Stalefish Labs

Building Confidence Into Uncertain Verdicts

This is the final article in the Building a Weather Decision Engine series. The previous articles covered the drying model, multi-app architecture, edge cases, and the Yardwise inversion. This last one is about the output side, how the engine communicates decisions to people who just want to know if they should go ride.

Flipping the Question: From 'Is It Too Wet?' to 'Is It Too Dry?'

When I first built the Groundwise engine for the Ridewise app, it answered one question: is it too wet to ride? Low wetness meant good conditions. High wetness meant stay home. Exactly what I needed for mountain biking, skateboarding, and other outdoor wheeled activities where surface conditions impacted by weather mattered.

Edge Cases That Break Your Weather Model

This is a continuation of a deep-dive into the weather engine that drives our Groundwise apps. Last we looked into the core drying model, which handles most days well. Sun, wind, time, surface type — combine them sensibly and you get a reasonable verdict most of the time.

One Engine, Three Apps: Sharing a Swift Decision Engine Across Products

In the previous articles I’ve covered what the Groundwise engine does and how the drying model works. This article is about the architectural decision that turned one app into three: sharing a single decision engine across Ridewise (trail conditions), Fieldwise (sports field conditions), and Yardwise (watering guidance).

Turning Raw Weather Into a Drying Model

In the first article about my foray into using weather data to assess surface conditions for riding activities, I introduced the Groundwise engine, a software decision system that turns weather data into a yes/maybe/no verdict for outdoor conditions. This article goes deeper into the core moisture model: how the engine takes raw weather observations and calculates a wetness score that drives the verdict. The hope is to show how a simple question such as “can I ride my mountain bike today?” spiraled into a fairly complex yet intriguing design challenge. Totally fair if you aren’t entranced by weather math, I get it, but I feel like there’s some value in pulling back the curtain to reveal how much goes into solving a seemingly simple problem.

I Built a Weather Engine That Tells You When to Ride, Play, or Water

Every mountain biker knows the ritual. It rained last night. You check the radar, clear now. You check the trail association’s Facebook page, nobody’s posted. You text your riding buddy: “Think the trails are good?” They don’t know either. So you either stay home and miss a perfectly rideable day, or show up and chew through muddy trails that needed another six hours to dry.