Beyond bothering large language models (LLMs) with funny questions, there’s the general idea that they can act as supporting tools. Theoretically they should be able to assist with parsing and summarizing documents, while answering questions about e.g. electronic design. To test this assumption, [Duncan Haldane] employed three of the more highly praised LLMs to assist with circuit board design. These LLMs were GPT-4o (OpenAI), Claude 3 Opus (Anthropic) and Gemini 1.5 (Google).
The tasks ranged from ‘stupid questions’, like asking the delay per unit length of a trace on a PCB, to finding parts for a design, to designing an entire circuit. Of these tasks, only the ‘parsing datasheets’ task could be considered to be successful. This involved uploading the datasheet for a component (nRF5340) and asking the LLM to make a symbol and footprint, in this case for the text-centric JITX format but KiCad/Altium should be possible too. This did require a few passes, as there were glitches and omissions in the generated footprint.
When it came to picking components for a design, it’s clear that you’re out of luck here unless you’re trying to create a design that a million others have made before you in exactly the same way. This problem got worse when trying to design a circuit and ultimately spit out a netlist, with the best LLM (Claude 3 Opus) giving nonsensical suggestions for filter designs and mucking up even basic amplifier designs, including by sticking decoupling capacitors and random resistors just about everywhere.
Effectively, as a text searching tool it would seem that LLMs can have some use for engineers who are tired of digging through yet another few hundred pages of poorly formatted and non-indexed PDF datasheets, but you still need to be on your toes with every step of the way, as the output from the LLM will all too often be slightly to hilariously wrong.
Batman is a compelling superhero for enough reasons that he’s been a cultural force for the better part of a century. His story has complex characters, interesting explorations of morality, iconic villains, and of course a human superhero who gets his powers from ingenuity instead of a fantastical magical force. There are a number features of the Batman universe that don’t translate well to the real world, though, such as a costume that would likely be a hindrance in fights, technology that violates the laws of physics, and a billionaire that cares about regular people, but surprisingly enough his legendary Batwing jet airplane actually seems like it might be able to fly.
While this is admittedly a model plane, it flies surprisingly well for its nontraditional shape. [hotlapkyle] crafted it using mostly 3D printed parts, and although it took a few tries to get it working to his standards, now shoots through the air quite well. It uses an internal electric ducted fan (EDF) to get a high amount of thrust, and has elevons for control. There are two small vertical stabilizer fins which not only complete the look, but allow the Batwing to take to the skies without the need for a flight controller.
Not only is the build process documented in the video linked below with some interesting tips about building RC aircraft in general, but the STL files for this specific build are available for anyone wanting to duplicate the build or expand on it. There are plenty of other interesting 3D-printed models on [hotlapkyle]’s page as well that push the envelope of model aircraft. For some other niche RC aircraft designs we’ve seen in the past be sure to check out this F-35 model that can hover or this tilt-rotor Osprey proof-of-concept.
Thanks to [Keith] for the tip!
As far as model construction sets go, LEGO is by far the most popular brand for building not only pre-planned models but whatever the builder can imagine. There are a few others out there though, some with some interesting features. Meccano (or Erector in North America) is a construction set based around parts that are largely metal including its fasteners, which allows for a different approach to building models than other systems including the easy addition of electricity. [Craig], a member of the London Meccano Club, is demonstrating his model Enigma machine using this system for all of its parts and adding some electricity to make the circuitry work as well.
The original Enigma machine was an electronic cypher used by the German military in World War 2 to send coded messages. For the time, its code was extremely hard to break, and led to the British development of the first programmable electronic digital computer to help decipher its coded messages. This model uses Meccano parts instead to recreate the function of the original machine, with a set of keys similar to a typewriter which, when pressed, advance a set of three wheels. The wheels all have wiring in them, and depending on their initial settings will light up a different character on a display.
There are a few modifications made to the design (besides the use of a completely different set of materials) but one of the main ones was eliminating the heavy leaf springs of the original for smaller and easier-to-manage coil springs, which are also part of the electrical system that creates the code. The final product recreates the original exceptionally faithfully, with plans to create a plugboard up next, and you can take a look at the inner workings of a complete original here.
Thanks to [Tim] for the tip!
Alibaba’s EMO (or Emote Portrait Alive) framework is a recent entry in a series of attempts to generate a talking head using existing audio (spoken word or vocal audio) and a reference portrait image as inputs. At its core it uses a diffusion model that is trained on 250 hours of video footage and over 150 million images. But unlike previous attempts, it adds what the researchers call a speed controller and a face region controller. These serve to stabilize the generated frames, along with an additional module to stop the diffusion model from outputting frames that feature a result too distinct from the reference image used as input.
In the related paper by [Linrui Tian] and colleagues a number of comparisons are shown between EMO and other frameworks, claiming significant improvements over these. A number of examples of talking and singing heads generated using this framework are provided by the researchers, which gives some idea of what are probably the ‘best case’ outputs. With some examples, like [Leslie Cheung Kwok Wing] singing ‘Unconditional‘ big glitches are obvious and there’s a definite mismatch between the vocal track and facial motions. Despite this, it’s quite impressive, especially with fairly realistic movement of the head including blinking of the eyes.
Meanwhile some seem extremely impressed, such as in a recent video by [Matthew Berman] on EMO where he states that Alibaba releasing this framework to the public might be ‘too dangerous’. The level-headed folks over at PetaPixel however also note the obvious visual imperfections that are a dead give-away for this kind of generative technology. Much like other diffusion model-based generators, it would seem that EMO is still very much stuck in the uncanny valley, with no clear path to becoming a real human yet.
Thanks to [Daniel Starr] for the tip.
Sometimes, it’s time to shut down the oscilloscope, and break out the cardboard and paints. If you’re wondering what for, well, here’s a reminder of an Instructable from [CrazyScience], that brings us back to cardboard crafts days. They rebuild one of the most iconic components for an electronics tinkering beginner — an ultrasonic distance sensor, and what’s fun is, it stays fully functional after the rebuild!
This project is as straightforward as it gets, describing all the steps in great detail, and you can complete it with just a hot glue gun and soldering iron. With materials being simple cardboard, aluminum foil, popsicle sticks, some mesh, and a single ultrasonic sensor for harvesting the transmitter and receiver out of, this is the kind of project you could easily complete with your kids on a rainy day.
Now, the venerable ultrasonic sensor joins the gallery of classics given a size change treatment, like the 555 timer we’ve seen two different takes on, or perhaps that one Arduino Uno. Unlike these three, this project’s cardboard skeleton means it’s all that simpler to build your own, what’s with all the shipping boxes we accumulate.
The radio hackers in the audience will be familiar with a spectrogram display, but for the uninitiated, it’s basically a visual representation of how a range of frequencies are changing with time. Usually such a display is used to identify a clear transmission in a sea of noise, but with the right software, it’s possible to generate a signal that shows up as text or an image when viewed as a spectrogram. Musicians even occasionally use the technique to hide images in their songs. Unfortunately, the audio side of such a trick generally sounds like gibberish to human ears.
Or at least, it used to. Students from the University of Michigan have found a way to use diffusion models to not only create a spectrogram image for a given prompt, but to do it with audio that actually makes sense given what the image shows. So for example if you asked for a spectrogram of a race car, you might get an audio track that sounds like a revving engine.
The first step of the technique is easy enough — two separate pre-trained models are used, Stable Diffusion to create the image, and Auffusion4 to produce the audio. The results are then combined via weighted average, and enter into an iterative denoising process to refine the end result. Normally the process produces a grayscale image, but as the paper explains, a third model can be kicked in to produce a more visually pleasing result without impacting the audio itself.
Ultimately, neither the visual nor audio component is perfect. But they both get close enough that you get the idea, and that alone is pretty impressive. We won’t hazard to guess what practical applications exist for this technique, but the paper does hint at some potential use for steganography. Perhaps something to keep in mind the next time we try to hide data in an episode of the Hackaday Podcast.
“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.
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