TinyML air quality monitoring lets you build a low‑power, privacy‑first device that predicts indoor air quality on an ESP32‑C3. The guide covers sensor selection, data collection, model training, quantization, and deployment.
TinyML real‑time sign language recognition lets you build a low‑power translator that runs on an ESP32‑C3. The guide covers dataset prep, model training, quantization, and deployment.
TinyML indoor positioning lets everyday devices figure out where they are inside buildings using BLE beacons and a small neural net that runs on microcontrollers. This guide covers data collection, training, quantization, and deployment.
Edge AI audio event detection lets microcontrollers listen, classify, and react to sounds instantly—no cloud needed. This guide walks through data collection, feature extraction, model training, quantization, and deployment on Raspberry Pi 4 and ESP32.
Drone swarm AI lets you build small, autonomous fleets that can map crops, deliver medicine, or aid in emergencies. This guide covers data, training, deployment, and future trends.
TinyML neural architecture search automates the design of compact AI models that run on microcontrollers. This guide shows how to search, quantify, and deploy a model on an ESP32.
AI‑powered security monitoring uses machine learning to spot hidden threats in network logs, reducing false alarms and catching new attacks before they cause damage.
Federated learning for edge devices lets tiny gadgets learn together while keeping data private. This guide covers the basics, tools, and a smart‑home example.
TinyML on-device models let tiny devices run AI locally for privacy, low latency, and low cost. This guide covers tools, design tips, and a sample wake word project.
AI-generated API documentation automates the writing of clear, up‑to‑date docs directly from your code, saving time and boosting developer satisfaction.