Veteran Windows developer Dave Plummer is using a 47-year-old PDP-11 system with a 6 MHz CPU and 64KB of RAM to train a neural network called ‘Attention 11,’ developed in PDP-11 assembly language. The model is tasked with reversing a sequence of eight digits, requiring it to understand structural rules similar to those used in contemporary large language models like ChatGPT. Plummer emphasizes the importance of optimizing for the system's constraints, stating that constraints drive creative engineering. The model operates with 1,216 parameters and uses fixed-point math with 8-bit precision. After approximately 350 training steps, the model achieved 100% accuracy in the number-reversing task in about 3.5 minutes. Plummer argues that modern AI operates on the same mechanical principles as this vintage machine, just at a much larger scale, and suggests that companies focusing on efficiency and optimization may gain an advantage in the AI landscape.