I Tried to Build a Golf App with Gemini Ai Canvas — Here's What Happened

It started with a simple question: "How much revenue does the golf app Hole19 generate?" The answer led to a more ambitious thought: "Could I replicate its core features using Gemini?"

What began as a simple query quickly snowballed into a fascinating, half day collaborative coding session. In Google Gemini you can click the Canvas button to vibe code simple apps. I wanted to put it to the test after it gave me a good answer on research.

The goal was to build a functional, AI-powered golf application right inside the chat window. The result was a surprisingly powerful web app and a series of insightful lessons on the power and limitations of AI-assisted development.

Phase 1: Laying the Foundation

Every good app starts with the basics. The initial request was to create a React application that could act as a digital scorecard and track performance. Within minutes, Gemini generated the first version of the "Gemini Golf Coach."

It was a complete, self-contained React app with:

• A Digital Scorecard: A clean, 18-hole table to input scores, putts, fairway hits, and greens in regulation.

• Performance Tracking: A dashboard that automatically calculated key stats like score to par, putts per hole, and accuracy percentages.

• The AI Golf Coach: This was the magic. Using the Gemini API, the app could take a completed scorecard, send the data in a detailed prompt, and return a personalized analysis of the round, complete with strengths, weaknesses, and actionable tips.

Seeing the first AI-generated analysis pop up after filling out a scorecard was the "Aha!" moment. It wasn't just a data-entry app; it was an interactive tool that provided genuine value. The core concept was proven.

Phase 2: Scaling Up with Multi-Player Support

A solo golf app is useful, but golf is often a social game. The next logical step was to add multi-player support. This was more than just a minor tweak; it required a fundamental change to the app's data structure.

The request included:

• Multi-player support (up to 4 players).

• Customizable player names.

• An "auto-fill with par" button for quick scoring. As a golf player this was a a feature I wanted to add to save time.

  
  

This is where the collaborative process shined. I guided the logic, and Gemini refactored the code. The currentRound state was updated from holding a single player's data to managing an array of player objects. The UI was overhauled to handle multiple columns on the scorecard, and the performance and AI analysis views were updated to allow selecting which player to analyze.

Within one iteration, the app had transformed from a personal tracker into a tool for a whole group.

The GPS Conundrum: Hitting a Real-World Wall

Then came the million-dollar question: "Can you add GPS-based range finding?"

This is where we moved from the realm of pure code into the world of proprietary data. My response was immediate and clear: building a true, live GPS that works on any course is a monumental task, not because the code is impossible to write, but because it requires something I don't have: a massive, licensed database of golf course maps.

This was perhaps the most crucial lesson of the entire experiment. AI can write incredibly complex code, design user interfaces, and even debug its own work. What it can't do is conjure up proprietary, high-cost data that companies spend millions to create and maintain.

We discussed the reality of the industry: companies like Garmin and iGolf build and license this data through expensive commercial agreements. It's a business-to-business transaction, not a simple API call.

Instead of giving up, we pivoted. The proposed solution was a "Yardage & Club Suggestion" feature. Users could manually enter the distance to the pin for each shot, and this data would be fed to the AI coach. It was a practical workaround that still enhanced the app's core value without requiring an unobtainable resource.

Reflections on Building with an AI Partner

This project was a valuable lesson in what it means to develop software in the age of AI. It's not about handing over control; it's about intelligent collaboration.

• As a Prototyper, Gemini is Unmatched: The speed at which we went from an idea to a functional, multi-featured application was astounding. Generating boilerplate, creating components, and even suggesting UI layouts happened in minutes.

• It Understands Architecture: When we needed to shift from single-player to multi-player, Gemini correctly identified the necessary changes to the data structures and component hierarchy.

• It Knows Its Limits: When faced with the GPS request, it didn't pretend it could do it. It accurately explained the real-world data constraints, a critical skill for any developer, human or otherwise.
  

The Gemini Golf Coach now exists as a tangible artifact of this process—a feature-rich app that started on research in the chat. I then switched it to canvas mode in the prompt where it switched to building a protype app. Sign up to get access to try my Golf app and Google AI Creative Flow state Course.

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