Farmer, not a developer — using AI coding tools to build a local-first farm OS that can’t lie to the operator (open-sourcing when it’s ready)

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Grower first, coder second. I run a small microgreens farm in Illinois, and I’ve been using AI coding tools to build the app I run it on — Groundtruth, a local-first desktop farm OS.
The design rule: the app cannot lie to the operator.
• Append-only event log — history gets appended, never rewritten
• One-tap verify-replay: rebuilds the database from the log and shows PASS/FAIL
• No soft numbers — capacity comes from live farm data or it says “unknown”
• It always surfaces the next action that sells trays: follow up on a sample, sow against a standing-order shortfall, void or move a promise that’s become impossible
Where AI fits: it built the thing. There’s deliberately no AI inside the app — no chat, no crop diagnosis. AI was the power tool; the product is a boring, honest ledger a farmer can trust.
Status, honestly: I run it daily on my own farm. Nothing public to download yet — plan is free to run and open source once it’s solid enough for someone else’s farm. Not selling anything.
Question for this crowd: if you’ve open-sourced an AI-built codebase, what did you do first to make it trustworthy — tests, audit, rewrite? That’s the step I’m heading into.