The field of code intelligence and large language models is rapidly evolving, with a growing focus on improving the capabilities of these models in real-world scenarios. Recent developments have highlighted the importance of considering factors such as code sensitivity, code switching, and visual biases in the evaluation of large language models. One of the key areas of research is the development of more accurate and robust evaluation methods for large language models, particularly in the context of code evaluation. Studies have shown that current evaluation methods can be susceptible to biases and may not accurately reflect the true capabilities of these models. Another area of focus is the development of more advanced training datasets and methodologies, such as the use of counterfactual perturbations and incremental instruction fine-tuning. These approaches have been shown to improve the performance of large language models in a range of tasks, including code completion and feature-driven development. Notable papers in this area include StRuCom, which presents a novel dataset for Russian code documentation, and Fooling the LVLM Judges, which highlights the vulnerability of large vision-language models to visual biases. Additionally, SWE-Dev introduces a large-scale dataset for evaluating and training autonomous coding systems on real-world feature development tasks. Overall, the field of code intelligence and large language models is rapidly advancing, with a growing focus on developing more accurate, robust, and reliable models and evaluation methods.