See what developers are building with AI coding tools. Learn from their successes—and their security mistakes.
Complete customer dashboard with auth, billing integration, and analytics. Used Cursor for 80% of the code.
Productized Claude API for a specific use case. Full landing page, auth, and usage tracking.
Collection of internal tools for a startup: employee directory, PTO tracker, expense reports.
Custom storefront integrated with Shopify backend. Headless commerce with custom design.
Mobile-first community app with posts, comments, and real-time chat.
Vibe coding shines for rapid prototyping and MVPs where speed matters most
Fine for demos, but scan before accepting real user data
Great for internal dashboards and tools where you control the user base
Still need auth hardening—internal users can be curious
Can accelerate development but requires careful security review
Must scan thoroughly—you're responsible for user data
Speed benefits don't outweigh compliance and security requirements
Requires extensive manual review and compliance audits
Scan your project to find the security issues that AI missed. Most vibe-coded apps have 5-10 vulnerabilities on first deploy.
Free Security ScanAlmost anything a traditional developer could build: SaaS apps, e-commerce sites, mobile apps, internal tools, APIs, browser extensions, and more. The AI handles syntax and patterns while you provide direction and architecture decisions.
Simple MVPs can be built in hours to days. More complex applications take 1-2 weeks for a functional version. The speed gain comes from AI handling boilerplate, but you still need time for design decisions, testing, and security hardening.
Not automatically. The AI generates functional code quickly, but it often has security issues, performance problems, and edge case bugs. Before going to production, you need security scanning, load testing, and careful review of authentication and authorization flows.
Next.js + Supabase is currently most popular because AI has lots of training data for both. The key is choosing technologies with good documentation that the AI has been trained on. Newer or niche technologies may produce lower quality code.
Last updated: January 16, 2026