This video presents a demo of indoor localization in multiple settings. In the demo, a user walks with a smartphone and the user's location is shown on the phone's screen in real time. Our system, called Unsupervised Indoor Localization (UnLoc) utilizes the sensor data from smartphones to learn "invisible landmarks" in the environment. Example landmarks could be a unique magnetic fluctuation experienced when the phone is near a water-cooler, or a distinct gyroscope rotation when the user turns a corner. We use these indoor "landmarks" to periodically reset the user's location. To track the user between these landmarks, we use an optimized variant of dead reckoning, ultimately leading to a robust location tracking system. We call our system UnLoc, since the landmarks are generated in an unsupervised manner, requiring no manual effort or floorplan of the building. The demo describes the high level intuitions, shows UnLoc in operation, and shares experiences from running UnLoc in various real-world environments.