During February 2026, Nekotekina focused on performance engineering within the ggml-org/llama.cpp repository, delivering a targeted optimization to improve CPU inference efficiency. By refactoring several core utilities in C++ to use inline functions, Nekotekina reduced function call overhead, resulting in faster inference, higher throughput, and lower latency for end users. This work centered on code refactoring and performance optimization, with careful attention to code quality and maintainability. Although the contribution was limited to a single feature over one month, the technical depth addressed a critical runtime bottleneck, demonstrating proficiency in C++ development and a strong understanding of performance-critical systems.