Hacking and making are great fun due to their open ended nature, but being able to try anything can make the task of selecting your next project daunting. [Steph Piper] is here with her Maker Skill Trees to give you a map to leveling up your skills.
Featuring a grid of 73 hexagonal tiles per discipline, there’s plenty of inspiration for what to tackle next in your journey. The trees start with the basics at the bottom and progressively move up in difficulty as you move up the page. With over 50 trees to select from (so far), you can probably find something to help you become better at anything from 3D printing and modeling to entrepreneurship or woodworking.
Despite being spoiled for choice, if you’re disappointed there’s no tree for your particular interest (underwater basket weaving?), you can roll your own with the provided template and submit it for inclusion in the repository.
Want to get a jump on an AI Skill Tree? Try out these AI courses. Maybe you could use these to market yourself to potential employers or feel confident enough to strike out on your own?
[Thanks to Courtney for the tip!]
Controlling an e-bike is pretty straightforward. If you want to just let it rip, it’s a no-brainer — or rather, a one-thumber, as a thumb throttle is the way to go. Or, if you’re still looking for a bit of the experience of riding a bike, sensing when the pedals are turning and giving the rider a boost with the motor is a good option.
But what if your e-conveyance is more of the aquatic variety? That’s an interface design problem of a different color, as [Braden Sunwold] has discovered with his DIY e-kayak. We’ve detailed his work on this already, but for a short recap, his goal is to create an electric assist for his inflatable kayak, to give you a boost when you need it without taking away from the experience of kayaking. To that end, he used the motor and propeller from a hydrofoil to provide the needed thrust, while puzzling through the problem of building an unobtrusive yet flexible controller for the motor.
His answer is to mount an inertial measurement unit (IMU) in a waterproof container that can clamp to the kayak paddle. The controller is battery-powered and uses an nRF link to talk to a Raspberry Pi in the kayak’s waterproof electronics box. The sensor also has an LED ring light to provide feedback to the pilot. The controller is set up to support both a manual mode, which just turns on the motor and turns the kayak into a (low) power boat, and an automatic mode, which detects when the pilot is paddling and provides a little thrust in the desired direction of travel.
The video below shows the non-trivial amount of effort [Braden] and his project partner [Jordan] put into making the waterproof enclosure for the controller. The clamp is particularly interesting, especially since it has to keep the sensor properly oriented on the paddle. [Braden] is working on a machine-learning method to analyze paddle motions to discern what the pilot is doing and where the kayak goes. Once he has that model built, it should be time to hit the water and see what this thing can do. We’re eager to see the results.
Here at Hackaday, we love it when someone picks up the ball from a previous project and runs with it. That’s what we’re all about, really — putting out cool projects that just might stimulate someone else to extend and enhance it, or even head off in an entirely new direction. That’s how the state of the art keeps moving.
This DIY spectrometer project is a fantastic example of that ethos. It comes to us from [Michael Prasthofer], who was inspired by [Les Wright]’s PySpectrometer, a simple device cobbled together from a pocket spectroscope and a PiCam. As we noted at the time, [Les] put a lot of the complexity of his instrument in the software, but that doesn’t mean there wasn’t room for improvement.
[Michael]’s goals were to make his spectrometer a little easier to build, and to improve the calibration process and overall accuracy. To help with the former, he went with software correction of the color filter array on his Fuji X-T2. This has the advantage of not requiring a high-power laser and precision micropositioner to ablate the CFA, and avoids potentially destroying an expensive camera. For the latter, [Michael] delved deep into the theory behind spectroscopy and camera optics to develop a process for correlating the intensity of light along the spectrum with the specific wavelength at that location. He also worked a little machine learning into the process, training a network to optimize the response functions.
The result is pretty accurate spectra with no lasers required for calibration. The video below goes into a lot of detail and ends up being a good introduction to some of the basics of spectroscopy, along with the not-so-basics.
Image-generating AIs are typically trained on huge arrays of GPUs and require great wads of processing power to run. Meanwhile, [Nick Bild] has managed to get something similar running on a Commodore 64. (via Tom’s Hardware).
[Nick’s] model was trained on 100 retro-inspired sprites that he created himself. He did the training phase on a modern computer, so that the Commodore 64 didn’t have to sweat this difficult task on its feeble 6502 CPU. However, it’s more than capable of generating sprites using the model, thanks to some BASIC code that runs off of the training data. Right now, it takes the C64 about 20 minutes to run through 94 iterations to generate a decent sprite.
8×8 sprites are generally simple enough that you don’t need to be an artist to create them. Nonetheless, [Nick] has shown that modern machine learning techniques can be run on slow archaic hardware, even if there is limited utility in doing so. Video after the break.
[Thanks to Stephen Walters for the tip!]
When we think about self-driving cars, we normally think about big projects measured in billions of dollars, all funded by major automakers. But you can still dive into this world on a smaller scale, as [jmoreno555] demonstrates.
The build consists of a small RC car—an HSP 94123, in fact. It’s got a simple brushed motor inside, driven by a conventional speed controller, and servo-driven steering. A Raspberry Pi 4 is charged with driving the car, but it’s not alone. It’s outfitted with a Google Coral USB stick, which is a machine learning accelerator card capable of 4 trillion operations per second. The car also has a Wemos D1 onboard, charged with interfacing distance sensors to give the car a sense of its environment. Vision is courtesy of a 1.2-megapixel camera with a 160-degree lens, and a stereoscopic camera with twin 75-degree lenses. Software-wise, it’s early days yet. [jmoreno555] is exploring the use of Python and OpenCV to implement basic lane detection and other self driving routines, while using Blender as a simulator.
The real magic idea, though, is the treadmill. [jmoreno555] realized that one of the frustrations of working in this space is in having to chase a car around a test track. Instead, the use of a desktop treadmill allows the car to be programmed and debugged with less fuss in the early stages of development.
If you’re looking for a platform to experiment with AI and self-driving, this could be an project to dive in to. We’ve covered some other great builds in this space, too. Meanwhile, if you’ve cracked driving autonomy and want to let us know, our tipsline is always standing by!
You might be under the impression that machine learning costs thousands of dollars to work with. That might be true in many cases, but there’s more to machine learning than you might think. For instance, what if you could shower anything with a network of cheap machine-learning-enabled sensors? The 1 dollar TinyML project by [Jon Nordby] allows you to do just that. These tiny boards host an STM32-like MCU, a BLE module, lithium ion power circuitry, and some nice sensor options — an accelerometer, a pair of microphones, and a light sensor.
What could you do with these sensors? [Jon] has talked a bit about a few commercial and non-commercial applications he’s worked on in his ML career, and tells us that the accelerometer alone lets you do human presence detection, sleep tracking, personal activity monitoring, or vibration pattern sensing, for a start. As for the sound input, there’s tasks ranging from gunshot or clapping detection, to coffee roasting process tracking, voice and speech detection, and surely much more. Just a few years ago, we’ve seen machine learning used to comfort a barking dog while its owner is away.
Bottom line is, you ought to get a few of these in your hands and start playing with ML. You still might need a bit of beefier hardware to train your code, but it gets that much easier once you have a network of sensors waiting for your command. Plus, since it’s an open source project, you’ll have a much easier time adding on any additional capabilities your particular application might need.
These boards are pretty cost-optimized, which makes it possible for you to order a couple dozen without breaking the bank. The $1 target is BOM cost, especially if you opt to not include one of the pricier sensors. You can assemble these boards yourself, or get them assembled at a fab of your choice for barely a cost increase. As for software, they will work with the emlearn framework.
Everything is on GitHub — from KiCad sources to Jupyter notebooks. As for Hackaday.io, there are five worklogs of impressive insight — the microphone worklog alone will teach you about microphone amplification in low-power conditions while keeping the cost low. Not as price-constrained and want to try on some image processing tasks? Here’s a beautiful Pi Pico ArduCam board with a camera and a TFT screen.
[Andrej Karpathy] recently released llm.c, a project that focuses on LLM training in pure C, once again showing that working with these tools isn’t necessarily reliant on sprawling development environments. GPT-2 may be older but is perfectly relevant, being the granddaddy of modern LLMs (large language models) with a clear heritage to more modern offerings.
LLMs are fantastically good at communicating despite not actually knowing what they are saying, and training them usually relies on PyTorch deep learning library, itself written in Python. llm.c takes a simpler approach by implementing the neural network training algorithm for GPT-2 directly. The result is highly focused and surprisingly short: about a thousand lines of C in a single file. It is a highly elegant process that does the same thing the bigger, clunkier methods accomplish. It can run entirely on a CPU, or it can take advantage of GPU acceleration, where available.
This isn’t the first time [Andrej Karpathy] has bent his considerable skills and understanding towards boiling down these sorts of concepts into bare-bones implementations. We previously covered a project of his that is the “hello world” of GPT, a tiny model that predicts the next bit in a given sequence and offers low-level insight into just how GPT (generative pre-trained transformer) models work.
The educational sector is usually the first to decry large language models and AI, due to worries about cheating. The State Library of Queensland, however, has embraced the technology in controversial fashion. In the lead-up to Anzac Day, the primarily Australian war memorial holiday, the library released a chatbot intended to imitate a World War One veteran. It went as well as you’d expect.
Twitter users immediately chimed in with dismay at the very concept. Others showed how easy it was to “jailbreak” the AI, convincing Charlie he was actually supposed to teach Python, imitate Frasier Crane, or explain laws like Elle from Legally Blonde. One person figured out how to get Charlie to spit out his initial instructions; these were patched later in the day to try and stop some of the shenanigans.
From those instructions, it’s clear that this was supposed to be educational, rather than some sort of macabre experiment. However, Charlie didn’t do a great job here, either. As with any Large Language Model, Charlie had no sense of objective truth. He routinely spat out incorrect facts regarding the war, and regularly contradicted himself.
Generally, any plan that includes the words “impersonate a veteran” is a foolhardy one at best. Throwing a machine-generated portrait and a largely uncontrolled AI into the mix didn’t help things. Regardless, the State Library has left the “Virtual Veterans” experience up at the time of writing.
The problem with AI is that it’s not a magic box that gets things right all the time. It never has been. As long as organizations keep putting AI to use in ways like this, the same story will keep playing out.
“There goes the neighborhood” isn’t a phrase to be thrown about lightly, but when they build a police station next door to your house, you know things are about to get noisy. Just how bad it’ll be is perhaps a bit subjective, with pleas for relief likely to fall on deaf ears unless you’ve got firm documentation like that provided by this automated noise detection system.
OK, let’s face it — even with objective proof there’s likely nothing that [Christopher Cooper] is going to do about the new crop of sirens going off in his neighborhood. Emergencies require a speedy response, after all, and sirens are perhaps just the price that we pay to live close to each other. That doesn’t mean there’s no reason to monitor the neighborhood noise, though, so [Christopher] got to work. The system uses an Arduino BLE Sense module to detect neighborhood noises and Edge Impulse to classify the sounds. An ESP32 does most of the heavy lifting, including running the UI on a nice little TFT touchscreen.
When a siren-like sound is detected, the sensor records the event and tries to classify the type of siren — fire, police, or ambulance. You can also manually classify sounds the system fails to understand, and export a summary of events to an SD card. If your neighborhood noise problems tend more to barking dogs or early-morning leaf blowers, no problem — you can easily train different models.
While we can’t say that this will help keep the peace in his neighborhood, we really like the way this one came out. We’ve seen the BLE Sense and Edge Impulse team up before, too, for everything from tuning a bike suspension to calming a nervous dog.
Conectar