Whenever birds call outside, Fugleramme uses AI to identify them and displays only recently heard species as 19th-century illustrations on an e-paper frame. The Norway-based project runs BirdNET-Go on a Raspberry Pi 5.
Modern audio recognition works behind the scenes, while the visible result looks like an old natural-history book. Digital detections are transformed into a quiet, daily-changing bird guide for the immediate surroundings.
Modern AI is working hard in the background so the wall can show something that looks like an old bird book. That's luxuriously understated.
Practicing badminton alone creates a simple problem: someone still needs to feed the shuttlecocks. This machine solves it by putting a compact Denso VS050 industrial robot arm in charge of shuttle delivery.
A pneumatic gripper picks one shuttlecock from a hopper and carefully feeds it between two high-speed spinning discs for launch. Even metal 3D-printed parts are involved, turning the everyday lack of a practice partner into something closer to a factory automation cell.
No practice partner? Bring in an industrial robot. That's an impressively large equipment budget for a very ordinary problem.
Generative image models usually suggest large GPUs, but this project asks whether one can be squeezed into an RP2350 microcontroller with just 4 MB of flash. It produces 128×128-pixel faces at roughly one image every 20 seconds.
The model and inference code fit into the tiny memory budget, and output can even be displayed directly through a VGA adapter. Instead of scaling AI upward, the project explores how far it can be compressed downward.
While everyone else adds GPUs, someone is removing things until the model fits in 4 MB. It's satisfying to see the race run backward.
Modern AI is working hard in the background so the wall can show something that looks like an old bird book. That's luxuriously understated.