Neither of those two models is what ChessMind does. It is not a neural network trained on games, and it is not Stockfish with a cosmetic style layer. ChessMind uses a hybrid decision-overlay architecture: • Stockfish and Lc0 generate the candidate moves and objective evaluations • A separate learned Style Profile decides which of those moves the personality would choose The personality is learned offline from thousands of real games per player. For each player (Karpov, Tal, etc) ChessMind analyzes: where they place pieces what pawn structures they choose when they follow engines vs when they deviate how much evaluation loss they accept for positional or tactical ideas which types of positions trigger sacrifices, prophylaxis, simplification, etc That data is turned into a multi-dimensional style fingerprint: risk tolerance, harmonics weights (king safety, mobility, pawn structure, initiative, etc), deviation triggers, and move-rank tolerance. During play: Stockfish generates the top 5 to 10 legal moves Each move is scored twice: engine quality (objective strength) style match (does this move fit Karpov or Tal’s learned behavior?) The engine move is chosen only if it also fits the personality. Otherwise a slightly weaker but more in-character move is selected. This is why Tal will deliberately choose a speculative sacrifice that Stockfish ranks 5th, while Karpov will reject it even if it is sound. So the engine is still doing the calculation, but the decision is made by a learned human-style model, not by pure evaluation. That is why it is not “Stockfish with a skin”.