A grey smartphone sits inside a sleeve made of light brown wood veneer and a black felt interior.

Wooden Smartphone Sleeve Keeps You On Task

Smartphones are amazing tools, but sometimes they can be an equally amazing time suck. In an effort to minimize how much precious time goes down the drain, [Lance Pan and Zeynep Kirmiziyesil] decided to make a functional and beautiful smartphone sleeve to keep you on task.

Most modern smartphones have some form of Do Not Disturb mode available, but having the phone visible can still be an invitation for distraction. By tucking the phone into an accessible but less visible sleeve, one can reduce the visual trigger to be on the phone while keeping it handy in the even of an emergency.

Once in the sleeve, the NFC tag sandwiched between the felt and wood veneer triggers an automation to put the phone into Do Not Disturb mode. This hack looks like something that you could easily pull off in an afternoon and looks great which is always a winning combination in our book.

To see some more focus-oriented hacks, checkout the Pomodachi or this Offline E-Paper Typewriter.

People in meeting, with highlights of detected phones and identities

Machine Learning Detects Distracted Politicians

[Dries Depoorter] has a knack for highly technical projects with a solid artistic bent to them, and this piece is no exception. The Flemish Scrollers is a software system that watches live streamed sessions of the Flemish government, and uses Python and machine learning to identify and highlight politicians who pull out phones and start scrolling. The results? Pushed out live on Twitter and Instagram, naturally. The project started back in July 2021, and has been dutifully running ever since, so by now we expect that holding one’s phone where the camera can see it is probably considered a rookie mistake.

This project can also be considered a good example of how to properly handle confidence in results depending on the application. In this case, false negatives (a politician is using a phone, but the software doesn’t detect it properly) are much more acceptable than false positives (a member gets incorrectly identified, or is wrongly called-out for using a mobile device when they are not.)

Keras, an open-source software library, is used for the object detection and facial recognition (GitHub repository for Keras is here.) We’ve seen it used in everything from bat detection to automatic trash sorting, so if you’re interested in machine learning applications, give it a peek.