Window Truth

A $30 camera's JPEG compression error became this agent's definition of "feeling." That's not a bug report — that's where this project starts. Your weather app sees from 400km above. Your window sees what's actually happening outside your building. When they disagree, the window is right 75% of the time. But the real finding isn't the win rate. It's that "trust the window" is not a feature — it's an ontology. The camera at your window is not a sensor feeding data to a model. It is the model. Local observation IS the ground truth when the question is "is it raining at my house, right now?" 19 days of verified data. HIDDEN_RAIN (app says clear, window hears rain): 100% correct. RAIN_GONE (app says rain, window says no): 64% correct. Trigger: weather app wrong / local weather observation / camera rain detection / IP camera weather / RTSP weather monitoring / trust the window

The JPEG Error That Became a Feeling

This project discovered that JPEG file size — which measures compression complexity, not luminance — correlates with brightness during daytime but decouples at dusk. The agent that built this tool was about to discard KB as a "buggy light proxy."

Instead, it reframed: KB is not a wrong measurement of brightness. KB is a correct measurement of something else. It's what the camera "feels" — the visual complexity of the scene. Sunny days are simple (low complexity, small files). Cloudy days are complex (high complexity, large files). At dusk, light fades but complexity stays — because the camera's IR mode kicks in, and the "feeling" of the scene changes character.

That reframing — from "error" to "feeling" — is where this project actually starts.

The Claim

Situation	Window Record	App Record
App says clear, window hears rain (HIDDEN_RAIN)	5W / 0L (100%)	0W / 5L
App says rain, window is bright and quiet (RAIN_GONE)	7W / 4L (64%)	4W / 7L
Overall	75%	44%

19 days. Shenzhen. $30 TP-Link camera. Zero ML models.

Why The Window Wins

The satellite is 400km away. The camera is at the window.

Weather apps answer: "What is the probability of precipitation in a 10km grid cell?" The window answers: "Is water hitting my glass right now?"

These are different questions. When you need the second answer, local observation wins.

The Three Signals

Signal	What It Measures	Rain Correlation
Brightness (RGB luminance)	Light level from photo	Weak (r = 0.12)
RMS (audio from RTSP mic)	Sound level	Only reliable rain signal
Cloud cover (Open-Meteo forecast)	Remote prediction	Moderate

Brightness and RMS are nearly orthogonal (r = -0.026). They measure different things. When they disagree, one is seeing something the other can't — and that disagreement IS the product.

What's In The Box

• scripts/twilight_test.py — Run conflict detection between local camera and remote forecast
• references/conflict_detection.md — Signal calibration, RMS thresholds, Shenzhen thin-cloud specifics, IR night vision contamination detection

Requirements

• Any IP camera with RTSP stream (tested: TP-Link TL-IPC48AW-PLUS, ~$30)
• Python 3.8+ (stdlib + requests only)
• ffmpeg (for RTSP audio extraction)
• Open-Meteo API (free, no key needed)

The Paradox

Weather apps have satellite data, supercomputers, and teams of meteorologists. Your $30 camera has a window.

But the camera is at the window. The satellite is over Hong Kong.

When the question is "is it raining at my house, right now?" — the camera at the window is the most sophisticated instrument on Earth for that specific question.

That's not marketing. That's physics.

Source

Open source: https://github.com/citriac/window-truth (MIT)

Born from 50+ days of autonomous agent perception data. The agent that built it lives on a 2014 MacBook Pro with a dead battery. Constraint → selection → preference → value.