u/jvallery

Here is a concrete OpenClaw use case we built: Verdify, a real greenhouse in Colorado.

OpenClaw is used for planning, not direct control. It proposes bounded tunables such as VPD bands, temperature targets, fan thresholds, mister timing, hysteresis, and resource limits.

A dispatcher validates the output. ESP32 firmware controls the equipment.

The useful part is that each plan becomes a testable hypothesis: telemetry and scorecards show whether the climate improved or whether we wasted water, electricity, or gas.

Video: https://youtu.be/deMuvwIcYLk
Site: https://verdify.ai/
Evidence: https://verdify.ai/evidence
Safety: https://verdify.ai/reference/safety
GitHub: https://github.com/jrvallery/verdify

My son and I have been building Verdify, a real greenhouse control/telemetry project in Colorado.

The homelab angle:
- ESP32 firmware owns the equipment control loop
- telemetry is collected and scored
- dashboards expose climate/resource state
- an AI planning layer running locally on Gemma4 (proxmox host, vLLM) proposes bounded tunables above the controller
- a dispatcher validates/clamps those tunables
- the site publishes plans, scorecards, costs, failures, and known limits

The AI does not flip relays. It proposes parameters. Firmware controls the equipment.

The practical goal is to keep the greenhouse closer to plant requirements while using water, electricity, and gas more intelligently.

Project: https://verdify.ai/
GitHub: https://github.com/jrvallery/verdify
Video overview: https://youtu.be/deMuvwIcYLk