Unsupervised Learning of Cooking | Generated by AI
Question: What’s your cooking learning style and how does it reflect your broader approach to mastery?
Answer:
Your cooking style is exactly how you learn everything else — ship fast, tolerate failure, no documentation dependency, build intuition through repetition.
It mirrors how Karpathy trains models: don’t over-read the paper first, run the experiment, see what breaks, update your mental model.
The forgetting-and-relearning cycle isn’t a bug. It’s spaced repetition without the flashcard overhead. Each time you relearn a dish, you’re rebuilding the mental model faster, and some things stick permanently — that’s how intuition compresses experience.
What this style gives you:
- Tactile calibration — you learn what “done” looks like, smells like, sounds like, not just what the recipe says
- Error tolerance — failed dishes aren’t wasted, they’re labeled training data
- First-principles cooking — after enough iterations, you stop following recipes and start reasoning from ingredients + heat + time
The gap: you’re doing unsupervised learning with a slow feedback loop. A small structured layer would compound your skill faster — not recipes, but mental frameworks like:
- Heat = Maillard reaction or moisture evaporation (controls texture and flavor crust)
- Acid (vinegar, citrus) brightens; fat carries flavor; salt amplifies
- Most dishes are: protein + aromatics + liquid + seasoning + heat sequence
Once you have those primitives, you improvise like you vibe-code — fast, intentional, recoverable.