Automatic differentiation (autodiff) has become the backbone for a new wave of optimization-driven domains such as computer graphics and machine learning over the past decade. However, existing autodiff systems face limitations, either lacking support for in-browser development or failing to harness more recent, compiler-based approaches to achieve both expressiveness and size-preserving differentiation. This work introduces Rose, a portable, extensible autodiff language that runs on the web. Through Rose, we aim to increase accessibility to autodiff algorithms and empower end-user programming in optimization-driven domains. We plan to evaluate Rose by replacing the autodiff engines of real-world, client-side optimization systems and assess the improvements on the computation power, expressiveness, and efficiency of such systems.