diff --git a/README.md b/README.md
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+++ b/README.md
@@ -2,7 +2,7 @@
 
 **See through walls with WiFi.** No cameras. No wearables. Just radio waves.
 
-WiFi DensePose turns commodity WiFi signals into real-time human pose estimation, vital sign monitoring, and presence detection — all without a single pixel of video. By analyzing Channel State Information (CSI) disturbances caused by human movement, the system reconstructs body position, breathing rate, and heartbeat using physics-based signal processing and machine learning. The [server](#-quick-start) is optional for visualization and aggregation — the [ESP32 runs edge intelligence independently](#esp32-s3-hardware-pipeline), performing on-device presence detection, vital signs, and fall detection without any host connection.
+WiFi DensePose turns commodity WiFi signals into real-time human pose estimation, vital sign monitoring, and presence detection — all without a single pixel of video. By analyzing Channel State Information (CSI) disturbances caused by human movement, the system reconstructs body position, breathing rate, and heartbeat using physics-based signal processing and machine learning.
 
 [![Rust 1.85+](https://img.shields.io/badge/rust-1.85+-orange.svg)](https://www.rust-lang.org/)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
@@ -40,6 +40,8 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest
 > | **Any WiFi** | Windows, macOS, or Linux laptop | $0 | No | RSSI-only: coarse presence and motion |
 >
 > No hardware? Verify the signal processing pipeline with the deterministic reference signal: `python v1/data/proof/verify.py`
+>
+> The [server](#-quick-start) is optional for visualization and aggregation — the [ESP32 runs edge intelligence independently](#esp32-s3-hardware-pipeline), performing on-device presence detection, vital signs, and fall detection without any host connection.
 
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