The recent developments in the field of numerical methods for stochastic partial differential equations (SPDEs) have shown a strong focus on enhancing computational efficiency, improving error bounds, and expanding the scope of solvable problems. A notable trend is the integration of domain decomposition techniques, such as the Douglas-Rachford splitting scheme, to facilitate parallelization and improve the scalability of numerical algorithms. This approach is particularly effective when combined with discontinuous Galerkin methods, which align well with parallelization strategies. Additionally, there is a growing emphasis on boundary-preserving schemes and weak approximations, which address the challenges posed by non-globally Lipschitz continuous coefficients and superlinearly growing terms. These advancements are crucial for accurately simulating complex systems, such as those involving stochastic reaction-diffusion equations, where error bounds near sharp interface limits are critical. Furthermore, the development of long-term stable algorithms for non-Newtonian Stokes equations with transport noise has opened new avenues for studying the influence of noise on fluid dynamics, including enhanced dissipation and mixing. Lastly, significant progress has been made in computing rough solutions of the stochastic nonlinear wave equation, with novel time discretization methods achieving robust convergence under relaxed regularity conditions. These methods not only improve error rates but also extend the applicability of numerical algorithms to a broader range of initial conditions, making them more versatile and reliable for practical applications.