I build software close to where it actually runs, Python for building AI evaluation framework, C/C++/Rust for systems, shell for everything in between, and deep Linux fluency underneath all of it.
Most of my recent work sits at the intersection of evaluation and reliability: how do you actually verify what a language model produces, rather than just trust it. That question runs through my thesis project, my reading list, and the opensource organization I'm building on the side.
Languages
Python, C, C++, Rust, Shell
Systems
Linux, CLI tooling, Bash automation
ML / AI
LLM evaluation, Prompting strategy, Execution-based verification
Foundations
Data structures and algorithms, Software engineering, Applied research writing
LLM code-generation evaluation framework built from my undergraduate thesis, comparing zero-shot and chain-of-thought prompting with execution-based verification. Working toward an arXiv preprint.
Early-stage technology opensource organization spanning AI systems, firmware, and open-source tooling, designed around safety as a first principle rather than an afterthought.