Building an Open Source Project with 100% AI-Generated Code

Introduction

I wanted to customize an LLM by wrapping it with a system prompt that I define to create an API for specific processing. This led me to build this open-source project.

About This Open Source Project

This project allows you to create a backend API wrapping an LLM in just one minute. All you need to do is enter the API path, request schema, response schema, and system prompt—then it's ready to go!

How I Built It

Technology Stack

• Frontend: React + Vite, because I specialize in React, and Vite offers fast builds.
• Backend: Node.js.
• Database: PostgreSQL, as I am familiar with it.
• Migrations: Knex.js (I had never used it before—AI generated it for me).

Tools Used

• Cursor IDE combined with Claude 3.7 Sonnet.
• Custom Rules for precise data handling, sourced from: Awesome Cursor Rules.

Prompting Strategy

I used role-based prompting to make AI act as both a Senior Developer and a UI/UX Designer, ensuring a modern and aesthetically pleasing UI.

Development Steps

1. Define AI agent roles: I described the AI agent's role and the project requirements, then asked it to initialize the frontend and backend source code.
2. Frontend development: Since the role was predefined, I directly described the required features and workflows to generate the frontend.
3. Backend development: I instructed the AI to read the entire frontend codebase and generate a corresponding backend. Surprisingly, it generated exactly what I expected!
4. Docker deployment: I asked AI to generate Docker configurations.
5. Frontend-Backend Integration: I provided a prompt to make AI integrate the generated backend API into the frontend.
6. Testing & Debugging: Finally, I manually tested the project and asked AI to fix the bugs. This step could be automated, but I chose to review manually.

Results

• 100% of the code was generated by AI.
• I made approximately 150 requests to Claude 3.7 sonnet to complete the project.
• The entire development process took 3 hours, with an additional 2 hours for bug fixes (still fully AI-driven).

English grammar checker API demo:

Javascript mentor API demo

Conclusion

Building a fully AI-generated open-source project is not only fast but also entirely feasible. However, this poses a serious risk to junior developers, as AI can potentially replace them. Two years ago, no one could have imagined AI reaching this level. I strongly believe that in a year, AI will be capable of replacing mid-level developers. In the near future, a large project may only require one senior developer and multiple AI assistants to be successfully completed.

Open Sourcing and Community Support

I have open-sourced this project on GitHub: https://github.com/hungxplorer/ez-ai-agent

If you find it useful, please give it a ⭐ so I can continue building more AI-powered projects. Thank you!