The value of a mirror is in its clarity. If the reflection is cast by [danicakostic17]’s Uncooperative Mirror though, you’ll find anything but. It’s described as a useless machine, because it appears as a tiled mirror. As you approach it though, the tiles shake around and make it very difficult to follow what’s in front of you. It’s an art piece and a prank all in one, and we like it.

Behind the mirror is a 3D printed frame and a set of small servos with what look like some belts to hitch them up. There’s an ultrasonic sensor and an Arduino Uno, that sets those servos going as soon as the ultrasonic sensor sees anything. We can see this thing would be fun at a party.

Everything you’ll need is on the Instructables page linked above should you be foolhardy enough to want your own, and there’s even a YouTube video which we’ve placed below.

Tanaka Masayuki’s PCMFlow722 library enables (half-duplex) two-way real-time HD voice over ESP-NOW on ESP32 boards with a speaker and a microphone, effectively transforming them into walkie-talkies. The library implements a G.722 wideband codec add-on for PCMFlow lightweight audio decode and PCM flow library for Arduino, which already supports uncompressed PCM, MP3, and FLAC audio codecs. PCM and FLAC take too much bandwidth over ESP-NOW, and MP3 is not suitable for real-time audio, so the legacy G.722 audio codec was selected instead. The keyword here is “HD voice,” since two-way audio over ESP-NOW was previously implemented in projects such as Atomic14’s esp32-walkie-talkie (5 years ago) and, more recently, the well-documented Adafruit ESP-NOW Walkie-Talkie project, but these typically rely on lower-quality G.711 audio or compressed audio. The PCMFlowG722 library and G.722 codec enable HD voice with “7 kHz audio at 16 kHz sampling using the same 64 kbps wire budget as G.711 [...]

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Factory lighting can be brutal. A label looks perfect from one angle and unreadable from another. A reflective pouch catches glare. A conveyor casts shadows. A package edge disappears under mixed LED lighting.

Traditional industrial vision systems solve these very real problems, and that’s why they became expensive. However, many inspection tasks don’t require a closed, high-cost smart camera. They just need a reliable prototype path: collect images, train a model, deploy locally, trigger an action, and improve. 

On UNO Q, the Linux side of the board can run the camera pipeline, OpenCV preprocessing, an Edge Impulse object-detection or classification model, and a local web dashboard. Meanwhile, the MCU side can handle encoder pulses, trigger timing, stack-light outputs, and reject-actuator logic. You can already browse Arduino® Project Hub for a variety of practical vision examples that combine UNO Q with Edge Impulse models. We highly recommend the one for a robot arm that recognizes people and offers gadgets through intuitive interactions, and the one for OCR (optical character recognition) with a two-stage text detection and recognition pipeline running locally with Arduino® App Lab, plus image classification examples using a USB webcam and Edge Impulse Linux runner. 

Real-world industrial applications are within reach

Imagine the following setup: a small conveyor rig with an overhead camera pointed at the product as it passes through an end-of-line station. A quantized model running locally detects pass/fail – checking for the right label, a properly seated connector, a sealed cap, or a surface defect – with inference times under 50 ms. The microprocessor running Debian hosts the dashboard made with Python and logs every result; the MCU triggers the operator’s alert system without waiting for a round trip to the cloud. No frames leave the board, no proprietary software license is required, and the same fixture can be reconfigured for a different product without rearchitecting the system from scratch. Sound like a dream? Nope, it’s real: just check up the setup IDT Solution validated in their open-architecture AOI proof of concept for automotive end-of-line inspection.

Want to learn even more? You can also use the UNO Q to run a defect classification model, such as a missing label, wrong color, missing cap, or damaged package. Train the first model in Edge Impulse. Deploy through Arduino App Lab. Run the application as a Debian service or Arduino App Lab app. Use the MCU for deterministic reject timing.

UNO Q turns vision into action

UNO Q has the potential to become the leading SBC for its price and power category, because of the real value it offers. 

1. Industrial vision without industrial pricing. Build credible inspection prototypes without committing to proprietary smart-camera systems.

2. Better inspection under real lighting. Use multiple camera views, local preprocessing, and optimized vision models to improve robustness under glare, shadow, and reflective surfaces.

3. AI plus deterministic action. Run inference on Linux; trigger conveyors, lights, and reject mechanisms through the MCU.

The real promise of UNO Q is not just that it can run a vision model. It is that it can turn vision into action.

A traditional camera can capture an image. A cloud model can classify it later. But an industrial inspection system needs more than recognition. It needs timing, reliability, local decision-making, and a way to respond immediately when something is wrong.

Edge AI for machine vision: from concept to working prototype

By combining Debian Linux, Edge Impulse, local AI inference, and deterministic MCU control, developers can build inspection systems that see the product, understand the defect, log the result, and trigger a physical response – all at the edge.

This means a faster path from concept to working prototype. For developers, it means open tools, flexible deployment, and real-world control. For manufacturers, it means machine vision can move beyond expensive, closed systems and become something more accessible, adaptable, and scalable.

That is how industrial vision becomes practical, repeatable, and affordable – and it is exactly the kind of edge AI workflow UNO Q was built to unlock.

Ready to build your first AI camera inspection system? Explore UNO Q and start prototyping real-world inspection systems today.

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

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Carlo Prisco and Fabio Marchese from PriscoZen had a clear goal from the start: not a technical demo, but a real, working platform that could bring machine control, software logic, and visual quality inspection together in a single compact system. Something they could demonstrate live, evolve over time, and show that industrial automation doesn’t have to mean a traditional PLC in every scenario. The result is ZenCell – and its story is a good example of how innovation, more often than not, emerges through iterations and a progression of improvements, rather than a single eureka moment. 

Where it started: Arduino^®Mega board + Raspberry Pi

The first version of ZenCell was built around a practical architecture: a Mega 2560 Rev3 handling input and output signals, a Raspberry Pi as the central brain coordinating machine logic and the operating cycle, and a Keyence camera for visual inspection. Logic was distributed across two boards, but it worked – well enough that cycle times were in the range of 1.8 to 2.2 seconds.

The team tested the system first at home, validating the overall logic and machine behavior in early-stage conditions. Then they took it further: ZenCell was integrated alongside a three-axis industrial robot next to a molding press, where it held up just as well. The architecture proved its validity not just as a concept, but in conditions close to real production – and it was open, integrated, and flexible.

The shift: from two boards to one

Once the Arduino^® UNO Q board was released, PriscoZen saw an opportunity to greatly improve their solution. Thanks to its dual-brain architecture, UNO Q combines an MPU running Linux with an MCU handling real-time control – exactly the split that Raspberry Pi and Mega were covering separately, now unified on a single compact platform. This allowed Prisco and Marchese to rethink how the system was organized.

In ZenCell V1 – which will début at Maker Faire Caserta 2026 (May 30-31, 2026) – UNO Q is the true engine of the system: hosting the dedicated ZenCell software, managing the cycle logic, coordinating all connected devices, and handling identification peripherals like QR code and barcode readers directly. The result is a cleaner, more centralized architecture, with cycle times brought down to a range of 0.56 to 0.68 seconds.

ZenCore: the software layer that ties it all together

Alongside the hardware evolution, Prisco and Marchese developed ZenCore – the software platform running on ZenCell V1. Accessible through a local web interface with no complex client software required, ZenCore centralizes operational supervision, workflow and recipe management, I/O signal handling, diagnostics, and vision system integration in a single environment.

The long-term vision is a node-based, visual approach to automation logic – connecting devices, commands, states, and sequences in a way that is reusable and adaptable across future applications.

The next step for ZenCell

The roadmap for ZenCell V2 takes the platform further still, with plans to replace the Keyence camera with an open industrial camera and build a proprietary pipeline for image acquisition, dataset development, and defect-recognition model training – all running locally on the next-generation platform.

From a distributed two-board prototype to a centralized system built around a single board’s dual architecture: ZenCell is a clear example of what happens when engineers with the right skills and a concrete vision find the right tools to bring it to life.

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

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Makers and educators, we want to hear from you! Expert or novice, your perspective as an Arduino user matters – and right now, sharing it could win you something pretty special.

We’ve put together a short user survey to better understand how the community uses what we have to offer, what’s working well, and where we can do better. It takes about 10 minutes to complete, it’s open to everyone, and your answers directly inform how we develop our products, tools, and resources going forward.

The Arduino Mystery Box Giveaway is here!

To thank you for your time, we’re running a mystery box giveaway alongside the survey – from May 18 to September 1, 2026. 10 respondents will be randomly selected to receive one of 10 mystery boxes containing Arduino hardware and swag.

It’s quick, it’s open to all levels, and the more voices we hear from, the better we can serve the community. Share the link with your maker friends – the more the merrier!

Take the survey now!

Giveaway Full Official Rules are available here. No purchase necessary. Void where prohibited.

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

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M5Stack Core StopWatch is an ESP32-S3-powered WiFi and Bluetooth development board with a 1.75-inch round AMOLED touch display, 16MB flash, and 8MB PSRAM designed for portable IoT devices, electronic badges, and wearables. The device also features a 1W speaker and a MEMS microphone for voice interaction, two programmable buttons, a vibration motor, a 6-axis IMU sensor, an RTC, and expansion capabilities through a Grove connector and GPIO headers. It’s powered by a 450 mAh battery managed by an “M5PM1 multi-level power management” solution and charged over a USB-C port. M5Stack Core StopWatch specifications: SoC – Espressif ESP32-S3R8 CPU – Dual-core Tensilica LX7 microcontroller up to 240 MHz with vector instructions for AI acceleration Memory – 8MB PSRAM Wireless – WiFi 4 and Bluetooth 5.0 LE + Mesh connectivity Storage – 16MB flash Display – 1.75-inch AMOLED round touch display with 466 x 466 resolution Audio 1W speaker MEMS microphone ES8311 [...]

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Pen plotters used to be very common in the engineering world, as they were economical for “printing” large technical drawings. They’re still around, but digital-only workflows and alternative printing methods have reduced their presence. But Elliot Madsen is an engineering student fascinated by his discipline’s history, so he constructed his own pen plotter using an Arduino^® UNO Q board and parts from an old 3D printer.

A pen plotter is just a machine that moves a pen around, relative to the paper, in the X and Y axes (plus lifting in Z). That’s why they were ideal for technical drawings that tend to be large and composed entirely of lines.

That kind of motion system also closely matches that of an FFF/FDM 3D printer, which is why Madsen was able to build his pen plotter using parts — like stepper motors, aluminum extrusion, and pulleys — from a 3D printer. He chose a CoreXY kinematic setup, with a solenoid-actuated lifter for the pen. (As a bonus, the solenoid provides a nice “clacking” noise when in use.) Madsen then added a clever vacuum table to hold down paper via four 12V fans, eliminating the need for mechanical clamps.

To control it all, Madsen selected the UNO Q. By leveraging the board’s dual-brain architecture, he was able to run the control software on the Linux side and then interface with stepper motor drivers directly through its STM32U585.

After setup, this pen plotter runs based on simple commands from the terminal, which means it can be controlled over SSH. Just tell it to plot with any arguments you like and add the path to the file.

It works great and is especially well-suited to the kinds of technical drawings that were the bread-and-butter of pen plotters for decades.

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The ESP32-S3 PowerFeather V2 board is an ESP32-S3 WiFi and BLE IoT board with an Adafruit Feather form factor that supports LiFePO4/LFP batteries, as well as Li-Ion or LiPo batteries, and up to 18V DC input for solar panel connection. As one could have guessed, it’s an update to the ESP32-S3 PowerFeather board introduced in 2024 with support for solar panel input, Li-Ion, and LiPo batteries. The V2 design is virtually identical, except it features an Analog Devices MAX17260 fuel gauge and a TPS631013 buck-boost regulator that keeps 3.3 V stable to add support for LiFePO4 batteries. Lithium Iron Phosphate batteries are said to be safer and longer-lasting than Li-ion or LiPo batteries, albeit at the cost of lower energy density. ESP32-S3 PowerFeather V2 specifications: ESP32-S3-WROOM-1-N8R2 SoC – ESP32-S3 CPU – Dual-core Tensilica LX7 up to 240 MHz Memory – 512KB SRAM, 16 KB RTC SRAM Wireless – 2.4 GHz [...]

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A few months ago, UncleStem built a completely functional 7:1 scale Arduino UNO Rev3 development board. That was a big hit, for obvious reasons. But what can you do with an Arduino of that size? The answer is: build a matching 7:1 scale turtle robot for it to control.

This is a great pairing, because turtle robots are often the first big projects that new Arduino users tackle — usually soon after blinking an LED. Just like with the oversized Arduino, UncleStem wanted to honor that tradition with the power of enlargement. 

He started by building a classic example of a normal-sized turtle robot from a kit. That is, of course, controlled by an UNO Rev3. The Arduino turns the two motors through an L298N dual H-bridge driver and avoids obstacles using an ultrasonic sensor. The ultrasonic sensor is on a servo-actuated panning mount, so it can “look around” for the optimal path when it runs into anything. The frame is just a piece of laser-cut transparent acrylic.

With that as a reference, UncleStem began building the scaled-up version. Most of the components are 3D-printed shells that match the mundane originals, but that contain the closest functional matches. For example, the drive motors are 12-24V geared units intended for kids’ ride-on vehicles. The “L298N” hides a pair of high-power 300W drivers suitable for those motors. A laser-cut plywood panel replicates the L298N’s PCB.

UncleStem progressed through all of the components in the same manner. He even made giant jumper cables to connect the components together.

The best part is that the mega turtle robot functions just like the original. It looks around with its ultrasonic sensor to find ways around obstacles, which is really fun to watch. 

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Anthropic has opened its Claude Hardware Interface (Bluetooth API) to developers, enabling an ESP32-S3-based desk companion to connect directly to the Claude desktop app over Bluetooth Low Energy (BLE). To demonstrate this new feature, the company released an open-source reference project called Claude Desktop Buddy. It currently runs on the M5StickC Plus (an ESP32-based board from M5Stack) and works as a small interactive hardware companion for Claude. Also, during the recent “Build with Claude” event, the company recommended the ESP32-S3-based M5Stack Cardputer as one of the best hardware options for developers who want to build physical devices that interact with AI agents. Designed as a physical companion device for Claude Cowork and Claude Code on macOS and Windows, it stays on your desk and provides real-time updates on the AI agent’s activity. It also lets you respond to permission requests directly using its buttons, so you can approve or deny actions [...]

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