anchor.diff:                  fjs.parentNode.insertBefore(js, fjs);
               }
           }(document, 'script', 'twitter-wjs');
  -  script(async defer id="github-bjs" src="https://buttons.github.io/buttons.js")
     div(ng-controller="IndexController")
       div
         div(class="row section")
  
positive.code:                     li creating a free market prediction model where people buy movie stock
                      li mapping how connected are the members of the Academy 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 twits that contain previously defined # and @ from the 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 to explode :)
                  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!
  
    footer(class="color-white")
      div(class="m-t-lg") Made by
        a( href="http://www.freeportmetrics.com")
          img(src="/img/FM-Logo-White.png" height="20" alt=" Freeport Metrics" class="m-l-sm")
negative.code:     div(class="row")
        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 twits per movie grows in real time. The chart displays data from the last hour
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          div(id="aggregated_chart" )
  