#RaspberryPi Zero W experiment failed and it's back in its ziplok. Just could not get DietPi to configure correctly, kernel panic this, couldn't recognize driver that, kept hanging at boot. Fucking piece of shit.
Now prepping my old #Radxa Zero (the original!) for the task. Should still be ultra low wattage, but way faster. They said once upon a time that it was 70% of the speed of a Pi4. Installing some additional software and will boot to kiosk browser. Should be a better experience.
Radxa Taco Updated for Raspberry Pi CM5 with 5× SATA and RAID Support
This board turns a Raspberry Pi CM5 into a NAS with support for 5 HDDs
The Radxa Taco is a carrier board for a Raspberry Pi Compute Module that gives you ethernet, USB, and HDMI ports plus a microSD card reader and M.2 2280 and M.2 2230 slots. But this board also has SATA connectors that let your compute module to power a network-attached storage system with up to five hard drives.
When Radxa first launched the Taco board in 2022 it supported the Raspberry Pi […]
#cm5 #radxa #radxaTaco #raspberryPiCm5 Read more: https://liliputing.com/this-board-turns-a-raspberry-pi-cm5-into-a-nas-with-support-for-5-hdds/
Hardware photos. ASCII art. Tables of data. This post has it all! Updated my notes on my homelab hardware for those of you who are binary curious. #odroid #supermicro #radxa #raspberrypi #freebsd #debian #alpinelinux
OwO what's this?
This is my new Raxda Rock 5T
And it runs GNU Guix of course!
Honestly, I'm really suprised by how well and smooth my transition from x86_64 to arm64 went, everything pretty much just works.
Huge thanks to Collabora for their efforts on upstreaming RK3588 and these boards specifically!
Literally no one: –
Me: how about having some unnecessary #BGA #soldering practice in the shape of upgrading #RAM on a #Radxa 4B Plus from 2GB to 4GB? 🤣
Well that did not go as planned.
I used Samsung K4F6E3S4HMMGCJ 2GB RAM chips from #RaspberryPi 4B. With those Radxa would power on, but won't boot and won't go into recovery mode. Only Maskrom works but I wasn't able even to flash a firmware.
Originally it has two HYNIX H9HCNNN8KUMLHR-NME chips - I wonder what makes those different... 🤔
CIX ClawCore Armv9.2 CPU family targets OpenClaw deployments
OpenClaw was just introduced a few months ago, but we've already seen several low-footprint implementations, and some companies even ship mini PCs preloaded with OpenClaw. But today, I was just informed that CIX had gone further, and introduced the ClawCore Armv9.2 CPU family specifically designed/optimized for OpenClaw. The family will be comprised of three main SKUs: ClawCore-P (勁螯芯 "Powerful Claw") - High-performance model with 12-core CPU @ 3.2GHz, Immortalis-G720 GPU, 45 TOPS AI compute, and support for up to 64GB LPDDR5 RAM. Aimed at high-parallelism, large-capacity scenarios. Shipping starts now in March 2026. ClawCore-A (智螯芯 "AI/Smart Claw") - Octa-core CPU @ 3.0GHz, 80 TOPS AI compute (expandable to 200 TOPS via PCIe AI card), up to 64GB LPDDR5. It's designed for 24/7 use, supports full-chain ECC, hardware security (encryption/key management), and enables up to 50% reduction in model token costs via local inference. In practise, 80 to 90% of requests
This Tiny Board Can Replace Your #RaspberryPi 5
#Radxa Cubie A7Z is all looked at in detail.
👉Watch the full video : https://youtu.be/1nyaeCO8pH0
Radxa AICore DX-M1M M.2 2242 low-power AI module delivers 25 TOPS of edge AI performance for just 3W of power
Radxa AICore DX-M1M is a compact, low-power M.2 edge AI acceleration module built around the DeepX DX-M1M neural processing unit (NPU) and delivers up to 25 TOPS (INT8) of AI performance while consuming only 3W of power. Designed for industrial robot arms, autonomous mobile robots (ARM), edge servers, drones, and AIoT devices, the module delivers high-performance AI and ML capabilities without blowing the power budget. It relies on a PCIe Gen3 x2 interface and works with both x86 and Arm systems, including the Raspberry Pi 5 and Radxa ROCK SBCs. AICore DX-M1M specifications: AI Accelerator – DeepX DX-M1M neural processing unit (NPU) with up to 25 TOPS AI System Memory – 1GB LPDDR4X @ 4266 MT/s (on-chip, supports up to 8GB according to DeepX) Storage – 1Gbit QSPI NAND / NOR flash Host Interface - PCIe Gen 3.0 x4 (supports Gen 1/2/3 and x1/x2) via M.2 M + B Key connector
The Digital Polyglot™
LinuxGizmos.com
Brad Linder
in_sympathy
Mark McBride
Alexei Edelweiss
cnx-software.com
Smart Home Circle