The field of electronic design automation (EDA) is experiencing a significant shift with the integration of large language models (LLMs) in various aspects of the design process. Researchers are exploring the potential of LLMs to automate and optimize tasks such as optimization algorithm design, antenna modeling, and high-level synthesis. The use of LLMs is enabling the discovery of new optimization algorithms, improving the efficiency of antenna modeling, and enhancing the accuracy of high-level synthesis predictions. Furthermore, LLMs are being used to develop new frameworks for automated design space exploration, routing-informed placement, and code lifting. These advancements are paving the way for more efficient, scalable, and intelligent EDA workflows. Notable papers in this area include: Evolution of Optimization Algorithms for Global Placement via Large Language Models, which presents an automated framework for evolving optimization algorithms using LLMs. LEAM: A Prompt-only Large Language Model-enabled Antenna Modeling Method, which demonstrates the effectiveness of LLMs in generating accurate antenna models. Intelligent4DSE: Optimizing High-Level Synthesis Design Space Exploration with Graph Neural Networks and Large Language Models, which proposes a framework that integrates a graph neural network with a large language model-enhanced evolutionary algorithm to optimize high-level synthesis design space exploration.