AI App Builder

Turn natural-language prompts into web apps with AI-powered generation, live previews, iterative editing, and flexible NVIDIA and Nebius model routing.

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Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

Inspiration

We wanted to make web application development easier and more accessible. Building an application from scratch can take time, especially when developers need to write code, manage multiple files, and repeatedly make changes. This inspired us to build AI App Builder, a tool that turns natural-language prompts into web applications.

What We Built

AI App Builder allows users to generate application code from prompts, preview the generated application, and request changes using natural language. It organizes code into separate files to make projects easier to understand and maintain.

The platform also includes features such as undo and redo, application export, and agent-assisted repair for certain code errors. Our goal is to bring these capabilities together in one development workflow.

How We Built It

We built the application using React and Next.js, with a model-routing system that connects the app to configured AI providers. The project integrates NVIDIA models and Nebius Token Factory, allowing users to work with different AI models through the same interface.

We also developed a preview environment so users can inspect generated applications and refine them through iterative editing.

Challenges We Faced

One of our main challenges was integrating different AI model providers while keeping the application workflow consistent. Different models can respond differently to the same prompt, and generated code may require additional corrections.

We also worked on organizing generated code across multiple files, handling editing operations, and improving the reliability of the generation and repair workflow.

What We Learned

Through this project, we learned more about AI model integration, API routing, application generation, code organization, and error handling. We also learned that different models have different strengths and limitations, so choosing a model for a task and evaluating its output are important parts of building reliable AI-powered applications.

What's Next

We aim to continue improving generation quality, model selection, code repair, and the overall developer experience. Our long-term goal is to make AI App Builder a useful starting point for turning ideas into functional web application prototypes.

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