When you’re developing systems that support air navigation services, the margin for compromise is essentially zero. Reliability isn’t a “nice to have,” it’s the baseline. That’s the world in which Techno Sky operates, and it’s what makes their decision to build around the Arduino® Opta RS485 microPLC responsive to the project’s identified requirements.

Techno Sky is an ENAV Group company that manages and maintains hardware and software systems supporting air navigation services, and develops innovative technologies for the Air Traffic Management (ATM) sector. Their R&D department works on solutions for air navigation service providers (ANSPs), airports, aviation authorities, and organizations worldwide – systems designed for use under demanding operational conditions.

The challenge: a compact, reliable visual emergency signaling system

The project that brought Techno Sky to Arduino was the development of a visual emergency signaling system for integration within ENAV’s Digital Tower environment. The requirements were unambiguous: industrial-grade reliability, compact form factor, continuous operating capability, Ethernet and industrial communication interfaces, integrated I/O, and the ability to develop and maintain the system without building dedicated custom electronics from scratch.

A fully custom hardware design was on the table, but it would have added significant time and complexity – hardware design, integration, and validation cycles that would delay the project without necessarily improving the outcome. The team needed a platform that would let them focus on application logic and system integration, not on reinventing the controller underneath it.

Opta: a non-conventional, but versatile and effective path to innovation

The R&D team evaluated conventional PLCs, custom embedded electronics, and single-board computers before landing on Opta. It selected Opta based on its combination of industrial characteristics and software flexibility in a single package. The team determined that the conventional PLCs it evaluated offered less development agility for this project. Custom electronics would have added hardware design overhead. The single-board computers evaluated by the team did not meet the project’s identified specifications.

The Opta RS485 offered integrated Ethernet and RS485 communications, relay outputs, and the flexibility of the Arduino programming environment and library ecosystem – all in a compact, DIN rail-mountable form factor designed for industrial applications. Critically, it allowed new functionality to be implemented through software without adding dedicated hardware, a quality that would prove important as the system evolved.

An architecture for in-house deployment within the year

In the deployed system, Opta acts as the main embedded controller, managing signaling outputs, Ethernet communication, RS485 communication with external equipment, operating modes and timing, supervision functions, and communication integrity and security. The system currently implements eight signaling modes on a single controller – functions that the Techno Sky team determined could otherwise have required additional hardware or a more complex architecture.

The main engineering challenge during development was coordinating communications, physical outputs, signaling sequences, and supervision in parallel. The team addressed this through a modular software architecture and the use of watchdog and communication-validation mechanisms – approaches intended to support a manageable codebase and consistent system behavior. Development was carried out internally by Techno Sky’s R&D team using Arduino documentation, libraries, and development resources.

According to Techno Sky, the project started in October and reached operational deployment in under 12 months. One Opta controller integrating more than ten main control functions, designed for continuous operation, with the same architecture carried through from R&D prototype to operational system.

From prototype to production on the same platform

That last point deserves emphasis. R&D teams may prototype on one platform and later transition hardware when moving toward deployment. Techno Sky didn’t need to make that transition. The architecture based on Opta developed during R&D was the architecture retained for operational use – a decision that they report was based on its assessment of the platform for this application.

Techno Sky summarized their experience: “Opta enabled us to move rapidly from an R&D concept to an operational solution. Its industrial hardware, combined with the Arduino programming environment and library ecosystem, allowed us to integrate and expand multiple functions without increasing hardware complexity. The fact that we are retaining Opta in the operational system reflects our positive assessment of its effectiveness and reliability for this application.”

More ideas, more innovation

The Techno Sky team has been working with Arduino platforms across their R&D activities for several years, and this project showcases their reinforced confidence in their chosen Arduino product line. Future applications under consideration include monitoring systems, remote control, sensor integration, robotics, automated test equipment, and ATM infrastructure – a range of potential uses that Techno Sky is evaluating.

For teams facing similar integration challenges – multiple control and communication functions, demanding reliability requirements, and pressure to move quickly from concept to deployment – Techno Sky’s assessment is direct: Opta is well suited, in Techno Sky’s assessment, when those functions need to come together in a compact system, without the overhead of custom hardware design.

Ready to explore options for your industrial application? Find out more about the Opta family and Arduino’s industrial-grade range at arduino.cc/pro.

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

The post How Techno Sky and Arduino Opta brought an industrial visual emergency signaling solution to air traffic management appeared first on Arduino Blog.

It’s almost that time of year again! No chance to 3D print your Arduino®-powered pumpkin project? Didn’t get around to animating the LEDs on your costume? There’s still time, and here’s a half-dozen Hallowuino-themed projects to get you inspired.

Spooky, Sassy Pumpkin

You’ve heard of Spooky, Scary Skeletons, well here’s a Spooky, Sassy Pumpkin that is built to use a USB webcam to identify visitors as they approach, then answer their questions about what it sees using the Arduino App Lab Vision-Language Model (VLM) Brick. This jack-o’-lantern packs a whole lot more smarts than your average fruit thanks to the dual-brain VENTUNO Q concealed underneath it. All processing is designed to be handled offline, on-device without sending users’ faces and voices  across networks or to the cloud.

Arduino-Powered Animatronic Supergroup

Bands break up for all kinds of reasons, so we were pleased to see Arduino Project Hub member nerdabilly figure out a way to sync the Costco Skeleton Punk Rocker animatronic with the Lowe’s Reaper Band. The band members are centered around a Bluetooth speaker, while the rocker is triggered by a timer or motion sensor. Not wanting to permanently alter these expensive displays, nerdabilly used the existing analog signal from the band leader to trigger the pink in place of its motion-sensing trigger, so the whole crew can rock out with their skulls out. Nerdabilly, we salute you for keeping this spooky symphony in sync!

Simple Spookiness with Modulinos and MP3s

Here’s some simple steps to sprinkle some spookiness into your season from our own team. Using the UNO R4-based Plug and Make Kit, plus a DFRobot DFPlayer MP3 player module, we show you how to trigger spooky lights and sounds when the Modulino® Distance sensor detects a presence. You can even connect to the Arduino Cloud to activate, customize, and monitor your project remotely. And that’s just the start – the seven included modules and built-in Wi-Fi give you plenty of options for creating your own frightful projects!

Ultrasonic Spider

Everyone has to start their Arduino journey somewhere, and for Project Hub member ParzivalUK, that place was a half-decade old project video demonstrating a fake spider dropping on unsuspecting passers-by. Unfortunately, ParzivalUK found that the accompanying code no longer worked for their project, and there weren’t detailed steps on how to recreate it. Driven by a desire to delight their son with a fun Halloween prank, ParzivalUK pushed on, combining a servo with an ultrasonic sensor to drop a toy spider on anyone who comes within approximately 40cm of the trap, with a continuous rotation servo to wind it back up, ready for the next victim!

Portal Sentry Turret

We love seeing projects shared on the unofficial Arduino Discord server. User jaysicks’ Aperture Science Sentry Turret costume really caught our eye, and we asked for a little more detail. The resulting Instructable is a detailed step-by-step guide for recreating the iconic tripod robot from one of the most beloved puzzle platformers of all time. With no 3D printer at hand, the costume relies on cardboard construction but comes out looking great. A red LED in the eye responds to the audio output from the incorporated GD380D-M audio playback module when a button is pressed, and the whole thing is controlled by an ATtiny85V microcontroller, programmed using the Arduino IDE. We think jaysicks’ son will be the envy of the neighborhood this Halloween, or at least among those with a solid appreciation of classic video games!

Cyber Research Systems T-1-7 Warrior Robot

The T-800 and T-1000 may get most of the screen time, but Redditor u/parsupo recreated the more niche T-1-7 Warrior Robot for their annual Halloween project. Built around an electric wheelchair base, the bot uses an Arduino Mega 2560 to receive and process commands from a remote control, and the same ATmega328 chips found in the UNO to control lights, gun movement, and the head. It may not be made of mimetic polyalloy, but we wouldn’t want to run into it in a dark alley!

What will you make?

Got a spooky Arduino-based project to share? Or need help realizing your creation? Be sure to add it to Arduino Project Hub. Then head over to the Arduino subreddit and let us know!

The post How about a Hallowuino half-dozen? appeared first on Arduino Blog.

Arduino, Qualcomm Technologies, and Edge Impulse recently teamed up with Hackster to host the Invent the Future competition. The competition attracted 4,028 registrants and 1,202 hardware applications, culminating in 153 final project submissions across a wide range of real-world applications.

There were several categories in the competition, all unified by the UNO Q board and Arduino® App Lab. The judging is officially complete and the winners have been announced, which means we get to show you the top projects that most impressed the judges and took home the awards!

These winning creations illustrate a range of uses for the UNO Q – and stay tuned, because we’ll also be shining the spotlight on many other fantastic contest submissions in the coming days.

Best Social Impact

While sustainability was a consideration for every category, the Best Social Impact award went to a project built explicitly to make the world a better place. The winning project was the Smart Water Guardian AI, built by KOCHONI Biaou David Barthélémy.

This project puts the edge AI capabilities demonstrated by UNO Q to good use, by monitoring water-quality indicators in real time. Through a host of sensors, it is designed to measure everything from temperature and pH to turbidity and dissolved solids content, allowing for detection of anomalies that correlate with poor water quality – detection that could save lives.

Best in Home Automation

Home automation is a thriving industry, and there are all kinds of tech products you can use to build the smart house of your dreams. But what about your chickens’ house? That’s what Coders Cafe’s Smart Coop 2.0 is for.

Smart Coop 2.0 is a system design, driven by an UNO Q, for intended to support routine chicken care. It is built to handle the basics, like dispensing feed on a schedule and heating the space, using local AI models designed to detect eggs and potential predators. The creator designed Smart Coop 2.0 to make certain chicken-care tasks more convenient and to detect potential predators, including coyotes and foxes.

Best in Gaming

RollerCoaster Tycoon was a popular video game generally among two types of people: those who enjoy creative building and those who like maximizing metrics. Kevin Loeffler is the latter type, and he wondered how real-life roller coasters might stack up. The result is RCT IRL, which is a wearable sensor system for classifying and quantifying roller coaster rides.

“Wearable” here means an UNO Q, a SparkFun 9DoF IMU (inertial measurement unit), and a battery stuffed into a fanny pack. During a coaster ride, the dual-brain board from Arduino is structured to collect movement data through the IMU. Afterwards, three Edge Impulse models are designed to process the data and generate model-based scores labeled Excitement, Intensity, and Nausea. Together with interesting stats like ride time and maximum g-forces, those values populate a report card for the roller coaster, just like in the game.

Best in Industrial IoT

HaloSense V1 is pasquale887’s attempt to give humans new senses via neuroplasticity. In short, neuroplasticity is your brain’s ability to accommodate new functions. Of course, there isn’t anything supernatural or superhuman about that, and your brain can only work with the sensory input it receives. HaloSense V1 provides new information through the senses you already have.

Or, to put it another way, HaloSense V1 is a haptic feedback headset. Through an array of 13 haptic feedback motors, it pulses and buzzes to convey information gathered through sensors connected to an UNO Q. The project is intended to explore whether, over time, a wearer can learn to interpret the haptic patterns as information.

Best in Robotics

We’re in the midst of a shift in robotics towards human-friendly designs. Mohammed Akramuddin’s Curie is engineered to be as friendly as it gets. This little desktop robot is cute, with facial expressions displayed on an OLED screen. The creator designed it to help users, including those with ADHD, manage tasks. 

Like all of the other projects in this competition, Curie contains an UNO Q board. It connects to various APIs, including Groq (a cloud interface that is built to let developers run open-source artificial intelligence models at high speeds), to help its owner manage their workload. It is designed to do that in the usual digital assistant ways, by taking notes and setting reminders, but also by providing more tailored assistance. It can, for example, break a complex task up into small pieces that are more approachable. It is also designed to identify potential distraction and provide a nudge to help users return to their task.

Best in Show

The Best in Show award went to the overall competition winner, regardless of category. The winner was Jorge Eldis González for his work on the Edge-AI Spectrophotometer, which is, as its name indicates, a spectrophotometer that integrates AI on the edge.

A spectrophotometer measures the wavelength of light, in order to detect the properties of sampled materials. That is a non-destructive analysis and can, for instance, tell you if a sample contains contaminants.

The Edge-AI Spectrophotometer is built using commercially available components, using an UNO Q and a SparkFun Electronics AS7343 Triad Spectroscopy Sensor. It then runs a machine learning inference model on the edge, which detects deviations from known baselines. The creator designed the project to make its testing workflow more accessible to less-experienced operators and to explore additional spectrophotometry applications.

Congratulations to all the winners! 

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

The post Inside the winning Arduino® UNO™ Q projects from Hackster’s Invent the Future competition appeared first on Arduino Blog.

^{Linux mascot Tux the penguin was created by Larry Ewing using GIMP}

The idea that changed how we build technology

Happy Linux Day, Arduino Fans!

Since 1991, Linux has helped popularize open-source software. And then Arduino, inspired by that, extended that open source concept to hardware with its own launch in 2005. The open-source movement did not stop at the screen; it also influenced the development of physical computing. As we celebrate Linux Day, we honor the collaborative ethos that helped shape the internet and the Arduino movement. Just as Linux democratized software by opening the kernel, Arduino liberated hardware from corporate labs, proving that when we freely share our blueprints from C++ to circuit schematics, we empower a global community to build the future together.

Linux meets the physical world

Imagine building an autonomous robot where the engineering challenge isn’t just running computer vision, but seamlessly bridging that user-space intelligence with bare-metal physical execution. The Arduino^® UNO Q board solves this by unifying a Qualcomm Dragonwing QRB2210 microprocessor running a full Debian Linux environment with an STM32U585 microcontroller for real-time, deterministic hardware control. This dual architecture gives developers the established capabilities of Debian’s package ecosystem and networking stack for AI workloads, while the bare-metal microcontroller supports real-time control of motors, sensors, and actuators, including sub-millisecond operations where supported by the applicable configuration and workload.

This integrated architecture is designed to reduce the friction associated with bridging separate computing and control hardware.By unifying real-time control with Debian’s robust software ecosystem, the UNO Q can help developers focus on building applications across robotics, computer vision, and industrial automation.The platform illustrates how advanced computing and bare-metal execution can be combined in an open, collaborative development environment.

From ideas to real world builds

The real power of open source comes to life in what people can create with it. Across the Arduino community, makers and developers can explore a broad range of possibilities, from desktop companion robots to autonomous systems that tackle everyday problems, and beyond. Whether they’re experimenting with the UNO Q or Arduino^® VENTUNO Q board, sharing their ingenuity through competitions and hackathons, or inspiring others with creative builds, these projects turn bold ideas into working solutions. Here are a few builds that caught our attention, built by community members using Arduino hardware and Linux-based capabilities.

Curie: An AI companion for ADHD and cognitive focus grounding

Curie is an open-source desk companion built on UNO Q, intended to interact through audio and vision features to help users begin tasks and maintain focus. Learn more.

Autonomous pantry assistant

An autonomous edge AI assistant that identifies pantry products and reads their labels aloud for people with low vision. Learn more.

A robot arm that sees you

Imagine a robot arm designed to detect configured users and deliver selected objects. The arm runs on Feetech digital servo motors with a Waveshare control board, and a camera on the head handles the recognition. Learn more.

Depth multitool on the VENTUNO Q

One ordinary webcam, a depth model on the VENTUNO Q NPU, and four visualization modes for estimating the distance of objects within the camera’s field of view. Learn more.

Building on open foundations

Many community projects build on open-source software and hardware. Arduino continues to strengthen that foundation with new hardware, software, and tools that give developers more ways to experiment, create, and solve difficult problems. The 2026 Arduino Open Source Report highlights this motion and key contributions from our vibrant, passionate community. Here are just a few of the highlights, reported by Arduino as part of its open-source ecosystem:

• 6 new open source hardware boards
• 11 new official libraries
• Arduino^® App Lab for UNO Q

The community led with:

• 1,218 new libraries, bringing the total to 8,754
• 6,602 new versions of libraries
• 420 new open source projects

The next chapter

As AI descends from the cloud to power the next generation of autonomous robots and edge devices, open-source development continues to play an important role in this area. The convergence of Linux, Arduino, and edge intelligence is helping enable new physical-computing applications, and it is the global community that will define what is possible. That is the true spirit of Linux Day: a brilliant, decentralized future we are building together.

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

The post From open source to real world solutions: How Linux and Arduino bring big ideas to life appeared first on Arduino Blog.

You build something great on your Arduino^® UNO Q board. Now what? Arduino App Lab 0.11 is about taking your project further, whether that means shipping it to another board, getting help while you code, or teaching your device to recognize a tennis swing. 

Here is what’s new!

App Release: deploy to production, at scale

App Lab 0.11 deepens our integration with the Works with Arduino program. 

App Lab now supports exporting production grade “Apps Releases,” so your project built in App Lab can be packaged for deployment without leaving the tool. 

How does it work?

1. Package an app into a ready-to-run file.
2. Install it on another board.
3. Run it on compatible third-party hardware included in the Works with Arduino program.

What’s the difference between this new App Release and the export feature?

Export is great for sharing a project. An App Release is designed to provide a consistent, ready-to-run package. An App Release is a frozen, immutable package with locked dependencies to support consistent behavior across compatible devices. Sketches are pre-compiled, which can reduce startup times. Remote dependencies like AI models are downloaded at install time, which can streamline setup across multiple boards.

Check our documentation to learn more:

• Create an App Release
• Install an App Release

Agent Panel right inside the editor

Chat with an Agent Panel next to your code to build, troubleshoot, and brainstorm ideas. 

Agent Panel uses the app, files, and code open in your workspace as context, which can reduce the need to copy and paste or switch tabs. It will update files in your workspace as you chat with it in plain-language, and it keeps your chat history organized by app.

What’s the difference between the Agent Panel and Agent Mode?

Agent Mode, introduced in App Lab 0.10, is a full-window experience centered on the agent. You describe what you want and the agent creates and works on your app for you. The Agent Panel is a side chat that stays open while you work in your app. It uses the app and files you have open as context, updates them as you chat, and you run your app right there as usual.

New Pose Estimation Brick: teach your board the moves

The new Pose Estimation Brick runs locally on your Arduino^® VENTUNO Q and comes with two features: one with preconfigured capabilities and one that supports custom poses. 

Preconfigured skeleton tracking and pose detection

The Brick tracks a 17-point body skeleton at up to 10 frames per second and draws a live overlay on your video stream. It also recognizes standard poses like standing, sitting, or arms up and down, without a separate model-training step. The demo app is just six lines of code.

Make it yours: teach it a custom pose

Need something more specific? Give the Brick a folder of photos of the pose you want, plus a folder of photos that are not that pose. No separate model-training step or additional tool is required. The photos are the whole input, and App Lab performs a recognition check before the app starts.

Want to recognize a tennis stroke? Provide representative photos to configure the custom pose.

Bring your own model

Add language models for the LLM Brick straight from Hugging Face, using a model ID or URL. App Lab checks that the model is compatible and selects a support quantization based on the model. 

Check out the documentation at this link: Custom model import documentation

Your phone is now a sensor board

The Mobile Companion App now streams more than the camera. 

Microphone and IMU data now flow into App Lab through a new remote sensor peripheral, at high frequency over WebSocket, with password protection and encryption options. Data is sent as JSON, so you can define your own keys. Because it is built on WebSockets, compatible external devices can stream data to your board using the supported WebSocket interface, which can support remote-control and IoT use cases.

Know where you are, even when GPS struggles

In this short demo, Anthony Hebert and Stefano Implicito show how easy it is to get started and put TPS to the test by finding Andy in the middle of a crowded show floor.

We showed the Qualcomm Terrestrial Positioning Service (TPS) running on UNO Q at The Things Conference 2026 on September 28th. It uses nearby Wi-Fi, cellular, and Bluetooth LE signals to support positioning while managing power consumption. A new TPS brick and example provide a way to evaluate the service.

Also in Arduino App Lab 0.11

• VENTUNO Q flashing: flash your board from the built-in flasher in Settings.
• Board extensions: select and configure the carriers and JHAT add-ons attached to your board from the Settings page.
• Publish to Arduino Project Hub: upload your app as a project draft from the App menu.
• Editor improvements: Find All References for Python and C++, YAML syntax checking, any file or folder name, and a separate console tab for each Brick container.

Get started

Download Arduino App Lab 0.11 today and tell us what you build!

For the full list of changes, see the release notes.

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

The post Arduino® App Lab 0.11 is here appeared first on Arduino Blog.

UNO R4 WiFi and UNO R4 Minima boards as well as the Arduino® Alvik robot.

Every maker remembers the moment something they built first came to life: an LED blinking, a servo sweeping, a sensor reacting to a hand wave. Today, that moment gets a lot easier to reach thanks to a new collaboration between STEMpedia and Arduino!

We’re excited to reveal that PictoBlox 10.0, STEMpedia’s coding and technology creation platform, has expanded its Arduino compatibility to include the UNO R4 WiFi, UNO R4 Minima, and Alvik robot. 

And what better place to celebrate this news than at Maker Faire Rome 2026? Join us from October 23rd–25th to explore the new integration and try PictoBlox for yourself! 

From first-time coder to confident creator

Arduino has long been a starting point in electronics for makers, students and educators.PictoBlox brings a visual, drag-and-drop programming experience that helps users to focus on programming concepts without having to start with coding syntax. Together, they can help beginners go from “I’ve never coded” to “I built a working project” in a single session.

But PictoBlox isn’t just for beginners – or just for programming. Beyond block-based coding and Python, the platform is designed to let users explore AI and machine learning, experiment with tinyML and intelligent systems, program compatible robotics and electronic hardware, and develop IoT projects. It also supports game and animation development, opening up even more opportunities for hands-on creativity and experimentation.

Announcing the collaboration, Dhrupal Shah, CEO and Co-Founder of STEMpedia, said: “Great innovations begin with the freedom to experiment. By expanding Arduino board support in PictoBlox, we are making it easier for learners and makers around the world to turn their ideas into working projects. 

“Students can create AI-powered projects and train their own Machine Learning models, making complex technologies more accessible. We’re excited to see how this collaboration inspires the global maker community to explore, build, and innovate.”

What you can do with PictoBlox and Arduino

Combining visual programming with Arduino hardware opens up all sorts of creative possibilities:

• Program with blocks: snap together scripts instead of typing code, and see how your Arduino responds right away.
• Work with sensors: read inputs such as an IR sensor and use them to trigger actions.
• Control actuators: drive LEDs and servo motors from your scripts.
• Go wireless: control your projects from a smartphone using Dabble, a project interaction and Bluetooth controller app.

By bringing together Arduino’s hardware ecosystem and PictoBlox’s versatile coding environment, the collaboration aims to make technology creation more approachable and open up new possibilities for the global maker community to learn, build, share, and innovate. 

Built for classrooms, clubs and makerspaces

Whether you’re teaching in a classroom, running a coding club or experimenting in a makerspace, this expanded integration is all about trying to lower the learning barrier and open up new possibilities for creative exploration.

By helping reduce the need to manage syntax, block-based coding means learners can focus on developing essential skills like logical thinking, problem-solving and understanding how code works. And as their confidence grows, they can build on these foundations, explore text-based programming and take on increasingly ambitious projects within the Arduino ecosystem.

For educators, it’s a compelling combination: an approachable software environment and a familiar, well-supported hardware platform, with plenty of room for students to learn, experiment and grow – all in one workflow! 

Get hands-on at Maker Faire Rome! 

Heading to Maker Faire Rome this year? Be sure to stop by booth N.02, where you can see PictoBlox and Arduino in action, try out the new integration for yourself and chat with the team. We’d love to see you there!

Download PictoBlox at: https://thestempedia.com/product/pictoblox/download-pictoblox/

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

The post More ways to code, more ways to create: PictoBlox expands Arduino support appeared first on Arduino Blog.

Want real-world experience developing AI models and deploying applications using cutting-edge technologies such as the Arduino^® UNO Q board? Qualcomm Academy has a new second-level program for you! The AI Associate Certification: Model to App is a hands-on, online, and self-paced program designed to provide experience with the end-to-end ML workflow, including data preparation, model creation, optimization, deployment, and on-device inference. 

Through approximately five to six hours of build-along training, you’ll enjoy a combination of technical depth and practical application, helping you develop the confidence to create accurate, efficient, and production-ready AI solutions at the edge.

Three projects. One complete edge AI journey.

To achieve their certification, participants use a learning-focused development kit based on UNO Q to complete three real-world projects that span voice, vision, and sensor-based AI:

• Wake Word Detection: Create and optimize a voice-enabled application that recognizes predefined wake words.
• Person Recognition: Build a computer vision application that processes image data and performs real-time inference on an edge device.
• Gesture Recognition: Train a model using IMU sensor data to detect and classify movements in real time.

Each project connects model architecture, training, validation, quantization, deployment, and application integration so learners can explore the ML lifecycle in practice.

Build skills for real-world edge AI development

By completing the certification, you will gain hands-on experience with developing ML models, preparing data, quantizing and optimizing models to explore tradeoffs among accuracy, latency, power consumption, and performance, and visualizing model graphs. You will also gain experience with AI development toolchains, explore MCU and MPU capabilities, develop real-time applications, and work with FastRPC communication for AI workloads.

The program is designed for developers and engineers, embedded systems professionals, IoT and robotics developers, students, and industry professionals interested in gaining practical experience with ML and edge AI. Learners should have basic Python skills, familiarity with an IDE, introductory knowledge of PyTorch and ML concepts, and prior hands-on experience equivalent to the Qualcomm Academy foundational AI certificate programs.

Flexible learning, practical results

The AI Associate Certification: Model to App is available online and can be completed at each learner’s own pace. You may purchase the program via Qualcomm Academy. After successfully completing the certification requirements, you will receive a Qualcomm Technologies certificate and badge that can be displayed on your CV and LinkedIn profile.

Start building – from model to app!

Take the next step in your AI development journey. Enroll in the AI Associate Certification: Model to App and gain hands-on experience applying ML concepts to applications running at the edge.

Enroll now!

Visiting Maker Faire Rome (October 23rd–25th)? Make sure to stop by the Arduino booth to talk to the team about how you can get your AI Associate Certification: Model to App. 

Qualcomm Academy is a program of Qualcomm Technologies, Inc. and/or its subsidiaries. Arduino and UNO are trademarks or registered trademarks of Arduino S.r.l.

The post Discover the Qualcomm Academy new AI Associate Certification: Model to App at Maker Faire Rome! appeared first on Arduino Blog.

When running UNO Q as a standalone single-board computer, navigating its Linux desktop environment doesn’t have to mean reaching for a standard mouse and keyboard. One of the most rewarding aspects of combining microcontrollers with a full operating system is the freedom to design custom physical interfaces – turning buttons, knobs, and joysticks into direct, tactile controls for your software.

To support that experience, we’re introducing the UNO Q SBC Controller Bundle. Designed for makers, learners, and innovators, this kit brings together hardware, software, and step-by-step guides to help you build a customized control setup.

What’s included in the box?

At the heart of the bundle is the dual-brain UNO Q 4GB board. To create a physical input layout as an alternative to certain traditional input functions, the kit includes a curated set of Arduino^® Modulino nodes: Buttons, Joystick, Knob, and Pixels for dynamic visual feedback.

The included components connect through the Arduino^® App Lab development environment. To give your finished project a clean, product-like appearance, we also offer a downloadable, 3D-printable enclosure that you can print or modify for your design.

Bringing physical control to your Linux workflows

Running Linux directly on your development board supports a range of potential applications, as we recently explored in our overview of what Linux unlocks on the UNO Q board. Whether you’re managing local Home Assistant dashboards, launching retro games, or controlling edge AI routines, physical controls provide another way to interact with your system.

Building your own controller offers a hands-on way to explore human-machine interaction, helping you learn how physical inputs translate into operating system commands. Thanks to the modular nature of the Modulino ecosystem, the setup is designed to be flexible – you can reconfigure supported input mappings or swap in compatible components as your project evolves. Detailed walkthroughs are already available here on Arduino Project Hub with instructions for assembling the featured configuration.

Completing your desktop setup

If you’re turning your UNO Q into a complete desktop station, a few optional accessories can round out your build. The Arduino^® USB-C Hub (8 in 1) adds HDMI output, Ethernet, and extra USB ports, while the official Arduino USB-C Power Supply (45W) and Arduino USB-C Cable (24 pin) are designed to provide compatible power delivery for the UNO Q setup.

Head over to the Arduino Store to explore the new UNO Q SBC Controller Bundle, download the 3D-printable files, and start crafting your own custom hardware interface today!

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

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Sitting on my desk I have two boards. They carry the same logo, have a somewhat similar name. Their technology could not be more different, and yet the philosophy behind them is exactly the same.

On the left I have an early Arduino^® UNO Rev3, featuring a Microchip ATmega328P, a fingernail-sized piece of silicon dating back the 2000s, that powered the original Arduino revolution. On the right sits the Arduino^® VENTUNO Q, running a Qualcomm Dragonwing IQ-8 chipset with eight-core CPU and an NPU able to go up to 40 dense TOPS, making it the most capable AI-focused Arduino boards to date.

Both are Arduino. Both share the same pin headers. In just a few steps, you can blink an LED on one, or on the other deploy an 8-billion parameter LLM, then ask it how to program the original board to blink an LED.

Crazy how technology has evolved in the last 20 years, but naturally the obvious question is by how much exactly?

Well, you already know the answer from the headline, but getting to the real figure reveals a common pitfall in how performance specifications are presented and offers a better way to evaluate any performance claim.

The problem with specs

The Dragonwing chipset (IQ-8275) on VENTUNO Q carries a headline specification of 40 dense TOPS, representing forty trillion dense operations per second.

The 2009 ATmega328P is, on the other hand, the workhorse of the embedded electronics world: a single 8-bit AVR RISC core clocked at 20Mhz, known for its long-standing use in embedded applications, straightforward architecture, and broad accessibility.

The ATmega has no performance metric on its datasheet because nobody was tracking this type of stuff back then. But, if you estimate its compute ceiling manually, you land at roughly 8 million operations per second.

Divide 40 trillion by 8 million and you get 5 million. Case closed?

Not quite. That simple division hides a common trap in how performance numbers are interpreted: operations aren’t created equal.

Computers do math in different formats: think of them as different currencies. Small integer formats (often used for basic pixel brightness) require relatively few computational resources. High-precision floating-point decimals (often used for physics or navigation) typically require more computational resources. As a result, a chip optimized to blast through basic integers can look ordinary when forced to handle complex decimals.

Marketing materials for AI hardware usually quote the small-integer format, INT8 or INT16. Comparing those figures with an ATmega328P estimate derived from general-purpose clock cycles is similar to comparing different currencies without applying an exchange rate. The resulting number may appear dramatic, but it does not provide a meaningful like-for-like comparison.

So here’s Rule #1 of meaningful performance comparisons: both sides must be measured using the same “currency.”

Designing a fair race

To run a meaningful comparison, both chips should perform the same operation on the same data and produce equivalent results.

A representative workload for this operation is the mathematical backbone of modern machine learning: multiplying two numbers, adding the result to a running total. It’s called multiply-accumulate or, colloquially, MAC.

Repeat this loop millions (or trillions) of times, and you can get to things such as Image Classification (aka deciding whether a photo contains a dog, a car, or a birthday cake), Voice Recognition (aka continuously looking for a specific wake word while filtering out background noise) or even funkier stuff like Autonomous Navigation (aka evaluating sensor data in real time to distinguish a wall from a person).

These tasks are all the same underlying math, an aggregation of MAC operations, just at different scales, making them a useful ruler.

The actual benchmark

Let’s go for image classification to keep things simple.

We’ll need test images to be fed into both systems, originated as standard digital photos cropped and resized to a uniform resolution of 224 × 224 pixels across three RGB color channels. This preprocessing converts each photograph into an input data array (aka a tensor) containing 150,528 individual pixel values (224 times 224 times 3). The image data is formatted as 8-bit quantized integers (INT8), mapping every color value to a whole number between 0 and 255 to match the low-precision integer format required by modern hardware.

Evaluating one photo requires streaming this tensor through a vision model containing roughly 100 megabytes of stored parameters, generating a workload of 8 billion total operations (4 billion MAC steps). VENTUNO Q is designed to process roughly 5,000 photos every second; at that rate, the system could sort an entire photo archive (or just a fancy meal worth of pictures of a millennial) in a single heartbeat. Under the same benchmark assumptions, the original UNO Rev3 would take about 17 minutes to analyze the same photo, so think twice before taking a pic of that tiramisu.

To process the 5,000 photos that VENTUNO Q is built to handle in one second, UNO Rev3 would need approximately 58 days at the benchmarked throughput rate. If you started on January 1st, it wouldn’t finish until late February.

Within the context of this benchmark, the performance difference remains on the order of five million to one.

Why this is actually all meaningless

Here’s the catch: UNO Rev3 cannot practically execute this benchmark workload because the model exceeds the device’s available memory resources.

An AI vision model requires roughly 100 megabytes of memory to store its parameters. The ATmega328P has 32 kilobytes of flash memory and 2 kilobyte of RAM, which is insufficient to store the model.

The “58-day” metric just assumes the infinite external memory attached. It’s a mere thought exercise, but there is something even more relevant.

The gap between 2008 and today isn’t just that the old chip is slow. It’s that an entire category of work moved from impractical on earlier hardware to routinely achievable on modern AI platforms. No amount of patience can stretch 8 kilobytes of memory into 100 megabytes.

Real technological leaps rarely feel like “the same thing, faster.” They manifest as entirely new categories of capabilities opening up for the first time.

What does really matter

While this thought exercise suggests a five-million-fold difference in arithmetic capability, the significance of VENTUNO Q extends beyond raw compute performance.

One of the platform’s key contributions is helping make edge intelligence more accessible by lowering barriers to experimentation and development. The goal is to make it easier for developers to move from exploring AI software to building physical AI systems.

Whether toggling a GPIO pin on an 8-bit ATmega or deploying a computer vision model on a modern NPU, both boards are built around a similar development philosophy: connect the hardware, open an accessible development environment, and begin building.

That emphasis on developer accessibility is what matters most. Despite substantial differences in compute capability, both boards are designed to provide an approachable path into hardware development and experimentation.

Preserving accessibility while dramatically expanding compute capabilities represents a significant engineering achievement. For me, that is the real story. Making AI technologies more accessible helps more people move from using AI tools to building AI-enabled applications of their own.

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

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Dear Builders, Engineers, and Innovators,

At Arduino, we have always believed that the most powerful technology should be in the hands of the many, not the few. That belief is the very reason VENTUNO Q exists, to put real AI into the hands of more creators. This includes access to the product itself, and when we introduced VENTUNO Q back in March we made the promise of an introductory price of under $300. A promise we honored when we made the product available for order in August for $299.

The cost of the many key components that make a board like this possible, in particular memory modules, has risen sharply across our entire industry, and continues to climb. We absorbed those increases for as long as we possibly could, because a commitment is a commitment, and your response made every bit of that effort worthwhile. VENTUNO Q sold out (twice!) in no time at all, and the orders have not stopped since. To everyone who pre-ordered, built, and believed in this board from day one, thank you! You proved what we hoped: that there is a real, deep hunger for open, powerful, physical AI.

But, what moves me most is not the numbers, but what you are already doing with it. In these first weeks we have begun to see projects arrive from our online community, and in person at events: Agentic AI reasoning assistant, depth camera multitool, industrial production line vision inspection, humanoid service robot, JetArm face-tracking, voice control, etc. You even have Half Life 2 & Skyrim running on it. From personal robotics experiments to serious edge AI industrial applications, this range of ideas is exactly why the VENTUNO Q exists. Take a look at what the community is already making, it’s truly impressive!

So here is where we stand. From October 19, VENTUNO Q graduates from its special introductory price to its official retail price, at $399. This is not a number we chose lightly, it is the honest price of delivering this AI computer, and this much ambition, in today’s market. If you have already pre-ordered VENTUNO Q, nothing changes for you, the introductory price will be honored in full.

What I want you to know is this: at $399, VENTUNO Q still delivers unprecedented value. In a single open platform, it brings together an Qualcomm^® Hexagon NPU rated at 40 dense TOPS for local LLMs and vision AI running on-device, a dedicated STM32H5 sub-millisecond brain to act on the physical world, 16 GB of RAM, and a genuine path from prototype to production through the Works with Arduino program powered by Qualcomm Dragonwing processors.

And we are only getting started. The board you hold is one half of the story; the software keeps evolving continuously and at no extra cost. Our next Arduino^® App Lab release will bring new AI models, support for 3rd-party accessories like the Raspberry Pi HATs and MIPI CSI cameras, and many more new features. Qualcomm^® Linux^® software stack support for VENTUNO Q is already live in beta on GitHub for you to experiment with today. There is a lot more on the way in the coming weeks. So stay tuned.

Thank you, as always, for the trust you place in Arduino. I cannot wait to see what you build next.

Marcello Majonchi
Chief Product Officer, Arduino

Get VENTUNO Q from the Arduino Store. Also available from our official distribution partners: Conrad, DigiKey, Mouser, Farnell, RS, Robu.in, and many others worldwide.

Qualcomm branded products are products of Qualcomm Technologies, Inc. and/or its subsidiaries. Arduino, VENTUNO, and Works with Arduino are trademarks or registered trademarks of Arduino S.r.l. Linux® is the registered trademark of Linus Torvalds in the U.S. and other countries.

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