Removed bold style on repository links.

Author: Florian Lautenschlager

Gemfile

@@ -1,4 +1,3 @@
 source 'https://rubygems.org'
 gem 'github-pages'
 gem "jekyll"
-gem "html-proofer"

_includes/documentation.html

@@ -35,13 +35,13 @@ <h2>About</h2>
                     Chronix supports three different scenarios, pursuing different goals:
                 </p>
                 <ol>
-                    <li><b><a href="https://github.com/ChronixDB/chronix.storage/">Chronix Storage:</a></b> Use Chronix as a small storage library and plug it into your
+                    <li><a href="https://github.com/ChronixDB/chronix.storage/">Chronix Storage:</a> Use Chronix as a small storage library and plug it into your
                         application. It stores the time series using <a href="https://lucene.apache.org/core/">Apache Lucene</a>.
                     </li>
-                    <li><b><a href="https://github.com/ChronixDB/chronix.server/">Chronix Server:</a></b> Combine Chronix with <a href="https://lucene.apache.org/solr/">Apache Solr</a> for a typical client-server scenario.
+                    <li><a href="https://github.com/ChronixDB/chronix.server/">Chronix Server:</a> Combine Chronix with <a href="https://lucene.apache.org/solr/">Apache Solr</a> for a typical client-server scenario.
                         Apache Solr offers several useful features like scalability, fault tolerance, distributed indexing, or replication.
                     </li>
-                    <li><b><a href="https://github.com/ChronixDB/chronix.spark/">Chronix Spark:</a></b> Whenever you need a parallel and distributed time series processing,
+                    <li><a href="https://github.com/ChronixDB/chronix.spark/">Chronix Spark:</a> Whenever you need a parallel and distributed time series processing,
                     integrate Chronix with <a href="http://spark.apache.org/">Apache Spark</a>. Store the time series in a Chronix Storage and HDFS or in a Chronix Server cluster.
                     Leverage Apache Spark to process a time series in parallel.
                     </li>