The field of edge computing is moving towards more flexible and adaptive architectures, with a focus on improving real-time scheduling, privacy, and scalability. Researchers are exploring novel approaches to operating system design, such as modularized and demand-driven frameworks, to better manage heterogeneous platforms and limited resources. Additionally, there is a growing emphasis on privacy-aware and decentralized inference orchestration, as well as dynamic partitioning and placement of foundation models for real-time edge AI. These advancements aim to address the challenges posed by the rapid evolution of edge computing and the increasing demand for intelligent and autonomous edge systems. Notable papers in this area include: IslandRun, which introduces a multi-objective orchestration system for distributed AI inference, TenonOS, which proposes a self-generating intelligent embedded operating system framework, and Joint Partitioning and Placement of Foundation Models, which presents a framework for runtime-resolved partitioning and placement of foundation models.