The field of software development is witnessing a significant shift with the integration of large language models (LLMs). Recent research has focused on leveraging LLMs to enhance various aspects of software development, including code generation, type annotation, and proof development. The use of LLMs has shown promising results in improving the efficiency and accuracy of these tasks. Notably, the development of proactive runtime enforcement frameworks and deterministic LLM workflows has addressed significant safety risks and non-determinism limitations associated with LLMs. Furthermore, the application of LLMs in object-oriented programming and generative AI has opened up new avenues for exploration. While challenges persist, the advancements in LLM-driven software development have the potential to revolutionize the field. Noteworthy papers in this area include: A Survey on Code Generation with LLM-based Agents, which presents a systematic survey of the field of LLM-based code generation agents. Pro2Guard: Proactive Runtime Enforcement of LLM Agent Safety via Probabilistic Model Checking, which proposes a proactive runtime enforcement framework grounded in probabilistic reachability analysis.