Bit-banged 100 MBit/s Ethernet Transmission On Raspberry Pi Pico

The Raspberry Pi Pico is a very capable board, but it’s still a surprise to see bit-banged 100 MBit/s Fast Ethernet implemented on one. [Steve]’s Pico-100BASE-TX library allows an RP2040 (or RP2350) microcontroller to stream data at roughly 11 Mbyte/s, enough to implement 100 MBit/s Fast Ethernet transmission.

We’ve seen 10BASE-T implemented on a Pico, but it takes a lot more than just shoveling bits faster to get 100BASE-T working. 10BASE-T uses two voltage levels and Manchester encoding, but 100BASE-T uses three voltage levels, which [Steve] cleverly implemented on the Pico with two GPIOs, and far more complex encoding. Check out the repository’s README for details as well as a couple example applications.

[Steve] tells us that to the best of his knowledge, this is the first bit-banged 100 MBit/s Ethernet implementation using a microcontroller. It’s transmit-only — reception being an entirely different beast — but it’s possible some enterprising soul might find a solution. If you do, be sure to let us know all about it!

Channel Surfing Nostalgia Machine

As any generation of people get older, they tend to look back fondly on their formative years when there was less responsibility and more wonder. Even if things have objectively improved, we often have a fondness for the past. Such is the case for cable television, where even though ads were everywhere and nothing was on-demand, we can see that something was lost from this era in the modern streaming ecosystem. [Ricardo] brought back the good parts of this golden era of cable TV with this small channel surfing television.

The project attempts to keep the good parts of this era while discarding things we certainly don’t miss. The ability to channel surf is still here, with a rotary encoder standing in for an antique television channel selector knob, but dealing with any telecommunications company is out, including those of the Internet variety. Instead it is a fully offline machine with the user able to curate their own channels and programming with a Flask application, and [Ricardo]’s includes his own collection of commercials from Argentina.

The hardware itself is fairly straightforward as well, with a Raspberry Pi doing the heavy lifting, paired with a small screen and enclosed in a retro-themed television case. It’s a clever throwback to a time where we might not know what we wanted to see but there was always something on. Builds like this are gaining popularity right now as well, and we’ve even seen them recreate the cable company’s preview channel as well.

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A Trail Camera Built With Raspberry Pi

You can get all kinds of great wildlife footage if you trek out into the woods with a camera, but it can be tough to stay awake all night. However, this is a task you can readily automate, as [Luke] did with his DIY trail camera.

A Raspberry Pi Zero 2W serves as the heart of the build. It’s compact and runs on very little power, but also provides a good amount more processing power than the original Raspberry Pi Zero. It’s kitted out with the Raspberry Pi AI Camera, which uses the Sony IMX500 Intelligent Vision Sensor — providing a great platform for neural networks doing image classification and similar machine learning tasks. A Witty Pi power management module is used both for its real time clock and to schedule start-ups and shutdowns to best manage the power on offer from the batteries. All these components are wrapped up in a 3D printed housing to keep the Pi safe out in the wild.

We’ve seen some neat projects in this vein before.

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Two hands working a TekaSketch

TekaSketch: Where Etch A Sketch Meets Graph Theory

The Etch A Sketch was never supposed to meet a Raspberry Pi, a camera, or a mathematical algorithm, but here we are. [Tekavou]’s Teka-Cam and TekaSketch are a two-part hack that transforms real photos into quite stunning, line-drawn Etch A Sketch art. Where turning the knobs only results in wobbly doodles, this machine plots out every curve and contour better than your fingertips ever could.

Essentially, this is a software hack mixed with hardware: an RPi Zero W 2, a camera module, Inkplate 6, and rotary encoders. Snap a picture, and the image is conveyed to a Mac Mini M4 Pro, where Python takes over. It’s stripped to black and white, and the software creates a skeleton of all black areas. It identifies corner bridges, and unleashes a modified Chinese Postman Algorithm to stitch everything into one continuous SVG path. That file then drives the encoders, producing a drawing that looks like a human with infinite patience and zero caffeine jitters. Originally, the RPi did all the work, but it was getting too slow so the Mac was brought in.

It’s graph theory turned to art, playful and serious at the same time, and it delivers quite unique pieces. [Tekavou] is planning on improving with video support. A bit of love for his efforts might accellerate his endeavours. Let us know in the comments below!

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Fully-Local AI Agent Runs On Raspberry Pi, With A Little Patience

[Simone]’s AI assistant, dubbed Max Headbox, is a wakeword-triggered local AI agent capable of following instructions and doing simple tasks. It’s an experiment in many ways, but also a great demonstration not only of what is possible with the kinds of open tools and hardware available to a modern hobbyist, but also a reminder of just how far some of these software tools have come in only a few short years.

Max Headbox is not just a local large language model (LLM) running on Pi hardware; the model is able to make tool calls in a loop, chaining them together to complete tasks. This means the system can break down a spoken instruction (for example, “find the weather report for today and email it to me”) into a series of steps to complete, utilizing software tools as needed throughout the process until the task is finished.

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The New Raspberry Pi 500+: Better Gaming With Less Soldering Required

When Raspberry Pi released the Pi 500, as essentially an RPi 5 integrated into a chiclet keyboard, there were rumors based on the empty spots on the PCB that a better version would be released soon. This turned out to be the case, with [Jeff Geerling] now taking the new RPi 500+ to bits for some experimentation and keyboard modding.

The 500’s case was not designed to be opened, but if you did, you’d find that there was space allocated for a Power-Over-Ethernet section as well as an M.2 slot, albeit with all of the footprints unpopulated. Some hacking later and enterprising folk found that soldering the appropriate parts on the PCB does in fact enable a working M.2 slot. What the 500+ thus does is basically do that soldering work for you, while sadly not offering a PoE feature yet without some DIY soldering.

Perhaps the most obvious change is the keyboard, which now uses short-travel mechanical switches – with RGB – inside an enclosure that is now fortunately easy to open, as you may want to put in a different NVMe drive at some point. Or, if you’re someone like [Jeff] you want to use this slot to install an M.2 to Oculink adapter for some external GPU action.

After some struggling with eGPU devices an AMD RX 7900 XT was put into action, with the AMD GPU drivers posing no challenge after a kernel recompile. Other than the Oculink cable preventing the case from closing and also losing the M.2 NVMe SSD option, it was a pretty useful mod to get some real gaming and LLM action going.

With the additions of a presoldered M.2 slot and a nicer keyboard, as well as 16 GB RAM, you have to decide whether the $200 asking price is worth it over the $90 RPi 500. In the case of [Jeff] his kids will have to make do with the RPi 500 for the foreseeable future, and the RPi 400 still finds regular use around his studio.

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Moondream title with man's face visible in background.

Using Moondream AI To Make Your Pi “See” Like A Human

[Jaryd] from Core Electronics shows us human-like computer vision with Moondream on the Pi 5.

Using the Moondream visual language model, which runs directly on your Raspberry Pi, and not in the cloud, you can answer questions such as “are the clothes on the line?”, “is there a package on the porch?”, “did I leave the fridge open?”, or “is the dog on the bed?” [Jaryd] compares Moondream to an alternative visual AI system, You Only Look Once (YOLO).

Processing a question with Moondream on your Pi can take anywhere from just a few moments to 90 seconds, depending on the model used and the nature of the question. Moondream comes in two varieties, based on size, one is two billion parameters and the other five hundred million parameters. The larger model is more capable and more accurate, but it has a longer processing time — the fastest possible response time coming in at about 22 to 25 seconds. The smaller model is faster, about 8 to 10 seconds, but as you might expect its results are not as good. Indeed, [Jaryd] says the answers can be infuriatingly bad.

In the write-up, [Jaryd] runs you through how to use Moonbeam on your Pi 5 and the video (embedded below) shows it in action. Fair warning though, Moondream is quite RAM intensive so you will need at least 8 GB of memory in your Pi if you want to play along.

If you’re interested in machine vision you might also like to check out Machine Vision Automates Trainspotting With Unique Full-Length Portraits.

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