During May 2025, Zhang Syi enhanced the ai-dynamo/nixl and LMCache/LMCache repositories by focusing on backend development and reliability. Zhang refactored CUDA device configuration in ai-dynamo/nixl, using PyTorch’s torch.set_default_device to streamline tensor allocation and improve device assignment efficiency. In LMCache/LMCache, Zhang addressed cross-platform compatibility by implementing OS-agnostic file system operations and centralized parsing of file URIs, reducing environment-specific errors. Additionally, Zhang improved test data reliability by enhancing error logging and fixing value mismatches, which increased test accuracy and reduced CI flakiness. The work demonstrated depth in Python development, debugging, and configuration management for maintainable codebases.