RuView is an open-source “WiFi DensePose” implementation leveraging multiple ESP32 nodes to turn WiFi signals into real-time human pose estimation, vital sign monitoring, and presence detection without relying on video cameras. WiFi DensePose is a sensing technique, first explored in academic research, that leverages WiFi signals to reconstruct human pose. RuView implements this technique in Rust or Python, and relies on your WiFi router and several ESP32 nodes to track body pose, detect breathing rate, and measure heart rate even through walls. As we’ll discuss below, this project has its own controversy, as some claim it’s fake. The solution relies on Channel State Information (CSI) disturbances caused by human movement to reconstruct body position, breathing rate, heart rate, and presence in real time using “physics-based signal processing and machine learning”. That obviously means you need CSI-capable hardware, and not all consumer WiFi nodes implement it. The project description lists various […]
Firefly CAM-3576 series – Tiny Rockchip RK3576 SBCs for commercial, industrial, and automotive applications
Firefly Technology has introduced the CAM-3576 series of tiny (38 × 38 mm) SBCs based on the Rockchip RK3576 processor with a 6 TOPS NPU for AIoT, edge AI, smart vision, industrial, and automotive applications. It comes in three variants, which include the CAM-3576Q38 (commercial), the CAM-3576JQ38 (industrial), and the CAM-3576MQ38 (automotive) modules designed for smart cameras, intelligent security systems, dash cams, and private on-device AI model deployment. The CAM-3576 series supports up to 16GB of LPDDR5 RAM, up to 256GB eMMC flash, and also includes a microSD card for expansion. Additionally, the boards feature a MIPI CSI input for up to 16MP camera sensors with HDR support, Fast Ethernet, Wi-Fi 6, USB 2.0, USB-C (device), RS-485, UART, I²C, ADC, GPIOs, audio input/output, and RTC support. Firefly CAM-3576Q38 specifications: SoM – ICORE-3576Q38 SoC – Rockchip RK3576 (Q38 – Commercial) or Rockchip RK3576J (JQ38 – Industrial) or Rockchip RK3576M (MQ38 – Automotive) […]
CamThink NeoEyes NE301 – An ultra-low-power, STM32N6-based Edge AI camera
CamThink NeoEyes NE301 is an ultra-low-power Edge AI camera built around the STM32N6 Arm Cortex-M55 MCU with Neural-ART NPU that “offers significantly enhanced features and performance” compared to the company’s earlier ESP32-S3-based NeoEyes NE101. The camera ships with a 4MP MIPI CSI camera sensor by default, but USB camera sensors are also supported. It also features 64MB PSRAM, 128MB hyperflash, WiFi 6 and Bluetooth 5.4 wireless connectivity, optional support for a 4G LTE module (global or US), audio wafers, USB-C and UART debug, a 16-pin GPIO header, and support for either USB, battery, or PoE power. CamThink NeoEyes 301 specifications: MCU – STMicro STM32N6 MCU Core – Arm 32-bit Cortex-M55 CPU @ up to 800MHz with Arm Helium and Arm MVE GPU – Neo-Chrom 2.5D GPU, Chrom-ART Accelerator (DMA2D) NPU – ST Neural-ART accelerator @ 1 GHz, up to 600 GOPS; 3 TOPS/W enabling fanless operation BPU – Hardware-accelerated H.264 […]
Axelera Metis M.2 Max Edge AI module doubles LLM and VLM processing speed
Axelera AI’s Metis M.2 Max is an M.2 module based on an upgraded Metis AI processor unit (AIPU) delivering twice the memory bandwidth of the current Metis M.2 module for compute-intensive Edge AI inference applications such as large language models (LLMs) and vision language models (VLMs). The new Metis M.2 Max also offers a slimmer profile, advanced thermal management features, and additional security capabilities. It is equipped with up to 16 GB of memory, and versions for both a standard operating temperature range (-20°C to +70°C) and an extended operating temperature range (-40°C to +85°C) will be offered. These enhancements make Metis M.2 Max ideal for applications in industrial manufacturing, retail, security, healthcare, and public safety. Axelera AI Metis M.2 Max specifications and host requirements: Accelerator – Metis AIPU’ System Memory – 1GB, 4GB, 8GB, or 16GB memory Host Interface – M.2 2280 M-key edge connector with PCIe Gen. 3.0 […]
Xerxes Pi – A Raspberry Pi CM4/CM5 carrier board with a rack-friendly design (Crowdfunding)
Designed by Rapid Analysis in Australia, the Xerxes Pi is a cross-vendor compute module carrier board that fits into a 1U rack and supports Raspberry Pi CM4/CM5, Radxa CM5, Banana Pi CM4/CM5, and Orange Pi CM4/CM5 compute modules. Designed at just one-third the size of a Nano-ITX board (120 × 40 mm), it’s ideal for home lab and small business servers looking for a low-cost way to run Docker containers and other open-source software. For storage, the carrier board includes a microSD card, and an M.2 E-Key slot enables support for accelerators or peripherals. Additionally, it features an I²C/SPI header and optional PoE via add-on boards or splitters. The design is well thought out and comes with a thermally efficient design with ventilated enclosures, optional PLA or metal heatsinks, and open-source 3D printable rack cases (single or multi-board). With open schematics, 3D files, Xerxes Pi targets DIY electronics, clustered computing, edge servers, […]
Compulab MCM-iMX95 – A solder-down NXP i.MX 95 SoM
Compulab MCM-iMX95 is yet another NXP i.MX 95 system-on-module (SoM), whose main selling point is being offered as a solder-down QFN package with SMD pads. The hexa-core Cortex-A55 Edge AI module ships with 4GB to 16GB LPDDR5 memory, 16GB to 128GB eMMC flash, an NXP PF0900 PMIC, and an RTC. All I/Os are exposed through 180 QFN SMD pads, including LVDS and MIPI DSI display interfaces, two MIPI CSI camera interfaces, two Gigabit plus one 10 Gbps Ethernet MACs, two PCIe Gen3 x1 interfaces, and more. Compulab MCM-iMX95 specifications: SoC – NXP i.MX 95 CPU Up to 6x Arm Cortex-A55 cores @ up to 1.8 GHz Real-time co-processors – Arm Cortex-M7 @ 800MHz and Cortex-M33 @ 250MHz 2D/3D Graphics Acceleration 3D Arm Mali GPU with OpenGL ES 3.2, Vulkan 1.2, OpenCL 3.0 2D GPU Video Encode / Decode – 4Kp30 H.265 and H.264 AI/ML – 2 TOPS eIQ Neutron NPU System […]
Huginn is a self-hosted, open-source alternative to IFTTT and Zapier
IFTTT and Zapier automation tools enable users to create automated workflows connecting various apps, services, and devices. They are relatively easy to use, but their free tiers are now rather limited, and you have to rely on the cloud. Huginn is a self-hosted, open-source alternative to IFTTT or Zapier that can work on your own network without cloud connectivity. Andrew Cantino released the first version of the project 12 years ago (in 2013) by Andrew Cantino, but it now has a larger community of developers and users. Somehow, I only found out about Huginn when XDA Developers wrote about it earlier this week. Let’s have a look. Developers describe Huginn as a system for building agents that perform automated tasks for you online, and view it as a hackable version of IFTTT or Zapier hosted on the user’s server with full control over the data. Here are some of the […]
Firefly’s CSB1-N10 series AI cluster servers can deliver up to 1000 TOPS of AI power with Rockchip or NVIDIA Jetson Modules
Firefly has recently introduced the CSB1-N10 series AI cluster servers designed for applications such as natural language processing, robotics, and image generation. These 1U rack-mounted servers are ideal for data centers, private servers, and edge deployments. The servers have multiple computing nodes, featuring either energy-efficient processors (Rockchip RK3588, RK3576, or SOPHON BM1688) or high-performance NVIDIA Jetson modules (Orin Nano, Orin NX). With 60 to 1000 TOPS AI power, the CSB1-N10 servers can handle the demands of large AI models, including language models like Gemma-2B and Llama3, as well as visual models like EfficientVIT and Stable Diffusion. CSB1-N10 series specifications All CSB1-N10 AI servers have the same interfaces, and the only differences are the CPU, memory, storage, multimedia, AI capabilities, and related software support. So it’s likely Firefly has made Rockchip system-on-modules compatible with NVIDIA Jetson SO-DIMM form factor, and indeed we previously noted that Firefly designed Core-1688JD4, Core-3576JD4, or Core-3588JD4 […]


