The field of numerical methods for elliptic problems is rapidly evolving, with a focus on developing innovative and efficient methods for solving complex problems. Recent developments have centered around the creation of new numerical methods, such as kernel-free boundary integral methods and immersed penalized boundary methods, which have shown promise in solving elliptic interface problems and boundary value problems on curved domains. Additionally, there has been a growing interest in using machine learning techniques to improve the accuracy and efficiency of numerical methods. Noteworthy papers in this area include: A kernel-free boundary integral method for elliptic interface problems on surfaces, which presents a generalized boundary integral method for elliptic equations on surfaces. Walk-on-Interfaces: A Monte Carlo Estimator for an Elliptic Interface Problem, which introduces a grid-free Monte Carlo estimator for a class of Neumann elliptic interface problems with nonhomogeneous flux jump conditions. Energy minimisation using overlapping tensor-product free-knot B-splines, which studies a nonlinear approximation scheme based on overlapping tensor-product free-knot B-spline patches for solving PDEs with localized features.