Arduino UNO Q: From blink to think

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Arduino.cc-Blogpost-Featured(385x289)

Arduino.cc-Blogpost-Cover(1100x600)

<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="559" src="https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600-1-1024x559.jpg" alt="" class="wp-image-42416" srcset="https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600-1-1024x559.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600-1-300x164.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600-1-768x419.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600-1.jpg 1100w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



We are thrilled to announce the release of version 0.90.0 of the ArduinoCore-zephyr. This is a major milestone: we are officially exiting the beta phase and marking the core as stable. Achieving this goal is the culmination of months of testing, architectural refinement, and community feedback. As we establish this core as our primary framework for advanced hardware, we are also beginning the deprecation phase for the corresponding legacy cores based on mbedOS, which has just now reached End of Life status. Join us in this next chapter! Compile your existing sketches with version 0.90.0, and let us know how it performs.



<h2 class="wp-block-heading">Expanded board and hardware support</h2>



Welcome, Nano RP2040 Connect! The <a href="https://store.arduino.cc/collections/interactive-games/products/arduino-nano-rp2040-connect">Arduino® Nano™ RP2040 Connect</a> board officially joins our lineup. You can now leverage the full RTOS power of Zephyr on RP2040 hardware with standard Arduino API compatibility.



SD card support for the Arduino® Portenta™ H7 and C33 boards. Native SD card support is now enabled for both <a href="https://store.arduino.cc/products/portenta-h7">Portenta H7</a> and <a href="https://store.arduino.cc/products/portenta-c33">Portenta C33</a>, making it simple to log data and host local files on external memory.



PDM library support. Official support for the PDM library on the <a href="https://store.arduino.cc/products/nano-33-ble-rev2">Nano 33 BLE</a> board is here, unlocking digital microphone input for audio sampling, noise detection, and edge AI voice processing.



<h2 class="wp-block-heading">Universal automatic loader sync</h2>



With version 0.90.0, automatic loader-and-sketch synchronization is now enabled across all supported boards. The core automatically verifies and handles loader updates behind the scenes during every sketch upload. This means you can simply open the Arduino® IDE, connect a board running a sketch based on mbedOS, switch to the new Zephyr-based core, and upload your sketch. The IDE will automatically update the loader as needed to ensure compatibility with Zephyr.



If you are running Zephyr for the first time, <a href="https://support.arduino.cc/hc/en-us/articles/29180434600476-Install-the-Zephyr-loader-on-your-board">check out our step-by-step guide here</a>. 



<h2 class="wp-block-heading">Full hardware compatibility list</h2>



<ul class="wp-block-list">
<li><a href="https://www.arduino.cc/product-uno-q">Arduino® UNO™ Q board</a></li>



<li>Portenta H7</li>



<li>Portenta C33</li>



<li><a href="https://www.arduino.cc/pro/hardware-product-family-opta-family/">Arduino® Opta™ microPLC</a></li>



<li><a href="https://store.arduino.cc/products/giga-r1-wifi">Arduino® GIGA R1™ WiFi board</a></li>



<li><a href="https://store.arduino.cc/products/nano-matter">Nano Matter board</a></li>



<li>Nano RP2040 Connect (New!)</li>



<li>Nano 33 BLE</li>



<li><a href="https://store.arduino.cc/products/nicla-sense-me">Arduino® Nicla™ Sense ME module</a></li>



<li><a href="https://store.arduino.cc/products/nicla-vision">Nicla Vision module</a></li>
</ul>



<h2 class="wp-block-heading">How to get started</h2>



To update your environment: open the <a href="https://docs.arduino.cc/software/ide/">Arduino IDE</a>, search for “zephyr” in the Board Manager, and install version 0.90.0.



<h2 class="wp-block-heading">Thank you!</h2>



This stable milestone wouldn’t be possible without our community. Check out the <a href="https://github.com/arduino/ArduinoCore-zephyr/releases/tag/0.90.0">full release notes on GitHub</a>, report issues, and help us build the next era of Arduino on Zephyr!



Arduino, GIGA R1, Nano, Nicla, Opta, Portenta, and UNO are trademarks or registered trademarks of Arduino S.r.l.

Arduino Core on Zephyr 0.90.0 is officially stable and leaving beta

LIDS_2575 - Mission 44 - Silverstone - June 26 - @lids_harper for @parcferme.co

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LIDS_2555 - Mission 44 - Silverstone - June 26 - @lids_harper for @parcferme.co

LIDS_0819 - Mission 44 - Silverstone - June 26 - @lids_harper for @parcferme.co

LIDS_0677 - Mission 44 - Silverstone - June 26 - @lids_harper for @parcferme.co

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Attendees

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<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="576" src="https://blog.arduino.cc/wp-content/uploads/2026/07/image-3-1-1024x576.png" alt="" class="wp-image-42389" srcset="https://blog.arduino.cc/wp-content/uploads/2026/07/image-3-1-1024x576.png 1024w, https://blog.arduino.cc/wp-content/uploads/2026/07/image-3-1-300x169.png 300w, https://blog.arduino.cc/wp-content/uploads/2026/07/image-3-1-768x432.png 768w, https://blog.arduino.cc/wp-content/uploads/2026/07/image-3-1-1536x864.png 1536w, https://blog.arduino.cc/wp-content/uploads/2026/07/image-3-1.png 1920w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



On July 2nd, three days before the British Grand Prix, Arduino brought a hands-on workshop to Silverstone for 40 students aged 16 to 19, supported by <a href="https://mission44.org/">Mission 44</a> and Qualcomm Technologies, Inc. in the framework of the <a href="https://mission44.org/mission-44-drives-change-and-inspires-the-next-generation-at-the-british-grand-prix/">Igniting Inclusion Summit</a>. The setting couldn’t have been more fitting, and as part of the bigger Qualcomm family, we are proud to have had the chance to empower young and bright minds with an activity that was such a natural fit for our team. 



Qualcomm Technologies, Inc., through its Snapdragon® brand, is a Founding Corporate Sponsor of Mission 44 – a global foundation established by Sir Lewis Hamilton with a clear belief: that it’s possible to succeed no matter your background. Through inclusive education and improving access to STEM careers, the foundation works to build a fairer, more inclusive future – and events like this one are a direct expression of that mission. Not to mention, the workshop was an exciting moment for students (and us!) to meet the 7-time F1 world champion himself!


<div class="wp-block-image">
<figure class="aligncenter size-large is-resized"><div class="image-post"><img loading="lazy" decoding="async" width="819" height="1024" src="https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_0677-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-819x1024.jpg" alt="" class="wp-image-42403" style="width:671px;height:auto" srcset="https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_0677-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-819x1024.jpg 819w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_0677-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-240x300.jpg 240w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_0677-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-768x960.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_0677-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-1229x1536.jpg 1229w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_0677-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-1638x2048.jpg 1638w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_0677-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-scaled.jpg 2048w" sizes="auto, (max-width: 819px) 100vw, 819px" /></div></figure>
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<h2 class="wp-block-heading">From unboxing to AI model in under an hour</h2>



Many of the students, who are supported by Mission 44 partners <a href="https://afbe.org.uk/">AFBE-UK</a> and <a href="https://upreach.org.uk/">upReach</a>, had never worked with sensors, AI, or machine learning before. Yet within the first hour, they were already collecting live data and exploring how AI models are trained.



The goal was simple: to understand how an F1-inspired telemetry system works. Through hands-on experiments with <a href="https://www.arduino.cc/product-uno-q">Arduino® UNO™ Q 4GB</a>, <a href="https://store.arduino.cc/pages/modulino">Arduino® Modulino™</a> and using <a href="https://docs.arduino.cc/software/app-lab/">Arduino® App Lab</a> and <a href="https://www.edgeimpulse.com/developers?utm_platform=23675660573-194972799496&amp;utm_source=google&amp;utm_medium=cpc&amp;utm_campaign=&amp;utm_content=801314931074&amp;utm_term=edge%20impulse&amp;hsa_acc=6610359976&amp;hsa_cam=23675660573&amp;hsa_grp=194972799496&amp;hsa_ad=801314931074&amp;hsa_src=g&amp;hsa_tgt=kwd-670726942748&amp;hsa_kw=edge%20impulse&amp;hsa_mt=e&amp;hsa_net=adwords&amp;hsa_ver=3&amp;gad_source=1&amp;gad_campaignid=23675660573&amp;gclid=CjwKCAjwuuPRBhAnEiwA2Ji8enTJ3Rf7a63wEBKI7yHSQ3jqb3StoQQ9ZRvkmobt2plXH7prtuamYBoC5D4QAvD_BwE">Edge Impulse</a> as their development environments, students experienced how information is collected, analyzed, and turned into insights in real-time.


<div class="wp-block-image">
<figure class="aligncenter size-large is-resized"><div class="image-post"><img loading="lazy" decoding="async" width="819" height="1024" src="https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_2575-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-819x1024.jpg" alt="" class="wp-image-42392" style="width:667px;height:auto" srcset="https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_2575-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-819x1024.jpg 819w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_2575-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-240x300.jpg 240w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_2575-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-768x960.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_2575-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-1229x1536.jpg 1229w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_2575-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-1638x2048.jpg 1638w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_2575-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-scaled.jpg 2048w" sizes="auto, (max-width: 819px) 100vw, 819px" /></div></figure>
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<h2 class="wp-block-heading">Discovering the tools that bring a future in racing within reach</h2>



For the final part of the session, some of the students raced miniature cars around a custom-built track while accelerometers streamed motion data over Bluetooth. UNO Q processed telemetry at the edge, computer vision tracked each car, and leaderboards displayed lap times, crashes, and performance metrics in real-time.



For many students, seeing their race transformed into live telemetry was the moment it all clicked.


<div class="wp-block-image">
<figure class="aligncenter size-large is-resized"><div class="image-post"><img loading="lazy" decoding="async" width="819" height="1024" src="https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_0819-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-819x1024.jpg" alt="" class="wp-image-42401" style="aspect-ratio:0.7998102639884699;width:669px;height:auto" srcset="https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_0819-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-819x1024.jpg 819w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_0819-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-240x300.jpg 240w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_0819-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-768x960.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_0819-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-1229x1536.jpg 1229w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_0819-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-1638x2048.jpg 1638w, https://blog.arduino.cc/wp-content/uploads/2026/07/LIDS_0819-Mission-44-Silverstone-June-26-@lids_harper-for-@parcferme.co_-scaled.jpg 2048w" sizes="auto, (max-width: 819px) 100vw, 819px" /></div></figure>
</div>


Telemetry is exactly the kind of real-world application that we are seeing with UNO Q. Not only in our demos and workshops, but also in professional motorsport contexts (have you read about <a href="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/">Star Stream’s success in bringing F1-level telemetry to race teams and fleet operators around the world</a>?).



<h2 class="wp-block-heading">Ready to build your own future?</h2>



STEM skills are increasingly central to careers across every industry – and access to hands-on learning experiences like this one can make a real difference in who gets to build that future. Giving young people from all backgrounds the tools, the context, and the confidence to engage with technology is something we care about deeply at Arduino.


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<figure class="aligncenter size-large is-resized"><div class="image-post"><img loading="lazy" decoding="async" width="762" height="1024" src="https://blog.arduino.cc/wp-content/uploads/2026/07/Screenshot-2026-07-20-at-18.17.32-762x1024.png" alt="" class="wp-image-42408" style="aspect-ratio:0.7446086510893503;width:666px;height:auto" srcset="https://blog.arduino.cc/wp-content/uploads/2026/07/Screenshot-2026-07-20-at-18.17.32-762x1024.png 762w, https://blog.arduino.cc/wp-content/uploads/2026/07/Screenshot-2026-07-20-at-18.17.32-223x300.png 223w, https://blog.arduino.cc/wp-content/uploads/2026/07/Screenshot-2026-07-20-at-18.17.32-768x1031.png 768w, https://blog.arduino.cc/wp-content/uploads/2026/07/Screenshot-2026-07-20-at-18.17.32-1144x1536.png 1144w, https://blog.arduino.cc/wp-content/uploads/2026/07/Screenshot-2026-07-20-at-18.17.32.png 1172w" sizes="auto, (max-width: 762px) 100vw, 762px" /></div></figure>
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Visit <a href="https://mission44.org/">Mission 44</a> to learn more about their work, and if today’s post made you want to start your own journey into AI, sensors, and real-world data&#8230; just <a href="https://store.arduino.cc/products/uno-q-4gb">get your own hands on an UNO Q</a>!



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

Mission 44 students at Silverstone go from zero to AI-powered racing telemetry in 90 minutes!

Arcade Bundle

Arcade Bundle 1

arcade_bundle_assembly

<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="559" src="https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600-1024x559.jpg" alt="" class="wp-image-42340" srcset="https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600-1024x559.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600-300x164.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600-768x419.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600.jpg 1100w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



Just a few weeks after launching the <a href="https://store.arduino.cc/products/uno-q-diy-synth-bundle?utm_source=content&amp;utm_medium=blogpost&amp;utm_campaign=unoq_is_the_answer&amp;utm_id=mktg-content">UNO™ Q DIY synth bundle</a>, we’re adding a new classic project you can pick up in one click, and get done in a weekend.&nbsp;



Meet the <a href="https://store-usa.arduino.cc/products/uno-q-arcade-bundle?utm_source=content&amp;utm_medium=blogpost&amp;utm_campaign=unoq_is_the_answer&amp;utm_id=mktg-content">UNO™ Q Arcade bundle</a>: a rewarding new way to start building with UNO Q, following a complete step-by-step tutorial towards a clear objective –&nbsp;and gaining hands-on tech experience as you go!



Not only is the bundle designed to make your experience frictionless and rewarding, but it comes out during a special sale with a 20% discount built right in to sweeten the deal. Don’t sleep on it! If the DIY synth bundle taught us anything, it’s these sell out fast.



<h2 class="wp-block-heading">The UNO Q Arcade bundle: one board, infinite games</h2>



Now anyone can build their own arcade cabinet, in just a few hours of fun. Follow the <a href="https://projecthub.arduino.cc/Arduino_Genuino/uno-q-arcade-bundle-b272c5">instructions available on Arduino® Project Hub</a>, and you’ll quickly have UNO Q run RetroArch – the well-established open-source multi-system emulator – directly on its Debian Linux side, outputting via HDMI and handling audio over USB. No separate single-board computer, no extra compute module, no compromises: the Qualcomm Dragonwing™ QRB2210 processor manages multiple emulation cores, controller input, video, and audio simultaneously, while the onboard eMMC provides plenty of room for ROMs and configuration files.



But one board does not a bundle make! We’ve included three easy-to-use Arduino®&nbsp;Modulino™ nodes – Buttons, Joystick, and Movement – to make the controller side of UNO 4GB really shine. Modulino eliminate complex wiring: just daisy-chain them together via Qwiic and you’re ready to configure. Use Joystick to simulate D-Pad or mouse movement, Buttons for single or multi-key shortcuts, and Movement for motion-based mouse control. Every input is customizable to your playstyle.&nbsp;



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="653" src="https://blog.arduino.cc/wp-content/uploads/2026/07/Arcade-Bundle--1024x653.jpg" alt="" class="wp-image-42353" srcset="https://blog.arduino.cc/wp-content/uploads/2026/07/Arcade-Bundle--1024x653.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/07/Arcade-Bundle--300x191.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/07/Arcade-Bundle--768x489.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/07/Arcade-Bundle-.jpg 1395w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<h2 class="wp-block-heading">Create your own games with open-source Godot</h2>



So you can build the cool hardware, and leverage ready-to-use software to play your favorite retro-style games. But how about creating your own?



Thanks to its Debian Linux environment, UNO Q can run projects built with the popular open-source game engine <a href="https://godotengine.org/">Godot</a>. This makes the UNO Q the heart of a compact game-development platform and, ultimately, a super-portable DIY video game console.



Develop your game with Godot, connect UNO Q to a display via HDMI, add a USB audio device, and build your own controller using Modulino nodes or other Arduino-compatible hardware. Buttons, joysticks, motion sensors, knobs, LEDs, and even environmental sensors can become part of the gameplay –&nbsp;not just accessories connected to it.



Put everything inside a <a href="https://www.printables.com/model/1786091-arduino-uno-q-arcade-bundle">3D-printed</a> or custom enclosure, add a small HDMI display and a portable power source, and your project can move from desktop prototype to a self-contained gaming device you can carry, demonstrate, and play almost anywhere.



Check out these inspiring GitHub repos to get a better idea of how you can stretch the UNO Q Arcade bundle:&nbsp;



<ul class="wp-block-list">
<li><a href="https://github.com/KsaweryL/Hackathon-project-spear">Hackathon Project Spear</a></li>



<li><a href="https://github.com/marchellodev/arduino_summer_game">Arduino Summer Game</a></li>



<li><a href="https://github.com/TimofeySukh/summerengine_hackathon">SummerEngine Hackathon</a></li>



<li><a href="https://github.com/vishalshan23/penguin-modulino-explorer">Penguin Modulino Explorer</a></li>
</ul>



<h2 class="wp-block-heading">Ready? Set? Play!</h2>



While the community has been building these kinds of playful and retro projects for years, UNO Q makes them more capable than ever. With the bundle, we want to make it easier to get started, and more affordable to dive in. Bring your creativity! We’ve taken care of the rest.



<a href="https://store-usa.arduino.cc/products/uno-q-arcade-bundle?utm_source=content&amp;utm_medium=blogpost&amp;utm_campaign=unoq_is_the_answer&amp;utm_id=mktg-content">Get the UNO Q Arcade bundle</a> (with 20% discount until September 10th!).



<a href="https://projecthub.arduino.cc/Arduino_Genuino/uno-q-arcade-bundle-b272c5">Check out the full tutorial on Arduino Project Hub</a>.



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

Build a retro home arcade with an Arduino® UNO™ Q board!

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<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="559" src="https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600-4-1024x559.jpg" alt="" class="wp-image-42300" srcset="https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600-4-1024x559.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600-4-300x164.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600-4-768x419.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/07/Arduino.cc-Blogpost-Cover1100x600-4.jpg 1100w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



Most machines warn you before they fail. A motor begins to vibrate differently. A pump slowly drifts out of balance. A bearing develops a new mechanical signature. A cooling fan starts producing frequencies that were not present during normal operation.



These changes can appear well before a complete breakdown. The challenge is detecting them early, reliably, and at a cost that makes monitoring practical across more than just the factory’s most expensive machines.



UNO Q provides a flexible platform for building a compact predictive maintenance node that collects vibration data, runs a machine learning model locally, and turns unusual machine behavior into actionable alerts. The result is a practical way to begin monitoring motors, pumps, fans, bearings, compressors, and other rotating equipment without immediately deploying a complex cloud infrastructure.



<h2 class="wp-block-heading">Pickin’ up good (and bad!) vibrations</h2>



Vibration is one of the most useful signals for understanding the condition of rotating machinery. When a machine is operating normally, its motor, bearings, shafts, and mechanical components produce a relatively consistent vibration pattern. Changes in alignment, balance, friction, mounting, or component wear can alter that pattern.



A traditional monitoring system might trigger an alert whenever vibration exceeds a predefined value. That approach can be useful, but machines rarely operate under perfectly fixed conditions. Speed, load, product type, temperature, mounting position, and operating mode can all influence the vibration signal. This is where anomaly detection becomes especially valuable.



A vibration sensor does not automatically identify every mechanical fault. It provides the raw signal from which meaningful patterns can be extracted. UNO Q – perhaps starting with an <a href="https://store.arduino.cc/collections/modulino/products/modulino-movement">Arduino® Modulino™ Movement sensor</a> and later adding a more precise vibration sensor – can capture acceleration data across three axes. Mounted securely on a motor or pump housing, it can record how the machine behaves during normal operation and how that behavior changes when a fault begins to develop.



The sensor can capture more than a single vibration value. It can record a time series that describes the direction, amplitude, and frequency of the movement. This gives a machine learning model more information than a simple threshold alarm.



A team can collect representative data from a healthy machine, train an anomaly detection model, and deploy it to the edge. When the model sees a vibration pattern that is sufficiently different from what it learned, it produces a higher anomaly score.



The system does not need to know the name of every possible fault before deployment. It can begin by answering a more practical question: “Is this machine still behaving as expected?”



See this pattern in action: This sense-infer-respond loop is exactly what’s demonstrated in the <a href="https://projecthub.arduino.cc/shivaylamba/ai-guard-demo-with-arduino-uno-q-modulino-sensors-and-local-npu-face-recognition-dcfcfe">AI Guard Demo with Arduino UNO Q, Modulino sensors, and local NPU face recognition</a>, published on Arduino Project Hub by user shivaylamba. The project is built around face recognition, but the underlying architecture – a Modulino sensor triggering local inference, which then drives a physical response – is the same blueprint a vibration-based anomaly detector would follow, just with different sensors and a different model.



Or check out how <a href="http://hackster.io/felix-setiono/audiolog-predictive-maintenance-75da5f">AudioLog uses Arduino UNO Q Edge AI to &#8220;listen&#8221; to machines, detecting early signs of failure to prevent costly industrial downtime</a>.



<h2 class="wp-block-heading">From raw data to business intelligence</h2>



A first predictive maintenance project can begin with one machine and a limited number of operating conditions.



The out-of-the-box example shipped with <a href="https://docs.arduino.cc/software/app-lab/">Arduino®&nbsp;App Lab</a> is a great starting point.



The Modulino Movement sensor is mounted firmly on the equipment. UNO Q records acceleration data while the machine is idle, starting, running normally, operating under different loads, and shutting down.



The microcontroller side manages sensor acquisition, sending the data to the Linux side that manages data logging, model execution, dashboards.



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="573" src="https://blog.arduino.cc/wp-content/uploads/2026/07/image-1024x573.jpg" alt="" class="wp-image-42297" srcset="https://blog.arduino.cc/wp-content/uploads/2026/07/image-1024x573.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/07/image-300x168.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/07/image-768x429.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/07/image-1536x859.jpg 1536w, https://blog.arduino.cc/wp-content/uploads/2026/07/image.jpg 2012w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



Collecting this range of normal behavior is important. A model trained on only one operating condition may incorrectly classify a legitimate speed or load change as a fault.



Depending on the application, the project can use classification or anomaly detection.



Classification is useful when the team already has examples of known conditions, such as normal operation, imbalance, misalignment, or a loose mounting.



Anomaly detection is useful when fault data is limited or when intentionally damaging equipment to create training examples would be unsafe or impractical. In this case, the model learns normal behavior and highlights signals that do not fit that baseline.



Worth a look: For a hands-on look at what running ML locally on UNO Q actually feels like in practice, check out<a href="https://www.hackster.io/vsupacha/running-ml-ai-on-arduino-uno-q-59ff07"> Running ML/AI on Arduino UNO Q</a> on Hackster. It’s a capability demo rather than a predictive maintenance project specifically, but it walks through the Arduino App Lab sample apps and on-device inference experience that the classification and anomaly detection workflows above are built on top of.



Looking for even more inspiration? Check out <a href="https://www.linkedin.com/posts/glenn-norris-95b1a78a_glennnorriscan-systemsengineering-arduino-activity-7415717319441862656-pjUR/">this predictive maintenance project </a>that reads automotive CAN bus raw data to determine systematic drifts early, and alert you when the line is not in sync with specs.



<h2 class="wp-block-heading">A large model is not necessarily a better model</h2>



The model for a vibration monitoring application has a narrow job. It does not need to understand images, natural language, or hundreds of unrelated machine types. It only needs to distinguish the relevant operating patterns of the equipment being monitored. This focus on smaller, task-specific models helps make always-on monitoring practical.&nbsp;



Continuous vibration monitoring can generate a large amount of data, but sending every raw sample to the cloud is not always necessary – especially considering it can increase bandwidth consumption, introduce recurring infrastructure costs, and make the monitoring system dependent on network availability.



UNO Q processes vibration windows locally and stores or transmit only useful information, such as the current health state, anomaly score, operating mode, timestamp, or alert event. A local dashboard can show recent machine behavior and event history. When an anomaly exceeds a validated threshold for a defined period, the system can activate a warning light, sound a buzzer, log the event, or send a message to a maintenance service.



Cloud connectivity can still be added when it provides value. The difference is that the core detection process does not need to stop when the internet connection is unavailable.



Going deeper: Edge Impulse has specific<a href="https://docs.edgeimpulse.com/experts/predictive-maintenance-and-defect-detection-projects/compressor-predictive-maintenance-nordic-thingy53"> predictive maintenance guidance</a> around monitoring equipment while it runs, so service teams can act before failure occurs. Its optimization tools are designed precisely for constrained edge deployments like this one: quantized int8 models and RAM-optimized compilation matter here because always-on monitoring needs lower compute, lower memory use, and better power behavior over the long run.



<h2 class="wp-block-heading">The real value of predictive maintenance</h2>



Predictive maintenance works when people are given enough warning to inspect a machine before an unexpected stoppage: there still has to be time to act. The great news is UNO Q now brings the sensing, local intelligence, Linux applications, connectivity, and machine-facing control needed to build that workflow on a single platform. It allows teams to start with a simple question – “Is this machine still behaving normally?” – and to develop the answer into a scalable maintenance system.



Ready to never be caught off guard by a faulty machine or worn-down part again? Build a custom predictive maintenance system that you can easily train on your specific data, with <a href="https://store.arduino.cc/products/uno-q-4gb">UNO Q</a> and <a href="https://docs.arduino.cc/software/app-lab/">Arduino App Lab</a>.



UNO Q is available to order from the&nbsp;<a href="https://store-usa.arduino.cc/pages/uno-q" target="_blank" rel="noreferrer noopener">Arduino Store</a> as well as<a href="http://www.digikey.com/en/product-highlight/a/arduino/uno-q-microcontroller-board"> DigiKey</a>,<a href="https://referral.element14.com/OrderCodeView?url=%2Fnew-products%2Fembedded-computers-education-maker-boards%2Farduino-uno-q"> Farnell</a>,<a href="https://www.mouser.de/new/arduino/arduino-uno-q-platform/"> Mouser</a>,<a href="https://referral.element14.com/OrderCodeView?url=%2Fnew-products%2Fembedded-computers-education-maker-boards%2Farduino-uno-q"> Newark</a>,<a href="https://uk.rs-online.com/web/content/m/arduino-unoq-uk"> RS Components</a>, and<a href="http://robu.in/"> Robu.in</a>; along with our other<a href="https://store.arduino.cc/pages/distributors"> authorized distributors and resellers</a>.



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

How to achieve cost-effective predictive maintenance with the Arduino® UNO™ Q board

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Version 0.56.0 of the Arduino® Core on Zephyr is live – and it’s a sizable update to <a href="https://blog.arduino.cc/2026/05/15/arduino-core-on-zephyr-0-55-getting-ready-for-the-final-mile/">the earlier release</a>. Think optimized performance, expanded hardware capabilities. We’re still smoothing some edges towards the official Stable release, but if you’ve been testing the beta, prepare for a meaningful upgrade.



<h2 class="wp-block-heading">Multimedia expansion: Arduino® GIGA™ Display Shield &amp; Arduino® Portenta H7 board video support</h2>



Multimedia capabilities take a major step forward in this release, establishing native support for advanced visual outputs.



We have introduced official video support for <a href="https://store.arduino.cc/products/portenta-h7">Portenta H7</a> alongside full compatibility for <a href="https://store.arduino.cc/products/giga-display-shield">GIGA Display Shield</a> when paired with the <a href="https://store.arduino.cc/products/giga-r1-wifi">Arduino®&nbsp;GIGA R1™ WiFi</a>. Whether you are building industrial interfaces, interactive kiosks, or custom dashboards, these display features are now fully integrated into the core and ready for your application code.



<h2 class="wp-block-heading">Core optimizations and network bug fixes</h2>



For our supported board lineup, this release delivers several updates to performance, pin management, and connectivity:



<ul class="wp-block-list">
<li>RAM usage optimization: We have optimized internal memory management across the core. This effectively lowers the core’s background footprint, freeing up more RAM for your sketches, complex variables, and larger application buffers.</li>



<li>Dynamic pin-muxing improvements: Runtime pin multiplex configurations have been refined. This improvement allows for more flexible and reliable dynamic pin reassignment, ensuring better stability when managing hardware peripherals programmatically.</li>



<li>Network fixes and improvements: Rather than adding new features, this time around we focused on essential bug fixing within the network stack. These improvements resolve ongoing connection issues and optimize socket management to make your connected prototypes more dependable.</li>
</ul>



<h2 class="wp-block-heading">How to get started</h2>



To update, open the Arduino® IDE, search for “zephyr” in the Board Manager, and install the 0.56.0 release. For a granular breakdown of specific code commits and fixes, you can view the full <a href="https://github.com/arduino/ArduinoCore-zephyr/releases/tag/0.56.0">release notes on GitHub</a>.



<h2 class="wp-block-heading">First use: flashing the Zephyr loader</h2>



To prepare a supported board for running Zephyr-based sketches for the first time, you must install the Zephyr loader firmware onto your hardware. Follow these steps within the Arduino IDE 2:



1. Enter bootloader mode: Double-click the physical RESET button on your board. 2. Select a programmer: Go to the Tools -> Programmer menu and select any available programmer. 3. Burn the loader: Navigate to Tools and click Burn Bootloader to write the Zephyr loader to the board. 4. Upload your first sketch: Once the loader is successfully installed, put the board into bootloader mode by double-clicking the RESET button one more time, and upload your sketch. After this initial setup, subsequent uploads will happen automatically without manual resets.



Important reminder: It is highly recommended to update the Zephyr loader with each new core release. Keeping the loader aligned with the current core version ensures your board remains fully functional, secure, and compatible with future framework modifications.



<h2 class="wp-block-heading">A streamlined workflow for the Arduino®&nbsp;UNO™ Q board</h2>



If you are using UNO Q, you can completely skip the manual process above. Starting with version 0.56.0, the core automatically checks the loader version behind the scenes during every single sketch upload and handles any necessary updates natively. You can write your code, click upload, and let Arduino IDE and Arduino®&nbsp;App Lab take care of the rest.&nbsp;



We are actively working to bring this automated behavior to all other supported boards in future releases.



<h2 class="wp-block-heading">Help us shape the final release!</h2>



Your real-world testing continues to be invaluable as we head towards the Stable milestone. Please share your feedback, report bugs, or contribute on our <a href="https://github.com/arduino/ArduinoCore-zephyr/issues">GitHub Issues page</a>. Thank you for being an active part of the Arduino community!



Arduino, GIGA, Portenta, GIGA R1, and UNO and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

Arduino Core-zephyr 0.56.0: try it out now, and help us get closer to Stable

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A message from Arduino CPO &#8211; Marcello Majonchi



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Dear Builders, Engineers, and Innovators,



Arduino is built on one belief: powerful technology should be accessible to everyone who wants to make, learn, or innovate with it. Staying true to that principle in every decision we make sometimes means making tough calls about what we charge for it. So let me get straight to the point and break the news: the time has come where at Arduino we finally have to increase the price for UNO Q.&nbsp;



Effective July 6th, we are adjusting the price of UNO Q. <a href="https://store.arduino.cc/products/uno-q" target="_blank" rel="noreferrer noopener">UNO Q 2GB</a> will increase from $44 to $59, while <a href="https://store.arduino.cc/products/uno-q-4gb" target="_blank" rel="noreferrer noopener">UNO Q 4GB</a> from $59 to $79.&nbsp;



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If you follow the news across the tech sector you’ll surely know that memory prices have been on a rally, fueled by the enduring demand for AI applications. Over the last six months alone, our memory component costs have more than doubled. Arduino is not immune to these effects but, thanks to support from Qualcomm Technologies, Inc., we held off increasing the price for as long as possible, enabling the community to do what it does best: innovating and sharing projects. However, with no near-term signs of relief in memory supply or pricing, continuing to absorb these costs is no longer possible.



Since its launch in October 2025, I’ve been amazed at the scale of uptake for UNO Q and by the amazing breadth of projects developed using the board. You’ve exceeded every expectation! Personally, I have loved the ingenious innovation from the community – my personal favorites being the <a href="https://projecthub.arduino.cc/marina_fujiwara/invisible-mess-glasses-b3e6f0" target="_blank" rel="noreferrer noopener">Invisible Mess Glasses</a> based on UNO Q 2GB and the <a href="https://projecthub.arduino.cc/ingeimaks/ai-plant-guardian-the-smart-plant-that-waters-itself-sees-with-ai-and-talks-to-you-on-telegram-f5f73c" target="_blank" rel="noreferrer noopener">AI Plant Guardian</a> based on UNO Q 4GB.&nbsp;



The price increase takes effect on July 6th, so you still have the opportunity to take advantage of the current pricing for a few more days, and for a limited time you can get a hold of a 4GB board for what will be the 2GB board’s new price!



Thank you for the trust you place in Arduino, and for the energy you bring to every build. What you create with these boards is why we do this work. I look forward to seeing what you all will build, channeling your creativity into your next amazing project on UNO Q.



&#8211; Marcello Majonchi, Arduino Maker and Arduino Chief Product Officer



UNO Q is available to order from the <a href="https://store-usa.arduino.cc/pages/uno-q" target="_blank" rel="noreferrer noopener">Arduino Store</a> and<a href="http://www.digikey.com/en/product-highlight/a/arduino/uno-q-microcontroller-board" target="_blank" rel="noreferrer noopener"> DigiKey</a>,<a href="https://referral.element14.com/OrderCodeView?url=%2Fnew-products%2Fembedded-computers-education-maker-boards%2Farduino-uno-q" target="_blank" rel="noreferrer noopener"> Farnell</a>,<a href="https://www.mouser.de/new/arduino/arduino-uno-q-platform/" target="_blank" rel="noreferrer noopener"> Mouser</a>,<a href="https://referral.element14.com/OrderCodeView?url=%2Fnew-products%2Fembedded-computers-education-maker-boards%2Farduino-uno-q" target="_blank" rel="noreferrer noopener"> Newark</a>,<a href="https://uk.rs-online.com/web/content/m/arduino-unoq-uk" target="_blank" rel="noreferrer noopener"> RS Components</a>, and<a href="http://robu.in" target="_blank" rel="noreferrer noopener"> Robu.in</a>, as well as our other<a href="https://store.arduino.cc/pages/distributors" target="_blank" rel="noreferrer noopener"> authorized distributors and resellers</a>.



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

A heads-up on the Arduino® UNO™ Q board pricing – straight from Marcello Majonchi

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The <a href="https://store-usa.arduino.cc/collections/modulino" target="_blank" rel="noreferrer noopener">Modulino family</a> keeps growing, to allow you to easily expand your projects with new tiny modules that bring additional functionalities –&nbsp;in a snap!&nbsp;



With Modulino Hub, Modulino Extender, and Modulino Motors joining the range, you now have no less than 15 easy options to make building your next idea easier and more fun than ever. Think of them as ready-to-deploy functionalities you can add, swap around, and link together to learn, create, or prototype interactive and automated devices.



<h2 class="wp-block-heading">Let’s get to know the three new modules!</h2>



After releasing <a href="https://store.arduino.cc/products/modulino-led-matrix" target="_blank" rel="noreferrer noopener">Modulino LED Matrix</a>, we tackled three pain points the Arduino community has been voicing.&nbsp;



<ul class="wp-block-list">
<li>Not enough I2C channels? The I2C protocol supports up to 127 devices in theory, but in practice address conflicts between components can become a real bottleneck long before you get there – especially in complex builds where multiple sensors or actuators share the same default address. When your project hits that wall, you can now add a <a href="https://store.arduino.cc/products/modulino-hub" target="_blank" rel="noreferrer noopener">Modulino Hub</a>. By daisy-chaining it to your existing setup, you gain 8 new independent I2C channels, each with its own isolated address space, so you can keep adding components without conflicts and without changing a line of code in your existing logic.</li>



<li>Have a project that’s too large for I2C to handle? I2C is great for short-range communication, but if you have longer distances between devices or larger installations you have two choices: either change protocol and start over, or simply boost the signal. Of course, you had us at simply. Get a <a href="https://store.arduino.cc/products/modulino-extender" target="_blank" rel="noreferrer noopener">Modulino Extender</a>, place it on the I2C bus, and it will amplify the signal to approximately 30 meters (?100 ft) when operating at 100 kHz. No additional libraries needed, because this is a 100% hardware module.</li>



<li>Have a robot or automated mechanism to control? Try <a href="https://store.arduino.cc/products/modulino-motors" target="_blank" rel="noreferrer noopener">Modulino Motors</a> to skip the spaghetti wiring between central controller and devices, and control two DC motors or a single stepper motor with precision. Now you can adjust speed, direction, and position right where the action happens.</li>
</ul>



<h2 class="wp-block-heading">What exactly is Modulino?&nbsp;</h2>



Modulino is our range of plug-and-play smart components designed to take the friction out of building with electronics. Each node connects via Qwiic cable and supports daisy-chaining, so you can combine sensors, actuators, and controllers without redesigning your hardware every time you add something new. A dedicated library – supporting both Arduino language (based on C++) and MicroPython – means the learning curve stays gentle, no matter where you are on your path.



The design philosophy is consistency: every Modulino node shares the same (tiny!) form factor and connection standard, so swapping components between projects is straightforward, and breakout pins give advanced users room to push customization further.&nbsp;



Modulino nodes connect directly to&nbsp; <a href="https://www.arduino.cc/product-uno-q" target="_blank" rel="noreferrer noopener">Arduino UNO Q</a>, <a href="https://store.arduino.cc/products/nesso-n1" target="_blank" rel="noreferrer noopener">Nesso N1</a>, <a href="https://store.arduino.cc/collections/uno/products/uno-r4-wifi" target="_blank" rel="noreferrer noopener">UNO R4 WiFi</a>, and <a href="https://store.arduino.cc/collections/nano-family" target="_blank" rel="noreferrer noopener">Nano</a> boards, and integrate naturally with <a href="https://cloud.arduino.cc/" target="_blank" rel="noreferrer noopener">Arduino® Cloud</a> tools and the broader Arduino ecosystem – making them equally valuable in a classroom, a maker workshop, or a professional edge IoT prototyping lab.&nbsp;



<h2 class="wp-block-heading">Get your new Modulino today</h2>



Modulino Hub, Modulino Extender, and Modulino Motors are available today from the <a href="https://store.arduino.cc/" target="_blank" rel="noreferrer noopener">Arduino Store</a> today, as well as through trusted official distributors worldwide.&nbsp;



Arduino, Modulino, UNO, Nesso, and Nano are trademarks or registered trademarks of Arduino S.r.l.

Three new Arduino® Modulino™ modules are here! Bigger ideas now come with zero added stress

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<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="559" src="https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost-Cover1100x600-3-1-1024x559.jpg" alt="" class="wp-image-42218" srcset="https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost-Cover1100x600-3-1-1024x559.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost-Cover1100x600-3-1-300x164.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost-Cover1100x600-3-1-768x419.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost-Cover1100x600-3-1.jpg 1100w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



When talking about large language models (LLMs), people usually imagine a general-purpose assistant: something that can answer questions about weather, politics, software, history, travel, cooking, electronics – and almost any other topic. The model is expected to know a little bit about everything, follow open-ended conversations, and respond to a very broad range of prompts. That’s the experience most of us are used to, since cloud-based AI tools have become so widespread.



Embedded systems work in a much more “narrow” world. A robot does not need to discuss politics, an inspection system does not need to suggest vacation destinations, and a maintenance assistant installed near a machine does not need to explain ancient history. The system needs to understand the device, the task, the possible commands, the local data, and the actions that are safe to suggest or execute. The goal is to give an edge device enough language intelligence to become more useful, more understandable, and more independent from the network.



This is the framework in which we can think about local LLMs on <a href="https://www.arduino.cc/product-uno-q">UNO Q</a>: a practical platform to explore this idea because it brings together a Debian Linux environment and the Arduino® hardware ecosystem. The Linux side can run local AI tools, command-line workflows, Python applications, web services, and inference runtimes. The Arduino side connects that intelligence to sensors, actuators, shields, <a href="https://store.arduino.cc/pages/modulino">Arduino®&nbsp;Modulino™ nodes</a>, and real-world signals. This combination makes it possible to experiment with language models not as isolated chatbots, but as part of real embedded workflows.



The most important question to consider is not how to force a large model to run, but what kind of useful intelligence can live close to the data, close to the device, and close to the physical action?



<h2 class="wp-block-heading">Step 1: choose the right model for your use case</h2>



The edge is where smaller, optimized models become interesting. On the cloud, a large general-purpose model makes sense because it is expected to answer almost anything. On the edge, a model that has been trained, fine-tuned, distilled, or quantized for a specific domain can be more practical. It carries less unnecessary weight, focuses on the type of language the device actually needs, and can be integrated into a controlled application flow.



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="682" src="https://blog.arduino.cc/wp-content/uploads/2026/06/DSCF4915-1024x682.jpg" alt="" class="wp-image-42422" srcset="https://blog.arduino.cc/wp-content/uploads/2026/06/DSCF4915-1024x682.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/06/DSCF4915-300x200.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/06/DSCF4915-768x512.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/06/DSCF4915-1536x1024.jpg 1536w, https://blog.arduino.cc/wp-content/uploads/2026/06/DSCF4915.jpg 2000w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



For example, in robotics the interaction can often be reduced to a limited set of useful instructions: move forward, stop, inspect this object, return to base, report battery level, explain the last error, switch to manual mode. The model can help interpret natural language, but the system should still map that interpretation to a controlled set of valid commands. This makes the behavior easier to test, easier to validate, and easier to trust.



That narrower scope is one of the reasons local LLMs can make sense on embedded platforms.



<h2 class="wp-block-heading">Step 2: understand your memory and storage constraints</h2>



A large language model usually has many parameters, and every parameter represents data that must be stored, loaded, and processed during inference. Model weights are only part of the story. During generation, the runtime also needs working memory for the prompt, the intermediate computation, and the key-value cache used by transformer models to keep track of previous tokens. As the context grows, memory usage grows too.



A 1B-parameter model in 4-bit quantization (such as Llama 3.2 1B Q4) occupies roughly 600–700 MB on disk and requires around 1 GB of RAM at runtime, including the KV cache for a short context window. A 3B model at the same precision pushes past 2 GB. These are numbers that matter on a board with fixed memory and storage, where the model must coexist with the OS, the runtime, and the rest of the application.



Quantization is one of the techniques that makes this more realistic. Instead of storing model weights with high-precision numerical values, a quantized model uses lower-precision representations. This reduces memory usage and can make inference possible on hardware that would otherwise be too constrained. In practical terms, quantization helps move a model from “too large to run locally” towards “small enough to experiment with” –&nbsp;while accepting a trade-off in accuracy, fluency, or speed depending on the model and runtime.



Model distillation is another important concept. In simple terms, distillation is a training approach where a smaller model learns from a larger teacher model. The goal is to keep useful behavior while reducing inference cost and memory footprint. A distilled model will not have the full breadth of the teacher, but it can be much more suitable when the application needs a focused capability on-device.



<figure class="wp-block-image size-full is-resized"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="768" src="https://blog.arduino.cc/wp-content/uploads/2026/06/781ae253-fc34-4fed-a9b4-7ffbbe5fe9cf.png" alt="" class="wp-image-42423" style="width:813px;height:auto" srcset="https://blog.arduino.cc/wp-content/uploads/2026/06/781ae253-fc34-4fed-a9b4-7ffbbe5fe9cf.png 1024w, https://blog.arduino.cc/wp-content/uploads/2026/06/781ae253-fc34-4fed-a9b4-7ffbbe5fe9cf-300x225.png 300w, https://blog.arduino.cc/wp-content/uploads/2026/06/781ae253-fc34-4fed-a9b4-7ffbbe5fe9cf-385x289.png 385w, https://blog.arduino.cc/wp-content/uploads/2026/06/781ae253-fc34-4fed-a9b4-7ffbbe5fe9cf-768x576.png 768w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



This example of <a href="https://projecthub.arduino.cc/marc-edgeimpulse/running-local-llms-and-vlms-on-the-arduino-uno-q-with-yzma-74e288">running local LLMs and VLMs on UNO Q with yzma </a>expands the conversation beyond text chat and explores local LLM and VLM workflows using yzma and llama, pointing toward a wider class of edge AI experiments where language models can work together with images, local data, and device context.



<h2 class="wp-block-heading">Step 3: identify where a local LLM adds real value</h2>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="682" src="https://blog.arduino.cc/wp-content/uploads/2026/06/DSCF5128-1024x682.jpg" alt="" class="wp-image-42424" srcset="https://blog.arduino.cc/wp-content/uploads/2026/06/DSCF5128-1024x682.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/06/DSCF5128-300x200.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/06/DSCF5128-768x512.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/06/DSCF5128-1536x1024.jpg 1536w, https://blog.arduino.cc/wp-content/uploads/2026/06/DSCF5128.jpg 2000w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



Local LLMs become even more useful when they are combined with other edge workflows. OCR is a good example. A camera connected to an UNO Q may extract text from a label, display, document, or machine interface. A compact language model can then summarize that text, classify it, or turn it into a structured response. The model only needs to process the relevant context, which keeps the workflow lighter and more focused.



The same principle applies to an UNO Q that collects logs, sensor readings, error states, or system events. A local model can turn that information into a short human-readable summary directly on the device. For a technician, this can transform raw data into something immediately useful – a compact explanation of the current status or a short description of the last error condition.



<h2 class="wp-block-heading">Step 4: design the architecture and set your boundaries</h2>



One of the most practical ways to think about local LLMs on UNO Q is to treat the model as an occasional reasoning layer. It can be called when language understanding, summarization, or interpretation adds value. Fast control loops, continuous monitoring, and timing-critical actions remain better suited to deterministic software running on the appropriate side of the system.



When working with local LLMs on UNO Q, developers should take into consideration a few practical parameters. Memory usage comes first: the model must fit comfortably together with the runtime and the rest of the application. Response latency comes next: a model that runs may still feel too slow if the use case expects instant answers. Storage should also be planned carefully, because model files and dependencies can be large.



The best entry point is the Arduino Project Hub tutorial <a href="https://projecthub.arduino.cc/robuinlabs/local-llm-ai-chatbot-on-arduino-uno-q-043aa9">Local LLM AI Chatbot on UNO Q</a>, which walks through installing a small LLM and running it offline. It is a useful starting point because it demonstrates the basic shape of a local LLM application



There is also a natural bridge toward local agents. Agentic workflows can move beyond a simple chat interface and start coordinating tools, files, scripts, and actions. On UNO Q, this direction is especially interesting when the agent is treated as an orchestrator on the Linux side. It can inspect logs, prepare files, call scripts, interact with local tools, or help drive development workflows, while the hardware-facing layer keeps direct control over physical I/O.



This kind of setup requires clear boundaries. Giving an agent access to tools means giving it the ability to change things, so the environment should be designed carefully. A dedicated board can be a useful sandbox for this type of experimentation, with limited credentials, limited data access, and a specific set of allowed tools. This makes it possible to explore agentic workflows while keeping the system understandable and controlled.



If you prefer a familiar developer workflow, <a href="https://blogm.tinivelli.com/installing-ollama-on-arduino-uno-q-d7b63a12b1e9">Installing Ollama on Arduino UNO Q</a> covers a practical detail that matters a lot on embedded Linux systems: how to efficiently manage the resources available on the UNO Q to get the most out of it.



<h2 class="wp-block-heading">Step 5: run it, measure it, iterate</h2>



Pick one model, run it on the board, and pay attention to memory usage and response time for your specific prompt. That real-world data will tell you more than any benchmark – and it will give you a much clearer picture of where a local LLM fits in your next embedded project.



Local LLMs on UNO Q always balance power, cost, size, latency, privacy, reliability, and connectivity. The most interesting question is how much useful intelligence can be placed close to the data, the hardware, and the user. Because edge AI is not about more power. It’s about smarter choices. With the right model, the right architecture, and the flexibility of UNO Q, you can test local AI where it matters most: on real hardware, in real projects.



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This video demonstrates how you can deploy lightweight LLMs on the Arduino UNO Q, opening the door to a new generation of intelligent, private, and responsive devices.



<a href="https://store.arduino.cc/products/uno-q-4gb">Start building with UNO Q</a> and bring your AI ideas closer to the real world. UNO Q is available to order from <a href="http://www.digikey.com/en/product-highlight/a/arduino/uno-q-microcontroller-board">DigiKey</a>, <a href="https://referral.element14.com/OrderCodeView?url=%2Fnew-products%2Fembedded-computers-education-maker-boards%2Farduino-uno-q">Farnell</a>,<a href="https://www.mouser.de/new/arduino/arduino-uno-q-platform/">Mouser</a>, <a href="https://referral.element14.com/OrderCodeView?url=%2Fnew-products%2Fembedded-computers-education-maker-boards%2Farduino-uno-q">Newark</a>, <a href="https://uk.rs-online.com/web/content/m/arduino-unoq-uk">RS Components</a>, and <a href="http://robu.in/">Robu.in</a>; along with our other <a href="https://store.arduino.cc/pages/distributors">authorized distributors and resellers</a>.



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

Running local LLMs on the Arduino® UNO™ Q board: a practical guide

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<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="559" src="https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost-Cover1100x600-2-1024x559.jpg" alt="" class="wp-image-42204" srcset="https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost-Cover1100x600-2-1024x559.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost-Cover1100x600-2-300x164.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost-Cover1100x600-2-768x419.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost-Cover1100x600-2.jpg 1100w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



Most developers reach for a single-board computer, only to discover they still need a microcontroller for real-time I/O. Then they need eMMC and extra storage. Then a separate AI accelerator. Then comes the custom wiring nightmare just to make everything talk to each other. As your app grows, you find yourself stacking extra boards, external controllers, and shaky communication links. The bill of materials skyrockets, and the integration headaches follow, all just to bridge the gap between Linux computing and real-time control.



<a href="https://www.arduino.cc/product-uno-q">UNO Q</a> starts where that frustration ends. One board. Qualcomm Dragonwing™ QRB2210 Linux-capable processor. Real-time STM32 MCU. Qualcomm® Adreno GPU 3D graphics accelerator. Forget unreliable SD cards with the built-in 32GB eMMC. It’s simple: one board, double the value.



When you can slash system complexity right out of the box, you instantly accelerate your development pipeline and cut down your total deployment costs. No extra fluff, just pure efficiency.



Let’s look under the hood: here are four reasons why UNO Q completely crushes overpriced, complicated hardware with a single, unified platform.



<h2 class="wp-block-heading">#1 Expect more from the right board</h2>



Many modern applications need two very different kinds of computing. On one side, there is the need for AI inference, computer vision, networking, data processing, and cloud connectivity, which all benefit from a Linux environment running on a powerful application processor. On the other side, there are sensors, actuators, motors, industrial signals, and timing-critical tasks that require deterministic real-time control.



Traditionally, developers solve this challenge by combining multiple boards and creating custom communication layers between them. UNO Q puts both worlds on the same board. The Linux MPU handles AI, vision, and data. The STM32 MCU handles sensors, actuators, and timing. They work together –&nbsp;and you don’t even need to design and debug the communication layer.



Of course there is value in having “two boards in one.&#8221; But there is potentially even more value in eliminating external hardware, reducing integration effort, and simplifying system architecture.



This approach is already proving valuable in real-world applications. <a href="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/">Star Stream</a>, for example, uses UNO Q to process and analyze high-speed racing telemetry data at the edge. The platform combines Linux-level processing power for data handling and visualization with the deterministic control needed to interact with physical systems in real-time. The result is a solution capable of delivering professional-grade, real-time insights without requiring a complex multi-board architecture: it’s Formula 1 results, without the Formula 1 budget.



<h2 class="wp-block-heading">#2 Lessen the cost with fewer components&nbsp;</h2>



As you move on to more complex endeavors, it’s important to keep the bigger picture in mind: the purchase price of a board is only a small part of a project&#8217;s budget! Additional controllers, interface boards, communication modules, power supplies, and custom integration all contribute to the final cost of a solution.&nbsp;



UNO Q consolidates that BOM. Linux processing, real-time control, high-speed connectivity, and industrial I/O, all on one board. Fewer components means fewer integration points, fewer failure modes, and a bill of materials that doesn&#8217;t keep growing. A lot more applications can be built with fewer components and fewer integration challenges. The result is a lower total cost of ownership (TCO), even before considering maintenance, deployment, and long-term support.



A great, concrete example of this comes from <a href="https://blog.arduino.cc/2026/05/26/zencell-replacing-two-boards-with-one-to-build-a-better-quality-inspection-solution/">ZenCell</a> from PriscoZen, a company developing automated quality inspection systems. By leveraging the dual-brain architecture of UNO Q, they were able to consolidate functionality that would traditionally require multiple devices into a single platform. Fewer boards meant lower BOM cost, fewer integration points, simplified maintenance, and a cleaner overall system design. What’s not to love?



<h2 class="wp-block-heading">#3 Develop AI-native projects with less second guessing&nbsp;</h2>



If you are running an AI model, the real complexity often begins after inference is complete. You need to collect sensor data, make a decision, and trigger a physical response. This needs to be done reliably, in real-time. That requires deterministic control, not a Linux process that can be preempted by the OS. Again, the dual-brain architecture of UNO Q can become particularly valuable from this perspective.



The Linux processor can handle AI models, vision pipelines, orchestration, and high-level decision making. Meanwhile, the microcontroller continues to manage sensors, actuators, and real-time interactions independently. The two processors complement each other naturally, allowing you to build systems that can both understand and react to their environment.



This architecture is especially relevant for applications such as <a href="https://blog.arduino.cc/2026/05/28/industrial-grade-vision-inspection-made-accessible-by-the-arduino-uno-q-board/">visual inspection</a>, predictive maintenance, robotics, smart gateways, and intelligent human-machine interfaces.



The RS DesignSpark team built a PCB inspection 4-part series (you can start with Part 1, <a href="https://www.rs-online.com/designspark/automating-pcb-inspection-with-arduino-uno-q-part-1-introduction">here</a>) that demonstrates this approach in practice. In the project, UNO Q handles image acquisition, machine learning workflows, and application logic while maintaining reliable interaction with the physical inspection environment. Rather than focusing solely on AI performance, the project showcases what is often more important in industrial deployments: connecting intelligence to action. The real kicker? Andrew told us he built the whole system for about $1,700 – with significant savings compared to similar systems starting at $3,000 and going all the way up to $20,000!



<h2 class="wp-block-heading">#4 Build it now, faster, and without friction</h2>



In many professional projects, engineering time can quickly exceed the cost of the hardware itself. Think about it: you need to prepare the environment, install the software stack, connect different tools, move data between systems, and create the infrastructure needed before real application development can even begin.



UNO Q helps reduce friction and speed up the process from the very first step. It ships with onboard eMMC and a preinstalled software environment. No SD card to format. No image to flash. No setup ritual before you can write a line of application code. With onboard eMMC storage and a preinstalled software environment, you can start working without first preparing removable media, flashing an operating system image, or assembling the basic development setup from scratch.&nbsp;



It also matters after deployment. In real-world systems, storage is not just a setup detail: it can affect reliability, maintenance, and uptime. Removable storage can be more exposed to corruption, wear, accidental removal, or failure, which may lead to downtime and additional recovery costs when a system needs to be reinstalled or reconfigured. By relying on onboard eMMC storage, UNO Q offers a more robust foundation for applications that are expected to run continuously and reliably. It also gives you flexibility in how you work with the board: it can be used connected to a PC during development, or run as a full standalone single-board computer (SBC) when the application needs to operate independently.



The advantage of adopting UNO Q becomes particularly clear in <a href="https://blog.arduino.cc/2026/05/28/industrial-grade-vision-inspection-made-accessible-by-the-arduino-uno-q-board/">machine vision and inspection projects</a>, where developers can focus on building the application itself rather than assembling the underlying infrastructure.



As author Andrew Back noted in his final words on <a href="https://www.rs-online.com/designspark/automating-pcb-inspection-with-arduino-uno-q-part-4-creating-the-application">Part 4</a> of the DesignSpark PCB inspection project, “UNO Q, Arduino® App Lab and Edge Impulse together provide a powerful combination, with a clear focus on convenience and enabling the rapid development of sophisticated applications.”



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<h2 class="wp-block-heading">Understanding the full value of your hardware</h2>



At the end of the day, the greatest value is found in building a simpler, more efficient, more versatile and scalable system. UNO Q – with its unique combination of Linux computing, real-time control, edge AI readiness, and industrial-grade flexibility – is designed to reduce complexity rather than add it. And for developers building the next generation of intelligent devices, that may be the most valuable feature of all.



The real cost of a system is not what you pay for the board.



It’s the external MCU you didn’t have to buy. The AI HAT you didn’t need. The communication layer you didn’t have to build. The hours you didn’t spend on integration. The maintenance call you avoided, because eMMC doesn’t fail the way SD cards do. Every component you skip is money saved. Every integration point you eliminate is a failure mode removed.



The ultimate system cost isn’t the price of the board, it&#8217;s everything you no longer have to buy, wire, and fix. By packing Linux, edge AI, and real-time control onto a single board, UNO Q slashes your component list, cuts power consumption, and eliminates integration headaches. Zoom in, it does more; zoom out, it saves you more.



Want to dive deep into real-world applications that are proving the value of UNO Q? Read more about:



<ul class="wp-block-list">
<li><a href="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/">How Star Stream created a smart, cost-effective solution to process and analyze high-speed racing telemetry data at the edge</a></li>



<li><a href="https://blog.arduino.cc/2026/05/26/zencell-replacing-two-boards-with-one-to-build-a-better-quality-inspection-solution/">How ZenCell replaced two boards with one, to build a better quality inspection system</a></li>



<li><a href="https://www.rs-online.com/designspark/automating-pcb-inspection-with-arduino-uno-q-part-1-introduction">How Andrew Back worked out automated visual inspection of PCBs</a>, leveraging user-friendly training of AI/ML models made available by Edge Impulse</li>
</ul>



UNO Q is available to order from&nbsp;<a href="http://www.digikey.com/en/product-highlight/a/arduino/uno-q-microcontroller-board" target="_blank" rel="noreferrer noopener">DigiKey</a>,&nbsp;<a href="https://referral.element14.com/OrderCodeView?url=%2Fnew-products%2Fembedded-computers-education-maker-boards%2Farduino-uno-q" target="_blank" rel="noreferrer noopener">Farnell</a>,&nbsp;<a href="https://www.mouser.de/new/arduino/arduino-uno-q-platform/" target="_blank" rel="noreferrer noopener">Mouser</a>,&nbsp;<a href="https://referral.element14.com/OrderCodeView?url=%2Fnew-products%2Fembedded-computers-education-maker-boards%2Farduino-uno-q" target="_blank" rel="noreferrer noopener">Newark</a>,&nbsp;<a href="https://uk.rs-online.com/web/content/m/arduino-unoq-uk" target="_blank" rel="noreferrer noopener">RS Components</a>, and&nbsp;<a href="http://robu.in/" target="_blank" rel="noreferrer noopener">Robu.in</a>; along with our other<a href="https://store.arduino.cc/pages/distributors" target="_blank" rel="noreferrer noopener">authorized distributors and resellers</a>.



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.

Ditch overpriced hardware: 4 ways the Arduino® UNO™ Q board helps you do more for less

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<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="559" src="https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost2-Cover1100x600-1024x559.jpg" alt="" class="wp-image-42186" srcset="https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost2-Cover1100x600-1024x559.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost2-Cover1100x600-300x164.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost2-Cover1100x600-768x419.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost2-Cover1100x600.jpg 1100w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



Artificial intelligence has already evolved from simple conversational assistants into autonomous systems capable of interacting with software, hardware, sensors, and even the physical world. The next frontier is not simply “chatting” with AI, but enabling AI agents to observe, reason, decide, and execute actions locally at the edge.



This shift becomes particularly interesting when combined with embedded systems. Why? Because traditionally, embedded development required deterministic programming, fixed automation logic, and carefully predefined behaviors. But now AI systems are capable of dynamically generating code, controlling peripherals, interacting with operating systems, and adapting behavior in real-time. And we see all of this happening in projects built around <a href="https://www.arduino.cc/product-uno-q">the Arduino® UNO™ Q board</a>.



<h2 class="wp-block-heading">“Think local” takes on a whole new meaning</h2>



What makes this especially compelling is that these agents can run locally.



Instead of relying entirely on cloud APIs and remote inference, developers are beginning to deploy autonomous AI systems directly on edge devices – reducing latency, improving privacy, lowering operational costs, and enabling offline functionality. Projects such as <a href="https://www.hackster.io/draider2001/qclaw-a-fully-local-agentic-assistant-on-the-arduino-uno-q-9f4b09">QClaw</a> and the broader <a href="https://www.hackster.io/news/openclaw-can-program-your-arduino-c73287a68858">OpenClaw ecosystem</a> are demonstrating how this model can work on UNO Q.



Our first <a href="https://blog.arduino.cc/2026/05/07/one-board-two-brains-three-ways-a-dual-architecture-board-makes-building-simpler/">dual-brain board</a>&nbsp;is particularly well suited for this new generation of AI-native embedded simple systems because it combines two different computing worlds on the same platform. In simple words, this means you have one board where, on one side, there is a Linux-capable Qualcomm Dragonwing™ QRB2210 processor capable of running Python applications, Docker containers, networking stacks. On the other side, an STM32 microcontroller handles deterministic real-time operations such as GPIO access, peripheral control, and low-level hardware timing. This hybrid architecture allows developers to separate high-level reasoning from low-level execution, effectively creating systems where AI can “think” on Linux while the microcontroller interacts directly with the physical environment.



<h2 class="wp-block-heading">Your word is AI’s command</h2>



One of the most interesting frameworks emerging in this space is OpenClaw. Rather than being a language model itself, OpenClaw acts as an orchestration layer that connects large language models with tools, terminals, filesystems, APIs, and hardware interfaces. As described in the <a href="https://www.qualcomm.com/developer/blog/2026/04/install-openclaw-hermes-agent-qualcomm-arduino-rubik-pi-snapdragon-pcs">OpenClaw installation guide for Qualcomm</a><a href="https://www.arduino.cc/product-uno-q">®</a><a href="https://www.qualcomm.com/developer/blog/2026/04/install-openclaw-hermes-agent-qualcomm-arduino-rubik-pi-snapdragon-pcs">and Arduino</a><a href="https://www.arduino.cc/product-uno-q">®</a><a href="https://www.qualcomm.com/developer/blog/2026/04/install-openclaw-hermes-agent-qualcomm-arduino-rubik-pi-snapdragon-pcs">platforms</a>, the framework enables AI models to move beyond pure text generation and become actionable agents capable of executing commands and interacting with their environment.



This changes the relationship between developers and embedded systems faster than ever. Instead of manually writing every line of code, users can increasingly interact with hardware through natural language. We’ve already showcased a good example of this approach <a href="https://blog.arduino.cc/2026/03/05/radical-accessibility-on-the-arduino-uno-q-board-with-openclaw">here</a>, demonstrating how OpenClaw can run on UNO Q to use conversational prompts to control hardware directly. Rather than opening an IDE and programming peripherals manually, you can ask the agent to blink LEDs, modify animations on the LED matrix, or generate entirely new interactions dynamically.



What makes these demonstrations powerful is the iterative workflow they enable. In one example, the AI agent generated code to display graphics on the LED matrix, uploaded the firmware, and then refined the visual output interactively through additional prompts. The interface to embedded systems effectively becomes conversational. Hardware prototyping starts looking less like firmware engineering and more like collaborative interaction with an intelligent assistant.



<h2 class="wp-block-heading">Fast, private, cheap AI agents? Cutting edge!</h2>



<a href="https://www.hackster.io/draider2001/qclaw-a-fully-local-agentic-assistant-on-the-arduino-uno-q-9f4b09">This concept evolves even further in the QClaw project</a>, where the Arduino UNO Q becomes a fully local agentic assistant. In this architecture, the system is capable not only of generating code, but also of compiling sketches, uploading the compiled code, interacting with local services, and managing workflows autonomously. Within this scenario, the agent orchestrates the full pipeline from intent to execution.



An especially interesting direction is the growing trend toward fully local AI deployments. While many AI systems depend heavily on cloud-hosted models, several developers are exploring combinations of OpenClaw with Ollama and lightweight open-source LLMs to build completely offline agents. The article <a href="https://amannnamm.medium.com/how-to-turn-openclaw-into-a-real-arduino-agent-using-a-free-local-llm-ae5a464a2deb">How to turn OpenClaw into a real Arduino agent using a free local LLM</a> demonstrates how developers can run local inference directly on the device without sending prompts or data to external APIs. In this model, Ollama hosts the local language model while OpenClaw orchestrates tools and actions, allowing UNO Q to function as an autonomous AI node entirely within the local network.



This approach is particularly attractive for edge AI applications because it addresses many of the practical concerns associated with cloud AI. Sensitive data remains local, recurring API costs disappear, internet connectivity becomes optional, and response times improve significantly. Of course, local models are generally smaller and less capable than frontier cloud models, but for many embedded automation tasks the tradeoff is worthwhile. In fact, for hardware-centric workflows, deterministic execution and local responsiveness often matter more than absolute model intelligence.



<h2 class="wp-block-heading">Local AI meets the real world</h2>



The implications become even more interesting when AI agents start interacting with sensors and perception systems. A compelling example comes from <a href="https://bandini.medium.com/dispositivo-para-verificar-hornallas-a-gas-con-ia-arduino-uno-q-openclaw-3d1876bd366d">this AI-powered gas stove monitoring project</a>, where computer vision and local AI reasoning were combined to monitor kitchen burners and detect potentially dangerous situations. Instead of relying on rigid rule-based automation, the AI system interpreted contextual visual information and made decisions dynamically.&nbsp;



This is an important shift because it demonstrates how edge AI agents can combine perception, reasoning, and physical interaction into unified systems capable of operating in real environments.



<h2 class="wp-block-heading">Safety first! Let UNO Q be your “hardware sandbox”</h2>



What emerges from all these projects is a broader transformation in how embedded systems may be designed in the future and how UNO Q allows this shift, acting as an accelerator.&nbsp;



Traditionally, embedded devices were deterministic endpoints executing carefully predefined logic. AI agents introduce adaptability. Developers no longer need to explicitly define every possible branch of execution. Instead, they can define goals, permissions, tools, and constraints, allowing the agent to determine dynamically how tasks should be completed.



This does not eliminate traditional engineering. Rather, it shifts engineering effort toward architecture, orchestration, safety, and supervision. The challenge becomes designing systems where AI agents can act autonomously while remaining constrained, secure, and reliable.



Security is in fact one of the most important considerations in this new paradigm. AI agents often gain access to terminals, filesystems, APIs, and hardware interfaces, which introduces entirely new attack surfaces. One advantage of platforms such as UNO Q is the possibility of creating isolated “<a href="https://www.youtube.com/watch?v=aA1oWSPh41Q">hardware sandboxes</a>” dedicated to agent execution. Instead of granting AI systems unrestricted access to personal laptops or production environments, developers can deploy agents on dedicated edge hardware with limited permissions and compartmentalized resources. This physical separation is one of the most important deployment strategies for safe personal AI agents.



<h2 class="wp-block-heading">The bigger picture</h2>



The broader trend is clear: AI is moving toward the edge, becoming increasingly autonomous, multimodal, and physically interactive. UNO Q plays a relevant role not because it replaces cloud AI, but because it makes AI tangible –&nbsp;empowering developers, makers, researchers, and students to experiment with autonomous embedded intelligence locally, affordably, and safely.&nbsp;



Ready to upgrade from chatbots to autonomous embedded collaborators? <a href="https://store.arduino.cc/products/uno-q-4gb">Get your hands on UNO Q</a> and play an active role in the AI revolution! UNO Q is available to order from <a href="http://www.digikey.com/en/product-highlight/a/arduino/uno-q-microcontroller-board" target="_blank" rel="noreferrer noopener">DigiKey</a>, <a href="https://referral.element14.com/OrderCodeView?url=%2Fnew-products%2Fembedded-computers-education-maker-boards%2Farduino-uno-q" target="_blank" rel="noreferrer noopener">Farnell</a>, <a href="https://www.mouser.de/new/arduino/arduino-uno-q-platform/" target="_blank" rel="noreferrer noopener">Mouser</a>, <a href="https://referral.element14.com/OrderCodeView?url=%2Fnew-products%2Fembedded-computers-education-maker-boards%2Farduino-uno-q" target="_blank" rel="noreferrer noopener">Newark</a>, <a href="https://uk.rs-online.com/web/content/m/arduino-unoq-uk" target="_blank" rel="noreferrer noopener">RS Components</a>, and <a href="http://robu.in/" target="_blank" rel="noreferrer noopener">Robu.in</a>; along with our other <a href="https://store.arduino.cc/pages/distributors" target="_blank" rel="noreferrer noopener">authorized distributors and resellers</a>.



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