I’m a bird photographer myself, and I love birds. But for a long time, the joy of a successful birding outing was always followed by a draining chore: coming home to thousands of images and the painstaking process of comparing them frame-by-frame.
The Cost of the Culling
For every eight hours spent in the field, I’d lose another few to the screen, just trying to decide which photos were worth editing. I’d spend hours squinting at bursts, trying to find the one shot where the eye was just a fraction sharper or the motion blur was non-existent. Blurry shots were cluttering my storage, and the barrier to my creativity was growing.
Built for the Community
Time is precious, especially as a student. I didn't have the budget for expensive corporate culling tools—none of which were actually built with the specific needs of bird photography in mind anyway. I realized that if I wanted a better workflow, I’d have to build it myself. That was the spark for Project Kestrel.
Over years of development, I’ve experimented with different ways to automate the most draining parts of the process: separating 'the best' from 'the rest.' I know that computers can never replace an artist’s vision, but they can certainly help us reach our goals faster. By using machine learning models tailored for bird photography, Kestrel organizes your library by quality and even helps tag species automatically.
Free & Open Source
I’m committed to keeping the core of Kestrel free and open-source. As a student, I only ever had access to the tools I could run for free, and I want to ensure every photographer has that same opportunity. This passion project has become a tool for the community.
Privacy by Default
Your photos are your memories and your intellectual property. That's why Kestrel is built from the ground up to run 100% locally on your machine. No cloud uploads, no external processing, and no data tracking. Just you and your photography.