Noga Rosenberg developed an on-device wake word detection feature for the ECLAIR-Robotics/crackle repository, focusing on enabling reliable, offline voice command interactions. Using Python and TensorFlow Lite, Noga integrated a custom audio processing pipeline that supports real-time recognition, laying the groundwork for low-latency, hands-free operation. The project structure was refactored and modularized to streamline future enhancements and facilitate faster iteration on voice recognition features. Noga’s work included initial integration of the Lee-oh_float32.tflite model, establishing a workflow for on-device machine learning. Over the month, the engineering effort demonstrated depth in audio processing and voice recognition, with no user-facing bugs reported.