The field of program synthesis and evolutionary computation is moving towards increased modularity, reusability, and autonomy. Recent developments have focused on creating unifying frameworks and libraries that enable the straightforward reapplication of existing methods and modules. This has led to significant advances in the automatic generation of code, scientific equation discovery, and evolutionary coding. Notably, the integration of large language models with evolutionary computation algorithms has shown great promise. The use of vision language models and evolutionary optimization has improved the generation of computer-aided design objects, while the elevation of large language models to autonomous AI scientists has enhanced scientific equation discovery. The development of open-source evolutionary coding agents has also demonstrated state-of-the-art performance on complex computational problems. Noteworthy papers include: EvoCAD, which presents a method for generating CAD objects through their symbolic representations using vision language models and evolutionary optimization. SR-Scientist, which introduces a framework that elevates the large language model from a simple equation proposer to an autonomous AI scientist. CodeEvolve, which introduces an open-source evolutionary coding agent that unites large language models with genetic algorithms to solve complex computational problems.