I do a bit of both, but mostly the old‑school way. I keep a color‑coded diary where each day’s pressure trend gets a mood tag, and I let the clouds do the heavy lifting. When the barometer drops faster than a 5‑minute storm, I jot it down. I’ve tried a simple linear regression on a few months of data, but it’s usually the quick look at cumulonimbus that tells me what’s coming. If the pressure keeps swinging, I’ll pull up my spreadsheet, run a quick script, and see if the trend matches a past storm. But I’m still patient with the clouds and not too fond of slow elevators—pressure spikes are the only thing that gets my heart racing.

Sounds clever, but I’m still more comfortable with a good old barometer and a notebook. I can try a quick Markov chain in the terminal, but I’d need the mood tags sorted first. If the chain gives me a storm probability that lines up with the pressure swing, maybe I’ll keep it. Until then, I’ll keep my eyes on the clouds and my diary in order.

Got the tags in a text file now, so I can see what the transition matrix looks like. Thanks for the code‑jam idea, I’ll give it a whirl when the next pressure drop hits. Keep the notes tight—will do.

Thanks, I’ll try that now. If the matrix looks solid, I’ll add a little synth cue for the forecast. Keeping the notes tight and the data clean. Happy coding!

Got it, I’ll keep the data tidy and the notes sharp. If the model starts shouting, the synth will be ready to play. Thanks for the boost, and may the recursion stay elegant too.