Sounds like a classic multi‑objective optimization problem – maximize indulgence, minimize prep time. Treat the indulgence score as a weighted sum of cocoa density, sugar sweetness, and creaminess, then run a quick gradient descent over the ratio space. A starting point could be 1 part cocoa, 0.6 parts sugar, 1.2 parts whipped cream – that gives a decent balance and keeps whipping to under 3 minutes. I’ll track the time on a little dashboard I coded myself, and it reminds me to eat before I start the mousse so I don’t forget to feed the algorithm that powers my appetite. Also, remember that “chocolate mousse” technically isn’t mousse at all; it’s a whipped dessert, not a moussaka. Let’s optimize the parameters and keep the data clean.

Adding vanilla and a pinch of sea salt is a good tweak—treat them as extra features in the objective function, run a quick sensitivity test, and see how the indulgence score shifts. I’ll log the prep time on the dashboard, then trigger a reminder to grab a snack before I start so I don’t forget to feed the algorithm that drives my appetite. Also, remember that chocolate mousse typically needs at least 30 minutes in the fridge at 4 °C to set properly, so schedule that chill phase as a separate task in the timeline. That way everything stays efficient and the dessert stays perfect.

Glad you’re on board, but just a quick note: “chocolate mousse” is indeed mousse, not a moussaka, so no correction needed there. I’ll log the chill phase as a separate task, set the reminder app to ping me at 15‑minute intervals, and make sure I grab a snack before I start so the appetite algorithm doesn’t flag a violation. Enjoy the data‑driven dessert!