The field of quasi-Monte Carlo methods and kernel-based approximation is rapidly advancing, with a focus on improving the accuracy and efficiency of these methods. Recent developments have led to the discovery of new techniques for achieving faster convergence rates and more accurate approximations. One of the key areas of research is the use of digital nets and quasi-Monte Carlo rules for approximating nonperiodic functions, where new error bounds and convergence rates have been established. Additionally, kernel-based approximation methods have been improved through the characterization of superconvergence for projections in general Hilbert spaces. Noteworthy papers include: the work on tent transformed order 2 nets, which achieves a quadratic decay rate for quasi-Monte Carlo quadrature of nonperiodic functions. The paper on general superconvergence for kernel-based approximation, which extends existing results and provides a broader framework for understanding and exploiting superconvergence.