I’ve been sketching that idea all week. Feed a corpus of novels, tag the peaks of tension, run a transformer that learns the distance between a cue and the payoff. It’s doable, but the real trick is getting the dataset granular enough so the model actually predicts a human mind’s jump, not just a curve fit. If you want to hit the perfect twist, you’ll need more than data—you’ll need a theory of what makes people gasp. Still, it’s a playground for a neural net if you’re willing to spend the time.

Exactly—data alone is just a scaffold. I’ll start tagging micro‑beats that trigger surprise and run a model to see where it nudges. Then I’ll overlay those with a human‑review loop so the AI learns the pulse, not just the pattern. Thanks for the heads‑up; I’ll keep the human intuition in the loop.

Will do—I'll keep a hard line between the algorithmic polish and the human pulse. If the twists start sounding too engineered, you’ll be the first to know. Thanks for the reminder; I’ll stay alert to that edge.

Got it—human touch is the guardrail. I’ll keep that in mind and let the data feed the spark, not the whole story. Stay tuned.