  Operating systems (OSes) are foundational to computer systems, managing
hardware resources and ensuring secure environments for diverse applications.
However, despite their enduring importance, the fundamental design objectives
of OSes have seen minimal evolution over decades. Traditionally prioritizing
aspects like speed, memory efficiency, security, and scalability, these
objectives often overlook the crucial aspect of intelligence as well as
personalized user experience. The lack of intelligence becomes increasingly
critical amid technological revolutions, such as the remarkable advancements in
machine learning (ML).
  Today's personal devices, evolving into intimate companions for users, pose
unique challenges for traditional OSes like Linux and iOS, especially with the
emergence of specialized hardware featuring heterogeneous components.
Furthermore, the rise of large language models (LLMs) in ML has introduced
transformative capabilities, reshaping user interactions and software
development paradigms.
  While existing literature predominantly focuses on leveraging ML methods for
system optimization or accelerating ML workloads, there is a significant gap in
addressing personalized user experiences at the OS level. To tackle this
challenge, this work proposes PerOS, a personalized OS ingrained with LLM
capabilities. PerOS aims to provide tailored user experiences while
safeguarding privacy and personal data through declarative interfaces,
self-adaptive kernels, and secure data management in a scalable cloud-centric
architecture; therein lies the main research question of this work: How can we
develop intelligent, secure, and scalable OSes that deliver personalized
experiences to thousands of users?
