Inspiration
The world of sports apparel has always been about self-expression and team identity. Yet finding unique, personalized jersey designs remains frustrating and expensive for athletes, teams, and sports enthusiasts. We saw an opportunity to democratize creative design by harnessing the power of artificial intelligence. The vision was simple: transform any text description into a stunning, professional-quality jersey design within minutes.
What it does
Fastjrsy transforms text descriptions into professional-quality jersey designs using advanced AI technology. Users describe their vision in natural language, and the system generates unique, high-resolution visuals within minutes. The asynchronous processing architecture maintains responsive interaction during AI generation, with real-time progress updates and intelligent polling.
Beyond design generation, the platform provides complete user management with multi-provider authentication, credit-based usage tracking, and a public gallery for showcasing creations. All designs are stored persistently, enabling users to build comprehensive design portfolios.
How I built it
The technical foundation centers on Next.js 15 with TypeScript, leveraging React 19 for the frontend experience. The core AI functionality integrates with Replicate's Flux model through a sophisticated asynchronous architecture.
The processing workflow operates in three phases: submission creates a Replicate prediction and stores status in Cloudflare KV; processing provides real-time updates through intelligent polling with exponential backoff; completion receives webhook updates, saves images to R2, and displays results instantly.
Data persistence utilizes Drizzle ORM with Cloudflare D1 for user data, orders, and work history. Cloudflare's ecosystem provides KV storage for temporary status tracking and R2 for permanent image storage. A dedicated Cloudflare Worker implements request rate limiting through Durable Objects, ensuring system stability under load.
The authentication system supports Google and GitHub providers, while the credit-based usage model manages resource allocation effectively. SWR handles data fetching with optimistic updates, creating a seamless user experience.
Challenges I ran into
The primary technical challenge involved orchestrating asynchronous AI processing while maintaining excellent user experience. Long-running AI generation tasks required careful balance between server resources and user expectations. Implementing intelligent polling with exponential backoff prevented server overload while keeping users informed of progress.
Resource management proved complex, particularly managing costs associated with AI generation and cloud storage. The credit system needed precise tracking of usage patterns while preventing abuse through rate limiting mechanisms.
Ensuring reliable webhook delivery and handling edge cases in the async workflow required extensive testing and error recovery mechanisms. The system needed to gracefully handle failures at any stage while providing clear feedback to users.
Accomplishments that I'm proud of
Successfully created a production-ready asynchronous architecture that handles AI processing elegantly without blocking the user interface. The intelligent polling system with exponential backoff demonstrates sophisticated understanding of both user experience and system efficiency.
Built a comprehensive user management system with authentication, credit tracking, and work portfolio features that rival commercial platforms. The public gallery creates a community aspect that encourages engagement and showcases the AI's capabilities.
Achieved seamless integration with Cloudflare's ecosystem, leveraging KV storage, R2, D1 database, and Workers to create a scalable, cost-effective solution. The webhook-based status updates provide near real-time feedback once processing completes.
