The post discusses an implementation of AlphaZero, a highly advanced AI developed by DeepMind for playing chess, within the MLX framework. AlphaZero utilizes deep reinforcement learning to master chess from scratch without human data, contrasting with traditional engines like Stockfish that rely on extensive databases and heuristics. It learns strategies and tactics autonomously through self-play, ultimately developing an intuitive understanding of the game that allows it to compete effectively. Users have commented on the performance comparison between AlphaZero and Stockfish, indicating an interest in how AlphaZero's approach stacks up against traditional chess engines, particularly in terms of creative play and adaptability. This discussion points towards a broader trend of AI systems gaining ground in complex strategy games, raising questions about the future of competitive chess strategies.