⚠️ ADR-028: Independent Capability Audit — 1,031 Tests, SHA-256 Proof, Witness Bundle #72

New Issue

2026-03-02T04:53:31+08:00

ruvnet commented

2026-03-02 04:53:31 +08:00

(Migrated from github.com)

What is this project, in plain English?

WiFi signals bounce off your body. When you move, the bounces change. This project reads those changes and figures out where you are, how you're standing, and whether you're breathing — using only a $8 WiFi chip and math.

No cameras. No wearables. No Bluetooth beacons. Just WiFi signals you're already swimming in.

It sounds like magic. It's not. It's physics that's been published in peer-reviewed papers for over a decade. We just built the open-source version — in Rust, with real firmware, real tests, and real proofs.

"Prove it."

Fair. We did.

We ran a full independent audit of every file, every claim, and every test in this repository. Three AI research agents examined the entire codebase simultaneously — one for hardware, one for signal processing and AI, one for deployment and security. They found:

The numbers

What	Result
Rust tests	1,031 passed, 0 failed
Python proof	VERDICT: PASS (deterministic SHA-256 hash match)
Witness bundle	7/7 self-verification checks PASS
Published crates	15 on crates.io @ v0.2.0
Docker images	2 on Docker Hub (132 MB Rust, 569 MB Python)
ESP32 firmware	606 lines of C, pre-built binaries included
Signal algorithms	10 peer-reviewed methods, 5,937 lines, 105+ tests
Training pipeline	10-phase, 9,051 lines, 174+ tests
Architecture decisions	28 ADRs documenting every design choice

What got audited (31 of 33 capabilities confirmed)

The witness log has a 33-row table. Each row is one capability claim, with a verdict and evidence. Here's the summary:

31 capabilities: Verified YES with passing tests or inspected code
2 capabilities: NOT MEASURED at audit time (benchmark throughput needs target hardware, Kubernetes deploy needs a cluster)
0 capabilities: Failed

Not "we think it works." Not "it should work." It compiles, it runs, the tests pass, the hashes match, and the bundle self-verifies.

How to verify it yourself

You don't need ESP32 hardware for steps 1-3. A laptop with Rust installed is enough.

Step 1: Run all 1,031 tests (~2 min)

git clone https://github.com/ruvnet/wifi-densepose.git
cd wifi-densepose/rust-port/wifi-densepose-rs
cargo test --workspace --no-default-features

Step 2: Run the pipeline proof (~1 sec)

cd ../../
python v1/data/proof/verify.py
# → VERDICT: PASS

This feeds a known reference signal through the actual production code (not a mock, not a test double — the same CSIProcessor that handles real WiFi frames), then hashes the output. If the hash matches, the pipeline is real and deterministic.

Step 3: Generate + verify the witness bundle

bash scripts/generate-witness-bundle.sh
cd dist/witness-bundle-ADR028-*/
bash VERIFY.sh
# → 7 passed, 0 failed — ALL CHECKS PASSED

Step 4: Flash an ESP32 and see it work (optional, requires ~$10 hardware)

pip install esptool
python -m esptool --chip esp32s3 --port COM7 --baud 460800 \
  write_flash 0x0 firmware/esp32-csi-node/build/bootloader/bootloader.bin \
  0x8000 firmware/esp32-csi-node/build/partition_table/partition-table.bin \
  0x10000 firmware/esp32-csi-node/build/esp32-csi-node.bin

Walk near the antenna. Watch the CSI amplitude change in real-time. That's your body affecting WiFi signals.

What's actually inside

The hardware layer — An ESP32-S3 chip ($8) runs custom firmware that captures raw WiFi Channel State Information (CSI) — amplitude and phase across 64 subcarriers — and streams it over UDP at 20-100 frames/sec. The binary protocol (ADR-018) is fully specified: magic number, node ID, antenna count, I/Q pairs. A Rust aggregator receives and validates the frames.

The signal processing layer — 10 algorithms based on published research (SpotFi, Hampel, Fresnel, BVP, etc.) clean and transform the raw CSI into meaningful features: Doppler shifts for motion, Fresnel zones for breathing, spectrograms for activity classification. 5,937 lines of Rust, 105+ tests.

The AI layer — A neural network (DensePose head + modality translator) maps CSI features to 17 body keypoints and 24 body surface regions. The training pipeline has 10 phases. Domain generalization (MERIDIAN/ADR-027) ensures models work across rooms without retraining. Self-supervised learning (AETHER/ADR-024) bootstraps from raw WiFi data alone.

The deployment layer — Docker images, WASM browser build, 15 published Rust crates, 4 CI/CD workflows, 15+ security scanners, and a deterministic proof system with SHA-256 verification.

Links

Document	What's in it
ADR-028 (full audit)	Complete findings: ESP32 specs, binary frame format, signal algorithms, NN architecture, training phases, deployment infra, security posture
Witness Log	11 reproducible verification steps + 33-row attestation matrix
Bundle generator	Creates offline-verifiable proof package
PR #71	The merged PR with all audit artifacts
ESP32 tutorial	Step-by-step hardware setup guide
"Is it fake?" FAQ	The original skeptics thread (with 20+ comments)
crates.io	All 15 published Rust crates
Docker Hub	Pre-built container images

The code is open. The tests pass. The proofs hash. The firmware flashes. The physics is peer-reviewed. The bundle self-verifies.

We built the receipts. Your move.

— rUv

## What is this project, in plain English? WiFi signals bounce off your body. When you move, the bounces change. This project reads those changes and figures out where you are, how you're standing, and whether you're breathing — **using only a $8 WiFi chip and math.** No cameras. No wearables. No Bluetooth beacons. Just WiFi signals you're already swimming in. It sounds like magic. It's not. It's physics that's been published in peer-reviewed papers for over a decade. We just built the open-source version — in Rust, with real firmware, real tests, and real proofs. --- ## "Prove it." Fair. We did. We ran a **full independent audit** of every file, every claim, and every test in this repository. Three AI research agents examined the entire codebase simultaneously — one for hardware, one for signal processing and AI, one for deployment and security. They found: ### The numbers | What | Result | |------|--------| | **Rust tests** | 1,031 passed, 0 failed | | **Python proof** | VERDICT: PASS (deterministic SHA-256 hash match) | | **Witness bundle** | 7/7 self-verification checks PASS | | **Published crates** | 15 on crates.io @ v0.2.0 | | **Docker images** | 2 on Docker Hub (132 MB Rust, 569 MB Python) | | **ESP32 firmware** | 606 lines of C, pre-built binaries included | | **Signal algorithms** | 10 peer-reviewed methods, 5,937 lines, 105+ tests | | **Training pipeline** | 10-phase, 9,051 lines, 174+ tests | | **Architecture decisions** | 28 ADRs documenting every design choice | ### What got audited (31 of 33 capabilities confirmed) The [witness log](https://github.com/ruvnet/wifi-densepose/blob/main/docs/WITNESS-LOG-028.md) has a 33-row table. Each row is one capability claim, with a verdict and evidence. Here's the summary: - **31 capabilities:** Verified YES with passing tests or inspected code - **2 capabilities:** NOT MEASURED at audit time (benchmark throughput needs target hardware, Kubernetes deploy needs a cluster) - **0 capabilities:** Failed Not "we think it works." Not "it should work." **It compiles, it runs, the tests pass, the hashes match, and the bundle self-verifies.** --- ## How to verify it yourself You don't need ESP32 hardware for steps 1-3. A laptop with Rust installed is enough. **Step 1: Run all 1,031 tests** (~2 min) ```bash git clone https://github.com/ruvnet/wifi-densepose.git cd wifi-densepose/rust-port/wifi-densepose-rs cargo test --workspace --no-default-features ``` **Step 2: Run the pipeline proof** (~1 sec) ```bash cd ../../ python v1/data/proof/verify.py # → VERDICT: PASS ``` This feeds a known reference signal through the **actual production code** (not a mock, not a test double — the same `CSIProcessor` that handles real WiFi frames), then hashes the output. If the hash matches, the pipeline is real and deterministic. **Step 3: Generate + verify the witness bundle** ```bash bash scripts/generate-witness-bundle.sh cd dist/witness-bundle-ADR028-*/ bash VERIFY.sh # → 7 passed, 0 failed — ALL CHECKS PASSED ``` **Step 4: Flash an ESP32 and see it work** (optional, requires ~$10 hardware) ```bash pip install esptool python -m esptool --chip esp32s3 --port COM7 --baud 460800 \ write_flash 0x0 firmware/esp32-csi-node/build/bootloader/bootloader.bin \ 0x8000 firmware/esp32-csi-node/build/partition_table/partition-table.bin \ 0x10000 firmware/esp32-csi-node/build/esp32-csi-node.bin ``` Walk near the antenna. Watch the CSI amplitude change in real-time. That's your body affecting WiFi signals. --- ## What's actually inside **The hardware layer** — An ESP32-S3 chip ($8) runs custom firmware that captures raw WiFi Channel State Information (CSI) — amplitude and phase across 64 subcarriers — and streams it over UDP at 20-100 frames/sec. The binary protocol (ADR-018) is fully specified: magic number, node ID, antenna count, I/Q pairs. A Rust aggregator receives and validates the frames. **The signal processing layer** — 10 algorithms based on published research (SpotFi, Hampel, Fresnel, BVP, etc.) clean and transform the raw CSI into meaningful features: Doppler shifts for motion, Fresnel zones for breathing, spectrograms for activity classification. 5,937 lines of Rust, 105+ tests. **The AI layer** — A neural network (DensePose head + modality translator) maps CSI features to 17 body keypoints and 24 body surface regions. The training pipeline has 10 phases. Domain generalization (MERIDIAN/ADR-027) ensures models work across rooms without retraining. Self-supervised learning (AETHER/ADR-024) bootstraps from raw WiFi data alone. **The deployment layer** — Docker images, WASM browser build, 15 published Rust crates, 4 CI/CD workflows, 15+ security scanners, and a deterministic proof system with SHA-256 verification. --- ## Links | Document | What's in it | |----------|-------------| | [**ADR-028** (full audit)](https://github.com/ruvnet/wifi-densepose/blob/main/docs/adr/ADR-028-esp32-capability-audit.md) | Complete findings: ESP32 specs, binary frame format, signal algorithms, NN architecture, training phases, deployment infra, security posture | | [**Witness Log**](https://github.com/ruvnet/wifi-densepose/blob/main/docs/WITNESS-LOG-028.md) | 11 reproducible verification steps + 33-row attestation matrix | | [**Bundle generator**](https://github.com/ruvnet/wifi-densepose/blob/main/scripts/generate-witness-bundle.sh) | Creates offline-verifiable proof package | | [**PR #71**](https://github.com/ruvnet/wifi-densepose/pull/71) | The merged PR with all audit artifacts | | [**ESP32 tutorial**](https://github.com/ruvnet/wifi-densepose/issues/34) | Step-by-step hardware setup guide | | [**"Is it fake?" FAQ**](https://github.com/ruvnet/wifi-densepose/issues/37) | The original skeptics thread (with 20+ comments) | | [**crates.io**](https://crates.io/crates/wifi-densepose-core) | All 15 published Rust crates | | [**Docker Hub**](https://hub.docker.com/r/ruvnet/wifi-densepose) | Pre-built container images | --- The code is open. The tests pass. The proofs hash. The firmware flashes. The physics is peer-reviewed. The bundle self-verifies. We built the receipts. Your move. — rUv

👍 1 👎 4

Sign in to join this conversation.

1 Participants

Notifications

Due Date

No due date set.

Dependencies

No dependencies set.

Reference: dearsky/wifi-densepose#72