I Built a Parking App Using Only AI. The UI Was Great, but the Logic Fell Apart
I wanted to test a simple question: can AI build a real app for me, with me writing no code by hand?
My project was a parking app, something that shows parking spots on a map so users can find a place to park. I used AI tools for everything: planning, design, and code.
The short version: the UI impressed me. The logic didn’t.
What worked: the UI
Within a short time, I had a clean, modern interface. The layout, colors, spacing, and screens all looked professional. If I had judged the project only by screenshots, I’d have called it a success.
For anyone who struggles with design, this is a real advantage. The AI turned plain descriptions like “a simple screen with a search bar and a list of nearby parking spots” into something that looked polished. It saved me hours that I would normally spend on layout and styling.
What failed: the logic
Once I started actually using the app, the cracks showed. Here’s where it broke down.
1. The map library was outdated
The AI generated code using an old version of the map library. That sounds minor, but it caused real trouble. When I upgraded to the newer version, I had to fix things manually because the AI’s code didn’t match how the new version works.
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This is a core limitation: AI tools learn from data up to a certain point, so they often suggest older versions, deprecated functions, or outdated syntax. Libraries like map SDKs change quickly, which makes this problem worse.
2. Updating didn’t fix everything
After I moved to the newer version, I found that the changes in the new version weren’t reflected in the rest of the code. So I had to go through and update those parts by hand too. One upgrade turned into a chain of fixes, and I ended up editing code I hadn’t written and didn’t fully understand.
3. Zoom in and zoom out problems
Map behavior is where the logic really struggled. Zooming in and out didn’t behave correctly, and a few other interactions were off too. Maps involve many moving parts, such as coordinates, markers, and events. When the AI misunderstands even one of them, the whole experience feels broken.
4. Confident code that doesn’t actually work
The frustrating part is that none of this was obvious from the code itself. Everything looked reasonable. The AI never warned me that a method was outdated or that behavior might differ across versions. I only found the problems by running the app and testing it.
The lesson: AI is better at looks than at logic
This project taught me something specific:
- UI is pattern-heavy. There are countless examples of good layouts, so AI does well here.
- Logic is context-heavy. It depends on exact library versions, how different parts interact, and real-world behavior. That’s where AI is much shakier.
In other words, AI gave me a beautiful car with an unreliable engine.
What I’d do differently next time
- Specify versions up front. Tell the AI exactly which version of the map library to use, and share the official documentation if possible.
- Check the docs yourself. For fast-changing libraries, treat the official documentation as the source of truth, not the AI.
- Build and test one feature at a time. Get the map working properly before adding search, markers, or filters.
- Test interactions early. Zoom, pan, and marker clicks should be checked at every step.
- Keep versions pinned. Avoid upgrading mid-project unless you have to.
Final thoughts
AI can get you a good-looking app quickly, and that’s genuinely valuable. But building something that works still takes understanding, testing, and debugging. For my parking app, the AI handled the paint job well, but I had to fix the engine myself.
Would I use AI again for this? Yes, but as a fast assistant, not a replacement for knowing how the pieces fit together.
Have you built something with AI and hit similar problems? Share your experience in the comments.
