LILYGO’s T-Watch Ultra is an ESP32-S3-based IP65-rated smartwatch development platform that appears to be an upgrade over the previous T-Watch-S3 Plus (1.3-inch display and a 940mAh battery), with a larger 2.01-inch AMOLED touch display, a higher-capacity 1,100mAh battery, and an IP65 waterproof and dustproof rating. The device integrates a u-blox MIA-M10Q GNSS module for positioning, a SX1262 LoRa transceiver for long-range communication, and a Bosch BHI260AP smart sensor for motion-based AI applications. Additionally, it features an RTC chip, NFC, a built-in microphone, a haptic driver, a microSD card slot, and a USB Type-C port for programming and charging. The watch targets applications such as Meshtastic nodes, GPS tracking, wearable IoT interfaces, edge AI sensing, and custom smartwatch firmware development. LILYGO T-Watch Ultra specifications: SoC – Espressif ESP32-S3R8 CPU – Dual-core Tensilica LX7 microcontroller up to 240 MHz with vector instructions for AI acceleration Memory – 512KB SRAM, 8MB PSRAM Wireless – [...]

The post LILYGO T-Watch Ultra – An IP65-rated ESP32-S3 smartwatch with 2.01-inch AMOLED, LoRa, and GNSS appeared first on CNX Software - Embedded Systems News.

A single microcontroller can interface with many other components and handle several different tasks — that’s kind of the whole point. But microcontrollers do have limits and sometimes it makes sense to divvy up tasks to get a logical system architecture. That led Michael Rigsby to put a whopping 11 different Arduino boards into his trash-handling robot.

This robot, called Zeno, responds to voice commands and can “see” its environment, all to accomplish the goal of collecting trash and depositing that trash into a specified receptacle. The user simply wakes Zeno with a voice command, then they can tell Zeno to follow them or grab trash from their hand.

Zeno’s two DFRobot HuskyLens AI cameras enable it to recognize people and marked trash bins, while five VS53L0X ToF distance sensors help it avoid obstacles. It has two driven wheels actuated by geared DC motors, plus a pair of serial-controlled servo motors for the gripper. That gripper mounts on an arm that rotates on another servo and extends with a 10” linear actuator.

That is a lot of hardware to control, but it could still be done with just a couple of Arduino UNO boards or maybe even a single Arduino Mega 2560 — if you were trying to optimize for efficiency/cost and made use of all of the available pins. Rigsby, however, chose to structure things differently. He dedicated an UNO Rev3 to each subsystem, with a Mega 2560 acting as a central controller. Each HuskyLens, for example, has its own UNO Rev3 that doesn’t do anything else.

When space and cost aren’t an issue, this kind of architecture can be sensible. It let Rigsby keep the subsystems self-contained, so each Arduino’s sketch and wiring is simple and manageable. But when all of the Arduino boards work together, they can accomplish the complex behavior we see from Zeno.

The post This trash-handling robot contains a whopping 11 Arduino boards appeared first on Arduino Blog.

BeagleBoard.org Foundation’s BeagleConnect Zepto “$1 computer” is an upcoming open-source hardware board powered by Texas Instruments MSPM0L117 Cortex-M0+ MCU, part of the MSPM0 family introduced in 2023. It’s a tiny board with mikroBus-compatible headers, a TAG-CONNECT JTAG connector, two Qwiic connectors for expansion (or one Qwiic connector + USB-C depending on the variant), Boot and Reset buttons, and an RGB LED. BeagleConnect Zepto specifications: MCU – Texas Instruments MSPM0L117 CPU – 32MHz Arm Cortex-M0+ core Memory – 16KB SRAM Storage – 128KB dual-bank flash Package – QFN32 (5×5 mm) USB – Optional USB-C port for power (multiplexed with one of the Qwicc JST connectors) Expansion mikroBUS headers supporting a choice of about 2,000 ClickE add-on boards; one of the sides is compatible with some Raspberry Pi HATs (note limited to 12 pins) Up to 2x Qwicc connectors with full Grove function: I2C, UART, ADC, GPIO Debugging – 8-pin TAG-CONNECT JTAG [...]

The post BeagleConnect Zepto – A “$1 computer” based on TI MSPM0L1117 Cortex-M0+ MCU appeared first on CNX Software - Embedded Systems News.

Ditch the smart phone and communicate with your buddy by building this set of walkie talkies! A Feather ESP32-S3 Reverse TFT runs Arduino code that uses the ESP-NOW wireless protocol to send and receive I2S audio packets, up to 10 seconds each. The w.FL antenna adds extra range to communicate at longer distances indoors or outdoors.

Read more at ESP-NOW Walkie Talkies

Project Video

MicroPython is a well-known and easy-to-use way to program microcontrollers in Python. If you’re using an Arduino Uno Q, though, you’re stuck without it. [Natasha] saves the day by bringing us a a subset reimplementation of machine for the Arduino Uno Q.

In the past, microcontrollers were primarily programmed in C, but since MicroPython’s popularity increased over the years, it has become more and more common for introductory microcontroller programming to be in Python. Python, of course, is generally considered more beginner-friendly than C. [Natasha] presumably wanted to teach this way using an Uno Q, but the usual MicroPython APIs weren’t available. And so, in true hacker fashion, they simply made their own library to implement the most important bits of the familiar API. It currently implements a subset of the machine module: Pin, PWM, ADC, I2C, SPI and UART. While not complete, this certainly has potential to make the Uno Q easier to use for those familir with MicroPython.

In the automotive industry, “drive-by-wire” means that a traditionally mechanical linkage, like a throttle cable, has been replaced by an electronic actuator. That can eliminate design constraints and even save money. SciFientist was able to apply those same drive-by-wire principles to this 3D-printed micro milling machine.

Machine tools, including vertical mills, are usually either CNC, manual, or power-assisted. In that last scenario, there is usually a simple motor that rotates a lead screw, so the user doesn’t have to crank the handle a bunch of times to traverse long distances. The motor can feed more consistently than a person can as well.

But this 3D-printed micro mill is different, because it entirely replaces the traditional manual cranks with motors and can only be controlled electronically — just like a drive-by-wire car.

Each axis has a lead screw turned by a stepper motor, controlled by an Arduino UNO Rev3 with a CNC Shield. The Arduino moves the motors in response to user input through a joystick and buttons. But in this incarnation, there isn’t any provision for true CNC operation — though SciFientist has plans for a second version with that capability.

What also stands out about this micro mill is its 3D-printed frame. That isn’t rigid at all by machine tool standards, but it should be good enough for the PCB milling that SciFientist plans to tackle with the machine.

While this is just the first step on the way to more conventional CNC milling, the drive-by-wire control is interesting. With linear position feedback on each axis — essentially a DRO— and fine motor movement, it would allow for many of the benefits of manual milling, but in a compact and affordable package that ignores the design constraints of manual mills.

The post A 3D-printed “drive-by-wire” micro mill for your desktop appeared first on Arduino Blog.

This is an easy to use light / lux sensor which features an ultra-wide 22-bit dynamic range from 0.045
lux to 188,000 lux. That means you can use the Adafruit MAX44009 Wide-range Lux Sensor in darkness or in bright outdoors sun, without having to tweak the integration time or gain: the sensor will auto-range for you so you get smooth readings no matter the light level.

Most light sensors just give you a number for brighter/darker ambient lighting. The MAX44009 makes your life easier by calculating the lux, which is an SI unit for light. You’ll get more consistent readings between multiple sensors because you aren’t dealing with some unit-less values.

The guide has pages covering the pinout, how to use the breakout with CircuitPython and Arduino. Datasheet and fab files are available on the downloads page.

Read more at Adafruit MAX44009 Lux Light Sensor

Managing IoT devices at scale is hard, but we believe finding the right resources at the right time shouldn’t feel like searching through a haystack! That’s why we built Smart Folders in Arduino Cloud – saved searches that stay alive and update automatically in real-time. This builds on recent improvements announced in Arduino Cloud, including a dark theme and a new Thing page.

What are Smart Folders?

Smart Folders bring dynamic, rule-based organization to Arduino Cloud, an all-in-one platform to bring your IoT projects to life quickly. If you’ve used Smart Folders in macOS or Google Drive, the concept will feel familiar – but we’ve tailored it specifically for IoT fleet management.

Instead of manually organizing resources into static folders that get outdated immediately, you create folders based on criteria that matter to your workflow: device status, location, connection type, or custom keywords. Arduino Cloud automatically populates these folders and keeps them updated as your fleet evolves.

Where you can create Smart Folders

Smart Folders are available across all major IoT listing pages:

• Things
• Devices
• Dashboards
• Triggers

This universal availability means consistent organization across your entire Arduino Cloud workspace.

How to create a Smart Folder

Creating a Smart Folder is straightforward: here’s a quick step-by-step guide.

1. Define your criteria

Use search and filter controls to narrow down your resources. You can combine multiple filter types:

• Keywords: Multiple search strings as individual criteria (press Enter after each)
• Status filters: Device status
• Technical filters: Device type, connection type, timezone
• Metadata filters: Creation date, tags, and attributes

^{Select multiple active filters such as keywords, status, device or connection type}

2. Save as a Smart Folder

Save your filters with a descriptive name. Arduino Cloud immediately creates a folder that automatically includes all matching resources.

^{Arduino Cloud instantly generates a folder containing all matching resources}

3. Manage and refine

Here are the key actions you can take to keep your Smart Folders always functional.

• Duplicate: Create variations quickly
• Edit filters: Adjust criteria when requirements change
• Delete: Remove unused folders

^{The Smart Folder management options menu}

Why Smart Folders matter in Arduino Cloud

^{Your Smart Folders appear at the top}

Traditional folders become stale immediately and require constant manual maintenance. Smart Folders stay current automatically. Add a new device matching existing criteria? It appears in the right folders instantly. Change a device’s status? It moves between folders automatically. Update tags? Your organization adapts in real time.

Watch Marta Barbero, our Lead Product Manager, explain what you can do with Smart Folders in Arduino Cloud.

Getting started 

Smart Folders are available now. Here’s how to start:

1. Log in to Arduino Cloud.
2. Navigate into your IoT builder side bar: Things, Devices, Dashboards, or Triggers.
3. Select the Smart Folder folder icon.

4. Apply filters using search and filter controls.
5. Save as Smart Folder with a descriptive name.
6. Pin important folders to your sidebar for quick access.

Start simple with one or two folders for your most common searches, such as “My Active Projects” or “Offline Devices,” and see how instant filtered views change your workflow!

Try Smart Folders today in Arduino Cloud, as well as our AI Assistant. Share your feedback in the Arduino community forums or reach out to our support team.

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

The post Organize your IoT fleet in Arduino® Cloud with Smart Folders appeared first on Arduino Blog.

NocKinematics by Muhammad Ikhwan Fathulloh is a modern, modular, and lightweight C++ Inverse Kinematics library for Arduino and ESP32 utilizing the powerful FABRIK (Forward And Backward Reaching Inverse Kinematics) algorithm.

Original Paper Reference: FABRIK: A fast, iterative solver for the Inverse Kinematics problem (Andreas Aristidou)

Inspired by industry-standard game engine tools, NocKinematics brings professional-grade kinematics calculations to microcontrollers in a highly memory-efficient way. It uses dynamic memory allocation specifically crafted to optimize heap usage—a critical requirement on limited systems like AVR, ESP8266, and older boards.

See more on GitHub.

ArtusIndus shares an interesting way to trap tiny squeakers. They do a share a disclaimer that the trap is for educational purposes and to follow local regulations regarding animal handling. (We strongly agree!)

In this project, I will show you how to build a simple automated mouse trap using an Arduino and a servo motor.

The system uses a phototransistor and an LED as a light barrier. When a mouse interrupts the light beam, the trap is triggered instantly.

Additionally, the Arduino records how long the system was running before activation using EEPROM.

This project is simple, low-cost, and great for learning basic electronics and sensor systems.

You can also check out their GitHub repository.