Wireless-Tag WT32P4C5-43S (ZX4D30CE405-V1.3)  is a fully integrated 4.3-inch touch display development board that combines an ESP32-P4 MCU with an ESP32-C5 wireless module supporting dual-band WiFi 6 (2.4/5 GHz), Bluetooth LE, and 802.15.4 connectivity. Unlike previous ESP32-P4 + ESP32-C5 solutions, this one comes with a built-in touchscreen, so you don’t need to connect a separate display. Designed for industrial automation, smart control panels, and edge AI vision terminals, the board also integrates a 15-pin MIPI CSI camera interface, a microSD card slot, two USB Type-C ports (USB 2.0 High-Speed and Debug/JTAG), and a 14-pin GPIO expansion header. Wireless-Tag WT32P4C5-43S (ZX4D30CE405-V1.3) specifications: Core module – Wireless Tag WT0132P4-A1-N16R32 SoC – Espressif Systems ESP32-P4 CPU Dual-core 32-bit RISC-V HP (High-performance) CPU @ up to 400 MHz with AI instructions extension and single-precision FPU Single-RISC-V LP (Low-power) MCU core @ up to 40 MHz with 8KB of zero-wait TCM RAM Memory 768 KB HP [...]

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M5Stack has just introduced the ToughC5, a rugged, weatherproof outdoor IoT controller built around the ESP32-C5. The device features a bright 2.0-inch capacitive touch IPS display and RS485 connectivity. The ToughC5 is an upgrade to the ESP32-base3d M5Stack TOUGH introduced in 2021, with the upgraded model featuring a more modern ESP32-C5 MCU with dual-band (2.4/5 GHz) Wi-Fi 6, Bluetooth 5 LE, and an IEEE 802.15.4 radio for Zigbee and Thread. Other changes include faster Octal PSRAM, an upgraded RTC chip with a dedicated coin cell backup battery, a passive buzzer replacing the previous 1W speaker, and a custom power management system (M5PM1 + M5IOE1) replacing the AXP192 PMU. These features make it suitable for industrial field control, outdoor edge data collection, and smart building applications. M5Stack ToughC5 specifications: Wireless SoC – Espressif Systems ESP32-C5HR8 CPU Single-core 32-bit RISC-V processor @ up to 240 MHz Low-power RISC-V core @ 40 MHz [...]

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Developed by Zed (M-Abozaid), the esp32-c3-adblock is an open-source, Pi-hole-style DNS ad blocker that runs on a $2 ESP32-C3 microcontroller board/module without requiring PSRAM. It blocks ads and tracking domains across devices on a home or small office network by filtering DNS requests. Instead of storing domain strings in RAM, the project stores them as sorted 40-bit FNV-1a hashes in flash memory and uses binary search to check DNS requests. When a UDP DNS query comes in, the C++ firmware extracts the domain, hashes it (including parent suffixes), and performs a binary search against the flash hash table. If the domain is on the blocklist, the ESP32-C3 returns 0.0.0.0 to block the request. Otherwise, it forwards the query to an upstream DNS server and returns the response to the client. This method fits over 140,000 domains into ~0.7 MB of flash memory while using only ~50 KB of RAM. A [...]

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GIGA R1 WiFi is built as a powerful development board, thanks to its dual-core, 32-bit STM32H747XI microcontroller, designed to operate at clock speeds of up to 480 MHz and equipped with 1 MB of RAM. Arduino Forum users KurtE and Merlin513 developed a ZephyrOS-based weather station app for it, showcasing its capabilities. What’s more, they recently published a demonstration that it also works on the less powerful UNO R4 WiFi.

The original project with GIGA

ZephyrOS is an RTOS (Real-Time Operating System), which we chose as a replacement following the end of active Mbed development in 2024. With the Arduino Core on Zephyr 0.90.0 now officially stable, it provides a foundation for applications designed to support process management, task scheduling, and real-time operation.

KurtE and Merlin513 built their weather app for ZephyrOS on the GIGA R1 WiFi with a paired touchscreen (like the GIGA Display Shield). That app is engineered to show the local weather through a nice GUI, with touch-selectable detailed information on specific days.

The result was relatively resource-intensive for a microcontroller, but the GIGA R1 WiFi’s processing and memory resources were designed to support the application.

The new, limit-stretching version

The UNO R4 WiFi is a more resource-constrained board, featuring a single-core Renesas RA4M1 microcontroller operating at 48 MHz and 32 kB of RAM. It also includes 256 kB of flash storage, compared with the GIGA R1 WiFi’s 2 MB.

This translates into two challenges: shaving the package size down to fit in the available flash storage and reducing resource usage for the available RAM. Fortunately, solving the first challenge also eliminated the second. KurtE did that by switch display libraries and simplifying the GUI elements. Most of that came down to reducing the color palette and minimizing image sizes where possible.

With these optimizations in place, KurtE demonstrated that the weather application could run on the UNO R4 WiFi and described the result as operating “reasonably well.”

The great challenge of smaller platforms

Optimizing software for constrained hardware isn’t just an exercise in trimming code – it’s a mindset that encourages thoughtful engineering. While high-spec boards give you room to dream big, figuring out how to squeeze a full RTOS, graphical interface, and real-time data onto a board with 32 kB of RAM demonstrates how practical constraints can inspire creative solutions.

Porting projects like this weather station down to smaller platforms helps make complex applications more accessible, showing that with the right optimizations, you don’t always need massive compute power to build impressive, responsive systems.

Have a similar project to share? Add it to your next post on the Arduino Forum, or document it on Arduino Project Hub so the entire community can benefit from your experience and build on it!

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

The post Porting a weather station app from the Arduino® GIGA R1 to the Arduino® UNO R4 boards appeared first on Arduino Blog.

A year ago, we shared two pieces of news at once. Arduino was joining the Qualcomm Technologies family, and the Arduino® UNO Q board was on its way.

The promise was simple: bring real computing power and edge AI to makers, students, and professionals, without losing the openness and joy that make Arduino what it is.

Twelve months later, you have responded to our biggest announcement ever in ways we could not have scripted.

Thank you.

The year in numbers*

You didn’t just try UNO Q. You built an ecosystem around it.

• 270,000+ UNO Q shipped worldwide
• 400+ events across the ecosystem
• 4,000+ contest participants, who delivered 250+ official builds
• 850+ topics about UNO Q on the Arduino Forum

One year in, the Arduino community continues to grow and thrive. The number of projects shared on Project Hub has nearly doubled year on year, while Arduino library downloads grew roughly 50% year on year, reaching 244+ million downloads in the last 12 months, compared with 165+ million in the previous year. The Arduino IDE also reached 35+ million downloads

Behind those numbers is something more interesting: a year of people discovering what happens when Arduino meets serious computing and AI. 

*As of September 29, 2026

Where it started

UNO Q brought together two worlds that had rarely lived on the same Arduino board: a Qualcomm Dragonwing QRB2210 microprocessor, with AI acceleration, quad-core performance and graphics, coupled with a real-time STM32U585 microcontroller. 

That combination is what makes UNO Q different. One side can run Linux, AI models, and demanding applications. The other can handle deterministic, real-time control. Together, they let developers bring the two worlds together in a way that feels like Arduino.

UNO Q launched alongside Arduino® App Lab, introducing a new way to build: an Arduino sketch on the MCU working together with a Python program on the MPU, packaged as one application. Suddenly, projects that once needed separate boards, computers or more complicated setups could start from a single Arduino.

Debian Linux for UNO Q brought a curated Linux environment to the board, making it easier to get started with the MPU side of the board. Arduino App Lab evolved throughout the year, adding new capabilities and workflows. We also brought Qualcomm Linux to UNO Q, giving developers another path into the Qualcomm ecosystem while keeping Arduino at the center of the experience. 

In August, Agentic Mode brought AI-assisted development to an Arduino desktop IDE for the first time. 

Your ideas, our proudest moments

From computer vision and local LLMs to music, healthcare, and smart everyday tools, we’ve seen an incredible range of ideas take shape.

We selected 12 standout projects that capture just some of the creativity and ambition of the UNO Q community.

And the creativity is still unfolding. Our Invent the Future with Arduino UNO Q and App Lab contest with Hackster is wrapping up, with the winners set to be announced on October 9. See the contest on Hackster

• JBR-001, a desktop companion robot
• XGO-Duck, a fully open-source robot
• DIY-ECG, exploring real-time health monitoring
• Local LLMs and VLMs on UNO Q
• NetraHz: Camera-free fall detection
• DIY Synth
• Gesture Detection
• Arcade Cabinet
• Inverted Pendulum
• Local Weather Station
• Smart Mirror
• AI Plant Guardian

Open by design: UNO and beyond

When Arduino joined Qualcomm, some wondered what it would mean for Arduino’s open approach. One year later, the answer is simple: openness remains at our core.

Arduino remains open source, with open hardware schematics and software under MPL, AGPL, and GPL. And this year, we continued to open up the tools behind UNO Q, including Arduino App Lab, available on GitHub for developers to explore, contribute to, and build on.

And the work this year has not been limited to UNO Q. Throughout this time period, we also unveiled several new products under the same ethos, ranging from the Arduino® Starter Kit R4 and Arduino® Nesso N1 to no less than eight (eight!) new Arduino® Modulino nodes. Motors, Joystick, Latch Relay, Light, LED Matrix, Vibro, Hub, and Extender may be tiny in size, but easily add so many different functionalities to your projects: you can check out the Arduino® UNO Q DIY synth bundle and Arduino® UNO Q Arcade bundle to get an idea of how well they work to bring ideas to life. Plus, Arduino Cloud got even better!

Meanwhile, Arduino Core on Zephyr reached version 1.0, moving beyond its beta phase and providing a solid RTOS foundation for Arduino boards with an MCU, including UNO Q as well as the new Arduino^® VENTUNO Q board.

Stronger together

Qualcomm is deepening its own open source commitment as well. Its business success depends on open standards, upstream collaboration, and transparent participation in the ecosystem.

A couple of months ago, we introduced VENTUNO Q, bringing substantially more computing and AI capability to Arduino developers. It opens the door to more demanding applications while keeping the development experience familiar.

A year ago, UNO Q was a new board. Today, it’s a growing platform, a new way of developing with Arduino, and, most importantly, a community of people finding out what it can do. Arduino and Qualcomm are stronger together!

A prototype is only the beginning

You can start with a prototype on UNO Q or VENTUNO Q and, as your requirements evolve, move to more advanced platforms and commercial-ready solutions while keeping the tools, models, and applications you already built. 

Beyond it, the wider Qualcomm Dragonwing family supports industrial and production-scale deployments.

The software stack follows you, too: 

• Arduino App Lab for building and packaging your application
• Edge Impulse for production-grade pipelines
• Foundries.io for secure updates and fleet management once you deploy at scale.
• Qualcomm AI Hub for optimized models

Finally, this year we also launched Works with Arduino, a program enabling partners to certify their System on Modules and developers to take apps built with UNO Q or VENTUNO Q to compatible hardware without changing a line of code. With App Lab, applications are packaged as a single App Release, making it possible to move from prototype to production in minutes, not weeks.

It is a direct bridge from prototype to commercial-ready hardware, and a path to the 33M+ Arduino developer community for partners.

And the community came together beyond the projects too. Alongside Qualcomm, we brought UNO Q to CES, MWC, and the Mission44 Workshop, meeting developers, makers, students, and industry leaders, while showing what this new generation of Arduino-powered computing can do.

^{The Arduino team at the Qualcomm booth at CES (January 2026)!}

^{Mission 44 students at Silverstone getting hands-on with UNO Q and AI-powered racing telemetry (June 2026).}

It is just the beginning

Year one showed that powerful, open, and simple can go together. It showed us what happens when you put new tools in the hands of a curious community, and let them take it somewhere you didn’t expect.

Year two is about going further: more tools, more partners, and more ways to turn an idea into something real.

Happy first birthday, UNO Q! 

And to everyone who built, shared, taught, hacked, and dreamed with it this year: thank you. The best is still ahead.

Qualcomm branded products are products of Qualcomm Technologies, Inc. and/or its subsidiaries. 

Arduino, Modulino, Nesso, UNO, Starter Kit, and VENTUNO, and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

The post One year of UNO Q: built together, open by design appeared first on Arduino Blog.

ZeroWire Robotics Q8botOne is an open-source, palm-sized quadruped robot built around an ESP32-C3 MCU and ROBOTIS DYNAMIXEL smart actuators. Designed with a unique wire-free architecture, the robot integrates all components onto a central printed circuit board also used as the structural chassis. The robot is roughly the size of a smartphone and can jog, jump, and carry up to twice its 250-gram weight. The main PCB uses a dedicated 6V/16A step-down converter for motor power and includes a battery management system for two 14500 Li-ion cells. For control, the kit includes a separate ESP32-C3 controller PCB with an Adafruit Mini I2C Gamepad. Q8botOne specifications: Wireless module – Espressif Systems ESP32-C3-MINI-1-N4 module with: ESP32-C3 (ESP32-C3FN4) SoC CPU – 32-bit RISC-V single-core processor up to 160 MHz Memory – 400 KB SRAM (16 KB for cache), 8 KB SRAM in RTC Storage – 4 MB embedded flash, 384 KB ROM Connectivity – [...]

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Cyberdecks don’t all have to be chunky slabs of aluminum and plastic chasing after an ‘80s cassette futurism aesthetic. Cyberdecks can be whatever their builders want them to be — that’s the whole appeal. Jen Looper is a creative technologist who loves nature, plants, and cozy gardens, so she built this #cottagecore cyberdeck to do literal fieldwork.

As an aesthetic, cottagecore is all about cozy, rustic vibes. As a subculture, it is about comfortable sustainability and “slow living.” B.L.O.O.M. (Bridging Local Observations, Openly Mapped) is a cyberdeck designed to encompass all of that. Looper built it for collecting field notes on regional plants and animals. It harkens back to the descriptive logging and taxonomic classification work of Victorian naturalists.

The physical construction is also the polar opposite of what we typically see from the cyberdeck community. The enclosure is a “train case,” which is a traditional piece of luggage used for carrying cosmetics and grooming supplies. All of the hardware components nestle inside, with plenty of room to spare for some paper notebooks.

The hardware consists of an Arduino® UNOQ (4GB), a 5” HDMI screen, a Bluetooth keyboard, a Bluetooth mouse, and a power bank to power it all. It also has a USB camera and a USB microphone, which can be used for data collection.

To that end, it connects with a companion app (Field Notes) to compile that data in the Grinnell format. It can also run local AI models directly on the UNO Q to help with that work. As an example, Looper employed the Edge Impulse integration in Arduino App Lab to create a custom model that can recognize avian species based on their birdsong.

This is the kind of functionality that Victorian naturalists wouldn’t have even been able to dream of, but Looper was able to achieve it on a modest budget. At the same time, she proved that cyberdecks can come in any form.

The post This #cottagecore cyberdeck is perfect for literal fieldwork appeared first on Arduino Blog.

The single-board computing market is shifting rapidly, and we are moving into an era of unprecedented possibilities where edge AI, computer vision, real-time control, and rich sensor streams converge locally. Yes, it’s exciting – but even amidst this “explosion” of hardware, an age-old rule holds true: define what you want to build before picking your tool.

No single board can (nor should!) excel at everything. A low-power embedded sensor, an autonomous robot, and a high-throughput video server all demand different architectures. For example, if you are weighing hardware choices for a general-purpose Linux setup versus a hybrid microcontroller, read this in-depth review to evaluate how different design philosophies match your requirements.

In concrete terms, it’s great to get a sense of what each board can do – and even better if you can look at a variety of cool projects at the same time! So here is a selection of what we are seeing developers build across key domains with the Arduino^® UNO Q board, leveraging its dual-brain to bridge high-level AI workloads with deterministic physical control. 

Dive into perception-driven robotics

Robotics excels on hybrid platforms because physical movement requires tight, real-time microcontroller timing while visual recognition requires heavy AI processing.

•  A robot arm that sees you: Uses camera vision designed to recognize people and a robotic arm built to deliver items, with a Modulino LED Matrix providing visual feedback.
• UNO Q Braccio: Integrates Edge Impulse AI and ROS 2 in a platform engineered to support robotic arm control, including simulation in Gazebo.
• Face-following robot: Uses Edge Impulse computer vision designed to track faces and convert tracking data into physical servo movements.
• AI agent robot: Uses a local AI agent built to support physical navigation and decision-making tasks in real-world environments.

Explore the world through computer vision and spatial sensing

Instead of just streaming video, local machine learning turns cameras and sensors into environmental perception engines.

• Gesture-controlled input system: Uses a standard webcam and Edge Impulse in a workflow built to convert hand gestures into inputs for digital applications.
• Real-time LiDAR room mapper: Pairs LiDAR sensors with Edge Impulse ML in a system engineered to help interpret spatial data and interpret room layouts locally.

Create smart environments with interactive AI

Local AI allows devices to run full “sense, interpret, decide, act” loops without relying on cloud latency or external servers.

• Talk to your house: An all-in-one smart home hub engineered to support wake-word detection, voice commands, sensor management, and a web interface.
• Clawrophyll: A smart houseplant system designed to run a local AI agent directly on the board.

We don’t need every platform to do the same thing

The technological landscape is expanding, giving us more tools to build with: the goal was never to find one ultimate board “to rule them all”, but to match your project’s unique demands to the right architecture. Whether you need a raw Linux computing hub or a dual-brain setup like UNO Q, starting with a clear target can help you build smarter, faster, and more effectively. Today, we have more options, more possibilities – and this may ultimately be the most exciting part in the current era of single-board computing.

UNO Q is available on the Arduino Store, and can be ordered from DigiKey, Farnell, Mouser, Newark, RS Components, Robu.in, and other authorized distributors and resellers worldwide.

Qualcomm branded products are products of Qualcomm Technologies, Inc. and/or its subsidiaries. Arduino, UNO, and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

The post What can you build, when AI can interact with the real world? appeared first on Arduino Blog.

UNO Q was designed around a dual-brain architecture that is built to bring high-performance computing and real-time hardware control together on a single board. The Linux environment on the MPU side is constructed to let developers install and run familiar open-source software – the same tools they’d reach for on a server or any popular SBC. Meanwhile, the STM32H5 microcontroller runs a dedicated Arduino Core on Zephyr RTOS, handling sensors, actuators, and time-critical I/O with deterministic precision. Together, they make the UNO Q something more than the sum of its parts.

What follows is a set of concrete examples: real software platforms you can install on UNO Q today, what each one enables, and why having the microcontroller in the same system changes what’s possible.

#1 Agentic AI development

UNO Q is engineered to put an AI coding agent inside the hardware it is writing for. An agent running on the board can receive a goal, process context, call tools, interact with services, and – through the board’s hardware interfaces – turn decisions into real-world actions. The reasoning comes from a frontier model over the API, but the tool calls run on the device. That last part is what makes this different from running an agent on a laptop or a server: the machine executing the decisions is also wired into the physical world.

The practical starting point is installing an AI coding agent, such as OpenCode, directly on the board. With the right context added – for example, knowledge of the Arduino^® App Lab CLI and the board’s specific capabilities – it can set up Linux services for you, answer questions about Debian on UNO Q, and build physically connected Arduino apps end to end: create the app, write the sketch, deploy it, read the output, iterate. Any agent can write a sketch; only one running on the board can deploy it and see what happened.

More broadly, this creates a meaningful bridge between generative AI and embedded systems. Only the reasoning leaves the board: the sensors, actuators, and data stay where they are.

Ready to try the full tutorial? Find out here how to install and use Open Code on UNO Q.

#2 Home Assistant

Home Assistant dashboards inspiration

Home Assistant aims to turn UNO Q into the center of a local smart home or building automation system, connecting lights, climate controls, energy meters, and other compatible devices through a single dashboard and automation engine. Running entirely on the Linux side, it is built to handle scheduling, historical data, dashboards, and integrations – with no cloud subscription required.

The real advantage emerges when custom hardware enters the picture. A Qwiic temperature and air-quality sensor connected to the board’s Qwiic port could feed live data into Home Assistant, while an Arduino sketch running on the STM32H5 microcontroller is constructed to drive a ventilation fan through a relay based on those readings. Home Assistant is designed to manage the logic and interface on Linux; the microcontroller is engineered to handle the physical layer. The two communicate via the onboard RPC bridge, with no external Arduino board required.

Need a little guidance in trying this yourself? Check out this GitHub tutorial. 

#3 n8n automation

n8n is a visual workflow automation platform that is built to connect services, APIs, databases, and devices – think of it as a self-hosted alternative to Zapier or Make, with full data sovereignty because everything runs locally.

Running n8n on UNO Q is designed to bring those workflows to the edge. A single workflow could receive sensor data from the microcontroller side, process and store it in a local database, update a dashboard, send a notification to an external service, and trigger a physical output – all in sequence, all on the same device.

The combination makes UNO Q a practically designed platform for rapid prototyping, industrial automation, smart building control, and local edge orchestration where cloud latency or dependency would be a problem.

#4 Pi-hole network shield

Pi-hole is a DNS-level filtering service that is structured to block advertising, tracking domains, and unwanted traffic for every device on the local network that uses it as a DNS server. On a standard single-board computer, that’s where the story ends.

UNO Q is different: the Linux side runs Pi-hole and its web dashboard, while the microcontroller can drive a dedicated status display, visualize blocked-request activity in real time, trigger an alert if the DNS service becomes unavailable, or provide a physical button to temporarily pause filtering – without opening a browser interface. 

It’s a small example of a broader pattern: UNO Q aims to turn passive software services into systems with physical feedback and control.

Want to protect your home from ads, using UNO Q and Pi-hole? Check out this Project Hub tutorial. 

#5 Frigate camera computer vision

Frigate is a network video recorder built around real-time object detection. It can process camera streams locally to identify people, vehicles, animals, or other defined events – and running it on UNO Q keeps that processing at the edge, where it belongs. 

UNO Q is a practical host for Frigate for a few reasons. Cameras can be connected directly via USB or accessed remotely over RTSP (Real Time Streaming Protocol) when Frigate detects a relevant event, the microcontroller side can respond immediately – triggering a light, an alarm, a relay, or a Modulino-based indicator – without any additional hardware.

Sensitive footage stays local. Response times improve. And the physical response doesn’t require a round trip to the cloud.

#6 Local NAS with SMB

UNO Q can be configured as a lightweight network-attached storage server using SMB, making shared folders accessible to other computers and devices on the local network – useful for camera recordings, machine-learning datasets, sensor logs, or shared project files.

Paired with a dongle that gives cabled Ethernet connectivity and an external USB Disk, this can be a quick and instant NAS, flexible enough for sharing office documents with your colleagues or family. 

A platform, not just a single application

Each of these applications is useful independently. The more interesting territory is what opens up when they run together.

Here’s one example of how that could look in practice: Frigate detects a person entering a monitored area and publishes the event via MQTT. Home Assistant picks up that event and switches on a light. n8n extends the response by calling an external API, logging the event to a database, and sending a notification. Meanwhile, the raw footage remains accessible over the local network through the Samba share. The microcontroller drives an indicator throughout. All of this runs on one board. 

UNO Q is designed to provide a common foundation where Linux applications, local AI workloads, network services, and real-time embedded hardware control can be part of the same coherent system – rather than spread across multiple devices with their own power supplies, enclosures, and points of failure.

UNO Q is available on the Arduino Store or can be ordered from DigiKey, Farnell, Mouser, Newark, RS Components, Robu.in, and many other authorized distributors and resellers.

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

The post From Home Assistant to AI agents: what Linux unlocks on the Arduino® UNO Q board appeared first on Arduino Blog.

UNO Q was designed around a dual-brain architecture that is built to bring high-performance computing and real-time hardware control together on a single board. The Linux environment on the MPU side is constructed to let developers install and run familiar open-source software – the same tools they’d reach for on a server or any popular SBC. Meanwhile, the STM32H5 microcontroller runs a dedicated Arduino Core on Zephyr RTOS, handling sensors, actuators, and time-critical I/O with deterministic precision. Together, they make the UNO Q something more than the sum of its parts.

What follows is a set of concrete examples: real software platforms you can install on UNO Q today, what each one enables, and why having the microcontroller in the same system changes what’s possible.

#1 Agentic AI development

UNO Q is engineered to put an AI coding agent inside the hardware it is writing for. An agent running on the board can receive a goal, process context, call tools, interact with services, and – through the board’s hardware interfaces – turn decisions into real-world actions. The reasoning comes from a frontier model over the API, but the tool calls run on the device. That last part is what makes this different from running an agent on a laptop or a server: the machine executing the decisions is also wired into the physical world.

The practical starting point is installing an AI coding agent, such as OpenCode, directly on the board. With the right context added – for example, knowledge of the Arduino^® App Lab CLI and the board’s specific capabilities – it can set up Linux services for you, answer questions about Debian on UNO Q, and build physically connected Arduino apps end to end: create the app, write the sketch, deploy it, read the output, iterate. Any agent can write a sketch; only one running on the board can deploy it and see what happened.

More broadly, this creates a meaningful bridge between generative AI and embedded systems. Only the reasoning leaves the board: the sensors, actuators, and data stay where they are.

Ready to try the full tutorial? Find out here how to install and use Open Code on UNO Q.

#2 Home Assistant

Home Assistant dashboards inspiration

Home Assistant aims to turn UNO Q into the center of a local smart home or building automation system, connecting lights, climate controls, energy meters, and other compatible devices through a single dashboard and automation engine. Running entirely on the Linux side, it is built to handle scheduling, historical data, dashboards, and integrations – with no cloud subscription required.

The real advantage emerges when custom hardware enters the picture. A Qwiic temperature and air-quality sensor connected to the board’s Qwiic port could feed live data into Home Assistant, while an Arduino sketch running on the STM32H5 microcontroller is constructed to drive a ventilation fan through a relay based on those readings. Home Assistant is designed to manage the logic and interface on Linux; the microcontroller is engineered to handle the physical layer. The two communicate via the onboard RPC bridge, with no external Arduino board required.

Need a little guidance in trying this yourself? Check out this GitHub tutorial. 

#3 n8n automation

n8n is a visual workflow automation platform that is built to connect services, APIs, databases, and devices – think of it as a self-hosted alternative to Zapier or Make, with full data sovereignty because everything runs locally.

Running n8n on UNO Q is designed to bring those workflows to the edge. A single workflow could receive sensor data from the microcontroller side, process and store it in a local database, update a dashboard, send a notification to an external service, and trigger a physical output – all in sequence, all on the same device.

The combination makes UNO Q a practically designed platform for rapid prototyping, industrial automation, smart building control, and local edge orchestration where cloud latency or dependency would be a problem.

#4 Pi-hole network shield

Pi-hole is a DNS-level filtering service that is structured to block advertising, tracking domains, and unwanted traffic for every device on the local network that uses it as a DNS server. On a standard single-board computer, that’s where the story ends.

UNO Q is different: the Linux side runs Pi-hole and its web dashboard, while the microcontroller can drive a dedicated status display, visualize blocked-request activity in real time, trigger an alert if the DNS service becomes unavailable, or provide a physical button to temporarily pause filtering – without opening a browser interface. 

It’s a small example of a broader pattern: UNO Q aims to turn passive software services into systems with physical feedback and control.

Want to protect your home from ads, using UNO Q and Pi-hole? Check out this Project Hub tutorial. 

#5 Frigate camera computer vision

Frigate is a network video recorder built around real-time object detection. It can process camera streams locally to identify people, vehicles, animals, or other defined events – and running it on UNO Q keeps that processing at the edge, where it belongs. 

UNO Q is a practical host for Frigate for a few reasons. Cameras can be connected directly via USB or accessed remotely over RTSP (Real Time Streaming Protocol) when Frigate detects a relevant event, the microcontroller side can respond immediately – triggering a light, an alarm, a relay, or a Modulino-based indicator – without any additional hardware.

Sensitive footage stays local. Response times improve. And the physical response doesn’t require a round trip to the cloud.

#6 Local NAS with SMB

UNO Q can be configured as a lightweight network-attached storage server using SMB, making shared folders accessible to other computers and devices on the local network – useful for camera recordings, machine-learning datasets, sensor logs, or shared project files.

Paired with a dongle that gives cabled Ethernet connectivity and an external USB Disk, this can be a quick and instant NAS, flexible enough for sharing office documents with your colleagues or family. 

A platform, not just a single application

Each of these applications is useful independently. The more interesting territory is what opens up when they run together.

Here’s one example of how that could look in practice: Frigate detects a person entering a monitored area and publishes the event via MQTT. Home Assistant picks up that event and switches on a light. n8n extends the response by calling an external API, logging the event to a database, and sending a notification. Meanwhile, the raw footage remains accessible over the local network through the Samba share. The microcontroller drives an indicator throughout. All of this runs on one board. 

UNO Q is designed to provide a common foundation where Linux applications, local AI workloads, network services, and real-time embedded hardware control can be part of the same coherent system – rather than spread across multiple devices with their own power supplies, enclosures, and points of failure.

UNO Q is available on the Arduino Store or can be ordered from DigiKey, Farnell, Mouser, Newark, RS Components, Robu.in, and many other authorized distributors and resellers.

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

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