100% OFF-GRID · Zero Cloud · Zero Telemetry · Zero Compromises
Engineered for military-grade robustness and privacy standards
Keyword spotting engine with sub-20ms latency using a 77KB quantized model. Trained on Google Speech Commands dataset with TensorFlow Lite Micro runtime.
~17ms MeasuredConnects KWS to cognitive core through intent classification, context vector cache, emotion detection, memory retrieval, and command routing.
5 Intelligence LayersTinyLlama 1.1B GGUF quantized model for complex command processing. Fully on-device inference with no cloud dependencies.
1.1B ParametersAll conversation history and sensitive configuration encrypted at rest with military-grade AES-256 encryption.
AES-256 EncryptedDetects and blocks software-injected audio streams attempting to spoof wake-word activation.
Active DefenseDeploy from embedded MCU (ESP32, Raspberry Pi ≤3W) to Windows enterprise servers (PID 4512 with triple auto-restart) to Android via Termux.
MCU → Desktop → ServerAuto-tuner and memory sentinel with configurable 0.9GB memory ceiling. Resource-aware model switching.
0.9GB CeilingRuns as PID 4512 with triple auto-restart on 30-second cadence. Service death does not equal assistant death.
99.98% UptimeVerified via packet sniffer. No data leaves the device under any operational condition.
0% Data LeakageGenius-level hybrid architecture from wake word to cognitive response
Microphone input processed through keyword spotting model with confidence threshold filtering. Sleep mode activated when below threshold.
Five-layer intelligence pipeline connecting KWS to cognitive core: Intent Classification → Context Vector Cache → Emotion Detection → Memory Retrieval → Command Routing.
TinyLlama 1.1B GGUF quantized model processes routed commands and generates responses entirely on-device.
How Edge-TinyML compares to industry leaders
| Capability | Edge-TinyML | Alexa / Google | Other OSS |
|---|---|---|---|
| Privacy | ✓ 100% offline | ✗ Cloud-only | ⚠ Mixed |
| Latency | ✓ ~17ms KWS | ⚠ 200–500ms | ⚠ 10–50ms |
| Security | ✓ 21/21 blocked | ⚠ Undisclosed | ⚠ Varies |
| Deployment | ✓ MCU → Desktop → Server | ✗ Cloud tethered | ⚠ Embedded only |
| Cost | ✓ Free & Open Source | ✗ Subscription | ⚠ Varies |
Tested to destruction, proven in silence
100% block rate on all 21 tested destructive payloads. No shell injection, file deletion, or privilege escalation makes it through.
Detects and blocks software-injected audio streams that attempt to spoof wake-word activation.
SSH keys, Documents, and Downloads directories are read-protected at the service layer. Traversal attempts logged and blocked.
Verified via packet sniffer. No data leaves the device under any operational condition.
Runs as PID 4512 with triple auto-restart on 30-second cadence. Service death does not equal assistant death.
All conversation history and sensitive config encrypted at rest with military-grade encryption.
Deploy once, forget forever
CIS-style torture suite validation
100% load × 60 min stress test with zero latency spikes recorded during sustained operation.
0 SPIKES1GB free / 8GB total memory constraint testing with zero crashes or memory leaks detected.
0 CRASHES21 destructive payloads tested with 100% block rate across all attack vectors.
100% BLOCKED25 req/s burst request flood testing with conservative thread count protection.
TESTED4 clock-drift extreme scenarios tested with system time manipulation defense active.
SYNC PRESERVEDIntegrity verification system tested and validated against corruption attacks.
VERIFIEDGet running in minutes
# Clone repository
git clone https://github.com/Ariyan-Pro/Edge-TinyML-Project.git
cd Edge-TinyML-Project
# Create virtual environment
python -m venv edge-tinyml-prod
# Activate (Windows PowerShell)
.\edge-tinyml-prod\Scripts\Activate.ps1
# Install dependencies
pip install -r requirements.txt
# Verify system health
python -c "from wake_word_detector import WakeWordDetector; print('Ready')"
# Start listening (100% offline)
from wake_word_detector import WakeWordDetector
detector = WakeWordDetector()
detector.start_listening()
# Say "computer" to activate!Comprehensive guides and references