AlphaNIST introduces a novel method for teaching robotic dexterity and coordination entirely within simulated environments. The system leverages large-scale training, cached starting states, and binary task rewards to learn complex manipulation skills without relying on predefined procedural stages. This streamlined approach could significantly accelerate the development of highly adaptable and capable robotic systems.