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.

AI as the new interface

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.

Natural language is becoming the new interface for industrial machines. 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.

A robotic arm you can talk to

Swiss startup Forgis, 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^® UNO 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.”

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.

Critically, none of this requires a cloud round trip. 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.

Why this matters for manufacturing

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.

The open Arduino ecosystem also means development teams like Forgis can deploy their proprietary models without getting bogged down in hardware integration. The gap between a working prototype and a production-ready system is narrowing.

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.

Curious about UNO Q? Find out more here or head directly to the Arduino Store to get started.

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

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Flip-disc displays used to be very common in the days before affordable LEDs, LCDs, and OLEDs. But as electromechanical devices with a lot of moving parts, they’re expensive and rare today. That’s a shame, because they’re charming. That charm is what led Zimm to the creation of this novel flip-squisk display called P.A.R (Pixel Art Robot) that accepts art from the community.

A “squisk” is a square disc, with one light-colored side and one dark-colored side. Zimm made his squisks by 3D printing the two sides with interlocking channels, so they come together and form one unit. The display is a 37 × 18 grid of those. By flipping them between light and dark sides, it can create a low-resolution raster monochrome image.

Other than using squares instead of circles, that is pretty much the same concept as a normal flip-disc display. But P.A.R. actually works in a very different way, and the “robot” part of the name hints at that.

A normal flip-disc display uses a solenoid to manipulate a permanent magnet on the axle of the disc, flipping it one way or the other. That requires a solenoid (and control over that solenoid) for every single disc, which results in a massive bill of materials and the high cost.

Zimm’s P.A.R. doesn’t do any of that. Instead, there is a robot behind the display that reaches over to each squisk and physically flips it. There are no solenoids or magnets. That robot is kind of like a pen plotter turned up vertically, with a servo-actuated pin that pushes squisks around. That operates under the control of an Arduino^® Nano ESP32 board, which also has a color sensor to detect the current state of the squisk in front of it.

The advantage of this design is the elimination of the solenoids. Zimm could scale up the size and resolution of the display by simple extending the axes and adding squisks. The disadvantage is that it is very slow compared to a traditional flip-disc display.

And because P.A.R. has a Nano ESP32, it can connect to the internet. Zimm set up a website where visitors can submit art, which P.A.R. will then “draw” onto the display where everyone can see it. The website contains a gallery of all of the drawn images. Take a look at those or submit your own art!

The post P.A.R. is a flip-squisk display that draws your pixel art appeared first on Arduino Blog.

MIG welding isn’t very difficult — at least not until you need to weld something at a weird angle or over a long seam. Those situations will make your welds look like they were done by an inebriated amateur. The best way to handle that is by setting yourself up to maintain stability through the entire weld. For achieving that on round objects, Greenhill Forge constructed an Arduino-controlled rotary welding table.

Tables like this have one job: to rotate the workpiece in front of your welding gun, so you can stay in a stable position while it moves. That’s a simple job, but there are a few important “gotchas” that Greenhill Forge cleverly took into account.

Structurally, this is just a mild steel frame with a heavy-duty lazy Susan bearing and a circular mild steel top. That’s the kind of thing even a beginner welder could throw together in an afternoon, assuming you have a way to cut the steel (that circular top might be challenging).

Those “gotchas” relate to the welding process. You want the top to spin at a consistent speed and you want that speed to be adjustable. You also want a good path for current to flow from the ground clamp to the wire.

Greenhill Forge handled the rotation with an Arduino UNO Rev3, a stepper motor with a gear reducer, and a spider coupler mating to a shaft welded to the underside of the tabletop. A rotary encoder lets the user set the rotation speed and a small OLED screen lets them see the speed value (for setting the same speed again later).

For the ground path, Greenhill Forge wrapped a ground strap around the shaft above the spider coupler. So, the current path is ground clamp, to ground strap, to motor shaft, to table, to workpiece, to wire. It lets the top spin freely without tangled cables, and avoids any danger of current going to the electronics.

The post This custom motorized turntable makes welding easier appeared first on Arduino Blog.

Pietro Maria Piazza, Alessandro Nesci, Davide Santucci, and Matteo Angiolillo are not waiting to finish school before starting to build something real. Based in Forlì, Italy, the four friends behind Blue Proton Initiative strive to develop an AI-powered livestock monitoring system designed to help farmers identify individual animals and detect early signs of health problems – automatically, in the field, in real-time.

They are 17. The project is four months old. They are far from ready to start their own business, but they sure have the spirit of budding innovators – and they are already pushing the Arduino® UNO Q board to its limits.

Agriculture 4.0 can’t wait

Livestock monitoring an area where technology has lagged behind what’s actually possible. Keeping track of individual animals, spotting health issues early, flagging anomalies before they become costly problems – most of it is still done by hand, which means it’s slow, inconsistent, and easy to miss.

Once they identified this gap, “None of us wanted to wait,” the team explains. “The tools, the knowledge, and the resources needed to tackle real problems are accessible today, and we saw no reason to postpone.”

What they want to build is a system that uses computer vision and custom-trained neural networks to identify animals and monitor their behavior – the kind of continuous, low-overhead monitoring that would be genuinely useful on a working farm. “In farming, catching something late can mean real losses. An automated system that runs in the background without needing constant attention is genuinely useful in that context.”

Four teens, one dream team

The four friends have divided responsibilities: Pietro leads software engineering, Matteo develops the backend using C, Davide handles CAD design and the physical structure of the system, and Alessandro focuses on neural network development and YOLO model training – the core of the system’s ability to detect and count animals automatically.

The models are being developed entirely in-house. Dataset composition is central to their approach: the more varied the training data – different lighting conditions, animal movement, weather, environments – the more robust the model becomes when deployed in real-world settings. As Alessandro puts it, “with enough diverse images covering a wide range of real-world scenarios, the model learns to generalize.”

UNO Q sits at the heart of the architecture. The entire system is housed in a custom 3D-printed enclosure designed specifically around specific requirements, keeping things compact and field-ready. The team is writing and deploying code directly via the Linux terminal – no IDE hand-holding, no App Lab. Just the board, a text editor, and a lot of iteration.

Why choose UNO Q?

Blue Proton didn’t come to Arduino cold. The team had already used the UNO and Arduino^® Nano hardware in earlier projects – a robotic arm, a model rocket – but they were quick to recognize that UNO Q was something different.

“The ability to run Linux on a compact, affordable board was a turning point for us. That combination – processing power, lightweight form factor, and low cost – made it a natural fit for a project like ours, where the hardware needs to operate in the field, potentially in remote environments, without being bulky or expensive to deploy.”

The ability to run AI inference locally, without depending on a constant server connection, is essential for agricultural deployment. UNO Q makes edge AI viable within real student-budget and field-deployment constraints.

That said, the team is candid about the hardware’s tech specs: “RAM and CPU are the main bottlenecks – running a system like ours on a board with those constraints requires every component to be as lean as possible.” Their approach: test, optimize, test again. “There is no shortcut to it – it just takes time and a lot of patience.” 

(Of course they started on the project before the launch of the Arduino® VENTUNO Q board, which would allow the Blue Proton Initiative team to have more headroom!)

Innovation is all about the journey

Four months in, Blue Proton has a working cattle-counting system, a clear technical direction to develop the health monitoring component of the project, and a level of honesty about their process that’s refreshing. “We are still working on it. We are 17, we have a math test tomorrow, and we are working on something that could maybe become our job one day – or maybe not. We don’t have a commercial product ready, and we are not going to pretend we have a five-year plan. But we are going to keep building, keep learning, and see where it goes. That is enough for now.” 

Their advice to other students thinking about starting something similar: “If you have an idea, chase it. Do not wait until you feel ready, because that moment will never come. You figure it out as you go, and that is kind of the point.”

Follow Blue Proton Initiative’s progress on their website and Instagram account – they are producing video documentation of the build as it develops. 

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

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Anyone working with electronics will, inevitably, end up with a large selection of various components — after all, you’ll buy a lot of them in bulk quantities that exceed what you actually need for the project at hand. Soon, you’ll have boxes and drawers full of components that you don’t even remember, which means you’ll order duplicates you don’t need. Akashdeep Singh’s solution to that problem is this mini inventory management system.

Inventory management is big business, but commercial inventory management systems are overkill for most hobbyists and even a lot of small businesses. All you really want to know is how many of each component you have on hand, right? That was the case for Singh and so he designed his device that keeps things simple.

This device isn’t much larger than an old iPod. And it is minimalist, too. There is just an LCD display and a knob. By interacting with that knob, Singh can select a component from the predefined list and view or adjust its quantity. Because it is so compact, it is easy for Singh to keep over by his workbench, where it is accessible during projects.

The build consists of an Arduino® UNO Q, which handles Singh’s inventory management software (essentially just a lightweight database), along with a TFT screen and a Modulino Knob. There’s also a buzzer for feedback and the enclosure was 3D-printed.

It is all very straightforward and sensible, which is perfect for a utility device meant to make life just a little bit easier. Now, if a project calls for a specific component, Singh can check to see if he already has one before placing an order.

The post A mini inventory management system perfect your electronic components appeared first on Arduino Blog.

Today is a big day. Pre-orders for VENTUNO Q 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 doesn’t just perceive the world, but interacts with it. A board where AI takes action.

“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.” – Fabio Violante, VP & GM, Arduino, Qualcomm Technologies, Inc.

What makes VENTUNO Q different

Most edge AI boards stop at perception. VENTUNO Q goes further. Its dual-brain architecture pairs a Qualcomm Dragonwing IQ8 processor – 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.

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^® 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.

VENTUNO Q is equipped for demanding real-world deployments. 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. 

ROS 2 compatibility is built in, making VENTUNO Q a natural fit for robotics development from day one. On the hardware compatibility side, VENTUNO Q works out of the box with Arduino^® UNO shields and carriers, Arduino^® Modulino 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.

Build your way with Arduino^® App Lab

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. 

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.

Scale from prototype to production

One of the things we’re most excited about is the path VENTUNO Q opens beyond the prototype stage. Under the Works with Arduino program, SECO and Toradex – 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.

Physical AI is more accessible than ever

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:

• Large language models (LLM)
• Vision-language models (VLM)
• Automatic speech recognition (ASR)
• Text-to-speech (TTS)
• Gesture recognition

And there’s more! Arduino App Lab 0.10.0 brings a wave of new features and improvements, including a new Agentic Mode experience. 

Pre-order now to get free accessories included!

Head to the Arduino Store 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 Arduino^® USB-C Power Supply (65W), and the Arduino® USB-C cable (24 pin). These are official accessories that together are designed to support the board in sustaining demanding AI workloads and connected peripherals without throttling.

VENTUNO Q is also available through our official distribution partners: DigiKey, Farnell, Mouser, Robu.in, and RS.

For full technical specs, use cases, and everything you need to know before ordering, read the full press note and visit the VENTUNO Q product page.

We built this for you, and we can’t wait to see what you build with it.

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.

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If you’re interested in robotics, a great way to get started is with an affordable kit that will let you focus on software and control, rather than frustrating and overwhelming hardware design. But once you play with that kit for while, you’ll probably reach a point where you want more capability than just line-following or remote control. At that point, you can take a page from Iulia Feroli’s book and use an Arduino® UNO Q to add real intelligence.

Feroli started with an entry-level robotics kit from Elegoo. It had an Arduino UNO Rev3 for control, a shield providing motor drivers and connections, and only a few sensors to perform tasks like obstacle avoidance and line-following. That’s fun at first, but one can do so much more with some enhancements.

The most important upgrade Feroli performed was swapping the UNO Rev3 out for an UNO Q. They have identical headers, so moving the shield over was easy. That gave the UNO Q the same control over the motors and Feroli could very simply reproduce the original functionality.

But the UNO Q also gave Feroli a lot more horsepower to work with. Its STM32 microcontroller is far more powerful than the ATmega328 and it has the entire microprocessor running Linux. That microprocessor is more than capable of running local machine learning models, which meant that Feroli could take advantage of AI to make the robot far smarter than it was.

She implemented that by connecting a standard USB webcam, so the UNO Q could see the world. She then pulled that video feed into the Arduino App Lab’s face tracking Brick. Some simple code translated that detection into movement commands for the robot. It looks for the position of a face in frame and turns to keep the face centered, then drives forward towards the face it sees.

That’s a bit aggressive, because it essentially chases any face it sees. But it proves how effortlessly—and affordably—you can turn a basic robot kit into something far more capable and sophisticated by taking advantage of UNO Q.

The post Affordable robot car kit becomes an intelligent creator-chaser  appeared first on Arduino Blog.

Arduino Cloud 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’s everything that shipped.

Build dashboards with multiple pages

^{Add new dashboard pages from the left sidebar and give each one its own name, icon, position}

If you’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’s your greenhouse sensors or your energy monitor or both at once.

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.

See your whole device fleet at a glance

^{Spaces overview now shows Things, Devices, Members, and Trigger activity at a glance}

This is the big one in this release. Before, if you wanted to know something as simple as “how many of my devices are online right now,” you had to dig through the Devices page and count. Now every Space’s home page has a Statistics section with four widgets: 

1. Total Things (active vs. inactive)
2. Total Devices (online vs. offline)
3. Total Members (by role)
4. Trigger activations over a time range you can flip between 1 hour and 15 days

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’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.

Track daily data usage before it’s a problem

^{Track daily ingested records against your plan’s limit, broken down by Thing or by Space}

Every plan comes with a Daily Ingested Records limit, but until now that number only existed on paper. 

That’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’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.

Decide who gets to claim a device next

^{A new toggle on every device’s detail page controls whether it can be claimed by another account}

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 too easy to reassign, since accidentally claiming a colleague’s hardware is a real risk.

The fix is a new “Lock to this account” toggle on every device’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.

Connect board to Arduino Cloud, right from Arduino App Lab

^{Provision UNO Q for Arduino Cloud directly from the Arduino App Lab’s device settings page}

If you’re building on UNO Q, registering with Arduino Cloud used to mean stepping outside Arduino App Lab to do it. Not anymore.

You can now register your UNO Q 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: dashboards, remote control, fleet management, all of it, without breaking your workflow to set it up.

Fleet device management with Smart Folders

In case you missed it, we also shipped Smart Folders earlier this year, letting you organize your device fleet in Arduino Cloud instead of losing track of things as it grows. Check out that post if you haven’t already.

Get started on Arduino Cloud

Everything we just talked about is live in production right now, ready for you to test out! Here’s what we recommend trying first:

• Log in to app.arduino.cc (it’s free) 
• Check out the new Statistics section on the home page of a Space you manage
• If you have more than one Space, open Spaces Overview and see your whole footprint in one place
• Building a busy dashboard? Try splitting it into pages

Arduino is a trademark or registered trademark of Arduino S.r.l.

The post Here’s what’s new in Arduino® Cloud: multi-page dashboards, Spaces Overview, and more! appeared first on Arduino Blog.

The industrial world runs behind everyone else, technologically speaking. Reliability, safety, and maintenance are the biggest concerns, which means tried-and-true control solutions reign supreme. Even new facilities are full of machines that rely on tech that is several decades old. Mukesh Sankhla’s Edge Link aims to bring that equipment into the modern era, without risky modifications.

Edge Link is a small device that fits onto a standard DIN rail inside any machine’s electrical cabinet. It can “speak” to many legacy machines and control systems via Modbus, RS-485, and more. Through those protocols, it can both receive data and send control commands — though, obviously, the specifics depend on the equipment in question.

At its most basic, Edge Link acts like an IoT gateway. It can log data and make that available on a network, so facilities managers can keep on eye on a machine’s status. It also allows for control along the same channels, if the equipment supports that. One could, for instance, remotely activate a startup routine before the morning shift gets in.

Data, such as sensor readings, transfer via MQTT messages and everything is organized through a structured UNS (Unified Namespace). That means Edge Link-equipped systems settle right into other systems that also take advantage of UNS, so they integrate nicely into the dashboards and cloud platforms a facility already uses.

But Edge Link also has real intelligence, thanks to the Arduino® UNO Q board it contains. That has the computing power to run local predictive maintenance machine learning models (built using Edge Impulse) that can make decisions on the edge — no outside network access necessary. One would need to be careful about how they implement that, but it would be ideal for tasks like safety shutdowns. For example, it could shut down machine B if it detects a problem with machine A. 

Sankhla even programmed a PLC Simulator, which has a complete Siemens S7-300 PLC digital twin to test with. Grab an UNO Q and you can use that to see how Edge Link works under different conditions.

More details on Edge Link can be found in Sankhla’s Hackster tutorial.

The post Edge Link is a DIY solution to upgrade old industrial equipment appeared first on Arduino Blog.

We’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.

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.

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.  Here’s a breakdown.

The big one: Agentic Mode

^{Agentic Mode and session panel in Arduino App Lab 0.10}

Open Arduino App Lab and you’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.

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.

It’s what allows Agentic Mode to go beyond simple conversation and actually operate on your project.

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. 

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.

Faster code editing with Language Server Support

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, “go to definition”, and “find all references” work across Arduino sketches, Python, HTML, CSS, and JavaScript.

^{The new Language Server allows Inline code definition}

Connect your board to Arduino Cloud, right from Settings

You can now register your Arduino^® UNO 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.

^{Provision UNO Q for Arduino Cloud directly from the Arduino App Lab’s device settings page.}

A clearer path through Examples

The Examples catalog has been restructured into three tiers so you always know where to start and where to go next:

1. Inspirations: more than examples, full blown Applications that showcase what the board can really do. 
2. Examples: the fundamentals, taught through focused topics like LEDs, matrices, and the Bridge API.
3. Bricks Examples:  small, interactive apps that each focus on a single Brick’s functionalities, so you can see one building block in action at a time.

New inspiration page to show the full potential of Arduino UNO Q

Download Arduino App Lab 0.10

Update Arduino App Lab to 0.10.0. 

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

Resource

• Purchase the Arduino UNO Q.

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

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