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 week in Python on Microcontrollers, we have the CircuitPython 10.1.0 release candidate, a new guide for AI on microcontrollers, and more!
MimiClaw turns a tiny ESP32-S3 board into a personal AI assistant. It's a local-first, portable, privacy-first AI that runs on a $5 chip without requiring Linux, Node.js, or a server. It supports Anthropic (Claude) and OpenAI (GPT) and stores all data locally.
ESP32-S3 2inch Capacitive Touch Display Development Board, 240×320 Pixels, IPS Panel, 32-bit LX7 Dual-core Processor, Supports WiFi & Bluetooth, Onboard Camera Interface, ESP32 With Display
ESP32-S3 4.2inch RLCD Development Board, 300 × 400 Resolution, Supports Wi-Fi & BLE Dual-mode Communication And AI Voice Interaction
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.
Pixlpal is a hackable, ESP32-S3-based desktop device with an 11.25-inch LED matrix, high-fidelity audio, and Home Assistant integration, designed to be a smart AIoT desktop companion.
This project guides you through building a portable AI agent using the UNIHIKER K10, Xiaozhi AI firmware, and a custom 3D-printed case. It covers hardware overview, firmware flashing, console setup, and 3D printing services.
This article details how to train an image classification model on an ESP32 using both the SenseCraft AI platform and a custom TensorFlow Lite implementation. It covers setting up binary classification, training the model, and deploying it on ESP32-S3 devices.
