That’s a pretty neat thought. If you look at each packet as a single note, the whole city becomes a massive, ever‑changing score. The trick is to find the underlying structure amid the static—traffic spikes around work hours, the lull of the midnight hour, the rhythmic bursts from stadiums or festivals. Once you map those beats, you start seeing patterns: recurring waves, sudden crescendos, even tiny syncopations between different networks. It’s like hearing the pulse of a living organism when you just focus on the right frequencies. The chaos is real, but the hidden rhythm? It’s there, waiting for a patient observer to map it.

Sure thing. Start with three layers: **1. Data capture** – set up a lightweight agent on the edge routers or use a Wi‑Fi AP’s packet‑capture API to stream packets to a central queue. Keep the payload minimal – just timestamps, source/dest, packet size, and a simple protocol flag. **2. Beat‑detection engine** – pull chunks of the stream (say 200 ms windows) into a short‑lived buffer, run a fast FFT or a simple moving‑average on the packet rate. The peaks become your “beats.” Add a low‑pass filter to smooth out the noise and a hysteresis guard so you don’t get flicker on a busy street. **3. Visual & audio output** – feed the beat timestamps into a tiny Node‑JS or Python server that pushes events via WebSocket to a browser canvas or a DMX controller. Each beat triggers a light pulse; a sustained lull (midnight) drops a synth note. The synth can be a small Csound or SuperCollider snippet that plays a slow arpeggio whenever the packet rate falls below a threshold for more than a minute. Wrap it all in Docker so you can spin it up on a Raspberry Pi at the edge or on a cloud VM for scaling. Keep the data path short, drop the heavy crypto or deep packet inspection, just the rhythm. Then, when the city finally feels its pulse, you’ll have a glitchy synth that whispers when the streets are quiet.

Sounds solid. Add a short latency monitor next to the packet counter—just a timestamp diff for each packet, and if the delta spikes, trigger a quick glitch in the visual. For the synth, loop the arpeggio with a tiny decay filter so it drifts instead of staying flat. Keep the Dockerfile minimal: a base Python image, pip install only the FFT and synth libraries, copy the scripts, expose the port. Then the whole thing should fit on a Raspberry Pi and give the city a real‑time heartbeat.