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
NocKinematics is a modern, modular, and lightweight C++ Inverse Kinematics library designed specifically for Arduino and ESP32 microcontrollers. It utilizes the FABRIK (Forward And Backward Reaching Inverse Kinematics) algorithm to provide fast, iterative computations that are more efficient than traditional Jacobian Matrix approaches. The library is optimized for memory-constrained systems like AVR and ESP8266 by using specialized dynamic memory allocation to prevent RAM fragmentation.
Key features and topics:
* N-Joint Support for arbitrary numbers of connected joints.
* Memory-optimized architecture avoiding heavy std::vector usage.
* Platform agnostic compatibility with Arduino Uno, Nano, Mega, ESP8266, and ESP32.
* Practical implementation examples ranging from basic logic verification to multi-DOF servo motor control.
* Support for complex mechanisms like snake or tentacle simulations via the MultiJointSnake example.
Key features and topics:
* N-Joint Support for arbitrary numbers of connected joints.
* Memory-optimized architecture avoiding heavy std::vector usage.
* Platform agnostic compatibility with Arduino Uno, Nano, Mega, ESP8266, and ESP32.
* Practical implementation examples ranging from basic logic verification to multi-DOF servo motor control.
* Support for complex mechanisms like snake or tentacle simulations via the MultiJointSnake example.
The author explores the potential of running an AI agent framework on low-cost hardware by testing MimiClaw, an OpenClaw-inspired assistant, on an ESP32-S3 microcontroller. Unlike traditional AI setups, MimiClaw operates without Node.js or Linux, requiring the user to flash custom firmware using the ESP-IDF framework. The setup integrates with Telegram for interaction and utilizes Anthropic and Tavily APIs for intelligence and web searching. Despite the technical hurdles of installation and potential API costs, the project successfully demonstrates a functional, sandboxed, and low-power personal assistant capable of persistent memory and routine tracking.
PycoClaw brings full OpenClaw agent parity to embedded hardware — a MicroPython-powered AI agent that can run on a $5 microcontroller. It features one-click flashing, a full agent loop, hardware control, multi-channel chat, persistent memory, and ScriptOs skills.
PycoClaw is an open-source platform for running AI agents on microcontrollers. It brings OpenClaw workspace-compatible intelligence to embedded devices costing under $5. Built on MicroPython, it supports multi-provider LLM routing, multi-channel chat, tool calling, extensions, over-the-air updates, and battery operation.
This article details the process of running a personal AI assistant on a low-cost microcontroller. It covers the use of Ollama for running large language models (LLMs) locally and MimicLaw for optimizing the model for resource-constrained devices. The author shares their experience with porting and running the models, along with the challenges and solutions encountered.
This project details how to serve offline content (Wikipedia, etc.) from an ESP32 microcontroller, specifically using a LILYGO T-Dongle-S3. It covers the process from downloading ZIM files, processing them with Python scripts, transferring to an SD card, and loading a sketch onto the microcontroller to serve the content via a WiFi access point.
MicroPythonOS is a lightweight, fast, and versatile operating system designed to run on microcontrollers like the ESP32 and desktop systems. It features a modern Android-like touch screen UI, App Store, and Over-The-Air updates.
A demo for turning an ESP32-S3 microcontroller into a tiny, instant-on PC with a shell, editor, compiler, and app installer.
This article provides a comprehensive guide on choosing the best ESP32 LVGL development board, covering key features like RAM, CPU performance, display interface, and touch support. It also discusses different ESP32 variants and their suitability for LVGL projects, along with pros and cons, pricing, and sourcing tips.
