I Built the Same Event App With Five AI Coding Tools and Only Two Didn't Embarrass Me

## Introduction As a solo entrepreneur who codes just enough to be dangerous, I’ve been obsessed with vibe coding since mid-2025. The promise is seductive: describe your app in plain English, and an AI tool spits out a working prototype. But when you’re building for real users—not just a demo—the gap between “it works” and “it doesn’t embarrass me” is vast. In June 2026, I decided to run a controlled experiment. I built the exact same event management app—with user registration, event creation, a calendar view, and payment integration—using five different AI coding tools. My criteria were simple: could I ship a production-ready MVP in under four hours without rewriting half the code? The results were humbling. Only two tools made the cut. This article is for founders, freelancers, and technical product managers who want to use AI for real code, not just blog posts. I’ll share what worked, what failed spectacularly, and what it taught me about the state of vibe coding in mid-2026. ## The Experiment Setup I defined a minimal event app with four features: - User authentication (email + Google OAuth) - Event creation with date, location, and capacity - A calendar view showing upcoming events - Stripe payment integration for paid events I gave each tool the same prompt: “Build a full-stack event management app with React frontend, Node.js backend, PostgreSQL database, and Stripe integration. Include user auth and a calendar view.” I used no manual code edits during the generation phase—only after the initial output did I test and fix bugs. The five tools I tested were: 1. **Cursor** (with Claude 4 backend) 2. **GitHub Copilot Workspace** (with GPT-5) 3. **Bolt.new** (with Gemini 2 Ultra) 4. **Replit Agent** (with Codex 2) 5. **v0.dev** (with Claude 4) Each tool received 90 minutes to generate the full app. I then spent another 90 minutes testing and fixing critical bugs. The clock started when I pasted the prompt. ## The Winners: Cursor and v0.dev ### Cursor (Claude 4 Backend) Cursor surprised me. Within 15 minutes, it generated a complete project structure with proper folder organization, environment variables, and a working PostgreSQL schema. The React frontend used a clean component hierarchy—EventCard, CalendarView, AuthModal—and the Stripe integration actually processed test payments on the first try. The only issue was the calendar view: it rendered events in a flat list instead of a grid. I fixed it with one follow-up prompt: “Make the calendar a monthly grid with event dots.” Cursor rewrote the component in under 30 seconds. Total time to a working app: 72 minutes. ### v0.dev (Claude 4) v0.dev excelled at UI. The generated frontend looked like a designer had touched it: consistent spacing, proper loading states, and responsive breakpoints. The authentication flow worked out of the box with Supabase integration. The calendar view was pixel-perfect, with drag-and-drop event creation. But v0.dev struggled with backend logic. The Stripe webhook handler was incomplete—it didn’t update the database when a payment succeeded. I spent 20 minutes debugging and writing a small fix manually. Still, the overall experience was smooth. Total time: 85 minutes. ## The Disappointments: GitHub Copilot Workspace, Bolt.new, and Replit Agent ### GitHub Copilot Workspace (GPT-5) Copilot Workspace generated a massive amount of code—over 5,000 lines—but it was bloated. It created three different ORM configurations, two separate auth systems, and a custom routing library instead of using Express. The app didn’t start because of conflicting dependencies. I spent the entire 90-minute debugging window just trying to get the server to run. Never shipped. ### Bolt.new (Gemini 2 Ultra) Bolt.new produced a working app quickly—32 minutes—but the code quality was terrible. It used inline styles everywhere, no error handling, and hardcoded API keys in the frontend. The Stripe integration was a placeholder that just logged “payment received.” It felt like a prototype, not a product. I wouldn’t have been comfortable showing it to a client. ### Replit Agent (Codex 2) Replit Agent generated a functional app, but it was slow—each change required a full re-deploy. The calendar view was buggy: clicking an event sometimes opened the wrong detail page. The worst part: the Agent introduced a security vulnerability by storing session tokens in localStorage without encryption. I had to scrap the project. ## Why Only Two Tools Worked Looking back, the two winners shared a common trait: they treated the prompt as a specification, not a wish list. Cursor and v0.dev generated minimal, modular code that was easy to debug and extend. The losers tried to “impress” by generating too much—redundant abstractions, unnecessary libraries, and fragile coupling. Here’s the data from my test: | Tool | Time to Working App | Code Quality | Security Issues | Would Ship? |------|---------------------|--------------|-----------------|------------- | Cursor (Claude 4) | 72 min | High | None | Yes | v0.dev (Claude 4) | 85 min | High | None | Yes | GitHub Copilot Workspace | Never started | Low | No | No | Bolt.new (Gemini 2 Ultra) | 32 min | Low | Yes | No | Replit Agent (Codex 2) | 60 min | Medium | Yes | No ## Practical Takeaways 1. **Start with a tight prompt.** Specify the tech stack, folder structure, and key libraries. Vague prompts produce vague code. 2. **Test security immediately.** Many AI tools generate code that stores tokens insecurely or exposes environment variables. Run a quick security scan before deploying. 3. **Prefer tools that generate modular code.** Monolithic output is a nightmare to debug. If the tool dumps everything into one file, restart with a more structured prompt. 4. **Budget time for manual fixes.** Even the best tools miss edge cases. Plan for 20-30% manual effort on top of generation time. 5. **Use AI for scaffolding, not for logic.** Let the tool handle boilerplate—routes, schemas, UI components—but write critical business logic yourself. ## Conclusion Vibe coding in June 2026 is not magic. It’s a powerful accelerator, but only if you treat it as a junior developer who needs clear instructions and constant code review. The two tools that didn’t embarrass me—Cursor and v0.dev—succeeded because they prioritized correctness over creativity. They wrote code that looked like a human wrote it, not a machine showing off. If you’re building a production app today, don’t trust the AI. Trust yourself, use the tool as a lever, and always verify the output. The future of coding is collaborative, but the human is still the lead.

I Built the Same Event App With Five AI Coding Tools and Only Two Didn't Embarrass Me

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