The field of software development is witnessing significant advancements with the integration of Large Language Models (LLMs), artificial intelligence (AI), and modern programming languages. One notable trend is the adoption of modern C++ features, such as C++20 modules, which aim to address the limitations of traditional header-based interfaces. Researchers are also exploring the development of more efficient and effective tools for Python development, including static analysis techniques for detecting type errors and automated unit test generation. The use of Rust-compatible bindings toolchains is also being explored as a means of optimizing critical sections in Python. Furthermore, researchers are working on improving the robustness of current Python refactoring tools to ensure the correctness of automated code transformations. Noteworthy papers in this area include a study on converting large mathematical software packages to C++20 modules, which reports a reduction in compile time for the converted library itself, and an evaluation of Rust-compatible bindings toolchains for Python, which achieves state-of-the-art performance with no concern for API compatibility. The field of model-based systems engineering is also witnessing significant advancements with the integration of AI and machine learning techniques. Researchers are exploring innovative approaches to improve the development process, including the use of multi-agent systems, knowledge-guided frameworks, and automated requirements development. The application of DevOps practices in embedded systems and firmware development is also emerging as a key area of research. The development of large language models is driving new opportunities for AI-driven development, with a focus on software engineering approaches and knowledge protocol engineering. Noteworthy papers in this area include SysTemp, which presents a multi-agent system for template-based generation of SysML v2 models, and Knowledge Protocol Engineering, which introduces a new paradigm for AI in domain-specific knowledge work. Additionally, the field of hardware design verification and code synthesis is witnessing a significant shift towards leveraging LLMs and AI to improve efficiency and accuracy. Researchers are exploring innovative approaches to integrate LLMs with domain-specific knowledge and expertise to automate tasks such as SystemVerilog Assertions (SVAs) generation and Standard Verification Rule Format (SVRF) code synthesis. Overall, these developments are expected to have a significant impact on the field, enabling developers to create more efficient, reliable, and maintainable software systems.