In March 2026, Kel Maaro developed a performance optimization feature for the RobotecAI/rai repository, focusing on accelerating the startup of perception services. By leveraging Python scripting, ROS2, and multithreading, Kel introduced a concurrent initialization pattern that parallelized the loading of detection and segmentation services. This approach reduced startup latency and improved the system’s readiness for perception workloads, enabling faster time-to-value for downstream tasks. The implementation included performance instrumentation to validate the concurrency gains. While the work addressed a single feature within a short timeframe, it demonstrated a thoughtful application of concurrency to solve a targeted performance bottleneck.