Three different sources have something to say about Max's appetite this month. His smart bowl has logged five smaller-than-usual breakfasts. A vet note mentions "monitor appetite." A dog-care book says goldens often eat less in summer heat. Do we know Max's appetite has dropped?
A smart bowl reading looks like this: 120 g served, 82 g eaten, 340 seconds, 7:14 a.m. It is perfectly precise and almost meaningless on its own. A vet note is the opposite: rich with meaning ("mild tartar, monitor appetite"), structured not at all. To answer
At 7:14 this morning, a smart feeding bowl logged something small: Max, a six-year-old golden retriever, left a third of his breakfast. His collar tracker says yesterday's walk was completely normal. A vet note from two weeks ago mentions mild tartar buildup. And a dog-nutrition book on
Our IMU-based activity tracker clips to a dog's collar and, in principle, can tell you how far your dog walked today. In practice it needs a teacher. An accelerometer strapped to a moving neck produces a beautiful, noisy waltz of a signal, and turning that waltz into a
Our smart bowl fires its camera whenever anything comes within 40 centimetres — a dog's nose, a human's ankle, a broom. That is by design: in the data-collection phase, every visitor is a labeled training sample. But the product question is sharper than the trigger: the bowl
Everbowl logs a dog's day in numbers. Load-cell weights on every bowl visit, water refills, eye-temperature frames from the thermal camera, ambient temperature and humidity, audio classifications of chewing and licking. Across a fleet this runs into thousands of events per dog per day. Almost none of it
When you're building a thermal-detection system to watch over a dog, the sensor you pick is only half the story. The lens sitting in front of it — and specifically how wide it sees — quietly decides how far away you can read your pup and how forgiving the aim
Not every safety check belongs in the same place. Some only make sense on the user's question, some only on the model's answer, and some on both. Here's how we structure the guardrails on our veterinary chatbot, and why the input and output split
Presence detection sounds solved—motion sensors have shipped for decades. But a real living-space sensor must spot a person sitting dead still, ignore furniture, work in the dark, and not confuse the dog for a human. Our first trigger, a single-channel time-of-flight lidar, failed nearly all of these. Here'
A step counter on a dog's collar sounds like it should be a solved problem. After all, human pedometers have shipped in phones and watches for over a decade. But a dog is not a human walking upright with a phone in their pocket. The sensor sits on