Flying and driving simulators can go as far as money will take you, and at some point it might end up being cheaper to buy a plane or race car than to keep adding capabilities to some of the sim rigs we’ve seen. But it can also be a fairly affordable hobby as well, with entry-level components being within reach for many. The price can drop precipitously from there too, provided some parts can be sourced and a 3D printer is made available, and [Jason] is demonstrating one of the lowest-cost H-shifters we’ve seen which only uses two parts at its core.
The two components only cover the electronics for the build, but this gets almost everything needed for the shifter squared away. A joystick like those found inside many game controllers is paired with an Arduino Pro Micro, with a very straightforward wiring configuration between them. These components are paired with a prototype 3D printed case and shift knob which provides the H pattern of choice. From there it’s as simple as uploading some readily-available firmware, which [Jason] also demonstrates, and which has many options for various configurations of shifters.
GIGA R1 WiFi is built as a powerful development board, thanks to its dual-core, 32-bit STM32H747XI microcontroller, designed to operate at clock speeds of up to 480 MHz and equipped with 1 MB of RAM. Arduino Forum users KurtE and Merlin513 developed a ZephyrOS-based weather station app for it, showcasing its capabilities. What’s more, they recently published a demonstration that it also works on the less powerful UNO R4 WiFi.
The original project with GIGA
ZephyrOS is an RTOS (Real-Time Operating System), which we chose as a replacement following the end of active Mbed development in 2024. With the Arduino Core on Zephyr 0.90.0 now officially stable, it provides a foundation for applications designed to support process management, task scheduling, and real-time operation.
KurtE and Merlin513 built their weather app for ZephyrOS on the GIGA R1 WiFi with a paired touchscreen (like the GIGA Display Shield). That app is engineered to show the local weather through a nice GUI, with touch-selectable detailed information on specific days.
The result was relatively resource-intensive for a microcontroller, but the GIGA R1 WiFi’s processing and memory resources were designed to support the application.
The new, limit-stretching version
The UNO R4 WiFi is a more resource-constrained board, featuring a single-core Renesas RA4M1 microcontroller operating at 48 MHz and 32 kB of RAM. It also includes 256 kB of flash storage, compared with the GIGA R1 WiFi’s 2 MB.
This translates into two challenges: shaving the package size down to fit in the available flash storage and reducing resource usage for the available RAM. Fortunately, solving the first challenge also eliminated the second. KurtE did that by switch display libraries and simplifying the GUI elements. Most of that came down to reducing the color palette and minimizing image sizes where possible.
With these optimizations in place, KurtE demonstrated that the weather application could run on the UNO R4 WiFi and described the result as operating “reasonably well.”
The great challenge of smaller platforms
Optimizing software for constrained hardware isn’t just an exercise in trimming code – it’s a mindset that encourages thoughtful engineering. While high-spec boards give you room to dream big, figuring out how to squeeze a full RTOS, graphical interface, and real-time data onto a board with 32 kB of RAM demonstrates how practical constraints can inspire creative solutions.
Porting projects like this weather station down to smaller platforms helps make complex applications more accessible, showing that with the right optimizations, you don’t always need massive compute power to build impressive, responsive systems.
Have a similar project to share? Add it to your next post on the Arduino Forum, or document it on Arduino Project Hub so the entire community can benefit from your experience and build on it!
Arduino, GIGA, and UNO are trademarks or registered trademarks of Arduino S.r.l.
The promise was simple: bring real computing power and edge AI to makers, students, and professionals, without losing the openness and joy that make Arduino what it is.
Twelve months later, you have responded to our biggest announcement ever in ways we could not have scripted.
Thank you.
The year in numbers*
You didn’t just try UNO Q. You built an ecosystem around it.
270,000+ UNO Q shipped worldwide
400+ events across the ecosystem
4,000+ contest participants, who delivered 250+ official builds
850+ topics about UNO Q on the Arduino Forum
One year in, the Arduino community continues to grow and thrive. The number of projects shared on Project Hub has nearly doubled year on year, while Arduino library downloads grew roughly 50% year on year, reaching 244+ million downloads in the last 12 months, compared with 165+ million in the previous year. The Arduino IDE also reached 35+ million downloads
Behind those numbers is something more interesting: a year of people discovering what happens when Arduino meets serious computing and AI.
*As of September 29, 2026
Where it started
UNO Q brought together two worlds that had rarely lived on the same Arduino board: a Qualcomm Dragonwing QRB2210 microprocessor, with AI acceleration, quad-core performance and graphics, coupled with a real-time STM32U585 microcontroller.
That combination is what makes UNO Q different. One side can run Linux, AI models, and demanding applications. The other can handle deterministic, real-time control. Together, they let developers bring the two worlds together in a way that feels like Arduino.
UNO Q launched alongside Arduino® App Lab, introducing a new way to build: an Arduino sketch on the MCU working together with a Python program on the MPU, packaged as one application. Suddenly, projects that once needed separate boards, computers or more complicated setups could start from a single Arduino.
Debian Linux for UNO Q brought a curated Linux environment to the board, making it easier to get started with the MPU side of the board. Arduino App Lab evolved throughout the year, adding new capabilities and workflows. We also brought Qualcomm Linux to UNO Q, giving developers another path into the Qualcomm ecosystem while keeping Arduino at the center of the experience.
In August, Agentic Mode brought AI-assisted development to an Arduino desktop IDE for the first time.
We selected 12 standout projects that capture just some of the creativity and ambition of the UNO Q community.
And the creativity is still unfolding. Our Invent the Future with Arduino UNO Q and App Lab contest with Hackster is wrapping up, with the winners set to be announced on October 9. See the contest on Hackster
When Arduino joined Qualcomm, some wondered what it would mean for Arduino’s open approach. One year later, the answer is simple: openness remains at our core.
Arduino remains open source, with open hardware schematics and software under MPL, AGPL, and GPL. And this year, we continued to open up the tools behind UNO Q, including Arduino App Lab, available on GitHub for developers to explore, contribute to, and build on.
And the work this year has not been limited to UNO Q. Throughout this time period, we also unveiled several new products under the same ethos, ranging from the Arduino® Starter Kit R4 and Arduino® Nesso N1 to no less than eight (eight!) new Arduino® Modulino nodes. Motors, Joystick, Latch Relay, Light, LED Matrix, Vibro, Hub, and Extender may be tiny in size, but easily add so many different functionalities to your projects: you can check out the Arduino® UNO Q DIY synth bundle and Arduino® UNO Q Arcade bundle to get an idea of how well they work to bring ideas to life. Plus, Arduino Cloud got even better!
Meanwhile, Arduino Core on Zephyr reached version 1.0, moving beyond its beta phase and providing a solid RTOS foundation for Arduino boards with an MCU, including UNO Q as well as the new Arduino® VENTUNO Q board.
Stronger together
Qualcomm is deepening its own open source commitment as well. Its business success depends on open standards, upstream collaboration, and transparent participation in the ecosystem.
A couple of months ago, we introduced VENTUNO Q, bringing substantially more computing and AI capability to Arduino developers. It opens the door to more demanding applications while keeping the development experience familiar.
A year ago, UNO Q was a new board. Today, it’s a growing platform, a new way of developing with Arduino, and, most importantly, a community of people finding out what it can do. Arduino and Qualcomm are stronger together!
A prototype is only the beginning
You can start with a prototype on UNO Q or VENTUNO Q and, as your requirements evolve, move to more advanced platforms and commercial-ready solutions while keeping the tools, models, and applications you already built.
Finally, this year we also launchedWorks with Arduino, a program enabling partners to certify their System on Modules and developers to take apps built with UNO Q or VENTUNO Q to compatible hardware without changing a line of code. With App Lab, applications are packaged as a single App Release, making it possible to move from prototype to production in minutes, not weeks.
It is a direct bridge from prototype to commercial-ready hardware, and a path to the 33M+ Arduino developer community for partners.
And the community came together beyond the projects too. Alongside Qualcomm, we brought UNO Q to CES, MWC, and the Mission44 Workshop, meeting developers, makers, students, and industry leaders, while showing what this new generation of Arduino-powered computing can do.
The Arduino team at the Qualcomm booth at CES (January 2026)!
Mission 44 students at Silverstone getting hands-on with UNO Q and AI-powered racing telemetry (June 2026).
It is just the beginning
Year one showed that powerful, open, and simple can go together. It showed us what happens when you put new tools in the hands of a curious community, and let them take it somewhere you didn’t expect.
Year two is about going further: more tools, more partners, and more ways to turn an idea into something real.
Happy first birthday, UNO Q!
And to everyone who built, shared, taught, hacked, and dreamed with it this year: thank you. The best is still ahead.
Qualcomm branded products are products of Qualcomm Technologies, Inc. and/or its subsidiaries.
Arduino, Modulino, Nesso, UNO, Starter Kit, and VENTUNO, and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.
Cyberdecks don’t all have to be chunky slabs of aluminum and plastic chasing after an ‘80s cassette futurism aesthetic. Cyberdecks can be whatever their builders want them to be — that’s the whole appeal. Jen Looper is a creative technologist who loves nature, plants, and cozy gardens, so she built this #cottagecore cyberdeck to do literal fieldwork.
As an aesthetic, cottagecore is all about cozy, rustic vibes. As a subculture, it is about comfortable sustainability and “slow living.” B.L.O.O.M. (Bridging Local Observations, Openly Mapped) is a cyberdeck designed to encompass all of that. Looper built it for collecting field notes on regional plants and animals. It harkens back to the descriptive logging and taxonomic classification work of Victorian naturalists.
The physical construction is also the polar opposite of what we typically see from the cyberdeck community. The enclosure is a “train case,” which is a traditional piece of luggage used for carrying cosmetics and grooming supplies. All of the hardware components nestle inside, with plenty of room to spare for some paper notebooks.
The hardware consists of an Arduino® UNOQ (4GB), a 5” HDMI screen, a Bluetooth keyboard, a Bluetooth mouse, and a power bank to power it all. It also has a USB camera and a USB microphone, which can be used for data collection.
To that end, it connects with a companion app (Field Notes) to compile that data in the Grinnell format. It can also run local AI models directly on the UNO Q to help with that work. As an example, Looper employed the Edge Impulse integration in Arduino App Lab to create a custom model that can recognize avian species based on their birdsong.
This is the kind of functionality that Victorian naturalists wouldn’t have even been able to dream of, but Looper was able to achieve it on a modest budget. At the same time, she proved that cyberdecks can come in any form.
The single-board computing market is shifting rapidly, and we are moving into an era of unprecedented possibilities where edge AI, computer vision, real-time control, and rich sensor streams converge locally. Yes, it’s exciting – but even amidst this “explosion” of hardware, an age-old rule holds true: define what you want to build before picking your tool.
No single board can (nor should!) excel at everything. A low-power embedded sensor, an autonomous robot, and a high-throughput video server all demand different architectures. For example, if you are weighing hardware choices for a general-purpose Linux setup versus a hybrid microcontroller, read this in-depth review to evaluate how different design philosophies match your requirements.
In concrete terms, it’s great to get a sense of what each board can do – and even better if you can look at a variety of cool projects at the same time! So here is a selection of what we are seeing developers build across key domains with the Arduino® UNO Q board, leveraging its dual-brain to bridge high-level AI workloads with deterministic physical control.
Dive into perception-driven robotics
Robotics excels on hybrid platforms because physical movement requires tight, real-time microcontroller timing while visual recognition requires heavy AI processing.
A robot arm that sees you: Uses camera vision designed to recognize people and a robotic arm built to deliver items, with a Modulino LED Matrix providing visual feedback.
UNO Q Braccio: Integrates Edge Impulse AI and ROS 2 in a platform engineered to support robotic arm control, including simulation in Gazebo.
Face-following robot: Uses Edge Impulse computer vision designed to track faces and convert tracking data into physical servo movements.
AI agent robot: Uses a local AI agent built to support physical navigation and decision-making tasks in real-world environments.
Explore the world through computer vision and spatial sensing
Instead of just streaming video, local machine learning turns cameras and sensors into environmental perception engines.
Gesture-controlled input system: Uses a standard webcam and Edge Impulse in a workflow built to convert hand gestures into inputs for digital applications.
Real-time LiDAR room mapper: Pairs LiDAR sensors with Edge Impulse ML in a system engineered to help interpret spatial data and interpret room layouts locally.
Create smart environments with interactive AI
Local AI allows devices to run full “sense, interpret, decide, act” loops without relying on cloud latency or external servers.
Talk to your house: An all-in-one smart home hub engineered to support wake-word detection, voice commands, sensor management, and a web interface.
Clawrophyll: A smart houseplant system designed to run a local AI agent directly on the board.
We don’t need every platform to do the same thing
The technological landscape is expanding, giving us more tools to build with: the goal was never to find one ultimate board “to rule them all”, but to match your project’s unique demands to the right architecture. Whether you need a raw Linux computing hub or a dual-brain setup like UNO Q, starting with a clear target can help you build smarter, faster, and more effectively. Today, we have more options, more possibilities – and this may ultimately be the most exciting part in the current era of single-board computing.
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.
UNO Q was designed around a dual-brain architecture that is built to bring high-performance computing and real-time hardware control together on a single board. The Linux environment on the MPU side is constructed to let developers install and run familiar open-source software – the same tools they’d reach for on a server or any popular SBC. Meanwhile, the STM32H5 microcontroller runs a dedicated Arduino Core on Zephyr RTOS, handling sensors, actuators, and time-critical I/O with deterministic precision. Together, they make the UNO Q something more than the sum of its parts.
What follows is a set of concrete examples: real software platforms you can install on UNO Q today, what each one enables, and why having the microcontroller in the same system changes what’s possible.
#1 Agentic AI development
UNO Q is engineered to put an AI coding agent inside the hardware it is writing for. An agent running on the board can receive a goal, process context, call tools, interact with services, and – through the board’s hardware interfaces – turn decisions into real-world actions. The reasoning comes from a frontier model over the API, but the tool calls run on the device. That last part is what makes this different from running an agent on a laptop or a server: the machine executing the decisions is also wired into the physical world.
The practical starting point is installing an AI coding agent, such as OpenCode, directly on the board. With the right context added – for example, knowledge of the Arduino® App Lab CLI and the board’s specific capabilities – it can set up Linux services for you, answer questions about Debian on UNO Q, and build physically connected Arduino apps end to end: create the app, write the sketch, deploy it, read the output, iterate. Any agent can write a sketch; only one running on the board can deploy it and see what happened.
More broadly, this creates a meaningful bridge between generative AI and embedded systems. Only the reasoning leaves the board: the sensors, actuators, and data stay where they are.
Home Assistant aims to turn UNO Q into the center of a local smart home or building automation system, connecting lights, climate controls, energy meters, and other compatible devices through a single dashboard and automation engine. Running entirely on the Linux side, it is built to handle scheduling, historical data, dashboards, and integrations – with no cloud subscription required.
The real advantage emerges when custom hardware enters the picture. A Qwiic temperature and air-quality sensor connected to the board’s Qwiic port could feed live data into Home Assistant, while an Arduino sketch running on the STM32H5 microcontroller is constructed to drive a ventilation fan through a relay based on those readings. Home Assistant is designed to manage the logic and interface on Linux; the microcontroller is engineered to handle the physical layer. The two communicate via the onboard RPC bridge, with no external Arduino board required.
n8n is a visual workflow automation platform that is built to connect services, APIs, databases, and devices – think of it as a self-hosted alternative to Zapier or Make, with full data sovereignty because everything runs locally.
Running n8n on UNO Q is designed to bring those workflows to the edge. A single workflow could receive sensor data from the microcontroller side, process and store it in a local database, update a dashboard, send a notification to an external service, and trigger a physical output – all in sequence, all on the same device.
The combination makes UNO Q a practically designed platform for rapid prototyping, industrial automation, smart building control, and local edge orchestration where cloud latency or dependency would be a problem.
#4 Pi-hole network shield
Pi-hole is a DNS-level filtering service that is structured to block advertising, tracking domains, and unwanted traffic for every device on the local network that uses it as a DNS server. On a standard single-board computer, that’s where the story ends.
UNO Q is different: the Linux side runs Pi-hole and its web dashboard, while the microcontroller can drive a dedicated status display, visualize blocked-request activity in real time, trigger an alert if the DNS service becomes unavailable, or provide a physical button to temporarily pause filtering – without opening a browser interface.
It’s a small example of a broader pattern: UNO Q aims to turn passive software services into systems with physical feedback and control.
Frigate is a network video recorder built around real-time object detection. It can process camera streams locally to identify people, vehicles, animals, or other defined events – and running it on UNO Q keeps that processing at the edge, where it belongs.
UNO Q is a practical host for Frigate for a few reasons. Cameras can be connected directly via USB or accessed remotely over RTSP (Real Time Streaming Protocol) when Frigate detects a relevant event, the microcontroller side can respond immediately – triggering a light, an alarm, a relay, or a Modulino-based indicator – without any additional hardware.
Sensitive footage stays local. Response times improve. And the physical response doesn’t require a round trip to the cloud.
#6 Local NAS with SMB
UNO Q can be configured as a lightweight network-attached storage server using SMB, making shared folders accessible to other computers and devices on the local network – useful for camera recordings, machine-learning datasets, sensor logs, or shared project files.
Paired with a dongle that gives cabled Ethernet connectivity and an external USB Disk, this can be a quick and instant NAS, flexible enough for sharing office documents with your colleagues or family.
A platform, not just a single application
Each of these applications is useful independently. The more interesting territory is what opens up when they run together.
Here’s one example of how that could look in practice: Frigate detects a person entering a monitored area and publishes the event via MQTT. Home Assistant picks up that event and switches on a light. n8n extends the response by calling an external API, logging the event to a database, and sending a notification. Meanwhile, the raw footage remains accessible over the local network through the Samba share. The microcontroller drives an indicator throughout. All of this runs on one board.
UNO Q is designed to provide a common foundation where Linux applications, local AI workloads, network services, and real-time embedded hardware control can be part of the same coherent system – rather than spread across multiple devices with their own power supplies, enclosures, and points of failure.
UNO Q is available on the Arduino Store or can be ordered from DigiKey, Farnell, Mouser, Newark, RS Components, Robu.in, and many other authorized distributors and resellers.
Arduino, UNO, and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.
The Arduino core for Zephyr RTOS has reached version 1.0.0. This release marks the completion of the transition away from the old MbedOS-based core. The new core provides a modern and flexible foundation for current and future Arduino boards. It also lets you use Zephyr RTOS features and APIs directly from the Arduino IDE, Arduino CLI and Arduino App Lab tools.
The project is built on a two-component architecture. The core generates a standalone elf file that is loaded dynamically by a precompiled Zephyr firmware called the loader. The loader handles the interaction between sketches and the underlying Zephyr system. After the initial bootloader installation, the loader automates the sketch loading process. Version 0.90.0 made the Zephyr loader installation procedure fully automatic.
The loader and its operating modes
The loader’s behaviour is set through the IDE’s Mode menu. In Standard mode, the loader loads the sketch automatically. In Debug mode, on the other hand, it requires you to type ‘sketch’ in the Zephyr shell. This flexibility makes debugging much easier. The loader design is generic: board-specific changes are made in the DTS overlay or fixup files.
The core is validated with version v0.16.8 of the Zephyr SDK. Development uses the standard tools of the Zephyr ecosystem, such as west and sync-zephyr-artifacts. The core also relies on components such as llext, the dynamic extension mechanism, and zephyr-sketch-tool for compiling sketches. Everything rests on ArduinoCore-API to maintain compatibility with the Arduino ecosystem.
What you need to get started
To try the Zephyr core you need a supported board and an up-to-date development environment. Here are the main steps:
Install Arduino IDE 2.x.x or Arduino CLI
Add board support through the Board Manager
Install the Zephyr core from version 1.0.0
Configure the loader mode from the IDE’s Mode menu
Version 1.0.0 of the Zephyr core is a milestone release. The move from MbedOS to Zephyr is complete. The new core offers a more solid foundation for the future of Arduino boards. Among the new features, support for the project’s code repository includes all the loader and core sources. In addition, the new Arduino UNO Q board is among the first devices to benefit from this architecture.
Arduino’s Zephyr core, designed to replace the MbedOS version, has reached release 1.0.0 and now supports the VENTUNO Q.
The Zephyr core represents a paradigm shift for Arduino. It is not just an update: it is an infrastructure built to last. The separation between loader and sketch makes the system safer and easier to update. What’s more, using Zephyr RTOS opens the door to advanced features such as support for numerous protocols and optimised power management. For anyone developing IoT applications, this is a solid foundation to build on.
The transition from MbedOS to Zephyr was not only technical, but also strategic. Zephyr is an open source RTOS with an active community and a regular release cycle. This guarantees long-term support for Arduino boards. In addition, version 1.0.0 of the core is validated with the Zephyr SDK v0.16.8, ensuring stability and compatibility. The future of Arduino boards goes through here.
When you look into the night sky, you can’t help but wish to bring the outer worlds to your doorstep. One of the best ways to do that is to photograph the Moon’s surface to display in a frame. However, [Sebastian Lague] found a simple image too lackluster compared to something with a bit more style, such as a plotted image of the lunar terrain.
Why plotting? Well, the Moon is not defined in the same way with contrasting colors as Earth is. The Moon is peppered with craters and differing elevations that separate regions, so why not build an entire DIY plotter from scratch? [Sebastian Lague] did exactly this with a custom algorithm to take the elevation maps and create vector drawings which are plotted on his custom plotter.
Plottings of Earth, the Moon, and Mars
The plotter’s design is simple on its face, using an Arduino and stepper motors, as many other plotters have. [Sebastian Lague] also shares insights into their process and iterative designs, moving from a level arm to raise a marker to a linear method. While the Moon plots are impressive on their own, why stop there? [Sebastian Lague] decided to plot Mars and then return home with Earth plots.
While the plotter itself is rather simple, all the work behind the contour maps and turning the ideas into reality is nothing to take for granted. However, if you want a more complicated side of plotting, make sure to check out this multi-colored delta plotter!
A group of children aged 7 to 13 is building a tactile zoo with 3D-printed robotic animals. The creatures, more than 20 in total, are equipped with LEDs, motors, buttons, and gears. At Maker Faire Bay Area, the group will also display their printing errors and failed prototypes. The goal is to show the iteration process that leads from an idea to a working object.
The project turns 3D printing into an educational and interactive activity. Mistakes are not hidden but become an integral part of the exhibition. Visitors can thus understand that making errors is a normal step in the work, not a failure. The maker’s website details how the children faced technical difficulties and what solutions they found.
From choosing the animal to the first print
Each young maker chooses an animal, real, mythological, or completely invented. Then they decide what behavior the creature should have. Some design the animals from scratch, others start from existing 3D models and modify them, cutting or redesigning sections to make room for the electronics. Once the body is ready, they add a function such as an LED, a servo, a motor, a button, or a set of gears.
The group includes eight creatures, among them Em. To make Plate the Armadillo roll into a ball, 11 prints were needed. Each failed attempt taught something new: a joint too tight, a motor off-axis, a wall too thin. The children learned to observe the error and correct it in the next print.
An exhibition you can touch and open
The zoo is not a simple showcase. Visitors can press buttons, flip switches, and open the animals to see the wires, gears, and electronics inside. This choice makes the electronics transparent and understandable even to those who have never opened a device. Next to the finished animals, broken prints, melted parts, and earlier versions are displayed, so visitors can ask what went wrong and how it was fixed.
For those who want to recreate the project, the electronic part can be built with simple, modular components. For example, a servo motor with metal gears can move an animal’s legs, while a shield for controlling RC servos allows managing multiple movements with an Arduino board. For light effects, a WS2812 LED matrix offers endless color possibilities. Finally, a compact board like the Arduino Nano Matter can handle logic and connectivity in a small space.
Mistakes on display: the educational value of failure
The choice to display printing errors is the heart of the project. In a world that shows only perfect results, these children reveal the behind-the-scenes. The broken prints tell the real difficulties of 3D printing: material shrinkage, parts detaching from the bed, motors that don’t find space. Visitors to Maker Faire will be able to talk with the young makers and discover how each problem was tackled.
This approach also changes the way the children work. They learn that a failed prototype is not wasted time, but a step forward. Each error provides valuable information for the next version. The iteration process thus becomes a mental habit, useful not only in 3D printing but in every design activity.
We’re living in 2026 and everyone knows that they can ask a large language model (LLM) a question and get an answer. But most people assume that requires an internet connection, because those LLMs live in massive data centers. The truth is that LLMs can run on local hardware, answering questions without any internet at all. Cameron Coward, of the Serial Hobbyism YouTube channel, took advantage of that fact, connecting an Arduino UNO Q to a vintage terminal.
That terminal is a Texas Instruments Silent 700 (Model 745), which is a paper terminal that was made in the 1970s. Back then, it would have been used as an input/output device for something like a minicomputer, connected through either an acoustic coupler (remote) or RS232 (local). Users would type commands on the keyboard and computer output would print on the thermal paper.
For this project, Coward connected the Silent 700 to his custom-built device, Termi3, to give it the ability to answer questions. The user types a question and a moment later, the terminal prints out the answer.
As the name suggests, Termi3 is the third version of the device. The first two required internet access, because they retrieved answers through the Wolfram Alpha API.
Termi3 doesn’t need any internet connection at all, because it generates an answer locally on an UNO Q (2GB). The Arduino runs a local LLM, Llama 3.2-1B, on the Linux side without ever touching a network. It interfaces with the terminal though bit-banging on the STM32 side, which goes through a MAX3232 transceiver (for level conversion) to the terminal’s RS232 port.
It is amazing that an LLM can run on the small and affordable UNO Q at all, especially with 2GB of RAM. So, the LLM isn’t particularly sophisticated and its answers aren’t always accurate — or even coherent. But they are often entertaining, which makes Termi3 a lot of fun to talk to.
