I built When to Apply Pre-Emergent after getting tired of lawn advice tied to calendar dates rather than local weather.
The free tool maps 33,000+ US ZIP Code Tabulation Areas to nearby NOAA GHCN-Daily weather stations, recomputes base-50°F growing degree days each day, and turns that signal into a plain-language timing status. It also shows the source station, distance, data-through date, methodology, and limitations. No account required.
The hardest product decision wasn’t the calculation—it was how much uncertainty to expose without overwhelming homeowners. NOAA observations can arrive several days late, and the separate 2–4 inch soil-temperature view is modeled rather than measured, so the UI makes those limits explicit.
I’d especially value feedback on one question: do the station distance, data-through date, and uncertainty notes create enough trust, or is there still too much technical detail?
Try it: https://whentoapplypreemergent.com/
Built by me through Offshoot Labs.
The interesting challenge isn't calculating the right application window—it's helping homeowners trust a recommendation that isn't perfectly certain. I'd keep validating whether people value the prediction itself or the transparency behind it. In decision tools, explaining uncertainty well can become part of the product advantage.
That’s a useful framing. My current hypothesis is that the prediction earns attention, while the source station, data-through date, and uncertainty notes earn trust. I still need to test whether people actually use those details or simply want a clear recommendation. Thinking of transparency as part of the product is especially helpful. Thanks!
Appreciate the context.
The prediction vs. trust layer distinction is exactly where the interesting product question sits. I have a couple of thoughts on how decision products earn confidence, but I don't think I'd do it justice in a few comments here.
If you're open to it, what's the best email to reach you on?