The author demonstrates how to run Espressif's ESP-Claw agent framework on an ESP32-P4 microcontroller using a self-hosted Qwen 3.6 LLM. Unlike standard TinyML implementations that only use microcontrollers for simple tasks, this setup allows the chip to manage sensing, decision-making, and tool execution through Lua scripts triggered via Telegram. The project shows how hardware behavior can be modified in real-time through chat without needing to recompile firmware.
Main topics:
* Implementation of an agent loop directly on a microcontroller
* Using Lua modules for dynamic runtime skill acquisition
* Interfacing with LLMs via OpenAI-compatible APIs
* Controlling peripherals like GPIO, I2C, and sensors through natural language
* Utilizing Telegram as the primary user interface
Main topics:
* Implementation of an agent loop directly on a microcontroller
* Using Lua modules for dynamic runtime skill acquisition
* Interfacing with LLMs via OpenAI-compatible APIs
* Controlling peripherals like GPIO, I2C, and sensors through natural language
* Utilizing Telegram as the primary user interface
MimiClaw is an AI agent that runs on a $5 ESP32 board and delivers an OpenClaw-like experience, putting a private assistant in your pocket. It operates as a bare-metal AI agent, storing data in text files and interacting with the real world through GPIO pins.
