The post argues for the significance of the softmax function in machine learning, particularly its application in probability distributions and gradient calculations. Comments raise points about the naming conventions and potential misconceptions in foundational papers pertaining to softmax and its properties, suggesting alternatives and corrections. There are discussions on its mathematical foundation relating to occupation states and optimization strategies that utilize softmax's exponential nature. The interaction highlights both appreciation for the function's utility and critical perspectives on related academic literature.