The field is witnessing a significant shift towards enhancing the efficiency and precision of algorithmic differentiation tools, with a particular focus on mixed-precision methods and the incorporation of complex numbers. This movement is driven by the need for faster and more accurate computations in various applications, including linear systems solvers and matrix-matrix multiplications. Furthermore, advancements in formal verification are being made, with the development of new features and techniques that support the implementation of floating-point operations and mutable objects. Noteworthy papers include: Towards a mixed-precision ADI method for Lyapunov equations, which demonstrates the potential of mixed-precision methods for certain applications. Adding complex numbers to expression template algorithmic differentiation tools, which highlights the benefits of integrating complex number operations into modern operator overloading AD tools.