2026-02-26 17:00:55
The guitarist Jimi Hendrix had a unique sound which has influenced countless musicians over the decades. He achieved it through mastery not only of his instrument, but of the complex feedback relationship between amplifier, environment, and guitar — coupled with a series of effects pedals including some then-unique ones made for him. Musical commentators have pored over his work for decades, but a recent piece in IEEE Spectrum is particularly interesting as it examines things from a technical perspective.
It centers around an electrical simulation of Hendrix’s effects chain, and makes an assertion that’s obvious on consideration but not the usual take on a Hendrix performance; that in his hands it became a wave synthesizer rather than the instrument itself. Certainly for anyone with an interest in analogue audio electronics as they pertain to musical synthesis it helps in placing the influence of the different circuits on the sound, and in hearing the familiar performances in a new light.
This isn’t the first time we’ve seen someone take a modelling approach to a guitar effects chain, indeed it’s obvious something missing from the work above is the guitar itself.
Header image: Gemeente Rotterdam (Stadsarchief) CC-0.
2026-02-26 14:00:55
Over the last few centuries, behavioral psychologists have documented all kinds of ways of modifying our actions and the actions of various animals. From the famous Skinner boxes to many modern video game mechanics, animals and humans alike can learn through the addition or subtraction of various rewards and punishments. And it doesn’t only impact simple actions either; [Everything is Hacked] took this idea to the extreme, using painful electric shocks to teach himself to avoid making blunders while playing chess.
This positive punishment system uses a medical device called transcutaneous electrical nerve stimulation (TENS) to deliver an electric shock to the skin. The electrical jolt is routed through a custom-built, conductive chess board where each square is isolated from the others and controlled by its own relay. The pieces are conductive as well, so if one is placed on a square where it shouldn’t go a relay will switch on to quickly provide the behavioral modification. The control logic is provided by a Raspberry Pi running the Stockfish chess engine, and it keeps track of the locations of the positions of all the pieces by using MX switches in the base of each square on the board.
This project took [Everything is Hacked] over a year to get into a working condition, including having to rebuild the entire project twice after mishaps with baggage handling at an airline. But he was able to demo the board to the Open Sauce tech festival and even took it to his local park to play chess with the local hustlers. Unfortunately, he reports that he spent more time reworking and rewiring his board over that year than he did improving his chess game, so unfortunately he still hasn’t been able to win any of his money back yet. Perhaps combining this project with a chess-playing robot would help.
2026-02-26 11:00:09
Way, way back in the days when men wore beards and wide-lapelled suits in exotic colors, only NASA had access to photovoltaics and ‘solar’ meant solar thermal. In those days of appropriate technology, it was thought that the ultimate in thermal mass was a phase-change material– a salt or wax that in melting and re-freezing could hold far more heat than plain rock or water, which were more often used. Well, now that it’s the 21st century, we’ve got something even better. As Ars Technica reports about a recent paper in Science Magazine, Molecular Solar Thermal (MOST) energy storage can blow that old stuff right out of the water.
Molecular energy storage? That’s where the sunburn comes in. A sunburn occurs because proteins in your skin are denatured– kinked, twisted, and knocked out of shape– by ultraviolet light. The researchers realized that those kinky proteins are pretty energetic: like a spring, they’re storing energy in their distorted structure. Even better, certain chemicals, like the pyrimidone in the study, don’t ‘relax’ the way a phase change material does. It’s not a matter of warming up and giving up the energy stored in the molecular structure when cooling down– the energy needs coaxed out, in this case by an acidic solution.
That poses problems for a closed-loop system, since you’d be continuously diluting the pyrimidone with heat-releasing acid and neutralizing base. On the other hand, 1.65 MJ/kg of energy storage is nothing to sneeze at, especially when you’re collecting it with nothing more technically advanced than a fluid running through clear tubing. Conveniently enough, researchers found a way to make this stuff liquid at room temperature.
Comparing the heat in this MOST storage material to electrical potential in a battery is a case of apples and oranges, but in terms of pure energy density the pyrimidone cooked up for the paper is in the same range as Li-Ion batteries. There is some self-discharge, in that the altered “dewar” state of the pyrimidone decays naturally, but with a half-life of upto 481 days, you could imagine storing up a tankful UV-altered pyrimidone all year round to provide your winter’s heat.
It’s not perfect. Right now you get about 20 “charge cycles” before the molecules break down, but then, if you’re using this for seasonal load-spreading, a two-decade service life is nothing to shake a stick at. It’s only collecting energy from the UV range of the spectrum, which is a tiny fraction of the energy from our sun. The quantum efficiency of the molecule is rather poor as well– it takes a lot of photons to get a dewar transition.
With solar photovaltaics being as cheap as they are, thermal builds are few and far between– even solar water heaters are powered by PV these days. Of course if you’re somewhere that doesn’t get much sun, you could always go for wind power instead.
Thanks to [zit] for the tip! If you’ve seen a bright idea in the wild, or have one yourself, our tips line is open rain or shine.
2026-02-26 08:00:10
There was a time when only the richest ham radio operators could have a radio with a panadapter. Back in the day, this was basically a spectrum analyzer that monitored a broad slice of the receiver’s intermediate frequency so you could see signals on either side of the receiver’s actual frequency. Today, with SDR technology and computers, this is an easy thing for receivers to implement. But what if you want to refit a classic radio? It isn’t that hard, and [Mirko Pavleski] shares his notes on how he tackled the project. You can also check it out in the video below.
The plan is simple. A FET amplifier taps the radio’s IF stage before the first IF filter. This provides good isolation and buffering. Then, an emitter follower stage provides a matched output to the SDR through a low-pass filter. The SDR remains tuned to the IF frequency, of course. The rest is essentially software and procedures.
Of course, your exact connection to your radio will differ unless you have the same receiver shown in the video. A modern scope with an FFT should be able to help you quickly locate a good spot, though.
Of course, you could just listen through the SDR, but that doesn’t seem sporting but that’s what it looks like he does in the demonstrations. Essentially, he’s using the radio’s RF system via the first IF mixer, then letting the SDR handle the rest. But you could just use the display and tune the radio instead.
If you really wanted a cool system, you could frequency count the internal frequencies and display the correct frequencies in software. Then you could also track the current frequency. This would make it seem more like a traditional panadpater and less like just replacing most of the radio’s features with an SDR.
We’ve seen these before, of course. Many times.
2026-02-26 05:00:37
Random numbers are very important to us in this computer age, being used for all sorts of security and cryptographic tasks. [Theory to Thing] recently built a device to generate random numbers using nothing more complicated than simple camera noise.
The heart of the build is an ESP32 microcontroller, which [Theory to Thing] first paired with a temperature sensor as a source of randomness. However, it was quickly obvious that a thermocouple in a cup of tea wasn’t going to produce nice, jittery, noisy data that would make for good random numbers. Then, inspiration struck, when looking at vision from a camera with the lens cap on. Particularly at higher temperatures, speckles of noise were visible in the blackness—thermal noise, which was just what the doctor ordered.
Thus, the ESP32 was instead hooked up to an OV3660 camera, which was then covered up with a piece of black electrical tape. By looking at the least significant bits of the pixels in the image, it was possible to pick up noise when the camera should have been reporting all black pixels. [Theory to Thing] then had the ESP32 collate the noisy data and report it via a web app that offers up randomly-generated answers to yes-or-no questions.
[Theory to Thing] offers up a basic statistical exploration of bias in the system, and shows how it can be mitigated to some degree, but we’d love a deeper dive into the maths to truly quantify how good this system is when it comes to randomness. We’ve featured deep dives on the topic before. Video after the break.
2026-02-26 03:30:24
If you want smooth top surfaces on your 3D printed parts, a common technique is to turn on ironing in your slicer. This causes the head to drag through the top of the part, emitting a small amount of plastic to smooth the surface. [Make Wonderful Things] asserts that you don’t need to do this time-consuming step. Instead, he proposes using statistical analysis to identify the optimal settings to place the top layer correctly the first time, as shown in the video below.
The parameters he thinks make a difference are line width, flow ratio, and print speed. Picking reasonable step sizes suggested that there were 19,200 combinations of settings to test. Obviously, that’s too many, so he picked up techniques from famous mathematician [George E. P. Box] and also used Bayesian analysis to reduce the amount of printing required to converge on the perfect settings.
Did it work? Judging from the video, it appears to have done so. The best test pieces looked as good as the one that used traditional ironing. Compared to ironing, the non-ironed parts saved about 34% of print time. Not bad.
Of course, there are variations on traditional ironing, so your results may vary.
