggest a few things you can add to this, but this is a good starting point.

Installing the Rails App

Next step is to clone our Tweet Relevance Ruby on Rails on GitHub, and follow the installation instructions in the file, from there you’ll be guided through the next steps of setting up the app.


Once you’ve got things set up, your app will show you the tweets that are most relevant based on the rules we defined earlier. Each tweet will be scored like this: TweetRelevance.png

What’s Next?

Now that you’ve got this simple app working, what else can you do? If you want to play around with the app and, here are some other things you could try:

  • Add new rules that take into account your friends’ behavior: how many people you follow liked a tweet, how many people you follow shared a tweet.
  • Try combining different rules, for example if you want to identify tweets that are liked by your friends but not a lot of other people, that rule could be: IF number of likes by friends IS very high AND number of likes IS low THEN relevance IS very high.
  • Set up rules to increase relevance of tweets that include keywords you’re interested in and decrease relevance of tweets that include keywords you’re not interested in.
  • Add a feedback metric to train and improve the results: add thumbs up and thumbs down buttons next to each tweet that send positive or negative feedback to the API based on which tweets you find most or least relevant.


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