The field of software engineering is witnessing significant advancements with the integration of Large Language Models (LLMs). A key direction in this area is the exploration of ensemble methods, which combine the strengths of multiple LLMs to improve performance and reliability. Research has shown that ensemble approaches can lead to substantial gains in performance, with some studies demonstrating the potential for up to 83% improvement over single-model systems. However, achieving this potential requires careful consideration of selection strategies, as consensus-based approaches can fall into a 'popularity trap' and amplify incorrect outputs. Another important aspect of LLMs in software engineering is calibration, which aims to align model confidence with acceptability measures. While calibration can improve reliability, its effectiveness is highly dependent on the volume of user interaction data and the choice of calibration method. The reproducibility of LLM-centric studies is also a growing concern, with many studies failing to provide sufficient research artifacts or robust study designs. This highlights the need for stricter evaluations and more robust designs to ensure the reproducible value of future publications. Noteworthy papers in this area include: Wisdom and Delusion of LLM Ensembles for Code Generation and Repair, which demonstrates the potential of ensemble methods and the importance of diversity-based selection strategies. Does In-IDE Calibration of Large Language Models work at Scale, which investigates the feasibility of calibration in an in-IDE context and highlights the importance of personalized calibration and effective communication of reliability signals.