Recent advancements in edge AI are focusing on enhancing efficiency and adaptability across various applications, particularly in resource-constrained environments. Techniques like model stitching for multi-DNN inference are enabling systems to dynamically recombine model subgraphs, significantly reducing service level objective violations and improving throughput. On-board processing solutions, such as those utilizing compact segmentation networks for satellite imagery, are addressing the challenges of data transmission by generating actionable insights directly in orbit, thus minimizing energy costs. Furthermore, frameworks that facilitate on-device training of deep learning models are emerging, allowing for real-time adaptation while preserving privacy. Innovations in retrieval-augmented generation are also being tailored for edge devices, enhancing personalization in noisy environments. Collectively, these developments are poised to solve commercial challenges related to latency, energy efficiency, and data privacy, paving the way for more robust and responsive edge AI systems across industries.