The field of large language models (LLMs) is rapidly evolving, with a growing focus on applying these models to code generation and analysis tasks. Recent research has highlighted the potential of LLMs to automate various tasks in software development, including code generation, documentation, and unit testing. However, these models have also been shown to exhibit biases towards certain programming languages and libraries, which can limit their effectiveness in diverse programming contexts. To address these challenges, researchers are exploring new approaches to improve the adaptability of LLMs and develop mechanisms for mitigating programming language and library bias. Notably, some studies have demonstrated the effectiveness of LLMs in enhancing compiler-based code vectorization and automating code development tasks for scientific applications. Overall, the field is moving towards developing more robust and versatile LLMs that can be applied to a wide range of programming tasks. Noteworthy papers include: LLMs Love Python, which investigated LLM preferences for programming languages and libraries. VecTrans, which presented a novel framework for leveraging LLMs to enhance compiler-based code vectorization.