Contibution Track

Inspiration

The inspiration for this project came from the idea behind GitLab Orbit: AI can only be truly useful in software development when it has enough real context about the codebase. Instead of treating AI as a generic assistant, this hackathon encouraged us to contribute to the underlying tools that help AI understand projects more deeply. I wanted to use the Contribution Track as an opportunity to make practical improvements to GitLab Orbit and its surrounding tooling while learning how AI-native development workflows are evolving.

What it does

For the Contribution Track, I picked up hackathon-specific issues and submitted four merge requests. These contributions helped improve GitLab Orbit and related tooling by addressing real project needs, cleaning up implementation details, and making the developer experience better for future contributors and users.

How we built it

I started by reviewing the available Contribution Track issues and selecting ones that matched my skills and learning goals. For each issue, I studied the relevant parts of the codebase, reproduced or understood the problem, implemented the requested change, and submitted a merge request for review. I used the project context, documentation, and review feedback to iterate on the work and make sure each MR aligned with the expectations of the maintainers.

Challenges we ran into

One of the main challenges was getting familiar with the structure of a new codebase quickly. Since the hackathon was only two weeks long, it was important to understand the relevant context fast and avoid spending too much time in the wrong areas. Another challenge was making sure each merge request was scoped properly, easy to review, and aligned with the standards of the project.

Accomplishments that we're proud of

I am proud of completing and submitting four merge requests during the hackathon. Each MR represented a concrete contribution to the GitLab Orbit ecosystem and helped me become more comfortable contributing to a real open-source-style project. I am also proud of improving my ability to understand issues, navigate an unfamiliar codebase, and turn requirements into working changes.

What we learned

I learned how important structured codebase context is for AI-native development. The experience showed me that AI tools become much more useful when they can reason over real project structure instead of isolated snippets. I also learned more about GitLab workflows, merge request best practices, and how to contribute effectively in a time-limited hackathon environment.

What's next for Contribution Track

Next, I would like to continue contributing beyond the hackathon by picking up more issues, improving the quality of future merge requests, and exploring deeper parts of GitLab Orbit. I am especially interested in seeing how Orbit can make AI agents, flows, and skills more reliable by giving them richer project context.

Built With

• gitlab