How I Built a Native iOS App in a Weekend

Written by Steven Judge, Founder of GameLayer

The idea had been bouncing around in my head for a while. I wanted a simple way to motivate small micro-communities — families, friends, work teams — to stay active together. Nothing resembling a massive social network. Just daily nudges, light competition, and a shared sense of progress.

Because I knew GameLayer inside-out, I already knew the backend side of this problem was largely solved. Challenges, achievements, progression, leaderboards, rewards — all of that logic already existed as an API. So the real question became: how quickly could I turn that into a real, native app using today's AI tools?

For context, I'd tried something similar 12–18 months ago and gave up within a couple of hours. The tools just weren't there yet. The friction was too high, and progress stalled almost immediately.

This time, the ambition was very clear: build something real. Not a design prototype. Not a fake app with placeholder data. A native iOS app I could install on my phone, connect to real systems, and actually use.

With GameLayer as the backbone, the app itself didn't need much "intelligence". Its job was to integrate with HealthKit, sync step data, talk to the API, and present an interface that reflected real challenges, XP, and progression coming from the backend. That separation turned out to be crucial.

Once the API integration was in place, GameLayer's CMS handled the rest. Challenges, rewards, progression rules — all configurable without touching app code. That alone fundamentally changes how fast you can iterate. It's the difference between rebuilding and simply tuning.

On the AI side, I used ChatGPT for direction and Cursor for hands-on Swift development, environment setup, and the publishing pipeline. It wasn't flawless. Cursor needed time to "learn" the shape of the API, and there was plenty of time spent staring at console logs and untangling edge cases. ChatGPT and Cursor didn't always agree on the best approach either.

But here's the important part: it was never wildly wrong.

The output was consistently close enough to keep momentum. And that momentum matters more than perfection.

The hardest problem wasn't UI or API wiring — it was reconciling real-world HealthKit data with a backend that expects certainty. Health data arrives late, updates retrospectively, and changes underneath you. Keeping two systems in sync, and reacting correctly when one of them changes, took real testing and multiple iterations.

Still, by the end of the weekend, I had a native iOS app running on my phone. That felt genuinely satisfying.

A week later, testing is still ongoing. But the key outcome already happened: the idea was validated in days, not months.

For me, this reinforced where AI-accelerated development really shines — not as a replacement for systems or architecture, but as a force multiplier when it's paired with a dedicated API that already encodes business logic. When the heavy lifting is abstracted away, rapid build-and-test cycles stop being aspirational and start becoming practical.

That's a shift worth paying attention to.