The problem.
When my son's AFL coach was complaining about the difficulty of fairly rotating kids across different zones to ensure fair play amongst everyone, my first response was 'I could build an app to do that'. It was early 2026 and the AI tools had just entered the agentic era. Thirty minutes of prompting with Claude and the first prototype was built on Friday night, ready for game day on Saturday.
Nothing in the market was built for managing the intricacies of junior sport. Generic sub trackers did not account for the zones in AFL, the positions in Netball or the vests in Rugby League. When the cost of developing personalised apps is near zero, coaches deserved better.
The approach.
This was a real 'throw it in the hands of a parent and watch what they do' situation. My wife uncovered bugs and usability issues I never dreamed of testing. Despite hours of simulated testing, I picked up bugs on game day due to those 'weird' scenarios real life throws at you.
- Real games from day one.
- Every common action is one tap.
- AI agents handle the grunt work: UAT, regression testing, research.
- Built multi-sport from day one so the platform doesn't have to be rewritten as it grows.
- Avoid feature bloat even though building that feature is just a prompt or two away.
The outcome.
Siren is in coaches' hands at real games. The loop from coach to build and back is short enough that the app gets better every week, and the roadmap is whatever the coaches' chat is complaining about on Monday morning. Siren now has hundreds of weekly users, with no marketing beyond word of mouth.
What I learned.
AI-assisted engineering doesn't replace product skills, it amplifies them. The bottleneck stops being "can I build it" and goes back to "is this the right thing to build, and is it good enough that people will use it under pressure?" That's a product question, not a code question.