Pebble

The project started at an event in San Francisco, where I watched someone drink straight from a public water point. The water may well have been safe — but nothing around them showed why they should trust it. People judge with their eyes: color, smell, clarity. Most real risks are invisible to all three.

The design question that followed: in the few seconds before someone drinks, how can limited water-quality information become feedback that is fast, friendly, and open to follow-up — so uncertainty stops turning into distrust?

Research covered public drinking-water safety, the gap between objective data and human senses, an interview with an environmental expert, and competitive analysis across LARQ, TDS test pens, Tap Score, and public water-information platforms. The main insight: people don't lack data — they lack data that is tied to the moment, quick to grasp, and honest about its own limits.

Two rounds of in-person tests then compared intuition against information, and feedback formats against each other. The findings below shaped every later decision.

The final form is close to a real pebble: rounded, restrained, sized for a pocket and a palm. A translucent shell lets the electronics show through just enough to feel honest rather than toy-like, and the friendly geometry avoids the alarm posture of a typical meter. You dip it into a bottle, a cup, or the catch area of a fountain; it wakes on contact, reads a focused set of signals — TDS leading, with temperature and pH explored in concept — and answers with light before any number exists.

Under the shell, the prototype runs on Arduino with water and trigger sensors, an LED ring, and a Bluetooth link to the phone. It offers an immediate reference within a limited set of metrics — not a complete contaminant analysis, and the system says so.

Where the device answers in a second, the app answers "why". Three pages carry the experience: Home shows the connection and the current reading in plain language; Map gathers nearby fountains and accumulated readings; Ask lets an AI explain metrics, results, and next steps. The UI was designed and tested in Figma, then built in Flutter — running on the web in Chrome and packaged as an Android APK.

Honest caveat, by design: in the final prototype some TDS values were still entered manually during testing, so the automatic sensing chain remains an engineering prototype. The complete device-to-app experience, though, is real and working.

The product film follows one full loop of the system: a questionable-looking tap, a dip of the pebble, the glow, then the app's explanation and the map of what's around.

Alongside it, a vertical exhibition cut shows the device and the app together in the moment the system is designed for — phone-first, shot for the portrait screen.

The final presentation condenses the system into two portrait posters: one carries the promise — "Making water quality easier to trust" — and the other explains how it works, from the four-step use flow to the app's three pages. Beside them, a screen loops the exploded view of the device: shell, ring, stack, and core.

Pebble never claims that a small device solves water safety. Its value is a better relationship with information: abstract metrics become a state you can feel, quick judgment and deep explanation coexist, and the system is explicit about what it checked, what that means, and what it cannot say.