During August 2025, Migor focused on enhancing the robustness of the attention mechanism in the keras-team/keras repository. He addressed stability issues in deep learning workflows by improving error handling within the dot_product_attention module, ensuring that exceptions were logged and did not halt execution. By implementing hardware-aware fallback paths, Migor enabled seamless transitions between optimized and alternative implementations on TPUs and GPUs, maintaining consistent model training and inference. His work, primarily in Python, emphasized backend development and performance optimization, resulting in improved observability and reduced downtime when hardware-specific optimizations failed, reflecting a deep understanding of machine learning infrastructure challenges.