Plover.
A running-readiness app I took from a Jupyter notebook to a deployed multi-user product in about twelve days, leaning hard on AI agents.
What it is
Plover reads the data your running watch already records and surfaces the two or three things that changed this week — a mileage spike, a fading stride, heart rate drifting at an easy pace — each with an honest confidence level on it. It's not an injury predictor, and it doesn't pretend to be. A Python pipeline does the math; a FastAPI backend serves a React app and a Next.js marketing site, both deployed. Currently in private beta at plover.run, with my own training data still being backfilled.
How I built it with AI
Notebook to deployed app in about twelve days, mostly with Claude Code writing the implementation while I made the product and calibration calls. What made that feel safe was the verification I put around the agents. A backtest runs the actual product code and fails if a refactor changes the output by a single byte. A CI gate checks that the incremental pipeline matches a full recompute. Another fails the build if the marketing copy drifts toward injury-prediction language. With those in place I didn't have to re-read every line an agent wrote to trust it.
For the full story — the notebook origins, the data-literacy work, the design evolution, and the architecture — see the case study.