What they did
Roy (u/Roy3838 on Reddit) has spent about a year building Observer, an open-source agent that runs fully on a phone with small local models. He tells the agent in plain language what to watch for, and it writes a small watcher loop: each iteration grabs a camera frame, asks a sub-1B decision model how likely the condition is, and fires a notification above a threshold. The things he says he has watched this way: a stranger walking up to his car (send a photo), a bird landing on a nest (Telegram message with the picture), the washing machine reaching its rinse cycle (phone call), his dog finishing its food, and a free machine at the gym. In the demo he asked it to message him on WhatsApp when someone sat in his chair.
How it went
In the chair demo the decision model scored the empty chair at 0.014 and Roy standing in front of it at 0.064, so nothing fired. When he sat down it scored 0.990 and the WhatsApp alert went out. The agent stays around for a few iterations after setup to check for false positives. One frame was skipped because the model had not finished the previous one. He gives no numbers on how well the other watchers have held up over time.
Worth knowing
The poster is the developer and the post is a launch, so treat it as a maker's account. The default models are tiny (around 0.8B) so old phones work, but each frame takes a moment to score, and a fast event can fall between iterations. For several feeds at once he says to share the whole screen and have the agent crop each stream.