When I first envisioned the 3leaf Payment Terminal app, I wasn’t merely setting out to build a practical piece of mobile software — I was initiating an experiment. A challenge. A deliberate test of what artificial intelligence, in its current public incarnation, is truly capable of.
The premise was simple, yet ambitious:
Could I develop an entire iOS application — from core logic and user interface to localization, copywriting, and technical documentation — using nothing but ChatGPT?
No manual coding. No handwritten adjustments. No copy-pasting from Stack Overflow. Every single prompt, function, view hierarchy, and even App Store description would be generated by AI.
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🤖 Why Let AI Drive the Project?
We are living in an era of accelerating disruption. As generative models continue to evolve, they are increasingly capable of tasks that once required years of domain-specific expertise. Writing usable code is no longer the domain of seasoned developers alone — it is now within the reach of anyone capable of articulating a clear intention to an LLM.
This project wasn’t just about building an app. It was about interrogating a possibility:
Can AI actually replace the bulk of a developer’s workflow, and what would that mean for the future of software engineering?
The implications are profound. A world where small teams — or even individuals — can build secure, scalable, and specialized applications without formal coding knowledge has the potential to radically democratize software creation.
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🛠️ The Process — Patience, Precision, and Plenty of Prompts
Let me be clear: building an app exclusively with AI is not a plug-and-play experience.
While ChatGPT is extraordinarily capable, it is also a language model — not a compiler, not a project manager, and not a mind reader. The process demands a methodical and often painstaking iteration cycle. Prompts must be precise, expectations must be managed, and output must be carefully reviewed — line by generated line.
There were moments of brilliance.
A single prompt could yield a full SwiftUI layout, complete with @AppStorage integration, error handling, and localization scaffolding. AVFoundation camera integrations for QR scanning were completed in under 10 minutes. Multilingual .xliff files were exported with perfectly structured translations.
But then there were the pitfalls.
Sometimes, ChatGPT would inexplicably rewrite working code sections while fixing unrelated issues. Occasionally, it would forget previous logic, require re-prompting to recall context, or break @Binding chains without warning. Localization was particularly problematic — after four or five successful iterations, the model would begin to misinterpret .xliff structure, rendering entire files unusable.
And yet, these limitations didn’t make the project less compelling — they made it more fascinating. Because they illustrated not just what AI can do, but how it reasons, how it forgets, and how human guidance remains crucial even when creativity is machine-generated.
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🚀 What I Learned — and What Comes Next
The outcome? A real, functioning, App Store-ready Tap to Pay application. Built entirely through natural language prompts. Is it perfect? Of course not. But it works — and not in a toy sense. It handles live Stripe payments, onboarding, reader pairing, tipping logic, and post-payment receipts. It supports multiple currencies, languages, and fallback mechanisms.
It is not just a prototype. It is production software — generated entirely by AI.
But the app is only part of the story.
Surrounding it is a broader infrastructure — and it, too, was designed entirely through AI.
• The WordPress-based merchant portal, including subscription management, login systems, and automated generation of token URLs, was scaffolded and implemented through ChatGPT.
• The Znuny-based helpdesk, fully integrated with the WordPress userbase via API, REST, and automated ticket routing, was also developed via AI interaction — including ticket creation, user synchronization, and auto-responses.
• Even the support texts, onboarding instructions, and translated interface content were generated and refined in dialogue with AI.
From front-end to back-end, from content to code, from server configuration to transactional emails — every aspect of the 3leaf platform has been conceived, constructed, and iteratively improved in cooperation with ChatGPT.
This was never just about proving that AI could code an app.
It was about building a complete service — end-to-end — without touching a traditional IDE or writing a single manual line of code.
And that fact should give all of us something to think about.
AI won’t eliminate the role of human developers overnight, nor should it. But it will redefine the landscape. Developers will become prompt engineers, architects, and validators — shifting away from line-by-line logic toward high-level orchestration and semantic clarity. I believe we are witnessing the birth of a new paradigm, where code is not written, but requested.
This project is my small contribution to that vision.
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🔍 Final Reflection
To create is to collaborate. In this case, my collaborator was a neural network trained on billions of lines of code and human language. It didn’t always behave the way I hoped. But it never ceased to surprise me — and it never stopped iterating.
What began as a novelty has evolved into a genuine methodology.
The 3leaf Payment Terminal app is not just a product. It is a statement:
That AI can build. That humans can guide. And that together, we can create remarkable tools — faster, cheaper, and with fewer barriers than ever before.
But more importantly, it is not an isolated experiment.
The app is just one part of a larger system — all of which was created through AI collaboration. The merchant portal built in WordPress, the ticketing and helpdesk system powered by Znuny, the automated generation of onboarding links, translations, error flows, and even the tone of my customer service — all of it was designed, refined, and deployed using ChatGPT as the primary engine.
This project wasn’t just about proving AI could code an app.
It was about discovering that AI can participate meaningfully in every layer of software creation — from system architecture and server setup to frontend design and multilingual support.
And we are only just getting started.