diff --git a/src/components/LandingpageAbout/index.js b/src/components/LandingpageAbout/index.js
index 57e37bd..3d87cb5 100644
--- a/src/components/LandingpageAbout/index.js
+++ b/src/components/LandingpageAbout/index.js
@@ -8,9 +8,9 @@ export default function LandingpageAbout() {
         <div className="section-content-box">
           <div className="section-content-title">Introduction</div>
           <div className="section-content-text">
-            'OpenDataCam' is a tool that helps to quantify the world. It's here for everyone to use! With computer
-            vision OpenDataCam understands and quantifies moving objects. The simple setup allows everybody to become
-            an urban data miner.
+            OpenDataCam is an open source tool that helps to quantify the world. With computer
+            vision OpenDataCam understands and quantifies moving objects. The simple setup allows
+            everybody to count moving objects from cameras and videos.
           </div>
         </div>
         <div className="section-content-box">
@@ -23,35 +23,52 @@ export default function LandingpageAbout() {
       </div>
       <div className="section-content">
         <div className="section-content-box">
-          <div className="section-content-title">How can OpenDataCam see?</div>
+          <div className="section-content-title">What can you use it for?</div>
           <div className="section-content-text">
-            OpenDataCam consists of a camera attached to a mini computer. It's running an object detection algorithm
-            that counts and tracks moving objects. The board is equipped with a GPU (graphical processing unit), which
-            allows it to process many parallel image threads at once. The perfect tool for image analysis and video
-            processing.
+            <p>
+              People use OpenDataCam for many different <a href="use-cases">use cases</a>. It is 
+              especially popular for traffic studies (modal-split, turn-count, etc.) but OpenDataCam
+              detects 50+ common objects out of the box and can be used for many more things.
+            </p>
+            <p>
+              And in case it does not detect what you are looking for, you can always train your own
+              model.
+            </p>
           </div>
         </div>
         <div className="section-content-box">
-          <img className="section-content-img" src={require('@site/static/img/about-2.jpg').default} alt="RJ45 Cables" />
+          <img className="section-content-img" src={require('@site/static/img/about-3.jpg').default} alt="RJ45 Cables" />
         </div>
       </div>
       <div className="section-content section-content--reverse">
         <div className="section-content-box">
-          <div className="section-content-title">Yolo</div>
+          <div className="section-content-title">How does it work?</div>
           <div className="section-content-text">
-            On the software side OpenDataCam is running <a href="https://pjreddie.com/darknet/yolo/" target="_blank">YOLO</a> — an object detection library. YOLO is trained to detect
-            objects in images. The attached camera feeds YOLO with a video, YOLO then outputs all objects in each frame.
+            <p>
+              OpenDataCam uses machine learning to detect objects in videos and camera feeds.
+              It then follows the objects as they move accross the scene.
+            </p>
+            <p>
+              Users easily define counters via their browser, and every time an object crosses the
+              counter, OpenDataCam takes count.
+            </p>
           </div>
         </div>
         <div className="section-content-box">
-          <img className="section-content-img" src={require('@site/static/img/about-3.jpg').default} alt="YOLO neural network" />
+          <img className="section-content-img" src={require('@site/static/img/about-2.jpg').default} alt="YOLO neural network" />
         </div>
       </div>
       <div className="section-content">
         <div className="section-content-box">
-          <div className="section-content-title">Housing</div>
+          <div className="section-content-title">How do I get started?</div>
           <div className="section-content-text">
-            OpenDataCam is not tied to a specific housing, instead you can design your own or use an already existing casing. We used the <a href="http://naturebytes.org/our-tech/" target="_blank">Wildlife Cam Casing by Naturbytes</a> to protect the hardware in the wild. Have a look at this <a href="https://github.com/opendatacam/opendatacam/blob/master/documentation/jetson/HOUSING.md">tutorial</a> to see how the set up the the Jetson NANO in the Wildlife Cam Casing.
+            <p>
+              The simplest way to get started with OpenDataCam is downloading our <a href="https://hub.docker.com/r/opendatacam/opendatacam">Docker images</a> and run it your computer, server or Jetson device.
+              As long as it has a NVIDIA GPU, it can run OpenDataCam.
+            </p>
+            <p>
+              More information on how to run OpenDataCam on your device or machine can be found in our <a href="https://github.com/opendatacam/opendatacam">GitHub repository</a>.
+              </p>
           </div>
         </div>
         <div className="section-content-box">