No Code, Just Magic: Prototyping a Smarter Disneyland Day With AI Tools

That was the driving question behind this Disneyland planning app, created as part of our internal AI hackathon, by a team consisting  entirely of non-engineers. Fueled by curiosity (and a love for Disneyland), we set out to see how far we could get with building an app, using only AI-powered no-code and low-code tools.

The result? A mobile web app that pulls real-time ride wait times and helps users build a custom itinerary tailored to their arrival time, energy level, and ride preferences. It’s not perfect yet, but it’s functional, fun, and full of promise.

The Experiment / What We Did

We used Lovable as our primary AI prototyping tool, starting with a conversational prompt to describe our goal. We provided Lovable with a third-party API (Theme Park Wiki) which  allowed it to scrape live Disneyland ride wait times and incorporate that data into a real-time itinerary planner.

Users could pick their must-do rides, choose whether they wanted an efficient or relaxed day, and even insert breaks or plan to park-hop between Disneyland and California Adventure. Supabase handled our backend data storage, while GitHub gave us the ability to host a live, usable web app.

We uncovered a handful of challenges with the limits of prompting, including:

• Naming inconsistencies:
  AI didn’t always recognize differently phrased ride names and sometimes created duplicates.
• Lack of contextual awareness:
  The logic around park geography was naive; it often recommended bouncing from one side of the park to the other, ignoring extensive walking times.
• Lovable has no true multi-user collaboration:
  Only one person could work on the app at a time

One significant lesson learned was that AI often makes assumptions. Our app provided users with details - like ride intensity and duration - that weren’t in the API at all. Instead, the AI generated those arbitrarily,  based on the requests we provided in our initial prompt. This taught us how important it is to be specific about which data sources to use and to clearly define standards when writing prompts, so the AI doesn’t make assumptions or include inaccurate information.

Despite these quirks, the app’s ability to pull real-time data and generate usable plans was a major achievement for a no-code build.

Further Experiments:

As we continue experimenting with this project we hope to include geolocation awareness, ride grouping by area, filtering out closed rides, and better logic for breaks and meal planning. We'd also explore more efficient data validation and architecture using tools like Cursor.

What We Learned

This project proved that non-developers, armed with the right tools and ideas, can get surprisingly far in creating a working app.

Lovable handled a large portion of the logic and design with minimal friction, while Supabase and GitHub gave us enough backend power to bring it all together. We learned that data validation, naming conventions, and logic modeling still need human intervention—but building the skeleton of an experience like this is absolutely within reach.

This kind of tool has clear potential for expansion—not just at Disneyland, but in any scenario where real-time data and personal preferences intersect. We left excited, inspired, and convinced that AI has truly lowered the barrier to building software.