Recent discussions among researchers highlight the evolving utility of large language models (LLMs) in various applications. N. Carlini, a researcher at DeepMind, noted that he has become at least 50% faster in writing code for both research and side projects due to LLMs. This sentiment is echoed by others, who emphasize that while LLMs are primarily effective in generating text and code, their eloquence in text generation may not be as strong. Additionally, there is a growing interest in understanding the practical applications of LLMs beyond coding, with inquiries about their use in areas such as chess and math problem-solving. The Simple-Bench benchmark has also been referenced, suggesting that while LLMs can mimic human reasoning, their strengths lie in their designed functions rather than true reasoning capabilities.