Robotic Screw And Bolt Sorter Seeks A New Challenge

August 8, 2026 by Donald Papp 21 Comments

As someone who disassembles and repairs hardware, [Aad] eventually ended up with a huge collection of mixed bolts and screws. This led to creating the automatic bolt and screw sorting system you see here, although in a way it is just a proof of concept. Bolts and screws happen to be a useful application for now, but the system is capable of sorting just about any small objects.

A bit of machine vision detects the size and shape of each object. Weight can also be measured.

Mixed pieces go onto a large conveyor belt, shown on the right. This feeds a few screws at a time down a chute, where they roll onto an illuminated platform.

Above the lit platform is a camera, and machine vision is used to detect the size and shape and orientation of each screw. A robotic gripper on a gantry picks the screws up one by one — separating them first if they happened to clump together — and places each in a drop-off cart. The cart drops the object into a receptacle with its brethren, making sure similar ones are grouped together. Watch it in action in the video, embedded below the page break.

It’s a great build that shows fancy components aren’t necessary for good results. Servos and steppers are controlled with an ESP32-WROOM board, and a piezo sensor detects screws falling off the conveyor. Some of you may have noticed a repurposed Ultimaker 3D printer serving as the bulk of the system, its hot end having been replaced with a gripper that can raise and lower. The overhead camera is an ESP32-CAM adapted to accept M12 lenses so it can focus on the platform.

There’s one more feature worth mentioning — the system also has the ability to measure the weight of a picked object by placing it onto a moveable inspection platform, which can optionally put it under a USB microscope for a closer look. Everything is controlled by a nearby PC, so there’s a lot of flexibility built into the system.

We suppose that once all the screws and bolts in a shop are sorted, it only makes sense to sort all the nuts. Are there other objects besides screws and bolts that would be useful to sort with a system like this? If you have any ideas, don’t keep them to yourself! [Aad] would love to hear your comments and ideas, so share them below.

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Come With Me If You Want To Weed: Autonomous Weedinator Robot Back For 2026

June 19, 2026 by Tyler August 18 Comments

The WEEDINATOR agricultural robot is one of the longer-running projects we’ve featured here on Hackaday. We first featured it way back in 2017 for that year’s Hackaday prize, and after a nearly a decade of work on-and-off it has hit a very important milestone: it is now an effective horticultural instrument, as you can see in the latest demo video below.

There have been some big changes over the years. For one, the scope of the project narrowed considerably with the adoption of a commercial tractor as the base, specifically an Iseki 321 . They picked the Iseki after examining several competitors, and it won out because its hydrostatic drive was best able to handle the very low speeds desired. It looks like they’re now focused on cultivation — that is, tearing out weeds mechanically — rather than the flame weeder they started with. The cultivators are of the claw type, and has three claws powered via the tractor’s hydraulics for control in all three axis: X, Y and Z. Of course the project now leverages modern computer vision toolsets, using a combination of OpenCV and YOLO26n running on a Jetson Nano board. The robotics half of the equation is handled on an STM32 Nucleo.

Aside from being one of our longer-running submissions, we have to call out the team for being one of the very few — perhaps the only — to go to the effort of creating a theme song for their project. If you’ve only got a minute to see the robot run, you might as well look at the second video embedded below and give a listen.

While WEEDINATOR has got the most persistence, they’re not the only ones in the garden robot game. We’ve seen projects using everything from concentrated sunlight to precision-applied herbicides to clear unwanted plants over the years.

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But Just What Is This ‘Artificial Intelligence’?

June 3, 2026 by Maya Posch 61 Comments

In the world of buzzwords, the acronym ‘AI’ has absolutely been the buzziest of buzzing buzzwords for at least a few years now. Where previously terms like ‘smart’ and ‘intelligent’ sufficed to promote a product, we are now being told that we are living in an age where this supposedly newfangled ‘artificial intelligence’ is doing literally everything faster and better while also curing cancer on the side. Yet, as a wise man once said: “You keep using that word. I do not think it means what you think it means.”

The obvious implication of using a term like ‘artificial intelligence’ in this manner is that it brings to mind a modern version of early last century’s ‘electronic brain’ vernacular alongside the rise of digital computers. Yet rather than electrons in vacuum tubes and semiconductors propelling us into a brave new world of super-intelligence, we now just use said devices to doom scroll and to engage in passive-aggressive online communications like the typical primate groups in a virtual jungle defending their turf.

Similarly, the term AI is massively oversold today, least of all in the inherent presupposition that we somehow have finally cracked the mystery of the brain and have created an intelligence that can go toe-to-toe with humans and even our corvid dinosaur friends. Perhaps the worst part is that there is a veritable mountain of fascinating algorithms and other constructs that help us automate many tasks today, making it somewhat rude to just give up and call everything ‘AI’ like we learned nothing from the 1980s AI craze.

So what is exactly being smoothed over by the glossy marketing of ‘everything is AI’?

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The map in action, along with a sample of the video feeds.

Hardware Store Marauder’s Map Is Clarkian Magic

December 20, 2025 by Tyler August 25 Comments

The “Marauder’s Map” is a magical artifact from the Harry Potter franchise. That sort of magic isn’t real, but as Arthur C. Clarke famously pointed out, it doesn’t need to be — we have technology, and we can make our own magic now. Or, rather, [Dave] on the YouTube Channel Dave’s Armoury can make it.

[Dave]’s hardware store might be in a rough neighborhood, since it has 50 cameras’ worth of CCTV coverage. In this case, the stockman’s loss is the hacker’s gain, as [Dave] has talked his way into accessing all of those various camera feeds and is using machine vision to track every single human in the store.

Of course, locating individuals in a video feed is easy — to locate them in space from that feed, one first needs an accurate map. To do that, [Dave] first 3D scans the entire store with a rover. The scan is in full 3D, and it’s no small amount of data. On the rover, a Jetson AGX is required to handle it; on the bench, a beefy HP Z8 Fury workstation crunches the point cloud into a map. Luckily it came with 500 GB of RAM, since just opening the mesh file generated from that point cloud needs 126 GB. That is processed into a simple 2D floor plan. While the workflow is impressive, we can’t help but wonder if there was an easier way. (Maybe a tape measure?)

Once an accurate map has been generated, it turns out NVIDIA already has a turnkey solution for mapping video feeds to a 2D spatial map. When processing so much data — remember, there are 50 camera feeds in the store — it’s not ideal to be passing the image data from RAM to GPU and back again, but luckily NVIDIA’s “Deep Stream” pipeline will do object detection and tracking (including between different video streams) all on the GPU. There’s also pose estimation right in there for more accurate tracking of where a person is standing than just “inside this red box”. With 50 cameras, it’s all a bit much for one card, but luckily [Dave]’s workstation has two GPUs.

Once the coordinates are spat out of the neural networks, it’s relatively simple to put footprints on the map in true Harry Potter fashion. It really is magic, in the Clarkian sense, what you can do if you throw enough computing power at it.

Unfortunately for show-accuracy (or fortunately, if you prefer to avoid gross privacy violations), it doesn’t track every individual by name, but it does demonstrate the possibility with [Dave] and his robot. If you want a map of something… else… maybe check out this backyard project.

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Self-Driving Cars And The Fight Over The Necessity Of Lidar

October 30, 2025 by Maya Posch 72 Comments

If you haven’t lived underneath a rock for the past decade or so, you will have seen a lot of arguing in the media by prominent figures and their respective fanbases about what the right sensor package is for autonomous vehicles, or ‘self-driving cars’ in popular parlance. As the task here is to effectively replicate what is achieved by the human Mark 1 eyeball and associated processing hardware in the evolutionary layers of patched-together wetware (‘human brain’), it might seem tempting to think that a bunch of modern RGB cameras and a zippy computer system could do the same vision task quite easily.

This is where reality throws a couple of curveballs. Although RGB cameras lack the evolutionary glitches like an inverted image sensor and a big dead spot where the optical nerve punches through said sensor layer, it turns out that the preprocessing performed in the retina, the processing in the visual cortex and analysis in the rest of the brain is really quite good at detecting objects, no doubt helped by millions of years of only those who managed to not get eaten by predators procreating in significant numbers.

Hence the solution of sticking something like a Lidar scanner on a car makes a lot of sense. Not only does this provide advanced details on one’s surroundings, but also isn’t bothered by rain and fog the way an RGB camera is. Having more and better quality information makes subsequent processing easier and more effective, or so it would seem.

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Hackaday Links: June 29, 2025

June 29, 2025 by Dan Maloney 17 Comments

In today’s episode of “AI Is Why We Can’t Have Nice Things,” we feature the Hertz Corporation and its new AI-powered rental car damage scanners. Gone are the days when an overworked human in a snappy windbreaker would give your rental return a once-over with the old Mark Ones to make sure you hadn’t messed the car up too badly. Instead, Hertz is fielding up to 100 of these “MRI scanners for cars.” The “damage discovery tool” uses cameras to capture images of the car and compares them to a model that’s apparently been trained on nothing but showroom cars. Redditors who’ve had the displeasure of being subjected to this thing report being charged egregiously high damage fees for non-existent damage. To add insult to injury, if renters want to appeal those charges, they have to argue with a chatbot first, one that offers no path to speaking with a human. While this is likely to be quite a tidy profit center for Hertz, their customers still have a vote here, and backlash will likely lead the company to adjust the model to be a bit more lenient, if not outright scrapping the system.

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A piano is pictured with two hands playing different notes, G outlined in orange and C outlined in blue.

AI Piano Teacher To Criticize Your Every Move

June 22, 2025 by Ian Bos 15 Comments

Learning new instruments is never a simple task on your own; nothing can beat the instant feedback of a teacher. In our new age of AI, why not have an AI companion complain when you’re off note? This is exactly what [Ada López] put together with their AI-Powered Piano Trainer.

The basics of the piano rely on rather simple boolean actions, either you press a key or not. Obviously, this sets up the piano for many fun projects, such as creative doorbells or helpful AI models. [Ada López] started their AI model with a custom dataset with images of playing specific notes on the piano. These images then get fed into Roboflow and trained using the YOLOv8 model.

Using the piano training has the model run on a laptop and only has a Raspberry Pi for video, and gives instant feedback to the pianist due to the demands of the model. Placing the Pi and an LCD screen for feedback into a simple enclosure allows the easy viewing of how good an AI model thinks you play piano. [Ada López] demos their device by playing Twinkle Twinkle Little Star but there is no reason why other songs couldn’t be added!

While there are simpler piano trainers out there relying on audio cues, this project presents a great opportunity for a fun project for anyone else wanting to take up the baton. If you want to get a little more from having to do less in the physical space, then this invisible piano is perfect for you!