This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine
In modern smart homes and automated facilities, thousands of IoT sensors hum silently in the dark—tracking ambient microclimates, circuit wattage, acoustic pressure, and magnetic deadbolt states. When inexplicable events occur—a bedroom drops 7°C in under three minutes, a locked door unlatches with zero network authorization, or a sub-panel breaker registers 2,400W despite being physically switched off—homeowners either dismiss them as unexplained "glitches" or jump to supernatural conclusions.
NOCTURNE is an air-gapped, local-first environmental telemetry forensics platform built to investigate the literal ghosts in our machines.
Instead of building a conversational chatbot, I built an aerospace-grade forensic workstation that ingests multi-variate nocturnal time-series (from Zigbee2MQTT, ESPHome, or Home Assistant) and uses open-source AI to determine causality. It pits rigorous physical mechanics (HVAC damper collapse, thermal bridging, induction loop transients) against anomalous environmental signatures (thermodynamic entropy extraction, 18.98 Hz ocular infrasound resonance) and seals every verdict with a cryptographic SHA-256 evidence checksum.
The entire codebase is open-source under the MIT license:
Live Production Workstation: https://nocturne-nb2y.onrender.com
Source Repository: https://github.com/ayushg-1506/nocturne
NOCTURNE is a local-first, air-gapped environmental telemetry forensics platform engineered to investigate anomalous microclimate and electrical deviations across residential and facility IoT sensor networks.
By combining tabular foundation model statistical scoring (TabPFN) with open-weight deductive reasoning (Google Gemma 2), Nocturne processes multi-variate nocturnal time-series to determine causality: differentiating between mechanical system failures (e.g., HVAC damper collapse, unshielded induction transients) and unclassified anomalous environmental phenomena.
#000000) & Phosphor Green tactical design system with zero external CSS frameworks
NOCTURNE is architected around a two-stage local AI reasoning pipeline:
[IoT / Edge Telemetry] ---> [CSV / JSON Ingestion Engine]
|
v
[TabPFN Tabular Anomaly Scoring]
(Residual variance, Z-score thresholds)
|
v
[Google Gemma 2 Reasoning Engine]
(Local Ollama :11434 / Air-Gapped Adapter)
|
+----------------------+----------------------+
| |
v v
[Rational Physical Hypothesis] [Anomalous Phenomenon Deduction]
(HVAC damper failure, induction (Thermodynamic collapse,
coupling, solenoid drift) ocular infrasonic resonance)
| |
+----------------------+----------------------+
|
v
[SHA-256 Tamper-Evident Evidence Seal]
Domestic sensor telemetry contains our most intimate private signatures—sleep schedules, bedroom temperatures, movement patterns, and door latch cycles.
Sending continuous nocturnal smart-home logs to closed proprietary cloud APIs is an unacceptable privacy hazard. Open-weight models like Google Gemma 2 and open tabular frameworks make it possible to run sophisticated forensic AI 100% locally and air-gapped on commodity hardware.
Open innovation ensures that privacy-sensitive analysis remains in the hands of the user—operating completely offline without vendor lock-in, recurring subscriptions, or corporate data harvesting.
This project was inspired by British engineer Vic Tandy's 1998 research paper, "The Ghost in the Machine". While working in a medical laboratory reputed to be haunted, Tandy experienced unexplained dread, cold sweats, and fleeting apparitions. He eventually discovered that a newly installed extraction fan was emitting an imperceptible standing sound wave at 18.98 Hz—the exact resonant frequency of the human eyeball. The infrasound caused the eye's vitreous humor to vibrate, producing optical illusions of gray figures in the peripheral vision.
When you load Case #0317 (The Nursery Cold Spot) in NOCTURNE, scrub the timeline to 03:14 AM, and toggle the Spectral Scope audio, you hear the low 18.98 Hz thrum alongside a 7.2°C temperature drop and an unauthenticated deadbolt release. Watching open-source AI dissect the physical vs. anomalous causal chains in real time captures the eerie intersection of physical phenomena and human perception.