If you look at the github readme, it becomes clear that this technique can't infer _real world_ locations from wireless signals (which is already very much poss

If you look at the github readme, it becomes clear that this technique can't infer _real world_ locations from wireless signals (which is already very much possible and in widespread use), it's applying abstract labels to different regions of signal strengths. Someone has to train it themselves with those labels. It just knows, "I am fairly confident you are in 'bathroom' given the current patterns of signal strengths from the access points I can currently see". This might be useful as a low-power alternative to proper localization, for the cases that it's useful to know what room something's in, but not necessarily an actual position. So, if you wanted to automate things in your house to happen when you walk into a room with your smartphone, this would be less invasive than actually trying to triangulate a position or doing visual SLAM :-)