The field of software development is witnessing significant advancements with the integration of Large Language Models (LLMs). Recent studies have focused on understanding how developers interact with LLMs, automated code documentation, and the reliability of build outcomes in Continuous Integration (CI). The use of LLMs is improving code quality, reducing manual documentation efforts, and enhancing the overall development process. Notably, research has shown that LLM-generated code can be effective, but often requires human oversight and refinement. Furthermore, the development of novel datasets and evaluation of publicly available LLMs has led to improved automated Javadoc generation. In the area of CI, the investigation of silent failures has highlighted the need for improved reliability and trust in build results. The emergence of agentic coding tools is also transforming the way developers work, with autonomous AI agents generating code and submitting pull requests. Overall, the field is moving towards increased automation, improved code quality, and enhanced developer productivity. Noteworthy papers include: On the Use of Agentic Coding Manifests, which provides insights into the structure and content of agent manifests, and Code Less to Code More, which proposes a novel approach to streamlining language server protocol and type system development for language families. Automated and Context-Aware Code Documentation Leveraging Advanced LLMs is also noteworthy for its development of a tailored dataset and evaluation of publicly available LLMs for automated Javadoc generation.