The conversation highlights contrasting experiences and expectations surrounding LLMs. Supporters emphasize their practical utility in various tasks—even in cases where they are not perfect—pointing out that they significantly boost productivity in coding, research, and creative endeavors. Critics, however, tend to focus on the models' shortcomings, particularly in providing reliable citations or factual correctness. Several points of view arise, suggesting that the disconnect in perception may stem from differing expectations and use cases. While some see LLMs as revolutionary tools, others criticize their limitations too harshly, often forgetting the rapid advancements and improvements being made in AI technology. A consensus emerges that LLMs thrive in specific applications but still face challenges in reliability, especially in academic or high-stakes contexts. This underscores the need for users to adapt their approach based on the nature of the tasks at hand, and to remain patient as the technology evolves.