Over the past few weeks, I’ve been contributing to BasedHardware/omi, the open-source AI wearable project. As of this week, all three of my pull requests have been officially approved and merged into main by core maintainers and Omi's founder Nik Shevchenko (@kodjima33).
Here is a quick recap of the journey, the recipes built, and 3 key engineering lessons learned along the way.
The goal was to enable Omi users to seamlessly export and structure their conversational data without needing complex setups or external dependencies.
PR #13701: Conversations to Markdown
PR #14458: Memories Export with Categorization
PR #13960: Action Items to Markdown with Full Unit Tests
unittest (all 9 passed in 0.05s).origin/main to eliminate redundant commits.@kodjima33 and was merged directly into the core repo.
In open-source CLI tools, relying on third-party libraries introduces dependency bloat and breaking changes across user environments. Writing scripts purely with Python's standard library (urllib.request, json, datetime, argparse) guarantees that anyone can run the recipe out of the box with zero setup pain.
When a maintainer asks for tests or calls a branch dirty, don't get discouraged — treat it as free mentorship. Adding automated tests not only got the PR merged, but it also made the code unbreakable against future regressions.
Interactive rebasing and keeping commits atomic is a superpower. A clean, single-commit PR with passing CI makes a maintainer's job 10x easier to review and click "Merge".
I’m currently looking into extending these recipes to support direct third-party REST API payloads (like Todoist tasks and Notion blocks).
Contributing to active AI hardware/software projects is one of the fastest ways to level up real-world software engineering skills.
Have you contributed to open-source recently? What was your biggest takeaway from working with repository maintainers? Let's discuss below! 👇