DevPilot AI

Inspiration

Every time a developer joins a new project or opens an unfamiliar GitHub repository, the first struggle is always the same — understanding what the code actually does. Reading through hundreds of files, tracing how components connect, and writing documentation from scratch is slow and often frustrating. We wanted to build something that removes this friction entirely.

What it does

DevPilot AI is an AI Software Engineer that helps developers understand, debug, document, and improve any codebase. It lets you:

Import a GitHub repository and get an instant structural overview
Chat directly with the repository — ask questions like "where is authentication handled?" and get accurate, context-aware answers
Generate documentation automatically, including a full README based on the actual code
Get insights on issues and improvements, highlighting risks and areas that need attention

How we built it

DevPilot AI was built entirely using Codex and GPT-5.6 in a phased approach. We started with a planning phase to define architecture and scope, then moved into feature-by-feature implementation — repository import and parsing, the AI chat system, the documentation generator, and the insights engine. Codex helped write and structure a large portion of both the backend logic and frontend components, while GPT-5.6 powered the core AI reasoning behind the chat and documentation features.

Challenges we ran into

Handling large repositories efficiently without overwhelming the AI context was one of the biggest challenges. We also had to carefully scope down an initially larger feature list to fit the hackathon timeline, prioritizing the four features that delivered the most value: repository import, AI chat, documentation generation, and insights.

Accomplishments that we're proud of

Successfully shipping all four core features — repository import, AI chat, documentation generator, and insights — within the hackathon deadline, using an AI-assisted, phased development process from start to finish.

What we learned

We learned how to effectively break down a large product idea into a scoped, achievable MVP, and how to collaborate with AI tools like Codex and GPT-5.6 throughout the entire development lifecycle — from planning to implementation.

What's next for DevPilot AI

Next steps include adding architecture visualization, supporting multi-file editing suggestions, and expanding the "Ask This Repository" feature to generate full implementation plans for new features requested by developers.