laude-institute/terminal-bench
Oct 2025 – Oct 2025
1 Month active
Languages Used
CPythonShell
Technical Skills
BenchmarkingC programmingNumPyPerformance optimizationPortfolio optimizationPython extension development
Yanhao Li
PROFILE
Yanhao Li
Developed a performance-focused portfolio optimization feature for the laude-institute/terminal-bench repository, targeting large-scale financial analysis. The work involved designing a C extension to accelerate optimization calculations, building a Python baseline for reference, and implementing a wrapper to ensure correctness across language boundaries. By integrating C programming with Python and leveraging NumPy for benchmarking, the solution delivered measurable speedups and improved throughput for portfolio analysis workflows. Emphasis was placed on cross-language integration, validation, and reliability, resulting in an end-to-end pipeline that enables faster decision-making in risk and return scenarios. The project demonstrated depth in performance optimization and extension development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
827
Activity Months1
Your Network
61 peopleWork History
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 (2025-10) focused on delivering a high-impact performance enhancement for laude-institute/terminal-bench. Implemented a C extension to accelerate portfolio optimization, built a Python baseline, a high-performance C implementation, and a wrapper to ensure correctness across boundaries. The work targets large portfolios and yields significant speedups, enabling faster decision-making in portfolio analysis and improved throughput for optimization tasks. The change set emphasizes reliability, cross-language integration, and measurable performance gains.
1 Commits • 1 Features
Oct 1, 2025October 2025 (2025-10) focused on delivering a high-impact performance enhancement for laude-institute/terminal-bench. Implemented a C extension to accelerate portfolio optimization, built a Python baseline, a high-performance C implementation, and a wrapper to ensure correctness across boundaries. The work targets large portfolios and yields significant speedups, enabling faster decision-making in portfolio analysis and improved throughput for optimization tasks. The change set emphasizes reliability, cross-language integration, and measurable performance gains.
October 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture80.0%
Performance100.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
CPythonShell
Technical Skills
BenchmarkingC programmingNumPyPerformance optimizationPortfolio optimizationPython extension development
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