Banana Pi has announced the BPI-SM10, a compact computing system powered by the SpacemiT K3 RISC-V processor. This hardware is designed for users interested in exploring RISC-V architecture and high-performance AI tasks at the edge. The system features an 8-core AI accelerator capable of delivering up to 60 TOPS, which is sufficient to run 30 billion parameter AI models.
Key details include:
* BPI-SM10 consists of a SpacemiT K3 compute module and a versatile carrier board.
* The processor features an octa-core design at 2.4 GHz with support for up to 32GB LPDDR5 RAM.
* Carrier board I/O includes M.2 PCIe Gen 4 slots, USB 3.2 ports, DisplayPort, and Gigabit Ethernet.
* A forthcoming K3 Pico-ITX single-unit mini PC will also be released featuring a 10-gigabit Ethernet port.
Key details include:
* BPI-SM10 consists of a SpacemiT K3 compute module and a versatile carrier board.
* The processor features an octa-core design at 2.4 GHz with support for up to 32GB LPDDR5 RAM.
* Carrier board I/O includes M.2 PCIe Gen 4 slots, USB 3.2 ports, DisplayPort, and Gigabit Ethernet.
* A forthcoming K3 Pico-ITX single-unit mini PC will also be released featuring a 10-gigabit Ethernet port.
The MeshCore development team announces a formal split within the project following internal disputes regarding brand ownership and the use of AI-generated code.
A former team member is accused of attempting to claim the MeshCore trademark and rebranding components using "vibe coded" AI tools without team consensus. The core team clarifies that the only official source of truth remains the GitHub repository and has launched meshcore.io to serve as the new central hub for firmware, documentation, and community engagement.
Main points:
- Internal conflict regarding trademark filings and brand control.
- Dispute over the use of AI-generated code versus human-crafted software.
- Transition of official resources to the meshcore.io domain.
- Introduction of the core development team members responsible for future updates.
A former team member is accused of attempting to claim the MeshCore trademark and rebranding components using "vibe coded" AI tools without team consensus. The core team clarifies that the only official source of truth remains the GitHub repository and has launched meshcore.io to serve as the new central hub for firmware, documentation, and community engagement.
Main points:
- Internal conflict regarding trademark filings and brand control.
- Dispute over the use of AI-generated code versus human-crafted software.
- Transition of official resources to the meshcore.io domain.
- Introduction of the core development team members responsible for future updates.
Espressif Systems has introduced the ESP-Claw framework, designed to enable ESP32 devices to function as local AI agents. The framework allows hardware to interact with Large Language Models (LLMs) to make decisions and execute actions locally without requiring constant cloud connectivity. It supports natural language conversation for defining device behavior through chat coding and utilizes Lua scripts for deterministic execution.
Key features include:
- Local event bus driving millisecond-latency responses via Lua rules.
- MCP Server and Client capabilities for hardware exposure and external service calling.
- On-chip private memory for long-term context retention without data leaving the device.
- Support for various messaging platforms including Telegram, WeChat, and Feishu.
- Compatibility with LLMs such as OpenAI, Qwen, and ChatGPT.
- Current support for ESP32-S3 with upcoming support for ESP32-P4.
Key features include:
- Local event bus driving millisecond-latency responses via Lua rules.
- MCP Server and Client capabilities for hardware exposure and external service calling.
- On-chip private memory for long-term context retention without data leaving the device.
- Support for various messaging platforms including Telegram, WeChat, and Feishu.
- Compatibility with LLMs such as OpenAI, Qwen, and ChatGPT.
- Current support for ESP32-S3 with upcoming support for ESP32-P4.
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.
Monitor air quality with the Fermion BMV080, the world's smallest fanless PM2.5 sensor.
This ultra-compact breakout board utilizes innovative laser-based light scattering and a fanless measurement principle to deliver laboratory-grade precision in a footprint of just 24×20mm.
Capable of simultaneously measuring PM1, PM2.5, and PM10 mass concentrations with an accuracy of ±10μg/m³, the device operates with an ultra-low current consumption of 6μA in sleep mode.
It's perfect for space-constrained applications, ranging from portable air monitors and smart home thermostats to battery-powered IoT nodes.
This ultra-compact breakout board utilizes innovative laser-based light scattering and a fanless measurement principle to deliver laboratory-grade precision in a footprint of just 24×20mm.
Capable of simultaneously measuring PM1, PM2.5, and PM10 mass concentrations with an accuracy of ±10μg/m³, the device operates with an ultra-low current consumption of 6μA in sleep mode.
It's perfect for space-constrained applications, ranging from portable air monitors and smart home thermostats to battery-powered IoT nodes.
zclaw is a personal AI assistant running on an ESP32, backed by Anthropic, OpenAI, or OpenRouter. It allows for monitoring and control of connected devices via Telegram, scheduling tasks, and creating custom tools, all within an 888KB footprint.
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.
Build a voice-controlled AI assistant using an ESP32, Xiaozhi and MCP. This project focuses on safe hardware and software automation through the Model Context Protocol.
This software uses the Python logger to create a logfile for all messages for all topics to which this MQTT client has subscribed. It logs data as JSON or CSV and can be configured via command-line options.
This article details how to set up and use Machinechat JEDI with the Seeed Studio reTerminal DM for industrial IoT applications, including hardware/software preparation, installation, data pipeline creation, visualization, and MQTT integration.
