After working on AI apps for a while, I've found that a good tech stack and a proper workflow can boost efficiency and cut development time. As a solo developer, I've honed my method through experimentation and practice. Now, I share my technology stack. This guide shows how AI and modern tools can boost your development process.
Frontend Development
For frontend development, I found that Next.js and Tailwind CSS can better leverage AI. They can also improve the workflow for frontend development. This front-end framework makes SEO optimization easy given its server-side rendering capabilities. What's more, it integrates with Claude AI and Cursor very well. Claude AI can generate Next.js, Tailwind CSS, and shadcn/ui code. It can render a preview on its interface of the front-end page you are building without importing the code to the code editor. This great integration with AI saves me tons of time.
Here I have attached my GitHub repository of a Next.js SEO-optimized template: https://github.com/wudongjie/nextjs-seo-starter .
Backend Infrastructure
For back-end development, I used FastAPI, PostgreSQL, and Celery. They handled authentication, database operations, and AI-related business logic. This approach requires containerizing components with Docker and deploying containers to AWS.
However, this development workflow presented significant challenges. Without AWS knowledge, I needed outsourcing experts to set up the CI/CD pipeline and deploy containers. While functional, this approach significantly increased development time and costs. Also, building a REST API from scratch made my AI app prone to errors. This, in turn, extended development time during debugging.
I improved my development process by using AI, Supabase, and Vercel. Now, Vercel handles all my CI/CD needs. Meanwhile, Supabase offers strong user authentication and database support. For AI tasks, I use edge functions. This setup eliminates the need for a running server.
This change in my workflow has cut out complex server management. It has also reduced development time and costs. Now, instead of dealing with server settings and deployment, I can focus on developing features that users need.
AI Integration Layer
Let me share the most crucial aspect of my AI application development stack - the AI layer. Building AI applications usually involves domain-specific knowledge or business proprietary data. Successful AI application development goes beyond using general AI models like ChatGPT or Claude. The true value in my workflow lies in combining these models with business-specific data. General AI models often lack access to this crucial information.
This insight led me to use Retrieval-Augmented Generation (RAG) in my workflow. I've used AI through RAG systems in my app development projects. This has created a powerful way to combine large language models with specialized business data. Here's how I've optimized my development workflow for RAG implementation:
For data management, I use DynamoDB and Pinecone. DynamoDB manages structured business data, while Pinecone handles vector embeddings and indexing. When users engage with my AI apps, the system first queries Pinecone for relevant embeddings. This step identifies key business data. Next, it fetches detailed content from DynamoDB using the IDs returned. This ensures quick and accurate information retrieval.
Edge functions have proven perfect for this AI application development workflow. They efficiently orchestrate the entire process—from user interface interactions to vector searches, data retrieval, and AI response generation. By using edge functions, I've minimized development time while maintaining quick response times.
Development Experience and Workflow
AI tools like Cursor and Claude AI have transformed my development workflow. Claude AI quickly shows a prototype of the front-end page. Once I'm happy with the design, I import the code into Cursor for further refinement and testing. I always document each project thoroughly. I create a detailed README.md file that covers the application's architecture, features, and technical requirements. After writing the documentation, I use Cursor's AI composer. It generates, validates, and rewrites about 80% of my codebase based on the specifications. The efficiency of AI in application development with Cursor is impressive. It accurately turns documentation into everything from SQL queries for Supabase databases to edge functions and Next.js code. My role has shifted from writing boilerplate code to focusing on debugging and testing. This change has significantly reduced development time and improved overall code quality.
Conclusion
In conclusion, my AI application development process has been changing as AI gets better and stronger. Currently, my development workflow leverages Claude AI and Cursor, but in the future, things might change. I am very optimistic that we, as indie developers, are becoming more productive and efficient as AI progresses.
