The recent advancements in the field of large language models (LLMs) have shown significant promise across various domains, particularly in skill extraction, layout planning, plan generation, research ideation, and game development. The integration of LLMs into specialized tasks has demonstrated superior performance over traditional methods, often surpassing state-of-the-art techniques. Notably, the use of LLMs for skill extraction has seen improvements in precision and quality through fine-tuning, while customized LLMs for text-to-layout planning have outperformed existing baselines in graphical design tasks. Additionally, the incorporation of process mining techniques into LLM plan generation has enhanced flexibility and interpretability, addressing previous limitations in sequential execution and skill retrieval. In the realm of research ideation, LLM-based agents have shown remarkable efficiency in generating novel ideas, mirroring human research processes and offering a budget-friendly solution. Lastly, the development of instruction-driven game engines has democratized game creation, enabling complex game states to be predicted with high precision through progressive curriculum training. These developments collectively indicate a shift towards more intelligent, adaptable, and user-friendly applications of LLMs across diverse fields.