// CATEGORY: EDGE COMPUTING

Understand the landscape of on-device AI agents and tiny LLM systems, set up your development environment, and explore core tooling for edge AI.

Learn to implement robust, on-device speech-to-text functionality using Whisper.cpp, a high-performance C++ port of OpenAI's Whisper model, for edge …

Learn how to integrate a tiny, quantized Large Language Model (LLM) directly onto an edge device for natural language understanding, enabling …

Connect on-device Speech-to-Text (STT) output to a local TinyLLM for intent recognition and entity extraction, forming the core of your AI agent.

Prepare your development environment, install necessary tools, and run your first inference with Google's Gemma 4 QAT models for optimized edge …

Integrate your on-device AI agent with smart home systems to execute real-world actions using local APIs and tiny LLMs for intent mapping.

Master techniques for optimizing AI agent and tiny LLM performance and resource usage on constrained edge devices for real-world production …

Learn how to build robust, secure, and error-tolerant on-device AI agents and tiny LLM systems using modern edge AI tooling as of early 2026.

Learn production-grade deployment strategies, maintainability best practices, and advanced concepts for evolving on-device AI agents and tiny LLM …

Explore and build three distinct on-device AI agents—a voice assistant, a data summarizer, and an anomaly detector—using tiny LLMs and modern edge …

Explore 3 production-style project ideas for on-device AI agents and tiny LLMs, leveraging modern edge AI tooling and frameworks as of 2026 for …