anchor.diff:        div(class='col-xs-12')
           p(class="color-white chart-caption").
                 #[strong Chart 2. Buzz Trend Line in Time]#[br]
  -              This chart shows how number of tweets per movie grows in real time. The chart displays data from the last hour.
  +              This chart shows how the number of tweets per movie grows in real time. The chart displays data from the last hour.
           div(class='overlay-mask')
           div(id="aggregated_chart" )
   
  
positive.code:             div(class="col-xs-8 col-sm-8 col-md-4")
                  p(class="color-white").
                      #[strong Chart 1. Aggregated Number of Tweets Per Movie]#[br]
                      These numbers represent tweets per each movie aggregated since Feb 18th. We count all tweets that contain a movie hashtag, tweet at official movie profile or mention names of the cast.
        div(class='row section  text-center')
            div(class="col-xs-8 col-sm-4 col-md-2 col-lg-1" ng-repeat="counter in counters")
                div(class="laurel-wrapper {{applyClass(counter.name)}}")
  
negative.code:                 p(class="color-white").
                      We created this real time Twitter buzz meter as a side project of our company #[a(href="http://www.freeportmetrics.com") Freeport Metrics]. The challenge was to figure out who will win the best picture #[a(href="http://www.oscars.org") Oscar® 2016]. We wanted to make it real time to track changes in time, the closer to the Oscars®, the better. First we brainstormed different methods of predicting the winner:
                  ul(class="color-white")
                      li Measuring changes to the bookmakers’ scoring
                      li Creating a free market prediction model where people buy movie stock
                      li Mapping how connected the members of the Academy are with the nominees
                      li measuring the buzz on Twitter in real time
                  p The Twitter buzz meter turned out to be the most interesting from the technological point of view: a lot of data! We use #[a(href="http://www.nodejs.org") Node.js] server to retrieve tweets that contain previously defined # and @ from the #[a(href="https://dev.twitter.com/streaming/overview") Twitter Stream API]. For the database we use #[a(href="http://www.rethinkdb.com") RethinkDB] to store all the data. Is our solution scalable? Well, yes. We update the counters on the fly to avoid our servers from exploding :)
                  p(class="m-b-lg").
                      On the front-end we used #[a(href="http://www.socket.io") Socket.IO] to get data streamed from the server instead of making queries each time. For drawing charts we used #[a(href="http://c3js.org/") C3] library. That’s the whole magic!
  
  