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Verdify: A Longmont, Colorado AI greenhouse with public telemetry

5 min read

Verdify launch video: the greenhouse, AI planning loop, ESP32 safety boundary, Slack Ops, and public telemetry story.

We built a real greenhouse in Longmont, Colorado where an AI planner proposes bounded climate tactics, but an ESP32 controller, not the AI, owns the relays.

We publish the plans, telemetry, failures, costs, lessons, and scorecards so readers can check whether Verdify works instead of taking the claim on trust.

The greenhouse glowing through snowfall on a winter night Greenhouse interior aisle with red geraniums, canna lilies, hydroponic channels, and the north service wall The greenhouse, house, and rooftop solar system in Longmont

Right Now

The panels below are live Grafana trends from the public read-only telemetry path. They show 72 hours of observed greenhouse climate plus the forecast window Iris, the AI planner is planning against.

Climate control loop: green fill is the firmware compliance band, stitched from historical controller setpoints into the projected future band. The trend deliberately stays simple: actual greenhouse climate, outdoor pressure, forecast pressure, and the compliant band the ESP32 is expected to enforce.
VPD means vapor pressure deficit: how hard the air is pulling water from plant leaves. High VPD is dry-air stress; low VPD means the greenhouse is too humid for healthy transpiration.
Lighting control loop: lighting follows the same pattern: confirmed switch state for the two Lutron circuits against the planner-managed lux trigger bands, with the same solar background so all three trends share one visual story for how much sun is reaching the greenhouse.

Crop target provenance, dispatcher math, cfg readbacks, qualified-minutes accounting, and padding details stay in trace tables and tests instead of crowding the graph. The ESP32 still owns the physical state machines.

The AI does not control hardware directly. Iris writes documented AI-writable tunables: bounded setpoint biases, mist/fog limits, venting posture, and climate tactics. A dispatcher validates them. The ESP32 firmware decides relay state every 5 seconds.

What To Look At First

Does it work?Baseline vs Iris

The April 22-25 planner-offline window is compared with the following Iris-online window.

Is it safe?AI does not control relays

The planner writes intent. The dispatcher validates it. The ESP32 decides equipment state locally.

Can I inspect the data?CSV samples and planning archive

Public receipts include daily plans, scorecards, stress hours, water, energy, costs, failures, lessons, and sample exports.

Why This Is Worth Checking

Real physical stakes

The greenhouse sits at 5,090 feet in Boulder County: cold nights, intense sun, dry spring air, summer heat, winter snow, and fast shoulder-season swings.

AI planning loop

Iris reads telemetry, forecasts, prior plans, lessons, and site context through validated planning tools before writing bounded climate tactics.

Firmware safety boundary

Iris writes bounded climate tactics. The ESP32 safety controller owns the 5-second relay state machine.

Public receipts

Evidence pages publish live state, plans, scorecards, costs, failures, lessons, and sample data so readers can inspect the claims.

Human operations

Slack Ops turns plans, deviations, reminders, and operator tasks into human-readable messages without making Slack a safety layer.

Honest resource accounting

Resource Use separates solar timing, electricity, gas, and water so the environmental story does not hide the remaining utility bill.

What It Claims Now

ClaimAI-assisted tactics can be audited against physical outcomes.

Iris writes bounded plans, the ESP32 enforces safety every 5 seconds, and public receipts show climate, stress, cost, water, failures, and lessons.

Not claimed yetYield, profit, and full autonomy are roadmap claims.

The current public proof layer focuses on automating the greenhouse systems safely: climate tactics, relay-boundary enforcement, telemetry, costs, failures, and lessons. Yield attribution, profit optimization, and broader autonomy need more harvest records, crop-stage normalization, and comparable baselines before they become claims.

Improvement

Verdify publishes the evidence behind its claims: live state, current plan, scorecard history, costs, failures, and generated lessons. The claim is falsifiable: if Iris writes poor tactics, the daily plan, scorecard, stress hours, water use, and lesson record show it.

This is the planner audit. Compliance is measured as samples inside the firmware-enforced temperature band, the firmware-enforced VPD band, and both bands at once.
Stress is measured in hours. A useful planner is not judged by how often it writes plans; it is judged by whether those plans move the greenhouse closer to target while respecting water, energy, and physical limits.

The launch baseline is explicit: Baseline vs Iris compares the April 22-25 planner-offline window with the following Iris-online window. It is an operational comparison, not a controlled A/B test.

Start Here If You Are Skeptical