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I built NameSnap Picker’s animated selection in SwiftUI, with tactile interaction as part of the utility’s scope. Its simulator demo shows that behavior in motion, giving a prospective client a concrete example of my custom mobile interaction work.
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With NameSnap Picker, I treated release preparation as implemented scope—not as proof of a public launch. The case study can truthfully show the SwiftUI selection flow, a completed ranked result, StoreKit support, and simulator verification while keeping release status as its own claim. In freelance delivery, build evidence, QA evidence, and marketplace status should be reported separately.
This project started with a very specific idea for how a static product visual should move.
The base artwork was created with ChatGPT and Nano Banana, but before generating the final animation, I spent time refining the prompt and defining the exact movement, camera behavior and energy we wanted.
I tested the direction with different models, including Kling 2.5, using those iterations to improve the prompt before producing the final sequence with Seedance 2.5.
The result was extremely close to what we had planned, including the animation and even the sound effect that completed the motion.
For me, this is where AI production gets interesting: the real work is not simply generating. It is art direction, prompt design, testing and refinement until the model delivers the intended result.
That line about the real work being art direction and refinement rather than generation is something I don't think most clients grasp yet, they still see 'AI made it' as the whole story. Did you end up walking the client through that process, or did they only care about the final result?
This project started with a very specific idea for how a static product visual should move.
The base artwork was created with ChatGPT and Nano Banana, but before generating the final animation, I spent time refining the prompt and defining the exact movement, camera behavior and energy we wanted.
I tested the direction with different models, including Kling 2.5, using those iterations to improve the prompt before producing the final sequence with Seedance 2.5.
The result was extremely close to what we had planned, including the animation and even the sound effect that completed the motion.
For me, this is where AI production gets interesting: the real work is not simply generating. It is art direction, prompt design, testing and refinement until the model delivers the intended result.
