The field of data synthesis and foundation models is witnessing significant advancements, with a focus on developing scalable and reliable methods for generating high-quality datasets. Recent developments have led to the creation of frameworks that can synthesize diverse and comprehensive datasets from scratch, without human intervention, and have improved the performance of large language models. The notion of scaling laws has also been explored, revealing predictable relationships between dataset size and model performance. Furthermore, research has demonstrated the feasibility of developing multi-task foundation models that can be applied to various operational scenarios, including power systems. Noteworthy papers include: TreeSynth, which presents a tree-guided subspace-based data synthesis framework that surpasses human-designed datasets and state-of-the-art baselines. Scaling Laws of Synthetic Data for Language Models, which introduces a scalable framework for generating synthetic datasets that exhibit predictable scalability comparable to raw pre-training data.