A worked example of building software differently
How /pm-flow became a framework, and grew up alongside a fitness app.
I'm Regev — an iOS developer building FitMe, a privacy-first fitness app. To stop shipping fast-and-wrong, I wrote one command that enforced research, planning, testing, and learning. It grew into a measurable framework. This site is the guided tour.
A worked example of building software differently — one PM flow enforced research, planning, testing, and learning until it became a measurable framework.
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The numbers
Cross-feature velocity normalized by CU formula (R²=0.82).
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Measurement
Where the framework stopped estimating numbers and started capturing them — outcomes became provable.
v7.9v7.9 Promotion
Three advisory gates flip to enforced after a 14-day calibration window — the lifecycle hardening, end to end.
v5.2Parallel stress test
Four features, 54 minutes, four merged PRs, zero conflicts — process at speed.
About this project
FitMe is a personal project — an iOS app I built to track my own fitness and wellbeing the way I wanted: fast, privacy-first, and entirely owned by the person using it. Data stays on-device by default, analysis happens locally when possible, and no health signal gets silently shipped to a cloud AI. The interesting part isn't the app. It's what happened while building it.
I built an AI-orchestrated PM workflow — /pm-workflow — to enforce the planning discipline I kept abandoning. Then the workflow itself started evolving. Caches, eval layers, dispatch intelligence, measurement. By v7.0 it was routing to hardware-aware models. By v7.9 (shipped 2026-05-21) the framework was using its own Mechanism A telemetry as a gate on its own promotion decisions — the first version where the calibration discipline applied to itself, codified in honesty-ledger entry FT2-FH-003.
This site is the guided tour of that process. All docs are from real shipped work. All metrics are measured, not estimated. The regressions are as public as the successes.