The development effort required to implement and experiment with deep learning models has decreased enormously since the early days of neural network research. Tasks that once demanded months of low-level engineering in C++ and assembly code—such as building and improving SN, an early Lisp-based deep learning simulator created by Bottou and Le Cun in 1988—can now be accomplished in minutes using modern high-level frameworks. This extraordinary reduction in implementation overhead has democratized deep learning model development, making it accessible to a far broader community of researchers and practitioners.