CS student, building things at the intersection of software, AI, and finance.

I like problems where the interesting part isn't writing the code — it's figuring out what the code should actually do. Lately that's meant reading a lot of other people's codebases, and learning that most bugs hide in the cases nobody wrote a test for.

• bachelier — options pricing and risk in C++20. Black-Scholes-Merton with Greeks, an implied-volatility solver, and a binomial lattice with American exercise. No dependencies; 346 tests; CI across g++ and clang.
• Open source contributions — merged fixes into opam-repository, with work in flight on biome and icalendar
• Quantitative finance — working through awesome-quant and building intuition for how the models actually behave

I'm less interested in collecting languages than in getting good at a few. Right now:

• C++ — memory, performance, numerical methods, and why the abstractions leak
• Python — the default tool for anything data- or finance-shaped
• Applied AI — using LLMs as real engineering tools, not demos
• Reading unfamiliar codebases fast — underrated, and the thing that makes open source contribution possible

I'm early in my journey and actively looking for projects to contribute to, especially anything touching AI tooling, developer experience, or quantitative finance. If you maintain something and have a good first issue going unclaimed, I'd genuinely like to hear about it.