diff --git a/README.md b/README.md
index 9355fc9..254e428 100644
--- a/README.md
+++ b/README.md
@@ -54,18 +54,33 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest
 
 ## 🚀 Key Features
 
+### Sensing
+
+See people, breathing, and heartbeats through walls — using only WiFi signals already in the room.
+
 | | Feature | What It Means |
 |---|---------|---------------|
-| | ***Sensing*** | |
 | 🔒 | **Privacy-First** | Tracks human pose using only WiFi signals — no cameras, no video, no images stored |
 | 💓 | **Vital Signs** | Detects breathing rate (6-30 breaths/min) and heart rate (40-120 bpm) without any wearable |
 | 👥 | **Multi-Person** | Tracks multiple people simultaneously, each with independent pose and vitals — no hard software limit (physics: ~3-5 per AP with 56 subcarriers, more with multi-AP) |
 | 🧱 | **Through-Wall** | WiFi passes through walls, furniture, and debris — works where cameras cannot |
 | 🚑 | **Disaster Response** | Detects trapped survivors through rubble and classifies injury severity (START triage) |
-| | ***Intelligence*** | |
+
+### Intelligence
+
+The system learns on its own and gets smarter over time — no hand-tuning, no labeled data required.
+
+| | Feature | What It Means |
+|---|---------|---------------|
 | 🧠 | **Self-Learning** | Teaches itself from raw WiFi data — no labeled training sets, no cameras needed to bootstrap ([ADR-024](#self-learning-wifi-ai-adr-024)) |
 | 🎯 | **AI Signal Processing** | Attention networks, graph algorithms, and smart compression replace hand-tuned thresholds — adapts to each room automatically ([RuVector](#ai-backbone-ruvector)) |
-| | ***Performance & Deployment*** | |
+
+### Performance & Deployment
+
+Fast enough for real-time use, small enough for edge devices, simple enough for one-command setup.
+
+| | Feature | What It Means |
+|---|---------|---------------|
 | ⚡ | **Real-Time** | Analyzes WiFi signals in under 100 microseconds per frame — fast enough for live monitoring |
 | 🦀 | **810x Faster** | Complete Rust rewrite: 54,000 frames/sec pipeline, 132 MB Docker image, 542+ tests |
 | 🐳 | **One-Command Setup** | `docker pull ruvnet/wifi-densepose:latest` — live sensing in 30 seconds, no toolchain needed |