From Prompts to Products: Vibe Coding in Fitness Apps

Vibe coding is a fun term, but it reflects something serious: developers now use AI not just to help with code, but to build entire app features, sometimes whole products. The idea is simple. You describe what you want, and an AI model like GPT writes the code.

It started as a weekend experiment. Now it’s making its way into real projects, especially in fitness, where simple tools like water trackers, weight logs, and daily goals are enough to create value.

In this article, we’ll look at what vibe coding really is and where people are already using it in production. We’ll also show what works, what breaks, and what we’ve tried ourselves.

What Is Vibe Coding?

Vibe coding isn’t traditional development. You don’t start with wireframes, docs, or even code. You start with a prompt, and let the AI write most of it. This approach got its name from researcher Andrej Karpathy, who joked that it’s not even coding anymore. Still, for many developers, it’s quickly becoming a real tool.

The workflow is simple: describe what you want, and large language models like GPT will generate HTML, React components, backend logic, or even database schemas. You stay in the flow, the AI does the typing.

This style is a great fit for simple fitness apps with clear logic and repeatable features, such as habit trackers, step counters, or weight logs. These apps only need straightforward screens and basic data handling, not advanced integrations or complex systems. AI can already handle some of this work, but it still can’t build medium or complex apps by itself.

Founders we talked to say vibe coding helps them:

• build a simple prototype quickly and test it with early users
• collect feedback from the market before investing in a full product
• check if an idea has potential without needing a full development team

But it’s not all perfect. There are risks of:

• messy AI code that grows too fast and gets hard to manage
• code that is difficult to read and even harder to review, compared to writing it from scratch
• problems with outdated libraries or hallucinated syntax
• lack of real architectural thinking, especially in long-term projects

In short, vibe coding is great for prototyping, learning, and automating repetitive work, but risky if you lose control over structure. Several tools are pushing this forward:

• Bolt lets you generate and iterate on apps quickly through conversational prompts.
• Google AI Studio provides a workspace to design and test AI-powered features.
• Replit makes it possible to build and debug apps directly in the browser with AI assistance.
• v0 helps designers and developers turn prompts into functional UI components.
• Lovable.dev generates React apps from prompts and gives you clean code you can export.
• WindSurf turns a chat UI into working websites, useful for marketing pages or simple tools.

Vibe coding isn’t replacing developers. But those who use it well can move faster.  It’s best for simple apps with repeatable features like tracking, charts, reminders, onboarding, or settings screens. Large language models handle these tasks well. However, you won’t build something as complex as Apple Health this way. For advanced fitness apps with deep integrations and more complexity, you still need traditional development. AI can only support part of the process.

What You Can Already Vibe Code in Fitness

When we first explored vibe coding, what surprised us most wasn’t the technology, but how quickly it could be used for real projects. While it’s not yet powering apps like MyFitnessPal or Strava, it’s already making it possible to build small, useful tools in just a weekend.

Here are the types of apps that vibe coding already makes possible today:

• Water Trackers

Simple input forms, daily reminders, and streak counters are easy for AI to scaffold. The logic is straightforward: add an entry, calculate progress, display results.

• Weight Logs

With a prompt, you can get a React form for logging weight, a chart for progress visualization, and even a settings page for units. This is one of the most “AI-friendly” flows because it’s repetitive and clean.

• Step Counters

While integrations with Google Fit or Apple Health may require manual tweaks, AI can generate the basic UI, history view, and filtering options in minutes.

• Workout Timers

Interval timers or HIIT countdowns are another good candidate. The screens are simple, and the AI code usually works out of the box with only light refactoring.

These apps might not have millions of users, but they’re important building blocks in the fitness world. Being able to prototype them so quickly shows that vibe coding is more than a buzzword. It’s a practical way to turn ideas into real, usable tools.

Where AI Helps and Where It Struggles

What’s interesting about vibe coding is that it’s not just about speed. It’s also about where AI makes things easier and where it brings new challenges. Fitness apps highlight this contrast well.

Where AI Helps

1. Simple trackers and logs

If you ask an AI to make a weight log with a chart, it will usually create a React form for adding entries, a table or list to show them, and a chart to track progress over time. The same approach works for water intake or daily goals. These are repetitive patterns: input, store, display. Language models handle them well.

2. Reminders and streaks

Basic goal-setting features, like “remind me to drink water at 9am” or “show a 7-day streak counter,” are easy for AI to scaffold. The generated code might not be perfect, but you get working logic for timers, counters, and notifications.

3. UI drafts

Need a quick onboarding flow or a settings page? AI can spit out ready-made React components in minutes. It’s not production design, but it helps you see the flow on screen fast, instead of spending time sketching wireframes first.

Where AI Struggles

1. API connections

Here’s where things get messy. Asking AI to “connect step data from Apple Health” often gives you outdated code, hallucinated imports, or the wrong SDK methods. The same goes for Firebase or Stripe integrations. It gets you part of the way, but you’ll still need to fix it manually.

2. State management

Fitness apps are simple, but even they need multiple screens: a tracker, a chart, a reminder system, maybe a profile. AI tends to mix different state patterns (Context, Redux, local state) in the same project. That makes scaling or debugging much harder.

3. Styling and consistency

AI can generate CSS or Tailwind classes, but the result often feels off. Buttons aren’t aligned, fonts are inconsistent, and charts sometimes overlap other elements. It’s usable, but far from polished. You usually end up rewriting parts of the UI.

AI coding works best when features have clear rules and repeatable logic. That’s why logs, counters, and reminders are easy to build. But when you need deeper integrations, a solid structure, or a polished design, things can fall apart.

So the practical way to use vibe coding is this: let AI give you the first draft, then refine it manually. You get the speed of prototyping without falling into the trap of messy, unmaintainable code.

How to Stay in Control When Coding with AI

From our own experiments with AI-assisted coding, we’ve noticed the same pattern many other developers describe. On the one hand, AI can quickly build features for fitness apps, like logging workouts, setting reminders, making charts, or creating onboarding flows. What used to take a day can now take just an hour.

But there’s another side. The biggest challenge isn’t syntax or frameworks. It’s controlling what the AI touches. Ask it to add a new button, and suddenly it decides to refactor unrelated logic. Request a small CSS change, and it might quietly update your state management. These “helpful” improvements often break features that were already working fine.

That’s why we recommend following a structured approach when using vibe coding for fitness apps:

• Keep prompts specific — tell the AI exactly what to change, and what not to touch.
• Break down requests — ask for small pieces (a form, a chart, a timer) instead of big systems all at once.
• Test constantly — after every change, even minor ones, re-check core flows like logging, authentication, and reminders.
• Version control everything — it’s your safety net when the AI goes off track.

For developers, the main skill in vibe coding isn’t just knowing React or Node. It’s about working with AI without losing control. If you do it well, you save a lot of time. Done carelessly, it creates a debugging nightmare.

Final Thoughts

Vibe coding is not about replacing developers. It gives them a faster way to turn ideas into working code. In fitness apps, where many features repeat, such as trackers, reminders, charts, and onboarding, AI can be a real accelerator.

The key is balance: let AI handle the boilerplate, but keep control of the structure, design, and integrations. That’s where developers still make the difference.  When used well, vibe coding can turn weekend projects into real apps people use.