The field of systems programming and simulation is witnessing a significant shift towards the integration of large language models (LLMs) to enhance decision-making, improve memory safety, and increase efficiency. Researchers are exploring the potential of LLMs to automate tasks such as code translation, simulation, and data generation, with a focus on ensuring the security and reliability of the resulting systems. Notably, LLM-powered agents are being developed to facilitate collaboration between humans and machines, enabling more effective interaction with complex systems and improved forecasting capabilities. Overall, the trend is towards leveraging LLMs to create more robust, flexible, and user-friendly systems. Noteworthy papers include: SafeTrans, which presents a framework for LLM-assisted transpilation from C to Rust, mitigating memory-related vulnerabilities. AgentSGEN, which proposes a multi-agent framework for generating synthetic data tailored to safety-critical scenarios, addressing the shortage of realistic training data.