Ryan Hayashi is a Stanford junior studying Symbolic Systems and Computer Science with seven years of software engineering experience across front-end, back-end, and full-stack roles. He has shipped production features at startups and Fortune-scale firms, including building a C++20 functional query engine and transpiler at Bloomberg and contributing cross-platform window management and UX fixes to the popular Wails open-source project. Comfortable with systems, graphics, XR, and AI, he’s implemented end-to-end services, optimized performance-critical algorithms (e.g., triangulation code for generative art), and tuned LLM workflows from medical datasets. Based in Palo Alto, he pairs a product-oriented engineering mindset with hands-on low-level optimization and a knack for resolving long-standing bugs and cross-platform inconsistencies.
7 years of coding experience
2 years of employment as a software developer
International Baccalaureate Diploma, IGCSE, International Baccalaureate Diploma, IGCSE at UWC South East Asia
Bachelor of Science - BS, Symbolic Systems, Bachelor of Science - BS, Symbolic Systems at Stanford University
Contributions:7 commits, 7 PRs, 24 comments in 4 months
Contributions summary:Ryan primarily contributed to the Wails project by implementing features related to window management, specifically focusing on minimum/maximum window sizes, focus behavior, and fullscreen controls across different operating systems. They addressed several bugs and made adjustments to the build processes. The user also made several contributions to improve the user experience and corrected the Mac save dialog.
Contributions summary:Ryan primarily updated module paths and refactored fitness-related functions within the `triangula` repository. These changes include modifications to the `fitness`, `triangulation`, and `geom` packages, suggesting a focus on improving the core algorithm and data structures for generating triangulated art. The contributions involve changes to the `TrianglesImageEvaluator` and other related code which suggests a direct impact on the algorithm's performance and functionality.
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