Arduino VENTUNO Q: Where AI takes action

white

Arduino® VENTUNO™ Q Where AI takes action

Arduino is now a Qualcomm company

What is a board, how do I write code to it, and what tools do I need to create my own project?

Learn Arduino

The easy-to-use hardware and software platform

Our goal is to create a world where there are no barriers to technology and innovation

Mission & Vision

What is Arduino?

Latest News

<p>Discover our 2025 Open Source Report!</p>


Open source is love

Homepage

Arduino Homepage

Arduino Based Smart Security System 

Controlling led using HC-05 bluetooth module

Building an Automated Aquarium Dosing System with Arduino Uno Q

EchoAlert: Smart Obstacle Detector

VENTUNO Q Integration with JetArm

Real-Time Air Traffic Radar using ESP32 + ADS-B Data

Soil Knows Best: Build a Smart Irrigation System with Arduino Uno

Joystick-Controlled Football Goalkeeper

Interactive Robot Desk Buddy

Happy Birthday Melody with Arduino

Retro Game Console powered by the Arduino Q

03_AMR moving in warehouse

image

image-6

image-7

AMR-Alt2-Arduino.cc-Blogpost-Cover(1100x600)

AMR-Alt2-Arduino.cc-Blogpost-Featured(385x289)

Alt1-Arduino.cc-Blogpost-Cover(1100x600)

Alt1-Arduino.cc-Blogpost-Featured(385x289)


<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/2026/09/Alt1-Arduino.cc-Blogpost-Cover1100x600-1024x559.jpg" alt="" class="wp-image-42651" srcset="https://blog.arduino.cc/wp-content/uploads/2026/09/Alt1-Arduino.cc-Blogpost-Cover1100x600-1024x559.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/09/Alt1-Arduino.cc-Blogpost-Cover1100x600-300x164.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/09/Alt1-Arduino.cc-Blogpost-Cover1100x600-768x419.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/09/Alt1-Arduino.cc-Blogpost-Cover1100x600.jpg 1100w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="wp-block-paragraph">Physical AI is based on the idea that intelligence shouldn’t stop at perception: instead, it should bring to life systems able to sense their environment, reason about it, and act on it – all in one continuous loop. It’s what makes the difference between a device that observes and one that acts.</p>



<p class="wp-block-paragraph">Autonomous mobile robots are one of the clearest expressions of that idea in the real world. Imagine an autonomous delivery robot working around a warehouse, carrying items to the correct shelf or placing them on a conveyor belt for inspection or packaging. On its way, it may have to stop as someone steps into its path, or adjust its route if a cart has been moved. It must understand its surroundings, determine its position, calculate a path, and respond as the environment changes, until it reaches the right destination.&nbsp;</p>



<p class="wp-block-paragraph">It must control its wheels precisely, read sensors continuously, and react quickly to nearby obstacles –&nbsp;all at the same time.</p>



<p class="wp-block-paragraph">In all autonomous mobile robots, or AMRs, these tasks require two different kinds of computing: navigation, computer vision, and AI benefit from the flexibility and performance of Linux; motor control and time-sensitive sensor management require the responsiveness of a microcontroller.</p>



<p class="wp-block-paragraph"><a href="https://www.arduino.cc/product-ventuno-q">VENTUNO Q</a> brings these two worlds together, combining a Linux-capable MPU with a real-time MCU on a single platform. To explore the potential of this architecture, let’s imagine how it could become the central computing platform for a food-delivery AMR, where perception, ROS 2, Edge AI and physical control could work together.</p>



<h2 class="wp-block-heading">Two computing worlds, one robot</h2>



<p class="wp-block-paragraph">In a conventional AMR prototype, high-level computing and motor control are often distributed across separate boards. A Linux computer runs ROS 2 and processes data from cameras and LiDAR, while a microcontroller manages motors, encoders and local sensors. This approach works, but it also creates integration work. Developers must connect the boards, define their communication protocol, maintain multiple development environments and debug the interaction between systems designed independently from one another.</p>



<p class="wp-block-paragraph">VENTUNO Q provides a more integrated starting point. Its MPU can host Linux, ROS 2, navigation software, computer vision and AI workloads. Its MCU can remain close to the physical robot, managing wheel control, encoder feedback, inertial measurements, short-range sensing and communication with motor drivers. The separation of responsibilities is clear: the MPU determines where the robot should go, while the MCU turns that decision into controlled physical movement. Together, the two sides allow the system to <strong>sense, decide, and act.</strong></p>



<h2 class="wp-block-heading">Sense: giving the robot an understanding of its environment</h2>



<p class="wp-block-paragraph">As the delivery robot travels through a warehouse, hotel, or even a restaurant or public space where it is at work to deliver orders, it must continuously build a picture of what is happening around it. No single sensor can provide all the information it needs, so the architecture combines several complementary sources.</p>



<p class="wp-block-paragraph">A 2D LiDAR can provide the geometric foundation for navigation. Its laser scans describe walls, corridors, furniture and other structures around the robot. Combined with wheel odometry and inertial data, these scans can be used by SLAM software to build a map and later help the robot estimate its position within it. However, a LiDAR scanning on a single plane cannot describe every obstacle. A table surface, an open drawer or an object protruding into the robot’s path may sit above or below that plane.</p>



<p class="wp-block-paragraph">A stereo-depth camera can add three-dimensional information, helping the robot perceive shapes and obstacles that may not appear clearly in a planar scan. A MIPI camera can provide visual input for more application-specific tasks, such as detecting people, recognizing a delivery station or checking whether a tray is present.&nbsp;</p>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="683" src="https://blog.arduino.cc/wp-content/uploads/2026/08/image-6-1-1024x683.jpeg" alt="" class="wp-image-42580" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/image-6-1-1024x683.jpeg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-6-1-300x200.jpeg 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-6-1-768x512.jpeg 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-6-1-1536x1025.jpeg 1536w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-6-1.jpeg 2000w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="has-text-align-center wp-block-paragraph"><em><sup>Image shown for illustration purposes only</sup></em></p>



<p class="wp-block-paragraph">Closer to the robot, an <a href="https://store.arduino.cc/collections/modulino/products/modulino-distance">Arduino<sup>®</sup> Modulino<sup>™</sup> Distance</a> node monitors blind spots or detects objects immediately in front of the chassis. A <a href="https://store.arduino.cc/collections/modulino/products/modulino-movement">Modulino Movement</a> provides acceleration and angular-rate data, contributing to motion estimation and helping identify unusual vibration, collisions or unexpected movement.</p>



<p class="wp-block-paragraph">Wheel encoders complete this picture by measuring how the robot is actually moving. The MCU can use their feedback to regulate wheel speed, while the resulting odometry can be shared with the navigation software running on Linux. Rather than relying on a single “smart” sensor, <strong>the robot develops awareness by combining geometric, visual, inertial and mechanical information</strong>.</p>



<h2 class="wp-block-heading">Decide: ROS 2 as the software backbone</h2>



<p class="wp-block-paragraph">On the MPU, ROS 2 provides a common software environment for connecting all these elements. The LiDAR publishes laser scans. The camera pipeline provides images or depth information. The robot publishes odometry and inertial measurements. Navigation components consume this data to estimate the robot’s position, plan a route and respond when the environment changes.</p>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="683" src="https://blog.arduino.cc/wp-content/uploads/2026/08/image-7-1-1024x683.jpeg" alt="" class="wp-image-42581" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/image-7-1-1024x683.jpeg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-7-1-300x200.jpeg 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-7-1-768x512.jpeg 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-7-1-1536x1025.jpeg 1536w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-7-1.jpeg 2000w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="has-text-align-center wp-block-paragraph"><em><sup>Image shown for illustration purposes only</sup></em></p>



<p class="wp-block-paragraph">During its first exploration of a space, a ROS 2 SLAM package can combine LiDAR data and odometry to create a map. Once that map is available, the robot can localize itself and use Nav2 to move between destinations.</p>



<p class="wp-block-paragraph">If the application sends the robot from the warehouse to the packaging station, Nav2 calculates an appropriate route and continually updates it as new sensor information arrives. It then generates velocity commands describing how quickly the robot should move forward and rotate. Those commands express the intended movement, but they do not directly control the motors. That responsibility moves from the Linux domain to the MCU.</p>



<h2 class="wp-block-heading">Act: from a ROS 2 command to wheel movement</h2>



<p class="wp-block-paragraph">When Nav2 publishes a velocity command, an MPU-to-MCU communication layer transfers the request to the real-time control application. The MCU translates the requested linear and angular movement into targets for the left and right wheels. It then communicates with the motor controllers – potentially over CAN – and uses encoder feedback to maintain the required wheel speed.</p>



<p class="wp-block-paragraph">This control loop can run with predictable timing, independently of the larger ROS 2 software graph operating on Linux. Information also travels in the opposite direction. Encoder measurements, inertial data, short-range distance readings and drive-system diagnostics can move from the MCU to the MPU, where they become available to the ROS 2 application.</p>



<p class="wp-block-paragraph">This connection between the two computing domains is one of the most important elements of the architecture. It allows high-level navigation software to interact with the physical robot without asking Linux to manage every time-sensitive electrical and mechanical detail.</p>



<p class="wp-block-paragraph">It also creates space for local reactions. If a close-range sensor detects an object immediately in front of the robot, the MCU can place the drive system into a controlled stop state and report the event to the MPU. The navigation layer can then reassess the situation, update the route or notify the application. <strong>The result is a robot in which intelligence and responsiveness complement one another.</strong></p>



<h2 class="wp-block-heading">Adding context with edge AI</h2>



<p class="wp-block-paragraph">LiDAR, odometry and Nav2 can already allow the robot to navigate through a mapped environment. AI becomes valuable when the robot needs to understand the meaning of what it perceives. The navigation system may detect an obstacle, for example, but a vision model could determine whether that obstacle is a person, a chair or a delivery cart. This additional context enables more appropriate behavior.</p>



<p class="wp-block-paragraph">When a person approaches, the robot could slow down, wait or announce that a delivery is arriving. At its destination, a visual model might recognize the delivery zone or check whether someone has removed the tray. The same platform could also inspect the route for objects left on the floor or use vibration data to identify abnormal mechanical behavior.</p>



<p class="wp-block-paragraph"><a href="https://www.edgeimpulse.com/blog/announcing-edgeimpulse_ros-edge-ai-perception-native-in-ros-2/">Edge Impulse offers one possible workflow</a> for developing this kind of application-specific intelligence. Through its ROS 2 integration, an inference model can consume image data and make its results available to other parts of the robotic application. ROS 2 helps the robot decide how to navigate. Edge AI helps it interpret the context in which that navigation takes place. And with the integrated Edge Impulse platform, you can choose to deploy directly into <a href="https://docs.arduino.cc/software/app-lab/">Arduino<sup>®</sup> App Lab</a> – it just takes one click.</p>



<h2 class="wp-block-heading">Growing the prototype one capability at a time</h2>



<p class="wp-block-paragraph">Although the reference architecture includes navigation, depth sensing and AI, a developer would not need to introduce every capability at once. The first prototype could focus only on the mobile base, with the MCU controlling the motors and reading the wheel encoders. Once basic movement is reliable, the control layer could be connected to ROS 2 on the MPU so the robot can accept standard velocity commands and return odometry data.</p>



<p class="wp-block-paragraph">Adding LiDAR would introduce mapping, localization and autonomous navigation. Depth sensing and short-range sensors could then improve obstacle awareness. Finally, visual AI could transform the generic mobile platform into a delivery robot capable of recognizing people, destinations and application-specific events. This progressive approach keeps each development step understandable while preserving a clear path toward a more capable system.</p>



<p class="wp-block-paragraph">VENTUNO Q does not remove the engineering challenges involved in building an AMR. Mechanical design, power management, motor sizing, navigation tuning, sensor placement and safety still require careful work. <strong>Its value is in providing a more coherent place to begin.</strong></p>



<h2 class="wp-block-heading">From perception to motion</h2>



<p class="wp-block-paragraph">A food-delivery robot is only one expression of this architecture. The same computing model could support warehouse vehicles, inspection robots, hospital logistics systems, agricultural platforms, and other autonomous machines.</p>



<p class="wp-block-paragraph">The sensors, drive systems and AI models may change, but the fundamental challenge remains the same: connecting high-level intelligence with responsive physical action. By combining a Linux-capable MPU and a real-time MCU, VENTUNO Q gives developers a platform for exploring that complete chain. ROS 2, navigation, perception and edge AI can operate on the MPU, while the MCU manages the sensors and actuators that connect software decisions to the real world.</p>



<p class="wp-block-paragraph">From understanding the environment to controlling the wheels, <strong>VENTUNO Q offers a unified starting point for prototyping the next generation of intelligent mobile robots</strong>.</p>



<p class="wp-block-paragraph">Ready to kickstart your journey in robotics? <a href="https://store.arduino.cc/products/ventuno-q">Get your VENTUNO Q from the Arduino Store today</a>.&nbsp;</p>



<p class="wp-block-paragraph">VENTUNO Q is available through our official distribution partners: <a href="https://www.digikey.com/en/product-highlight/a/arduino/ventuno-q">DigiKey</a>,<a href="https://uk.farnell.com/new-products/embedded-computers-education-maker-boards/arduino-ventuno-q"> Farnell</a>,<a href="https://www.mouser.com/new/arduino/arduino-ventuno-q-sbc"> Mouser</a>,<a href="https://robu.in/product/arduino-ventuno-q/"> Robu.in</a>, and<a href="https://uk.rs-online.com/web/p/arduino/0878176"> RS</a>, along with our other authorized distributors and resellers.</p>



<p class="wp-block-paragraph"><em>Arduino, Modulino, VENTUNO, and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.</em></p>


Building smarter AMRs with the Arduino® VENTUNO™ Q board

Blogpost - Cover - 19 December 2023 at 14.45.21-5

Blog Feature Size (6)


<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="558" src="https://blog.arduino.cc/wp-content/uploads/2026/09/Blogpost-Cover-19-December-2023-at-14.45.21-5-1024x558.png" alt="" class="wp-image-42585" srcset="https://blog.arduino.cc/wp-content/uploads/2026/09/Blogpost-Cover-19-December-2023-at-14.45.21-5-1024x558.png 1024w, https://blog.arduino.cc/wp-content/uploads/2026/09/Blogpost-Cover-19-December-2023-at-14.45.21-5-300x164.png 300w, https://blog.arduino.cc/wp-content/uploads/2026/09/Blogpost-Cover-19-December-2023-at-14.45.21-5-768x419.png 768w, https://blog.arduino.cc/wp-content/uploads/2026/09/Blogpost-Cover-19-December-2023-at-14.45.21-5.png 1201w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="wp-block-paragraph">For years, bringing real intelligence to industrial automation meant expensive infrastructure, proprietary systems, and steep learning curves. That’s changing – fast. Foundation models powerful enough to run at the edge are turning natural language into machine control, and the factory floor is starting to look a lot more like a conversation.</p>



<p class="wp-block-paragraph"><strong>AI as the new interface</strong></p>



<p class="wp-block-paragraph">Think about how AI has changed the way you work at a desk. You describe what you need, and an agent handles the complexity underneath – the tools, the APIs, the data retrieval. The same shift is now happening in manufacturing.</p>



<p class="wp-block-paragraph"><strong>Natural language is becoming the new interface for industrial machines</strong>. An operator who once needed specialized training to reconfigure a production line can now just say what they need. The AI handles the translation from intent to instruction. </p>



<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe loading="lazy" title="From voice command to robotic arm: how agentic AI on the edge is changing the factory floor" width="500" height="281" src="https://www.youtube.com/embed/QZrBzgB3L-Y?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div></figure>



<p class="wp-block-paragraph"><strong>A robotic arm you can talk to</strong></p>



<p class="wp-block-paragraph">Swiss startup <a href="https://www.forgis.com/">Forgis</a>, which builds physical AI models for manufacturing, recently demonstrated exactly this. Using a smartphone, an operator sends a voice command to an AI agent running on the Arduino<sup>®</sup> UNO<sup>™</sup> Q board. Forgis’s foundation model processes the prompt, determines which object to pick and where to place it, calculates the full motion plan, and directs the robotic arm to execute the task – all from a single natural language instruction like “put each box in their respective compartments.”</p>



<p class="wp-block-paragraph">The foundation model ingests multimodal factory data – the robot’s CAD model, PLC I/O signals, project specifications – and translates it into structured, machine-readable instructions in real time. The robotic arm’s camera connects via USB directly to the board, which runs inference locally with a latency of just 20 ms. The LED matrix on the UNO Q even displays the agent’s current state, so the operator always knows what the system is doing.</p>



<p class="wp-block-paragraph"><strong>Critically, none of this requires a cloud round trip</strong>. The entire inference pipeline runs on the edge device, which matters enormously on a factory floor where network dependency, latency, and data sovereignty are real concerns.</p>



<p class="wp-block-paragraph"><strong>Why this matters for manufacturing</strong></p>



<p class="wp-block-paragraph">The practical benefits are straightforward. Fewer manual inputs mean fewer errors – a significant advantage in precision environments like aerospace, medical devices, and automotive. Operators spend less time on routine floor tasks and more on oversight and exception handling. And because the foundation model learns continuously from production data, the system should improve over time.</p>



<p class="wp-block-paragraph">The open Arduino ecosystem also means development teams like Forgis can deploy their proprietary models without getting bogged down in hardware integration. <strong>The gap between a working prototype and a production-ready system is narrowing.</strong></p>



<p class="wp-block-paragraph">Agentic AI has spent the last few years writing code and generating reports. It’s now moving machines. The Forgis and Arduino demo is an early signal of what industrial AI can look like when the edge is fast enough to keep up with the physical world – and when the interface is simply your voice.</p>



<p class="wp-block-paragraph">Curious about UNO Q? <a href="https://www.arduino.cc/product-uno-q">Find out more here</a> or head directly to the <a href="https://store.arduino.cc/products/uno-q-4gb">Arduino Store to get started</a>.</p>



<p class="wp-block-paragraph"><em>Arduino, and UNO, and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.</em></p>


From voice command to robotic arm: how agentic AI on the edge is changing the factory floor

Arduino App Lab - new examples

image-4-1

Arduino.cc-Blogpost-Featured(385x289)-4

Arduino.cc-Blogpost-Cover(1100x600)-5


<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/2026/08/Arduino.cc-Blogpost-Cover1100x600-5-1-1024x559.jpg" alt="" class="wp-image-42518" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/Arduino.cc-Blogpost-Cover1100x600-5-1-1024x559.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/Arduino.cc-Blogpost-Cover1100x600-5-1-300x164.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/Arduino.cc-Blogpost-Cover1100x600-5-1-768x419.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/Arduino.cc-Blogpost-Cover1100x600-5-1.jpg 1100w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="wp-block-paragraph">Today is a big day. <a href="https://store-usa.arduino.cc/products/ventuno-q" target="_blank" rel="noreferrer noopener">Pre-orders for VENTUNO Q</a> are open, and we couldn’t be more excited to share this with you. Our most advanced platform to date was built with one clear ambition: to give developers, makers, educators, and engineers a board that <strong>doesn’t just perceive the world, but interacts with it</strong>. A board where AI takes action.</p>



<blockquote class="wp-block-quote is-layout-flow wp-block-quote-is-layout-flow">
<p class="wp-block-paragraph">“<em>For twenty-one years, Arduino has taken complex technology and made it simple enough for anyone to build something real. VENTUNO Q is that same mission, applied to the most consequential technology of our time. With VENTUNO Q, we are giving every developer, educator and innovator the tools to build machines that don’t just think, but do.</em>” – Fabio Violante, VP &amp; GM, Arduino, Qualcomm Technologies, Inc.</p>
</blockquote>



<h2 class="wp-block-heading">What makes VENTUNO Q different</h2>



<p class="wp-block-paragraph">Most edge AI boards stop at perception. VENTUNO Q goes further. Its dual-brain architecture pairs a <a href="https://www.qualcomm.com/internet-of-things/products/iq8-series" target="_blank" rel="noreferrer noopener">Qualcomm Dragonwing<sup>™</sup> IQ8 processor</a> – delivering up to 40 dense TOPS of AI performance – with a dedicated STM32H5 real-time microcontroller, so the same board that runs a local LLM can also control a motor, communicate via CAN bus protocol, or trigger a physical response in milliseconds. Perception, decision, and action, unified on a single board, entirely offline.</p>



<p class="wp-block-paragraph">It ships with 16 GB LPDDR5 RAM and 64 GB of eMMC storage – expandable via an M.2 connector for NVMe Gen.4 – giving it the headroom to run concurrent AI inference, complex multitasking, and local data storage without compromise. It comes with pre-loaded Ubuntu, distributed by Canonical, and bundled with Ubuntu Pro license that delivers enterprise-grade security updates. Arduino<sup>®</sup> App Lab is your fastest path to a working prototype, but VENTUNO Q maintains full compatibility with VS Code, PyCharm, Jupyter, Docker, and native Linux tools. The Arduino Core runs on Zephyr RTOS on the MCU side for deterministic, real-time hardware control. No proprietary toolchain. No lock-in. Just a powerful, open platform you can build on your own terms.</p>



<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe loading="lazy" title="Arduino VENTUNO Q: Build AI for the Physical World" width="500" height="281" src="https://www.youtube.com/embed/jOs37MSag6A?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div></figure>



<p class="wp-block-paragraph"><strong>VENTUNO Q is equipped for demanding real-world deployments</strong>. On the connectivity side, it features Wi-Fi 6 tri-band (2.4/5/6 GHz) with onboard antenna, Bluetooth 5.3, and 2.5 Gb Ethernet. Video output is available via HDMI, DisplayPort over USB-C Alt mode, and MIPI DSI. For cameras, three MIPI CSI connectors are available alongside USB camera support. Audio I/O, USB-C with host/device and power role switching, two USB 3.0 Type-A ports, and additional USB 3.0 and power options on the JOMEGA header round out the interface set. For industrial applications, VENTUNO Q provides CAN-FD connectivity across multiple headers – including a CAN-FD PHY on a dedicated screw terminal – alongside I2C/I3C, SPI, PWM, and UART.&nbsp;</p>



<p class="wp-block-paragraph"><strong>ROS 2 compatibility is built in, making VENTUNO Q a natural fit for robotics development from day one.</strong> On the hardware compatibility side, VENTUNO Q works out of the box with Arduino<sup>®</sup> UNO<sup>™</sup> shields and carriers, Arduino<sup>®</sup> Modulino<sup>™</sup> nodes via the onboard Qwiic connector, and Raspberry Pi HATs. That’s how it ensures your existing ecosystem of sensors, actuators, and accessories carries forward without modification.</p>



<h2 class="wp-block-heading"><strong><strong>Build your way with Arduino<strong><sup>®</sup></strong> App Lab</strong></strong></h2>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="640" src="https://blog.arduino.cc/wp-content/uploads/2026/08/Arduino-App-Lab-new-examples-1024x640.jpg" alt="" class="wp-image-42476" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/Arduino-App-Lab-new-examples-1024x640.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/Arduino-App-Lab-new-examples-300x188.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/Arduino-App-Lab-new-examples-768x480.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/Arduino-App-Lab-new-examples-1536x960.jpg 1536w, https://blog.arduino.cc/wp-content/uploads/2026/08/Arduino-App-Lab-new-examples-2048x1280.jpg 2048w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="wp-block-paragraph">Whether you want to get started immediately, bring your own models, or train something custom, VENTUNO Q is the board for you. Out of the box, a growing library of NPU-optimized models is ready to run via Arduino App Lab powered by Qualcomm AI Hub – the current library includes: Qwen 3 4B LLM; Qwen 2.5 7B and 3 4B VLM; Gemma 4 E2B and E4B; Whisper ASR; Melo and Piper TTS; YoloX small object detection; and MediaPipe gesture recognition. Additional models and bricks are added continuously.&nbsp;</p>



<p class="wp-block-paragraph">You can also upload a GGUF-format model from Hugging Face directly into Arduino App Lab, or train your own with the integrated Edge Impulse Studio.</p>



<h2 class="wp-block-heading">Scale from prototype to production</h2>



<p class="wp-block-paragraph">One of the things we’re most excited about is the path VENTUNO Q opens beyond the prototype stage. Under the <a href="http://arduino.cc/pro/works-with-arduino/" target="_blank" rel="noreferrer noopener">Works with Arduino</a><sup>™</sup> program, <a href="https://www.seco.com/products-services/computer-on-module/smarc/som-smarc-dragonwing-iq8?utm_source=arduino&amp;utm_medium=referral&amp;utm_campaign=som_smarc_dragonwing_iq8" target="_blank" rel="noreferrer noopener">SECO</a> and <a href="https://www.toradex.com/computer-on-modules/aquila-arm-family/iq-8275" target="_blank" rel="noreferrer noopener">Toradex</a> – two leading providers of production-grade computing solutions – are among the first partners to offer production-certified SOMs based on the same Dragonwing IQ8 architecture. That means that the apps you prototype on VENTUNO Q with Arduino App Lab, you can scale without re-architecting your hardware stack. Your software, your models, your application logic – all carry forward.</p>



<h2 class="wp-block-heading">Physical AI is more accessible than ever</h2>



<p class="wp-block-paragraph">Arduino App Lab already features a library of 100+ examples available for VENTUNO Q, with more added regularly to always give you new starting points to explore physical AI out of the box. At launch, you already have everything you need to quickly develop applications with:</p>



<ul class="wp-block-list">
<li>Large language models (LLM)</li>



<li>Vision-language models (VLM)</li>



<li>Automatic speech recognition (ASR)</li>



<li>Text-to-speech (TTS)</li>



<li>Gesture recognition</li>
</ul>



<p class="wp-block-paragraph">And there’s more! <a href="https://blog.arduino.cc/2026/08/12/arduino-app-lab-0-10-meet-agentic-mode/" target="_blank" rel="noreferrer noopener">Arduino App Lab 0.10.0</a> brings a wave of new features and improvements, including a new Agentic Mode experience.&nbsp;</p>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="627" src="https://blog.arduino.cc/wp-content/uploads/2026/08/image-4-1-1024x627.png" alt="" class="wp-image-42479" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/image-4-1-1024x627.png 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-4-1-300x184.png 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-4-1-768x470.png 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-4-1-1536x941.png 1536w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-4-1.png 2048w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<h2 class="wp-block-heading">Pre-order now to get free accessories included!</h2>



<p class="wp-block-paragraph">Head to the <a href="https://store-usa.arduino.cc/products/ventuno-q" target="_blank" rel="noreferrer noopener">Arduino Store</a> to secure your VENTUNO Q today at its introductory price! Until September 30, 2026 (and while supplies last), you’ll see two freebies added automatically to your cart: the new <a href="https://store-usa.arduino.cc/products/usb-c-power-supply-65w" target="_blank" rel="noreferrer noopener">Arduino<sup>®</sup> USB-C Power Supply (65W)</a>, and the <a href="https://store-usa.arduino.cc/products/usb-c-cable-24-pin" target="_blank" rel="noreferrer noopener">Arduino® USB-C cable (24 pin)</a>. These are official accessories that together are designed to support the board in sustaining demanding AI workloads and connected peripherals without throttling.</p>



<p class="wp-block-paragraph">VENTUNO Q is also available through our official distribution partners: <a href="https://www.digikey.com/en/product-highlight/a/arduino/ventuno-q" target="_blank" rel="noreferrer noopener">DigiKey</a>, <a href="https://uk.farnell.com/new-products/embedded-computers-education-maker-boards/arduino-ventuno-q" target="_blank" rel="noreferrer noopener">Farnell</a>, <a href="https://www.mouser.com/new/arduino/arduino-ventuno-q-sbc" target="_blank" rel="noreferrer noopener">Mouser</a>, <a href="https://robu.in/product/arduino-ventuno-q/" target="_blank" rel="noreferrer noopener">Robu.in</a>, and <a href="https://uk.rs-online.com/web/p/arduino/0878176" target="_blank" rel="noreferrer noopener">RS</a>.</p>



<p class="wp-block-paragraph">For full technical specs, use cases, and everything you need to know before ordering, read the <a href="https://www.qualcomm.com/news/releases/2026/08/arduino-ventuno-q-is-here--bringing-agentic-ai-to-the-physical-w" target="_blank" rel="noreferrer noopener">full press note</a> and visit the <a href="https://arduino.cc/product-ventuno-q" target="_blank" rel="noreferrer noopener">VENTUNO Q product page</a>.</p>



<p class="wp-block-paragraph">We built this for you, and we can’t wait to see what you build with it.</p>



<p class="wp-block-paragraph"><em>Qualcomm branded products are products of Qualcomm Technologies, Inc. and/or its subsidiaries. Arduino and VENTUNO are trademarks or registered trademarks of Arduino S.r.l.</em></p>


The age of Physical AI is here, and it runs on the Arduino® VENTUNO™ Q board

ARDUINO-CLOUD_Blogpost-Featured_385x289

image-5

image-4

image-3

image-2

image-1

ARDUINO-CLOUD_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/2026/08/ARDUINO-CLOUD_Blogpost-Cover_1100x600-1024x559.jpg" alt="" class="wp-image-42493" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/ARDUINO-CLOUD_Blogpost-Cover_1100x600-1024x559.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/ARDUINO-CLOUD_Blogpost-Cover_1100x600-300x164.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/ARDUINO-CLOUD_Blogpost-Cover_1100x600-768x419.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/ARDUINO-CLOUD_Blogpost-Cover_1100x600.jpg 1100w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="wp-block-paragraph"><a href="https://cloud.arduino.cc/">Arduino Cloud</a> has been getting some serious upgrades behind the scenes. Dashboards can now have multiple pages, you can see the health of your whole fleet at a glance, and devices can be locked to your account (or freed up for someone else). Here&#8217;s everything that shipped.</p>



<h2 class="wp-block-heading">Build dashboards with multiple pages</h2>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="637" src="https://blog.arduino.cc/wp-content/uploads/2026/08/image-5-1024x637.jpeg" alt="" class="wp-image-42496" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/image-5-1024x637.jpeg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-5-300x187.jpeg 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-5-768x478.jpeg 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-5-1536x956.jpeg 1536w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-5.jpeg 1736w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="has-text-align-center wp-block-paragraph"><em><sup>Add new dashboard pages from the left sidebar and give each one its own name, icon, position</sup></em></p>



<p class="wp-block-paragraph">If you&#8217;ve ever tried to fit a real project onto one dashboard, you know the problem: every widget ends up crammed into the same view, whether it&#8217;s your greenhouse sensors or your energy monitor or both at once.</p>



<p class="wp-block-paragraph">Now you can split it up. When editing a dashboard, add new pages from the left sidebar (web app only, for now) and give each one its own name, icon, and position — group by room, by system, by whatever makes sense. When viewing the dashboard, switch pages from the sidebar on the web or the switch button on the IoT Remote app.</p>



<h2 class="wp-block-heading">See your whole device fleet at a glance</h2>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="637" src="https://blog.arduino.cc/wp-content/uploads/2026/08/image-1-1-1024x637.jpeg" alt="" class="wp-image-42498" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/image-1-1-1024x637.jpeg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-1-1-300x187.jpeg 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-1-1-768x478.jpeg 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-1-1-1536x956.jpeg 1536w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-1-1.jpeg 1736w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="has-text-align-center wp-block-paragraph"><em><sup>Spaces overview now shows Things, Devices, Members, and Trigger activity at a glance</sup></em></p>



<p class="wp-block-paragraph">This is the big one in this release. Before, if you wanted to know something as simple as &#8220;how many of my devices are online right now,&#8221; you had to dig through the Devices page and count. Now every Space&#8217;s home page has a Statistics section with four widgets:&nbsp;</p>



<p class="wp-block-paragraph">1.<strong> Total Things</strong> (active vs. inactive)<br>2. <strong>Total Devices</strong> (online vs. offline)<br>3. <strong>Total Members</strong> (by role)<br>4. <strong>Trigger activations</strong> over a time range you can flip between 1 hour and 15 days</p>



<p class="wp-block-paragraph">If you belong to more than one Space, for example: your personal Space plus an Organization, or a few Organizations at once, you also get Spaces Overview, which consolidates everything in one place: the same Statistics summed across all your Spaces, a Global Plan Usage table showing usage vs. limit for every Space so you know exactly where you&#8217;re closest to needing an upgrade, and an All Devices table listing every device you own, wherever it lives, with its status and Space. No more guessing which Space a device belongs to.</p>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="637" src="https://blog.arduino.cc/wp-content/uploads/2026/08/image-2-1-1024x637.jpeg" alt="" class="wp-image-42499" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/image-2-1-1024x637.jpeg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-2-1-300x187.jpeg 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-2-1-768x478.jpeg 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-2-1-1536x956.jpeg 1536w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-2-1.jpeg 1736w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<h2 class="wp-block-heading">Track daily data usage before it&#8217;s a problem</h2>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="637" src="https://blog.arduino.cc/wp-content/uploads/2026/08/image-3-2-1024x637.jpeg" alt="" class="wp-image-42501" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/image-3-2-1024x637.jpeg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-3-2-300x187.jpeg 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-3-2-768x478.jpeg 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-3-2-1536x956.jpeg 1536w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-3-2.jpeg 1736w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="has-text-align-center wp-block-paragraph"><sup><em>Track daily ingested records against your plan&#8217;s limit, broken down by Thing or by Space</em></sup></p>



<p class="wp-block-paragraph">Every plan comes with a Daily Ingested Records limit, but until now that number only existed on paper. </p>



<p class="wp-block-paragraph">That&#8217;s changing with a new chart in Statistics, plotting your daily ingested records (over 1D, 7D, 15D, or 30D windows) against a line marking your plan&#8217;s limit. Hover any day for the exact count and what percentage of your limit it represents, broken down by Thing in the single-Space view or by Space in Spaces Overview.</p>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="637" src="https://blog.arduino.cc/wp-content/uploads/2026/08/image-4-1-1024x637.jpeg" alt="" class="wp-image-42502" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/image-4-1-1024x637.jpeg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-4-1-300x187.jpeg 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-4-1-768x478.jpeg 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-4-1-1536x956.jpeg 1536w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-4-1.jpeg 1736w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<h2 class="wp-block-heading">Decide who gets to claim a device next</h2>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="540" src="https://blog.arduino.cc/wp-content/uploads/2026/08/image-5-1-1024x540.png" alt="" class="wp-image-42503" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/image-5-1-1024x540.png 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-5-1-300x158.png 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-5-1-768x405.png 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-5-1-1536x810.png 1536w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-5-1.png 1573w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="has-text-align-center wp-block-paragraph"><em><sup>A new toggle on every device&#8217;s detail page controls whether it can be claimed by another account</sup></em></p>



<p class="wp-block-paragraph">This one solves two opposite problems at once. In classrooms, the same physical board gets passed from student to student, but devices were silently and permanently tied to whoever set them up first — reclaiming one meant deleting it and often filing a support ticket. In business settings, it was the reverse concern: nobody wanted a device to be <em>too</em> easy to reassign, since accidentally claiming a colleague&#8217;s hardware is a real risk.</p>



<p class="wp-block-paragraph">The fix is a new <strong>&#8220;Lock to this account&#8221;</strong> toggle on every device&#8217;s detail page. Turn it on and the device stays permanently tied to your account; turn it off before deleting it and the next person can claim it under theirs. The default is set automatically — unlocked in Private and Education Spaces, locked in Business Spaces — but you can always override it.</p>



<h2 class="wp-block-heading"><strong>Connect board to Arduino Cloud, right from Arduino App Lab</strong></h2>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="615" src="https://blog.arduino.cc/wp-content/uploads/2026/08/image-6-1-1024x615.png" alt="" class="wp-image-42504" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/image-6-1-1024x615.png 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-6-1-300x180.png 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-6-1-768x461.png 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-6-1-1536x922.png 1536w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-6-1.png 2048w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="has-text-align-center wp-block-paragraph"><em><sup>Provision UNO Q for Arduino Cloud directly from the Arduino App Lab&#8217;s device settings page</sup></em></p>



<p class="wp-block-paragraph">If you&#8217;re building on UNO Q, registering with Arduino Cloud used to mean stepping outside <a href="https://docs.arduino.cc/software/app-lab/">Arduino App Lab</a> to do it. Not anymore.</p>



<p class="wp-block-paragraph">You can now <a href="https://blog.arduino.cc/2026/08/12/arduino-app-lab-0-10-meet-agentic-mode/">register your UNO Q with Arduino Cloud</a> directly from the Settings page, no need to leave Arduino App Lab. Once registered, your board is ready for IoT-enabled Apps: dashboards, remote control, fleet management, all of it, without breaking your workflow to set it up.</p>



<h2 class="wp-block-heading"><strong>Fleet device management with Smart Folders</strong></h2>



<p class="wp-block-paragraph">In case you missed it, we also shipped <strong>Smart Folders</strong> earlier this year, letting you organize your device fleet in Arduino Cloud instead of losing track of things as it grows.<a href="https://blog.arduino.cc/2026/04/15/organize-your-iot-fleet-in-arduino-cloud-with-smart-folders/"> Check out that post</a> if you haven&#8217;t already.</p>



<h2 class="wp-block-heading"><strong>Get started on Arduino Cloud</strong></h2>



<p class="wp-block-paragraph">Everything we just talked about is live in production right now, ready for you to test out! Here’s what we recommend trying first:</p>



<ul class="wp-block-list">
<li>Log in to <a href="http://app.arduino.cc/">app.arduino.cc</a> (it’s free) </li>



<li>Check out the new Statistics section on the home page of a Space you manage</li>



<li>If you have more than one Space, open Spaces Overview and see your whole footprint in one place</li>



<li>Building a busy dashboard? Try splitting it into pages</li>
</ul>



<p class="wp-block-paragraph"><em>Arduino is a trademark or registered trademark of Arduino S.r.l.</em></p>


Here&#8217;s what&#8217;s new in Arduino® Cloud: multi-page dashboards, Spaces Overview, and more!

AppLab Release - Arduino.cc-Blogpost-Cover(1100x600)

AppLab Release - Arduino.cc-Blogpost-Featured(385x289)


<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/2026/08/AppLab-Release-Arduino.cc-Blogpost-Cover1100x600-1024x559.jpg" alt="" class="wp-image-42452" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/AppLab-Release-Arduino.cc-Blogpost-Cover1100x600-1024x559.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/AppLab-Release-Arduino.cc-Blogpost-Cover1100x600-300x164.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/AppLab-Release-Arduino.cc-Blogpost-Cover1100x600-768x419.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/AppLab-Release-Arduino.cc-Blogpost-Cover1100x600.jpg 1100w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="wp-block-paragraph">We&#8217;re thrilled to announce Arduino App Lab 0.10, our biggest release yet. With this update, you can now build applications alongside an AI agent that actively works on your project, not one that just answers questions about it.</p>



<p class="wp-block-paragraph">Powered by MCP and integrated with App CLI, Agentic Mode understands your workspace, creates and edits files, runs your app, and interprets errors as they happen. It has deep knowledge of Arduino App Lab, Apps, Bricks, and Sketches.</p>



<p class="wp-block-paragraph">Alongside Agentic Mode, App Lab 0.10 also introduces Language Server support for code completion and navigation, a streamlined way to connect your board to Arduino Cloud and a restructured Examples library.&nbsp; Here&#8217;s a breakdown.</p>



<h2 class="wp-block-heading">The big one: Agentic Mode</h2>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="569" src="https://blog.arduino.cc/wp-content/uploads/2026/08/image-1024x569.png" alt="" class="wp-image-42448" title="image.png" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/image-1024x569.png 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-300x167.png 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-768x427.png 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/image.png 1440w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="has-text-align-center wp-block-paragraph"><em><sup>Agentic Mode and session panel in Arduino App Lab 0.10</sup></em></p>



<p class="wp-block-paragraph">Open Arduino App Lab and you&#8217;ll notice a new button at the bottom of the screen “Agentic Mode.” Through its MCP integration with App CLI, it understands your workspace and actively works on your project.</p>



<p class="wp-block-paragraph">If you need a reminder, MCP (Model Context Protocol) is an open standard that lets AI models interact with external tools and systems. Rather than just generating text, an MCP-connected agent can invoke tools, read state, and take actions, in this case, through Arduino App CLI. </p>



<p class="wp-block-paragraph">It&#8217;s what allows Agentic Mode to go beyond simple conversation and actually operate on your project.</p>



<p class="wp-block-paragraph">Agentic Mode can create and edit files, run your App, stop it when needed, and interpret errors as they happen. Ask it to build a feature and you can watch it assemble the project step by step, instead of handing you a wall of code to copy and paste. Watch this demo as an example.&nbsp;</p>



<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe loading="lazy" title="Arduino App Lab 0.10: Meet Agentic Mode" width="500" height="281" src="https://www.youtube.com/embed/zTLMNJG9eN4?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div></figure>



<p class="wp-block-paragraph">Agentic Mode works on a Bring-Your-Own-Key basis: you authenticate with your AI provider of choice, keeping you in control of your API usage and costs. Claude is supported at first, with more providers coming soon.</p>



<h2 class="wp-block-heading">Faster code editing with Language Server Support</h2>



<p class="wp-block-paragraph">Arduino App Lab 0.10 integrates a Language Server (not available in SBC Mode on UNO Q) that brings IDE-quality features to your Sketches and scripts: autocomplete, auto-formatting, hover documentation, &#8220;go to definition&#8221;, and &#8220;find all references&#8221; work across Arduino sketches, Python, HTML, CSS, and JavaScript.</p>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="678" src="https://blog.arduino.cc/wp-content/uploads/2026/08/image-1-1024x678.png" alt="" class="wp-image-42449" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/image-1-1024x678.png 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-1-300x199.png 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-1-768x508.png 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-1.png 1444w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="has-text-align-center wp-block-paragraph"><em><sup>The new Language Server allows Inline code definition</sup></em></p>



<h2 class="wp-block-heading">Connect your board to Arduino Cloud, right from Settings</h2>



<p class="wp-block-paragraph">You can now register your Arduino<sup>®</sup> UNO<sup>™</sup> Q board with Arduino Cloud directly from the Settings page, no need to leave Arduino App Lab. Once registered, your board is ready for IoT-enabled Apps.</p>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="615" src="https://blog.arduino.cc/wp-content/uploads/2026/08/image-2-1024x615.png" alt="" class="wp-image-42450" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/image-2-1024x615.png 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-2-300x180.png 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-2-768x461.png 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-2-1536x922.png 1536w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-2.png 2048w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="has-text-align-center wp-block-paragraph"><em><sup>Provision UNO Q for Arduino Cloud directly from the Arduino App Lab&#8217;s device settings page.</sup></em></p>



<h2 class="wp-block-heading">A clearer path through Examples</h2>



<p class="wp-block-paragraph">The Examples catalog has been restructured into three tiers so you always know where to start and where to go next:</p>



<p class="wp-block-paragraph">1. Inspirations: more than examples, full blown Applications that showcase what the board can really do.&nbsp;<br>2. Examples:&nbsp;the fundamentals, taught through focused topics like LEDs, matrices, and the Bridge API.<br>3. Bricks Examples:&nbsp; small, interactive apps that each focus on a single Brick&#8217;s functionalities, so you can see one building block in action at a time.</p>



<figure class="wp-block-image size-large"><div class="image-post"><img loading="lazy" decoding="async" width="1024" height="663" src="https://blog.arduino.cc/wp-content/uploads/2026/08/image-3-1024x663.png" alt="" class="wp-image-42451" srcset="https://blog.arduino.cc/wp-content/uploads/2026/08/image-3-1024x663.png 1024w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-3-300x194.png 300w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-3-768x497.png 768w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-3-1536x995.png 1536w, https://blog.arduino.cc/wp-content/uploads/2026/08/image-3.png 2048w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="has-text-align-center wp-block-paragraph"><em>New inspiration page to show the full potential of Arduino UNO Q</em></p>



<h2 class="wp-block-heading"><strong>Download Arduino App Lab 0.10</strong></h2>



<p class="wp-block-paragraph">Update Arduino <a href="https://www.arduino.cc/en/software/#app-lab-section">App Lab to 0.10.0</a>.&nbsp;</p>



<p class="wp-block-paragraph">For the full list of changes, see<a href="https://docs.arduino.cc/software/app-lab/release-notes/release-0-10/"> the release notes</a>.</p>



<p class="wp-block-paragraph"><strong>Resource</strong></p>



<ul class="wp-block-list">
<li>Purchase the <a href="https://store.arduino.cc/pages/uno-q?utm_source=blog">Arduino UNO Q</a>.</li>
</ul>



<p class="wp-block-paragraph"><em>Arduino and UNO are trademarks or registered trademarks of Arduino S.r.l.</em></p>


Arduino App Lab 0.10: Meet Agentic Mode

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>



<p class="wp-block-paragraph">We are thrilled to announce the release of version 0.90.0 of the ArduinoCore-zephyr. This is a major milestone: we are officially <strong>exiting the beta phase</strong> and marking the core as <strong>stable</strong>. Achieving this goal is the culmination of months of testing, architectural refinement, and community feedback.<br><br>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.<br><br>Join us in this next chapter! Compile your existing sketches with version 0.90.0, and let us know how it performs.</p>



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



<p class="wp-block-paragraph"><strong>Welcome, Nano RP2040 Connect! </strong>The <a href="https://store.arduino.cc/collections/interactive-games/products/arduino-nano-rp2040-connect">Arduino<sup>®</sup> Nano<sup>™</sup> 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.</p>



<p class="wp-block-paragraph"><strong>SD card support for the Arduino</strong><strong><sup>®</sup></strong><strong> Portenta</strong><strong><sup>™</sup></strong><strong> H7 and C33 boards. </strong>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.</p>



<p class="wp-block-paragraph"><strong>PDM library support. </strong>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.</p>



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



<p class="wp-block-paragraph">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<sup>®</sup> 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.</p>



<p class="wp-block-paragraph">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>. </p>



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



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



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



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



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



<li>Nano 33 BLE</li>



<li><a href="https://store.arduino.cc/products/nicla-sense-me">Arduino<sup>® </sup>Nicla<sup>™</sup> 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"><strong>How to get started</strong></h2>



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



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



<p class="wp-block-paragraph">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!</p>



<p class="wp-block-paragraph"><em>Arduino, GIGA R1, Nano, Nicla, Opta, Portenta, and UNO are trademarks or registered trademarks of Arduino S.r.l.</em><br><br></p>


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

Screenshot 2026-07-20 at 18.17.32

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

Attendees


<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>



<p class="wp-block-paragraph">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. </p>



<p class="wp-block-paragraph">Qualcomm Technologies, Inc., through its Snapdragon<sup>®</sup> 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!</p>


<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>
</div>


<h2 class="wp-block-heading">From unboxing to AI model in under an hour</h2>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">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<sup>®</sup> UNO<sup>™</sup> Q 4GB</a>, <a href="https://store.arduino.cc/pages/modulino">Arduino<sup>®</sup> Modulino<sup>™</sup></a> and using <a href="https://docs.arduino.cc/software/app-lab/">Arduino<sup>®</sup> 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.</p>


<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>
</div>


<h2 class="wp-block-heading">Discovering the tools that bring a future in racing within reach</h2>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph"><strong>For many students, seeing their race transformed into live telemetry was the moment it all clicked.</strong></p>


<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>


<p class="wp-block-paragraph">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>?).</p>



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



<p class="wp-block-paragraph">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.</p>


<div class="wp-block-image">
<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>
</div>


<p class="wp-block-paragraph">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>!</p>



<p class="wp-block-paragraph"><em>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.</em></p>


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>



<p class="wp-block-paragraph">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<sup>™</sup> Q DIY synth bundle</a>, we’re adding a new classic project you can <strong>pick up in one click, and get done in a weekend</strong>.&nbsp;</p>



<p class="wp-block-paragraph">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"><strong>UNO<sup>™</sup> Q Arcade bundle</strong></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!</p>



<p class="wp-block-paragraph">Not only is the bundle designed to make your experience frictionless and rewarding, but it comes out during a special sale with a <strong>20% discount</strong> 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.</p>



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



<p class="wp-block-paragraph">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<sup>®</sup> 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<sup>™</sup> 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.</p>



<p class="wp-block-paragraph">But one board does not a bundle make! We’ve included <strong>three easy-to-use Arduino</strong><strong><sup>®</sup></strong><strong>&nbsp;Modulino</strong><strong><sup>™</sup></strong><strong> nodes – Buttons, Joystick, and Movement</strong> – 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;</p>



<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>



<p class="wp-block-paragraph">So you can build the cool hardware, and leverage ready-to-use software to play your favorite retro-style games. <strong>But how about creating your own?</strong></p>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">Check out these inspiring GitHub repos to get a better idea of how you can stretch the UNO Q Arcade bundle:&nbsp;</p>



<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>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph"><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!).</p>



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



<p class="wp-block-paragraph"><em>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.</em></p>


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

Arduino.cc-Blogpost-Featured(385x289)-3

Arduino.cc-Blogpost-Cover(1100x600)-4


<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>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">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.</p>



<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe loading="lazy" title="How to Achieve Cost-Effective Predictive Maintenance with the Arduino UNO Q" width="500" height="281" src="https://www.youtube.com/embed/dbskHPOFWSM?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div></figure>



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



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">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<sup>®</sup> Modulino<sup>™</sup> 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.</p>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">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?”</p>



<p class="wp-block-paragraph"><strong><em>See this pattern in action</em></strong><em>: This sense-infer-respond loop is exactly what’s demonstrated in the </em><a href="https://projecthub.arduino.cc/shivaylamba/ai-guard-demo-with-arduino-uno-q-modulino-sensors-and-local-npu-face-recognition-dcfcfe"><em>AI Guard Demo with Arduino UNO Q, Modulino sensors, and local NPU face recognition</em></a><em>, 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.</em></p>



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



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



<p class="wp-block-paragraph">A first predictive maintenance project can begin with one machine and a limited number of operating conditions.</p>



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



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">The microcontroller side manages sensor acquisition, sending the data to the Linux side that manages data logging, model execution, dashboards.</p>



<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>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">Depending on the application, the project can use classification or anomaly detection.</p>



<p class="wp-block-paragraph">Classification is useful when the team already has examples of known conditions, such as normal operation, imbalance, misalignment, or a loose mounting.</p>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph"><strong><em>Worth a look:</em></strong><em> For a hands-on look at what running ML locally on UNO Q actually feels like in practice, check out</em><a href="https://www.hackster.io/vsupacha/running-ml-ai-on-arduino-uno-q-59ff07"><em> Running ML/AI on Arduino UNO Q</em></a><em> 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.</em></p>



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



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



<p class="wp-block-paragraph">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;</p>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph"><strong><em>Going deeper:</em></strong><em> Edge Impulse has specific</em><a href="https://docs.edgeimpulse.com/experts/predictive-maintenance-and-defect-detection-projects/compressor-predictive-maintenance-nordic-thingy53"><em> </em><em>predictive maintenance guidance</em></a><em> 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.</em></p>



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



<p class="wp-block-paragraph">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.</p>



<p class="wp-block-paragraph">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>.</p>



<p class="wp-block-paragraph">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>.</p>



<p class="wp-block-paragraph"><em>Arduino, UNO, Modulino and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.</em></p>



<p class="wp-block-paragraph"></p>


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


<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-Cover1100x600-1-1024x559.jpg" alt="" class="wp-image-42284" srcset="https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost-Cover1100x600-1-1024x559.jpg 1024w, https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost-Cover1100x600-1-300x164.jpg 300w, https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost-Cover1100x600-1-768x419.jpg 768w, https://blog.arduino.cc/wp-content/uploads/2026/06/Arduino.cc-Blogpost-Cover1100x600-1.jpg 1100w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /></div></figure>



<p class="wp-block-paragraph">Version 0.56.0 of the Arduino<sup>®</sup> 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.</p>



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



<p class="wp-block-paragraph">Multimedia capabilities take a major step forward in this release, establishing native support for advanced visual outputs.</p>



<p class="wp-block-paragraph">We have introduced official video support for <a href="https://store.arduino.cc/products/portenta-h7"><strong>Portenta H7</strong></a> alongside full compatibility for <a href="https://store.arduino.cc/products/giga-display-shield"><strong>GIGA Display Shield</strong></a> when paired with the <a href="https://store.arduino.cc/products/giga-r1-wifi">Arduino<sup>®</sup>&nbsp;GIGA R1<sup>™</sup> 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.</p>



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



<p class="wp-block-paragraph">For our supported board lineup, this release delivers several updates to performance, pin management, and connectivity:</p>



<ul class="wp-block-list">
<li><strong>RAM usage optimization:</strong> 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><strong>Dynamic pin-muxing improvements:</strong> 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><strong>Network fixes and improvements:</strong> 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>



<p class="wp-block-paragraph">To update, open the Arduino<sup>®</sup> 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>.</p>



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



<p class="wp-block-paragraph">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:</p>



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



<p class="wp-block-paragraph"><strong>Important reminder</strong>: 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.</p>



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



<p class="wp-block-paragraph">If you are using <strong>UNO Q</strong>, 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<sup>®</sup>&nbsp;App Lab take care of the rest.&nbsp;</p>



<p class="wp-block-paragraph">We are actively working to bring this automated behavior to all other supported boards in future releases.</p>



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



<p class="wp-block-paragraph">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!</p>



<p class="wp-block-paragraph"><em>Arduino, GIGA, Portenta, GIGA R1, and UNO and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.</em><br></p>


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

Arduino VENTUNO Q: Where AI takes action

The easy-to-use hardware and software platform

Mission & Vision

Learn Arduino

The easy-to-use hardware and software platform