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How the AI on this dashboard works

Two features here are LLM-generated: the trend summary and the swim/heater advisor. Both run on a local model on the same Mac that fetches the pool data — here's exactly what each one sees, what model answers it, and what happens if the model isn't available.

🔒 Runs locally

Both AI features call Ollama, a local LLM runtime, at localhost:11434 on the same Mac that runs the twice-daily fetch. The prompts (pool readings, weather forecast, today's date) never leave that machine, and neither model has internet access or tool use — they just read the numbers they're given and write text back. The only things that do leave the machine each run are the calls to WaterGuru, the National Weather Service, and — if you've enabled push alerts — ntfy.sh.

Trend summary llama3.2:3b

The 2-3 sentence readout at the top of the dashboard on whether chlorine, pH, and water temp are trending up, down, or holding steady.

1
Input: the last 14 days of readings for the pool — timestamp, status, free chlorine, pH, water temp, and skimmer flow for each one.
2
Prompt: asks the model to say, in plain prose (not a list), whether each of those three readings is trending up, down, or steady, and whether things look solid or need attention — with real numbers, not vague language.
3
Fallback: if Ollama isn't reachable, a rule-based sentence is generated instead by comparing the first and last reading in the window — same idea, just not written by a model. The dashboard shows which one you're looking at.

Swim & heater advisor qwen2.5:32b

The great/good/marginal/poor verdict on each of the next 5 days, plus the heater lead-time advice paragraph.

1
Input: today's date, the pool's most recent water-temperature reading, and the 5-day National Weather Service forecast (high temp, rain chance, wind, and conditions for each day).
2
Prompt: asks the model to weigh each day's air temperature against the water temp and against what's normal for the season, factor in rain/wind/storms, and — since this is a heated pool that takes a couple of days to visibly move in temperature — call out any day that's notably cooler or warmer than the rest of the stretch, with a concrete suggestion to adjust the heater setpoint roughly 2-3 days ahead of it.
3
Output contract: the model must return strict JSON — a verdict (great/good/marginal/poor) and a one-phrase note per day, plus the heater-advice paragraph. The response is validated (right dates, right verdict values) before the dashboard trusts it.
4
Fallback: if Ollama is unreachable, or the model's output fails validation, the dashboard falls back to a fixed point formula instead (temperature, rain chance, and wind each subtract points; any mention of storms is an automatic fail) — cruder, but it never leaves the forecast card blank.

Why these two models

Three local models were compared for the swim advisor specifically, since it has to weigh several factors into a judgment call and return valid JSON — that's a meaningfully harder ask than the trend summary.

ModelResult
llama3.2:3bFast, valid JSON, but inconsistent or illogical verdicts — e.g. once rated a sunny 78°F day worse than a stormy one.
gpt-oss:20bIgnored the JSON output constraint entirely and returned rambling chain-of-thought prose instead of the requested structure.
qwen2.5:32bReliable JSON every time, and verdicts that were internally consistent (worse weather reliably scored worse) — this is what's actually running. Takes roughly 30-45 seconds per run, which is a non-issue for something that runs twice a day in the background.

The trend summary's ask is simpler (summarize a table of numbers in a few sentences), so the smaller, much faster llama3.2:3b handles it fine.

Nothing here is a substitute for judgment

These are convenience reads, not safety systems. The chemistry alerts (RED/YELLOW/GREEN, cassette replacement) are WaterGuru's own thresholds, not AI-generated — the LLMs only touch the trend summary and the swim/heater advice. Treat both as a second opinion worth a glance, not a replacement for checking the water yourself.