In machine learning, overfitting happens when a model memorizes the noise in the training data instead of learning the underlying pattern. It becomes brittle and fails on new data. I think a lot of us do this with reversals. We memorize a list of "upside down" meanings and force the card to fit that script. When I pull the Five of Pentacles reversed, do I really have to assume it means "recovery" or "finding shelter"? Or is it just the Five of Pentacles, pointing to resource scarcity, but the energy is turning inward?

My experience suggests that reversals act more like a debugging flag than a complete negation. It's a pointer exception. Something's broken in the context, not necessarily a flip to the exact opposite definition. If I see a reversal, I treat it as an attention mechanism. It highlights a card that needs more scrutiny. "Hey, look here, the energy is stuck or internalized." That feels way more robust than trying to recall a separate dictionary entry for every card.

Also, let's talk about entropy. From a physics standpoint, the angle at which a card flips during a riffle shuffle is mostly random variance. Unless you're using a specific method like a perfect riffle or a controlled wash, the orientation is largely stochastic. Treating it as a deterministic signal introduces unnecessary bias into your reading pipeline. Instead of hard-coding reversed meanings, I recommend treating orientation as a weighting factor. It adjusts the magnitude or direction of the upright card's core archetype, rather than swapping it for an entirely new variable. This approach keeps your interpretive model generalizable across different spreads and contexts. Has anyone else shifted away from rigid reversal dictionaries? I'd be interested in hearing how you handle orientation variance in your own practice.

(There's a deeper dive on this in our wiki: Reversed Tarot Cards: What They Actually Mean | Mystic Wiki.)