I published my first repository at 15: a quality-of-life mod for a tactical shooter. The code was messy, but the pull requests kept coming. That feedback loop taught me how public work accelerates your learning curve.

Principles That Stuck

• ▹Document before you ship — README-driven development scales empathy
• ▹Automate every review gate you can — linters are teammates, not chores
• ▹Treat contributors like co-owners — roadmap transparency creates momentum

Those habits travelled with me into machine learning. Today my projects span C++, Python, and TypeScript, but the core loop is identical: ship publicly, invite feedback, iterate with humility.

What I'm Optimising For Now

Open source is still my fastest way to learn. I'm focused on workflow automation for ML Ops teams — packaging repeatable deployment pipelines, annotating datasets collaboratively, and instrumenting evaluation harnesses from day zero.