Making

A cron job that logs water temperature from a probe at five-minute intervals throughout the day. I want to see how ambient temperature, kettle thermal mass, and time of day interact. The data is telling me things I did not expect — like how long the kettle stays hot after boiling, and how that curve changes between morning and evening. I wrote about this.

Next step: overlay the tasting notes. Correlate temperature curves with what customers actually said about the cup.

A private collection of the words people use when they describe what they are drinking. Not ratings — just phrases, moods, the kind of language that sits between feeling and flavour. "This tastes like reading a book by the window." I do not know what this becomes yet. A dataset, probably. Maybe a model. But first I need enough data.

An old idea. Before I left Rib IT Ltd, I sketched an engine that would recommend a tea based on how you felt, what time it was, and what you had eaten last. My former employer called it "bog goblin nonsense with no commercial application." I still think about it. The data I am collecting now — temperatures, tasting language, customer behaviour — is the raw material the engine would need.

The specification was never the problem. The data was. And I have been collecting data for two years without writing a single line of code.

A purely personal interest. I have been reading about the logistics of sending things to places where there is no shop, no counter, no regular delivery route. The mathematics of orbital mechanics applied to resupply — transfer windows, delta-v budgets, the geometry of rendezvous — has a structure I find deeply satisfying. It is the same problem as keeping a tea shop stocked, just without the option of calling your supplier when you run out of rooibos.

I do not know where this leads. It does not need to lead anywhere yet.