Eight years ago, when I started university, I also began competing in triathlons for Dynamo Sports School in Kyiv. I came from a cycling background, so running was the new discipline for me — and I quickly became fascinated by training with power.
Back then there was essentially one serious running power meter on the market: Stryd. It was an impressive piece of technology, but at several hundred dollars it was far beyond what a broke engineering student could justify.
So I had a simple thought:
Why don't I just build my own?
I'd gone to a programming-focused high school and was studying Computer Science, specializing in Biomedical Cybernetics. That path gave me an early grounding in computer vision, working on medical-imaging problems — but it bored me quickly. Medicine is a tightly constrained field; you can't go full throttle with your creativity.
This project was the opposite. It's what truly pulled me into software engineering. I went deep into embedded systems, firmware, electronics, signal processing, and algorithms — all toward one goal: an affordable running power meter.
The first prototype was built around an nRF52832 microcontroller with an accelerometer and a Sensirion SDP810 differential pressure sensor. The idea was ambitious — estimate both motion and aerodynamic drag, and from them calculate running power.
It was incredibly difficult.
After months of experimentation, I got a rough estimate of running power onto my smartwatch. Seeing those first numbers appear on the screen felt magical.
But the project never became more than a personal experiment. Reality caught up with me. I had no experience designing custom hardware, writing production firmware, or building the kind of black-magic algorithms needed to estimate body drag from pressure. Every improvement meant redesigning PCBs, waiting for parts, debugging hardware, and starting over. Progress was painfully slow.
That experience taught me something I still believe today:
Custom hardware is often an artificial barrier.
It makes products more expensive, slows development dramatically, and limits how many people can benefit from new technology.
Over the following years, working professionally as a software engineer, that belief only grew stronger. Whenever I could, I built products that ran on devices people already owned — smartphones, GoPros, everyday consumer electronics — instead of requiring another dedicated gadget.
Then, eight years later, everything clicked. Apple had quietly turned AirPods into surprisingly capable motion sensors.
If I could estimate sports metrics with accuracy approaching dedicated sensors — using hardware that millions of people already carry every day — then the opportunity was far bigger than running.
Almost everyone owns a smartphone. Millions already own AirPods. Why should they buy another specialized sensor just to access advanced sports analytics?
That realization became Kinapod.
Kinapod isn't about one product. It's about turning everyday consumer devices into powerful sports sensors through software. Kinapod Disc — the AirPod-powered flying-disc tracker — is simply the first public demonstration of what's possible: proof that, with the right algorithms, the hardware people already own can do far more than anyone expected.