Founder George Hammel describes his Star Stream engineering team as “a pretty wild group.” A few of them aren’t certified engineers in the traditional sense, but all of them grew up solving problems in motorsports – an environment that rewards fast thinking, creative solutions, and zero tolerance for downtime. That background shows in what they’ve built.

Star Stream is a live video, telemetry, and data streaming service provider, built around Starlink connectivity and designed for the most demanding environments in racing. Their Streambox Pro Kit delivers ultra-low latency video from cockpit to pit wall on circuits and off-road courses alike. But as the demands of professional racing evolved, video alone stopped being enough.

After George took part in Arduino Days 2026, we had a chance to dive deeper with him into how Star Stream is meeting challenges and innovating – moving from prototype to track-ready deployment faster than expected – thanks to the Arduino ecosystem’s open-source flexibility.

From video to vehicle intelligence

Teams today want real-time granular telemetry – engine data, GPS, environmental sensors, driver biometrics – delivered fast enough to influence decisions mid-race. That meant Star Stream needed a processing platform that was rugged, versatile, and smart enough to keep up with a vehicle’s own systems.

The answer became the Telemetrybox Pro, their onboard telemetry and biometric system, built around the Arduino^® UNO Q board. UNO Q pulls CAN bus frames in real-time, automatically detects and maps signal parameters, and translates raw vehicle data into meaningful telemetry – without any manual configuration from the user. Its machine learning capabilities mean the system doesn’t just read data, it learns from it. As George puts it, “it’s also very smart with the machine learning capabilities that we can program from Star Stream into the device. And now we have the flexibility to make it learn, not just right now, but also well into the future. That gives us scalability.”

In practice, this means teams get real-time telemetry, biometrics, and course diagnostics through Star Stream’s mobile apps and web dashboards – something that until recently was exclusively available with Formula 1 budgets.

Built for the track, ready for the road

Star Stream has already put the Telemetrybox Pro through some of the most extreme conditions in motorsport. At Pikes Peak, where engines struggle with altitude and vehicles are tuned to the limit, a team running Star Stream’s system detected a loss of turbo boost pressure while the car was still climbing. The data reached the pit crew before the driver even reached the summit, giving the mechanics enough time to diagnose the problem, fix it, and send the car out for another run. Without real-time telemetry, George says, the team’s weekend would have been over.

In Baja, where connectivity doesn’t exist until Starlink makes it possible, the stakes are different but just as real. Mid-race, Star Stream’s system flagged a tire losing pressure and a fuel situation developing – the chase crew radioed the driver and co-driver in time to pull over and fix it before the tire went flat. At a recent NORA Rally, a voltage drop was caught early enough that the team switched to a backup alternator before any damage occurred. “We or the chase crew, mechanics, crew chief, can tell what’s going on with the car before the people that are in the car usually know what’s happening,” George explains.

The Telemetrybox Pro is already in commercial use, not just prototyping. Now, its same technology is being adapted well beyond motorsport: fleet services, automotive mechanics, and contractors – anyone who depends on vehicles to run a business – can use the same real-time vehicle health monitoring to reduce downtime, optimize workflows, and even feed data to insurers to demonstrate safe operation. 

Curious to find out more? Watch the full unveil video by Star Stream here and follow Star Stream on Facebook or Instagram.

Arduino and UNO are trademarks or registered trademarks of Arduino S.r.l

The post Star Stream is bringing F1-level telemetry to every race team – and every fleet – with Arduino UNO Q appeared first on Arduino Blog.

Read more here: https://blog.arduino.cc/2026/06/08/star-stream-is-bringing-f1-level-telemetry-to-every-race-team-and-every-fleet-with-arduino-uno-q/

The post Star Stream is bringing F1-level telemetry to every race team – and every fleet – with Arduino UNO Q appeared first on IPv6.net.

Read more here: https://blog.apnic.net/2026/06/08/from-workshop-to-deployment-how-bangladesh-cctld-implemented-dnssec/

The post From workshop to deployment: How Bangladesh ccTLD implemented DNSSEC appeared first on IPv6.net.

EMI (electromagnetic interference) can be a real nuisance in sensitive circuits. That might be from one device affecting another, but it can also happen when a circuit on a PCB interferes with another circuit on the same PCB (or another PCB in the same device). Engineering to prevent that entirely is really difficult and it helps a lot to be able to see where the interference is, which is why element14 Presents’ Clem Mayer converted an old 3D printer into a new EMI imager.

The goal here is to capture a “picture” of the device or PCB in question that shows the areas where EMI is highest. That picture looks like a heat map, with hot spots corresponding to areas of high EMI. But it isn’t something you can capture by snapping a photo with your Nikon. That’s where this EMI imager comes in.

Using an old 3D printer as a motion system, Mayer’s EMI imager moves a detector back and forth across the entire area, scanning EMI levels as it goes. At many points across that area, the system records both the XY coordinates and the EMI level. After scanning, a simple script can turn that data into a 2D image. 

A conventional camera mounted overhead also captures a normal photo. With the generated heat map overlaid onto the photo, the user can easily visualize the areas of the PCB or device that produce a lot of EMI.

The EMI detector is actually just an RTL-SDR module, which is affordable. It and the camera connect to a computer, which records the data. The 3D printer’s motion system operates under the control of an Arduino UNO Rev3 running Grbl 1.1, so it can run a simple G-code file that causes the toolhead (with detector) to move in a serpentine pattern across the scanning area.

If you have an old 3D printer gathering dust, this is an easy and affordable way to capture high-quality EMI images for diagnosing and improving designs.

The post An old 3D printer becomes a new EMI imager appeared first on Arduino Blog.

Read more here: https://blog.arduino.cc/2026/06/06/an-old-3d-printer-becomes-a-new-emi-imager/

The post An old 3D printer becomes a new EMI imager appeared first on IPv6.net.

AkiraOS is a Zephyr-based embedded OS that runs sandboxed WebAssembly applications on microcontrollers and lets users deploy and update firmware OTA without reflashing. In other words, it’s similar to Docker containers, but for microcontrollers. The open-source embedded platform separates the OS from the application. That means the firmware stays stable, while apps are independent .wasm binaries deployable over-the-air without touching the OS, and portable so a single binary works on ESP32-S3, nRF5x, or STM32 MCU boards. AkiraOS highlights: User space Up to 8 wasm apps can be installed Up to two apps can run at the same time Footprint: 50KB to 200KB per app Akiraz runtime – Custom WASM runtime App Manager UI Framework with 32 widgets Shell/console 18 API modules WebAssembly Micro Runtime (WAMR) – Two options: Interpreter or Ahead-Of-Time (AOT) compilation with 10 to 50x higher performance RTOS – Zephyr RTOS Scheduler Network stack HTTP for OTA updates […]

The post Docker for Microcontrollers? AkiraOS combines Zephyr RTOS with WebAssembly (WASM) applications appeared first on CNX Software – Embedded Systems News.

Read more here: https://www.cnx-software.com/2026/06/06/docker-for-microcontrollers-akiraos-combines-zephyr-rtos-with-webassembly-wasm-applications/

The post Docker for Microcontrollers? AkiraOS combines Zephyr RTOS with WebAssembly (WASM) applications appeared first on IPv6.net.

Edge AI is evolving quickly. It was the end of 2022 when the world saw the first Cloud AI tool available to everyone, accessible through a simple and intuitive chat. In less than four years, models have been refined, distilled, optimized, quantized – at record-breaking speed – to meet the needs of the first generation of edge systems focused mostly on detection and classification: identifying an object, recognizing a keyword, or triggering an action when a predefined event occurs.

The landscape is changing so quickly that the conversation is now already shifting toward something more interesting. Devices are starting to move from simple recognition to local understanding.

So instead of asking, “What is this?”‘ developers are beginning to ask:

• “What is happening here?”
• “What does this information mean?”
• “What action should the system take next?”

This is where local AI agents, LLMs, and intelligent workflows start becoming relevant at the edge.

That does not mean every device suddenly needs to run massive cloud-scale models. In most real-world scenarios, the goal is not running the biggest possible AI model – but running the right intelligence close to where data is generated. This is exactly the space where the Arduino^® UNO Q board shows its full potential.

By combining Debian Linux with a real-time STM32 microcontroller, UNO Q creates a hybrid platform where developers can experiment with practical local intelligence while still interacting reliably with sensors, actuators, cameras, industrial signals, and physical systems.

The Linux side can manage higher-level orchestration, local AI frameworks, APIs, dashboards, and model execution. The microcontroller side continues handling deterministic I/O, timing-sensitive interactions, and hardware control. That balance makes it possible to explore a new category of edge applications that don’t immediately depend on cloud infrastructure.

Now, let’s briefly explore three major directions in which UNO Q is contributing to reshaping edge computing. This is the introduction to a series of posts, where we’ll dive into each of these topics in more detail.

Building local AI agents on UNO Q

With the latest developments of agentic AI making headlines in the tech-world news, the next step is creating systems capable of reasoning about tasks and coordinating actions locally.

AI agents are essentially workflows where models interact with tools, hardware, APIs, sensors, or software services to complete specific objectives. On UNO Q, this means creating systems that observe the environment, interpret context, and trigger actions directly on the device. 

For example, David Groom ran OpenClaw on UNO Q to access embedded hardware conversationally, with a zero code approach – but an agent could also query and analyze information coming from sensors, summarize machine conditions, read visual states from a camera, or interact with connected services while still keeping the execution flow local. The interesting part is creating focused systems that are useful, understandable, and deployable in real products.

Because UNO Q combines Linux with real-time hardware control, these agents can move beyond chat interfaces and directly interact with the physical world. Interested in finding out more? Stay tuned for the dedicated article in this series.

Start experimenting with radical accessibility, following David’s example here.

Running local LLMs on UNO Q

Local language models are opening the door to a different type of edge interaction.

Instead of sending every request to the cloud, developers can run compact models directly on the device for task-specific workflows such as local assistants, OCR (optical character recognition), status summarization, command parsing, or contextual responses.

There are huge advantages to this in terms of privacy, any time keeping sensitive operational data on-device matters. But the real game-changer in this type of application is the reduced dependency on connectivity paired with improved responsiveness, resulting in systems that continue to operate without skipping a beat even when the network is unavailable.

UNO Q provides a practical platform for these experiments thanks to its Debian Linux environment, support for local AI frameworks, and compatibility with optimized inference workflows. Check out the documentation on this Project Hub entry by Robuinlabs to build your own private AI assistant, creating a local LLM chatbot that can run even when the internet is down or connection is not available.

There are, of course, constraints to what models can realistically run on the board, and on the computational power that can be expected from the nimble and cost-effective UNO Q. However, the trade-off may often prove perfectly acceptable for many experiments, prototypes, and a wide range of light applications. You don’t need a sledgehammer to crack a nut!

We’ll dive deeper into all of this in the next blog post in this series.

Build your own AI assistant with a local LLM chatbot, thanks to Robuinlab’s tutorial here.

Automating AI workflows on UNO Q 

The final step goes beyond single models or isolated agents.

Modern AI systems increasingly rely on workflows composed of multiple stages: capturing information, analyzing context, generating responses, triggering actions, and coordinating software execution. This includes workflows like local audio transcription and object recognition pipelines, multi-source data acquisition and automation systems: a great example of how all of this can fit together is Kevin McAleer’s “Nibsy” project, an AI agent that watches you work, listens to what you say, and at the end of a session writes the tutorial for you.

In these scenarios, the AI model becomes part of a larger orchestration pipeline rather than a standalone feature.

Using UNO Q is particularly interesting here because it allows developers to combine multiple layers together: Linux applications, Python environments, AI frameworks, cloud-connected services, local APIs, and deterministic microcontroller logic – all running side by side. Some workflows may use local models entirely on-device. Others may combine local execution with cloud-based reasoning depending on latency, privacy, or computational requirements.

The important shift is that UNO Q is no longer limited to simple inference. It enables solutions that coordinate complex operational workflows while remaining closely connected to the physical environment. We’ll see a few inspiring examples of how this can happen in a dedicated blog post. 

Explore how AI can automate complex projects in the example documented by Kevin here.

From AI demos to useful edge systems

One of the biggest misconceptions around AI at the edge is that success is measured by running the largest possible model. In reality, most successful deployments are built around smaller, focused systems designed for specific operational goals – such as:

• Reading text locally from a camera feed
• Recognizing gestures without streaming video to the cloud
• Summarizing machine states
• Interpreting operator commands
• Triggering actions from simple contextual understanding
• And many other practical examples of intelligence creating real value directly on-device!

UNO Q makes experimenting and building applications approachable by combining familiar Linux development with the flexibility of the Arduino ecosystem and real-time hardware interaction. All of it is built leveraging Arduino^® App Lab and the Bricks available there.

Over the next three articles in this series, we’ll explore how local AI agents, LLMs, and complex AI workflows can move from experimentation into practical edge applications running on UNO Q. Are you ready to explore with us?

Arduino, and UNO, and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

The post Beyond Edge AI: bringing local intelligence to Arduino UNO Q appeared first on Arduino Blog.

Read more here: https://blog.arduino.cc/2026/06/05/beyond-edge-ai-bringing-local-intelligence-to-arduino-uno-q/

The post Beyond Edge AI: bringing local intelligence to Arduino UNO Q appeared first on IPv6.net.

NetApp en Cisco presenteren nieuwe gevalideerde FlexPod-oplossingen voor AI-infrastructuur. De uitbreiding richt zich op enterprise AI-implementaties, inferencing, RAG-workflows en edge computing. Volgens de bedrijven levert de bestaande FlexPod-samenwerking klanten al tot twintig procent tijdwinst op bij infrastructuurbeheer. De nieuwe varianten zijn mede ontwikkeld samen met NVIDIA.

NetApp en Cisco kondigen een uitbreiding van hun FlexPod-samenwerking aan. De twee bedrijven brengen gevalideerde architecturen op de markt die AI-workloads moeten ondersteunen op het gebied van rekenkracht, netwerk en opslag. De oplossingen zijn beschikbaar voor organisaties van verschillende omvang en richten zich op drie use cases: enterprise AI-implementaties, AI-inferencing met RAG-workflows, en edge computing.

Enterprise AI-implementaties

Voor grootschalige AI-toepassingen, waaronder retrieval-augmented generation en semantische zoektoepassingen, biedt NetApp een architectuur met ontkoppelde opslag via NetApp AFX, waarbij performance en opslagcapaciteit onafhankelijk van elkaar opschalen. Volgens NetApp voorziet de NetApp AI Data Engine (AIDE) in toekomstige functionaliteit voor data discovery, -voorbereiding en -governance, geïntegreerd met het NVIDIA AI Data Platform referentiedesign.

De beveiliging verloopt via Cisco Secure AI Factory with NVIDIA, dat Zero Trust-gebaseerde controles toepast over de volledige AI-pipeline. Dallas Olson, Chief Commercial Officer bij NetApp, stelt dat de gecombineerde expertise van beide bedrijven organisaties in staat stelt AI sneller in te zetten met ingebouwde beveiliging. De Cisco AI-netwerkinfrastructuur met Nexus One positioneert het netwerk als een deterministisch fabric, wat volgens Cisco de benutting van XPU’s verbetert en de doorlooptijd van AI-taken verlaagt.

Samen met NVIDIA hebben NetApp en Cisco FlexPod-oplossingen op basis van NVIDIA Enterprise Reference Architectures ontwikkeld voor het ontwerpen, implementeren en opschalen van AI-fabrieken op enterpriseniveau.

AI-inferencing en RAG-workflows

Voor teams die AI willen inzetten op bestaande data introduceert het samenwerkingsverband een vooraf geïntegreerde oplossing. NetApp en Cisco stellen dat deze variant de kosten, complexiteit en behoefte aan gespecialiseerde vaardigheden bij het uitrollen van AI-infrastructuur terugdringt.

Jeremy Foster, GM en SVP bij Cisco, geeft aan dat beveiliging bij AI-implementaties vanaf het begin in de infrastructuur moet zitten. Volgens Foster pakt de uitbreiding naar FlexPod AI-specifieke risico’s aan, zoals datalekken en governance- en compliancevraagstukken.

Edge computing

De derde use case richt zich op AI-inferencing en gecontaineriseerde en gevirtualiseerde workloads op afgelegen locaties. De combinatie van Cisco Unified Edge met NetApp-opslagopties levert een geïntegreerde infrastructuuroplossing. Centraal fleetmanagement, beleidsgestuurde configuratie en geautomatiseerde orkestratie maken herhaalbare uitrol op gedistribueerde locaties mogelijk, stellen de bedrijven.

Rol van NVIDIA en partners

Jason Hardy, Vice President Storage Technologies bij NVIDIA, stelt dat data discovery, governance en voorbereiding in de infrastructuur moeten zijn ingebouwd om AI in productieomgevingen op te kunnen schalen. Volgens Hardy geeft NetApp AI Data Engine, gevalideerd met Cisco Secure AI Factory with NVIDIA, organisaties een basis om AI-fabrieken uit te rollen op FlexPod-infrastructuur.

WWT, een van de samenwerkingspartners, geeft aan de nieuwe architecturen te valideren in zijn AI Proving Ground. Brian Bartell, Practice Manager Compute en Storage bij WWT, stelt dat de uitbreiding de tijd tussen idee en uitvoering voor klanten verkort.

Het bericht NetApp en Cisco breiden FlexPod uit voor AI-workloads verscheen eerst op MSP Business.

Read more here: https://mspbusiness.com/technologie-en-architectuur/netapp-en-cisco-breiden-flexpod-uit-voor-ai-workloads/

The post NetApp en Cisco breiden FlexPod uit voor AI-workloads appeared first on IPv6.net.

Erqos EQSP32CE is a DIN rail-mountable industrial IoT PLC based on an ESP32-S3 WiFi and Bluetooth SoC and offering Ethernet, RS-485, RS-232, and CAN bus industrial communication interfaces. The IIoT logic controller also features several protected digital (16x) and analog (8x) inputs, eight current inputs, eight “special mode” analog inputs, and sixteen digital outputs with PWM support. A USB-C port is used for firmware flashing and monitoring, and the PLC takes a wide 7V – 26V DC input voltage and outputs 5V/1A for I/O expansion modules. Erqos EQSP32CE specifications: SoC – Espressif ESP32-S3 dual-core LX7 processor @ 240 MHz with 8MB Flash, 512kB RAM, wireless connectivity (so probably ESP32-S3FN8) Communication interfaces 10/100 Mbps Ethernet RJ45 port Wi-Fi + Bluetooth Low Energy (on ESP32-S3) with internal antenna RS232 (protected) and RS485 half-duplex (protected) with support for Modbus RTU, DMX512, and custom serial protocols CAN Bus (protected) USB – 1x USB-C power for […]

The post Erqos EQSP32CE – An industrial IoT ESP32-S3 PLC with Ethernet, RS232, RS485, CAN Bus, DIN Rail support appeared first on CNX Software – Embedded Systems News.

Read more here: https://www.cnx-software.com/2026/06/05/erqos-eqsp32ce-an-industrial-iot-esp32-s3-plc-with-ethernet-rs232-rs485-can-bus-din-rail-support/

The post Erqos EQSP32CE – An industrial IoT ESP32-S3 PLC with Ethernet, RS232, RS485, CAN Bus, DIN Rail support appeared first on IPv6.net.

Resellers die gebruikmaken van het My-Connect platform van BusinessCom kunnen vanaf nu managed datasim-oplossingen bestellen via een samenwerking met Q data solutions. De nieuwe dienst draagt de naam My-Connect Q Data Datasim en sluit aan op het bestaande aanbod van BusinessCom, dat onder meer cloud-, communicatie- en cybersecuritydiensten omvat.

Volgens BusinessCom groeit de vraag bij zijn partners naar mobiele connectiviteitsoplossingen voor toepassingen als failover, tijdelijke locaties, IoT-projecten en verbindingen met een vast publiek IP-adres. Shèr Hendrix van BusinessCom geeft aan dat de toevoeging van datasim-diensten resellers in staat stelt meer diensten vanuit één omgeving af te nemen. De eerste gezamenlijke projecten zijn volgens beide partijen al afgerond.

Geautomatiseerd platform voor partners

Q data solutions levert managed 5G-connectiviteit uitsluitend via telecom- en ICT-partners. Het bedrijf beschikt over een geautomatiseerd platform waarop verbindingen 24/7 besteld en beheerd kunnen worden, inclusief opties voor vaste publieke IPv4-adressen.

Jeroen Karel, directeur van Q data solutions, stelt dat het aanbod goed aansluit op de opzet van My-Connect, waarbij resellers één omgeving hebben voor cloud, communicatie, cybersecurity en connectiviteit. Hij geeft aan dat de API-integraties tussen beide platforms snel zijn gerealiseerd dankzij korte lijnen tussen de organisaties, en dat de eerste gezamenlijke projecten inmiddels zijn afgerond.

Beschikbare oplossingen en verdere integratie

Binnen het My-Connect portfolio zijn via de samenwerking onder meer de volgende datasim-oplossingen beschikbaar: Odido NL Data, IoT via KPN en Telefónica, en KPN EU Data.

BusinessCom meldt dat de komende periode verdere integratie in de My-Connect Portal plaatsvindt. Resellers kunnen dan abonnementen volledig online bestellen, beheren en monitoren, met inzicht in dataverbruik en automatische notificaties bij overschrijding van databundels.

Het bericht BusinessCom en Q data solutions brengen managed datasim samen verscheen eerst op MSP Business.

Read more here: https://mspbusiness.com/markt-en-strategie/businesscom-en-q-data-solutions-brengen-managed-datasim-samen/

The post BusinessCom en Q data solutions brengen managed datasim samen appeared first on IPv6.net.

We noticed the Rockchip RK3572 mid-range HMI SoC a couple of months ago, and Forlinx has launched the first system-on-module (FET3572-C SoM) based on the processor, along with a development board (OK3572-C) and BSP (Board Support Package) with a fairly recent Linux 6.12 kernel. The octa-core Cortex-A73/A53 processor features a 4 TOPS NPU (the same as in the RK3588) and targets HMI applications leveraging Edge AI for consumer electronics, industrial control, edge computing, smart security, and in-vehicle terminals. Forlinx FET3572-C Rockchip RK3572 system-on-module Specifications: SoC – Rockchip RK3572 or RK3572J Octa-core CPU – 2x Arm Cortex-A73 @ up to 2.2 GHz+ 2x Arm Cortex-A53 @ up to 2.1 GHz + 4x Arm Cortex-A53 @ up to 2.1 GHz GPU – Arm Mali-G310V2 MC1 with support for OpenGL ES 1.1/2.0/3.2, OpenCL 3.0, and Vulkan 1.4 VPU Hardware Encoding -H.264, H265, 4K @ 60fps Hardware Decoding – H.264, H.265, VP9, AV1, AVS2, […]

The post Forlinx launches Rockchip RK3572 system-on-module (SoM) and development board with Linux 6.12 BSP appeared first on CNX Software – Embedded Systems News.

Read more here: https://www.cnx-software.com/2026/06/04/forlinx-launches-rockchip-rk3572-system-on-module-som-and-development-board-with-linux-6-12-bsp/

The post Forlinx launches Rockchip RK3572 system-on-module (SoM) and development board with Linux 6.12 BSP appeared first on IPv6.net.

Gardening is a prime application for smart automation, because plant care requires a lot of monitoring, but is relatively simple to execute. Large-scale agricultural operations are already highly automated, so why not do the same thing with your house plants? Giovanni Mannara (AKA Ingeimaks) did so in style by building this smart planter system that does everything.

Before we even get to the technical details, we have to point out that this smart planter looks amazing. It is the kind of planter a person would be happy to display. Mannara had a talented friend design the 3D-printed outer shell of the planter, so he could focus on the engineering tasks.

The first step was selecting a brain and Mannara chose an Arduino® UNO Q (4GB) for the job. Like any “smart planter,” it does the basics of controlling lights and watering (powered via MOSFETs), plus monitoring soil moisture levels and ambient environmental conditions, the latter through a BME280 sensor.

But the use of the UNO Q allowed for dramatically higher intelligence. It looks at the plant through a USB webcam and is able to determine its health by running a computer vision model deployed with Edge Impulse. Sensor readings can do a lot, but the visual monitoring gives the smart planter even more capability. It can tell, for example, if the leaves wilt. Combined with the data from the sensors, that is very powerful. 

Once a plant is in place, the smart planter can do most of the care automatically. But there is an integrated display to show important status information and a web interface, which provides more detail and manual control. Real-time alerts are also sent over Telegram, enabling Mannara to take immediate action remotely.

It looks great and it should keep plants healthier than most of us manage!

The post This stunning smart planter tracks plant health and handles daily care appeared first on Arduino Blog.

Read more here: https://blog.arduino.cc/2026/06/03/this-stunning-smart-planter-tracks-plant-health-and-handles-daily-care/

The post This stunning smart planter tracks plant health and handles daily care appeared first on IPv6.net.