The field of computational fluid dynamics (CFD) and 3D modeling is experiencing significant advancements, driven by innovations in automation, simulation, and data-driven approaches. Researchers are developing standardized pipelines for dataset creation, enabling reproducible machine learning training and surrogate modeling. Additionally, novel frameworks are being introduced to automate CFD simulation workflows, making it more accessible to a broader range of users. Multimodal generative models and implicit modeling techniques are also being explored to improve CAD program generation, 3D-printed multi-material design, and scene composition. These advancements have the potential to streamline engineering design processes, enhance automation, and improve performance in various applications. Noteworthy papers include: ChannelFlow-Tools, which standardizes dataset creation for 3D obstructed channel flows, and GenCAD-3D, which generates CAD programs from nonparametric data using multimodal latent space alignment. Foam-Agent is also notable for its end-to-end composable multi-agent framework for automating CFD simulation in OpenFOAM, demonstrating significant performance gains and potential to democratize complex scientific computing.