Building high-performance Artificial Intelligence, next-generation Automotive Telemetry, intelligent ESP32 TinyML Micro-controllers, and ultra-scalable Web Cloud Platforms.
We are assembling our interactive flagship portfolio and live hardware demonstrations. In the meantime, direct technical inquiries and project collaborations are fully active via thaond@monnguyen.dev.
From silicon-level microcontroller neural networks to high-speed automotive bus telemetry and robust cloud web systems.
Deploying high-efficiency computer vision, intelligent agentic workflows, predictive neural modeling, and model quantization (INT8/FP16) optimized for cloud and edge processing.
Engineering connected vehicle architectures, real-time CAN/OBD-II telemetry logging, in-vehicle infotainment interfaces, battery management telemetry, and ADAS sensor fusion.
Harnessing the ESP32-S3 vector instructions for TinyML edge intelligence. Real-time neural inference on microcontrollers with milliwatt power consumption, Wi-Fi 6, BLE 5, and audio/vision AI.
Architecting high-concurrency web ecosystems, real-time WebSocket telemetry command centers, resilient REST/GraphQL APIs, and ultra-responsive cyber-grade user interfaces.
An intersection where automotive bus networks and low-power ESP32 silicon run localized AI models with instant decision-making.
Driver monitoring, occupant sensing, and predictive maintenance streaming over CAN-FD & 4G/LTE.
Running quantized TensorFlow Lite Micro models directly in SRAM with hardware vector DSP instructions.
Object recognition, lane detection, boundary segmentation, and autonomous spatial awareness.
Distributed WebSocket infrastructure streaming millions of concurrent sensor readings to web dashboards.
Inspect mock execution traces from our embedded firmware, CAN diagnostics, and edge AI runtime engines.
[0.002s] [INIT] Booting ESP32-S3 Vector Neural Engine...
[0.015s] [TFLITE] Loading quantized model: model_tinyml_quant_int8.tflite
[0.021s] [DSP] Initializing ESP-NN vector extensions: OK
[0.040s] [SENSOR] I2C 0x68 Accelerometer/Gyro initialized at 400kHz
[0.055s] [INFERENCE] Input buffer: 128 samples (3-axis vibration telemetry)
[0.072s] [INFERENCE] Result: Class 01 [NORMAL_OPERATION] Confidence: 99.41%
[0.073s] [POWER] Sleep current: 15uA | Active current: 48mA @ 240MHz
[0.089s] [BLE/WIFI] Broadcasting beacon packet to local telemetry receiver
[0.104s] [SYS] Free heap: 384,120 bytes | Internal Temp: 34.2 C
[0.120s] [LOOP] Cycle complete in 17.2ms. Next trigger in 50ms...
MonNguyen.Dev is actively undergoing architectural assembly. Follow our rollout phases:
Initial setup of hardware testbenches, ESP32-S3 breakout designs, automotive CAN simulation nodes, and base communication protocols.
Currently compiling on-chip TinyML models, real-time object detection models, and high-concurrency WebSocket data ingestion hubs.
Launching browser-accessible live telemetry dashboards streaming real-time metrics from our laboratory ESP32 test clusters.
Final deployment of client portals, end-to-end commercial solutions showcase, and interactive engineering consulting booking.
While the website is undergoing construction, our engineering team is actively collaborating on new AI solutions, automotive telemetry projects, ESP32 TinyML hardware, and web platforms.
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