Built an iOS app in two days using AI (Xcode 26.3)

• Key Takeaways:
• An app prototype was built in two days using agentic AI workflows alongside Xcode 26.3.
• AI accelerated scaffolding, UI layout, and routine code, but required active review and debugging.
• The experience was exhilarating for speed, and unsettling for correctness, security, and app-store readiness.
• Practical wins included rapid iteration; practical limits were testing, edge cases, and polish.

The experiment

The author set out to test how fast an iOS app could be assembled when AI agents drive development inside Xcode 26.3. The goal was not a finished product, but a working prototype produced in roughly two days of concentrated work.

How AI and Xcode 26.3 worked together

AI agents handled repetitive tasks: scaffolding project structure, generating view controllers, wiring UI elements, and producing boilerplate networking and persistence code. Xcode 26.3 acted as the IDE for running, debugging, and iterating on the generated code.

Using AI for design-to-code and feature scaffolds dramatically shortened the initial build phase. Where hand-coding could take hours, the AI often produced a usable implementation in minutes, allowing rapid user-flow testing.

What felt exhilarating — and what was terrifying

The exhilaration came from speed and momentum: watching screens populate, flows connect, and features appear after a short prompt felt like magic. Iteration velocity — make a change, regenerate, test — compressed development cycles.

The terrifying part was that AI sometimes produced plausible but incorrect code, introduced subtle bugs, or missed accessibility and security details. Reliance on agentic output required continuous human oversight: reading generated code, adding tests, and validating behavior across devices.

Lessons and practical tips

1. Control the prompts: precise, stepwise instructions produce more predictable results than open-ended requests.

2. Review every line: treat AI-generated code as a first draft, not a final artifact. Unit and UI tests are essential.

3. Prioritize safety and privacy: check networking, data storage, and permissions manually before any distribution.

4. Iterate UI/UX manually: AI can produce layouts fast, but human refinement is needed for polish and accessibility.

Why this matters

Two-day builds with AI show how developer productivity can jump, especially for prototypes and small features. The trade-off is responsibility: AI accelerates creation but doesn’t absolve developers from ensuring correctness, security, and long-term maintenance.

The experiment is an encouraging glimpse at agentic coding in practice — fast, powerful, and still very human-dependent.