Building Twisty: Testing a Vibe-Driven Coding Approach with Firebase Studio

In 2025, I set out to test whether it was possible to bring an app idea to life using a vibe-driven development approach, relying on visual tools, AI prompts, and minimal code to prototype quickly. The idea? Twisty, a mobile-first app that helps motorcyclists discover curated local routes and ride together.

This blog post documents my technical journey, not just building an app, but seeing how far AI-powered tools like Firebase Studio and Gemini could take me from idea to working MVP.

From Concept to Code: What I Wanted to Build

I had a rough vision: a tool for riders that could:

• Show great twisty, scenic motorcycle routes nearby
• Let people RSVP to upcoming rides with one tap
• Filter by engine size, road type, and riding style

I wasn’t setting out to build another social network or global ride tracker. I wanted something local, personal, and focused purely on good rides.

The Experiment: Letting AI Lead (Sometimes)

I wanted to see how much of this app I could build by prompting AI, not by writing code from scratch.

My tools:

• Firebase Studio (Gemini-based app builder with integrated backend)
• Next.js + Tailwind CSS for a clean, mobile-first UI
• Google Static Maps API for fast, lightweight route previews
• GitHub for version control and branching
• Firestore for flexible data storage

I started by prompting:

"Create a mobile-first app where riders can browse and RSVP to motorcycle routes based on engine size, pace, and twistiness."

Firebase Studio generated:

• A route model with distance, duration, pace, and twistiness
• RSVP functionality
• User profile support
• Sort, filter, and display logic

It was a strong foundation, and I didn’t touch a line of code until it was necessary.

Where I Had to Get Involved

Not everything worked out of the box. Here’s where human intervention made the difference:

• Git sync issues — dev and main had unrelated histories → fixed with --allow-unrelated-histories
• Branch switching in Firebase Studio → required dummy commits to force sync
• Google Maps not rendering → API key structure, image URL escaping, and .env config
• Auth persistence bugs post-login → needed Firebase Auth tweaks
• Error messages → custom prompts to improve clarity

Each of these was solved by working with the tools, not against them.

Building It Right: Security, Admin, and Auth

I was careful to:

• Keep sign-up minimal (just name and email)
• Use Firestore rules to secure user and route data
• Create an admin area for managing routes and users
• Add features like password reset, profile editing, and account deletion

The app feels lightweight but secure, ideal for a quick MVP.

Mapping the Vibe

Twisty’s heart is its ride cards, each with:

• A static Google Map preview
• Route length, time, and twistiness
• Suitable engine sizes
• Nearby towns

AI helped here too:

"Each route card needs to show the Google map, change the placeholder image to a static map preview."

One prompt, and I was off to the races, after fixing my .env and escaping the URL properly.

Safe Development: Dev First, Then Deploy

My branching strategy was simple:

• dev: all active work happens here in Firebase Studio
• main: production-ready, only updated via PR

Firebase Hosting only deploys from main. This reduced risk and gave me confidence to experiment without fear.

What Gemini & Firebase Studio Did Well

• Scaffolded working UI and backend logic from clear prompts
• Designed responsive layout and visual structure
• Built filtering and RSVP logic without needing code
• Created Firestore data models that matched my thinking

What I Brought to the Table

• Design thinking and UX experience
• Real-world motorcycle logic (engine size vs. road type)
• Git workflow best practices
• Debugging and refining what AI started

What Twisty Can Do Now

• Filter and browse local routes
• RSVP to scheduled rides
• View Google Static Maps for each ride
• Manage profiles and accounts
• Admin tools for creating and editing rides
• Secure hosting and version control via GitHub

What’s Next

• Segment-level tagging of roads by twistiness, pace, etc.
• AI-generated ride suggestions based on time and profile
• Calendar features for upcoming rides
• Rider feedback and meetup RSVPs
• Scaling to nearby towns

Final Takeaway: AI Builds Vibes, You Build Vision

Twisty is more than an app, it’s proof that you can go from concept to working MVP with mostly natural language prompts, a bit of Git discipline, and a clear use case.

Firebase Studio, Gemini, and GitHub have made it easier than ever to test a concept without over-engineering or overthinking. And while AI can generate a lot, it still needs a clear human vision to shape it.

So if you’ve got an app idea, try building it with vibe-first tools, and see where it takes you.