Building a Full App in Under an Hour with Cursor AI: A Deep Dive into AI-Powered Development

In the rapidly evolving world of software development, AI tools are making significant waves, promising to accelerate workflows and simplify complex coding tasks. One such tool garnering much attention is Cursor, an AI-powered code editor designed to help developers build applications faster and more efficiently. Recently, I embarked on an exciting challenge: to build a full-fledged app using Cursor, aiming to complete it within 20 minutes. Here’s a comprehensive look at the journey, insights, and lessons learned from that experience.

What is Cursor?

Cursor is an AI code editor that assists developers by generating, editing, and managing code across multiple files. It’s especially powerful when starting projects from scratch or adding new components. Having used it for a couple of months, I found it significantly speeds up development, particularly for building apps or features from the ground up.

The App Concept: Influencer Marketing Made Easy

For nearly two years, I’ve been exploring influencer marketing apps, a niche with undeniable potential but plagued by inefficient and expensive tools. The core problem is that brands struggle to identify their genuine advocates within their existing customer base.

The Opportunity

Many businesses already have valuable customer data, such as email lists from Shopify stores or subscriber databases. Among these customers are hidden influencers—people with significant social media followings who genuinely love the product and could serve as organic brand advocates or affiliates.

The Solution

The app I set out to build aims to:

• Allow businesses to upload their customer lists (CSV files).
• Identify influencers within that list by cross-referencing with social media data using an influencer search API.
• Provide a credit-based model where users get free credits to try the service and pay only for successful influencer matches.
• Offer downloadable results, including follower counts, platform, and profile URLs, enabling brands to reach out directly.

Setting Up the Project

To save time, I pre-set some groundwork before diving into Cursor:

• Created a new Vite project directory.
• Initialized a Firebase project with Google OAuth enabled.
• Linked Firebase to the local repo and added sensitive keys securely (not shared publicly).
• Used an existing Stripe account for payment processing.
• Prepared a detailed project specification to guide Cursor’s AI in generating accurate code.

The Importance of a Detailed Spec

One key takeaway is that writing a thorough and explicit specification upfront is crucial. It helps Cursor deliver a more accurate and usable first draft of the codebase. The spec included:

• Tech stack and color scheme.
• Data model with three Firebase collections (users, influencer matches, CSV uploads).
• UI pages: landing, login, dashboard, uploads.
• Backend logic, especially the core matching function implemented as Firebase Cloud Functions.
• Stripe integration for credit purchases.
• Error handling and UI states.

This upfront work is essential because once code is generated, extensive rewrites become challenging.

Building with Cursor: The Process

Using Cursor’s composer feature, I pasted the full spec and instructed it to generate the entire app without stopping or leaving TODOs. The AI completed the code generation in about 2 minutes, producing multiple files covering frontend, backend, and configuration.

Initial Setup

• Installed necessary npm dependencies as recommended by Cursor.
• Added Firebase configuration manually.
• Cleaned up default styles to match the design spec.
• Verified Google Login and navigation components.

Debugging and Iteration

While Cursor handled the heavy lifting, several issues required manual debugging:

• UI visibility problems due to color schemes and button variants.
• Callable Firebase functions not running due to missing emulator setup.
• Node version mismatches and syntax errors with dependencies like node-fetch.
• Environment variables and secret keys not correctly injected.
• Logical errors such as referencing non-existent functions.
• API response shape mismatches and error handling gaps.

These challenges highlighted that even with powerful AI assistance, developer knowledge and debugging skills remain indispensable.

Core Features Implemented

By the end of the first hour, the app had:

• User authentication with Google.
• CSV upload with a drag-and-drop interface.
• Matching logic connecting uploaded customer emails with influencer data.
• A dashboard displaying influencer matches with filtering options.
• Credit-based payment system integrated with Stripe.
• Downloadable CSV reports of influencer matches.

The app’s core functionality was solid, with only Stripe payment fine-tuning and deployment left.

Deployment and Stripe Integration

I deployed the app using Fly.io, a cloud hosting provider similar to Vercel. Stripe integration for purchasing credits was implemented manually, partly because Cursor’s generated code didn’t match my preferred workflow perfectly.

Stripe Checkout and Webhooks

• Created secure callable functions to generate Stripe checkout sessions.
• Implemented webhook listeners to update user credit balances upon successful payments.
• Added UI elements to let users select credit quantities.
• Attempted to integrate Stripe’s customer portal but found it better suited for subscriptions rather than one-time payments.

Lessons Learned and Final Thoughts

Pros of Using Cursor:

• Speed: Cursor can generate a substantial codebase very quickly.
• Multi-file editing: It’s capable of spanning multiple files and managing complex logic.
• Efficiency: Great for starting projects or adding large features when you have a clear spec.

Cons and Challenges:

• Context limitations: Cursor may miss nuances around environment variables, API docs, or specific frameworks.
• Debugging required: Generated code often needs manual fixes and refinements.
• Spec writing: Writing a detailed and clear spec is essential but takes time.
• Developer expertise: Users still need programming experience to handle errors and deployment.

Recommendations for Developers Exploring AI Tools

• Spend time crafting detailed specs before generating code.
• Use AI tools as accelerators, not replacements for developer judgment.
• Familiarize yourself with your tech stack’s quirks and environment setup.
• Leverage AI-generated code as a scaffold, then iterate and improve.
• Combine AI tools with existing resources like official docs and community support.

Bonus Resource: AI for Business Builders Guide

If you’re new to AI in software development, I recommend checking out the free AI for Business Builders Guide from HubSpot. It provides:

• A high-level overview of AI applications.
• Tips for effective AI prompting.
• Guidance on integrating AI with APIs and business workflows.

It’s a valuable resource whether you’re a beginner or looking to refine your AI usage skills.

Conclusion

Building a full app with Cursor AI in under an hour is not only possible but also practical for rapid prototyping and MVP development. While AI accelerates coding, it doesn’t eliminate the need for developer oversight, debugging, and careful planning. The combination of human expertise and AI assistance is where the true power lies.

If you’re interested in seeing more AI-assisted builds or want to share your experiences with Cursor and other AI tools, I’d love to hear your thoughts!

Happy coding with AI!