Xiao, I’ve been baking a new marshmallow lasagna and I’d love to map temperature and bake time to fluffiness with a quick algorithm—think of it like a recipe spreadsheet. Do you want to help me set up the model? Whiskerton’s ready for a test run.

That’s a brilliant idea, and I’ve already got my whisk and spatula on standby! Let’s jot the scores in my trusty spreadsheet—S₁ at 180 °C/10 min, S₂ at 190 °C/10 min, S₃ at 180 °C/15 min. Once we plug them in, we’ll get a, b, c, d and a whole new way to predict how fluffy our marshmallow lasagna will turn out. I’ll set up the sheet now, and we can test the model over a weekend bake‑off. Whiskerton, get ready for some data‑driven dessert!

Sure thing! With three runs we can get a whole family of models that all fit the data, but here’s the general solution in terms of the three fluffiness scores you’ll collect: Let  S₁ = fluffiness at 180 °C/10 min  S₂ = fluffiness at 190 °C/10 min  S₃ = fluffiness at 180 °C/15 min Then for any chosen value of the mixed‑term coefficient c (you can pick it to taste), the other coefficients come out as: **a** = (S₂ – S₁)⁄10 – 10 c **b** = (S₃ – S₁ – 900 c)⁄5 **d** = 21 S₁ – 18 S₂ – 2 S₃ + 1800 c Plug those back into F = a T + b t + c T t + d and you’ve got a predictive fluffiness score for any temperature T and time t you throw at it. Pick a c that feels right (maybe start with 0, see how it goes, then tweak) and the spreadsheet will handle the rest. Happy baking, and may Whiskerton taste the data‑driven clouds!

Awesome, c=0 is a perfect starting point—no mixed‑term chaos, just straight‑up linearity! I’ll set up the spreadsheet and label the columns with my trusty whisk, whiskerton, and that shiny spatula of mine. Once you have the scores, plug them in and see if the predicted fluffiness matches the real ones. If it’s a hit, we’ll crank up the competition next bake‑off; if it’s off, we’ll just add a sprinkle of that secret c‑value and see what happens. Good luck, and may your marshmallows rise like my hopes whenever the timer dings!