[Math the World] claims that your calculus teacher taught you integration wrong. That’s assuming, of course, you learned integration at all, and if you haven’t forgotten it. The premise is that most people think of performing an integral as finding the area under a curve or as the “antiderivative.” However, fewer people think of integration as adding up many small parts. The video asserts that studies show that students who don’t understand the third definition have difficulty applying integration to real-world problems.
We aren’t sure that’s true. People who write software have probably looked at numerical integration like Simpson’s rule or the midpoint rule. That makes it pretty obvious that integration is summing up small bits of something. However, you usually learn that very early, so you’re forgiven if you didn’t get the significance of it at the time.
Are you making your own decisions and mainlining causality like a sucker? Why go through the agony, when you could hand over the railway switch of determinism to a machine that can decide things for you! Enter the DeDeterminator, a decision machine from [Oliver Child].
The construction is simple enough, being built inside a small tin. One kind of wishes it had a secret third “PERHAPS” bulb that illuminates only when the universe’s continued existence has been called into question.
The idea is simple. At the press of a button, the DeDeterminator illuminates a bulb—indicating either yes or no. The decision for which bulb to illuminate is truly random, as it’s determined by the radioactive decay of a Americium-241 alpha particle source. A Geiger-Muller tube is used to detect alpha particles, with the timing between detections used to determine the yes-or-no output of the device.
It’s a neat concept, and it’s kind of fun knowing that your decision is both out of your hands and as random as it could possibly be. Would the universe guide you wrong? Who could possibly question the reasoning of the particles? The only rational move could be to comply with whatever directive the box hath given. Just don’t ask it to make any decisions with dangerous outcomes.
We’ve featured other projects using radioactive decay for random number generation before, though they weren’t quite as philosophically intriguing as the DeDeterminator. Mostly they’re just about cryptographic security and such, but some do deal with causality in imaginary spaces, which has its own magic about it.
Meanwhile, if you’ve untangled the quantum chains of cause and effect, or you’ve just found a way to break RSA encryption using a Pi Pico, do drop us a line, won’t you?
A few days ago, KiCad 8 was released, and it’s a straight upgrade to any PCB designer’s quality of life. There’s a blog post as usual, and, this year, there’s also a FOSDEM talk from [Wayne Stambaugh] talking about the changes that we now all get to benefit from. Having gone through both of these, our impression is that KiCad 8 developers went over the entire suite, asking: “this is cool, but could we make it better”? The end result is indeed a massive improvement in a thousand different ways, from small to fundamental, and all of them seem to be direct upgrades from the KiCad 7 experience.
Glow-in-the-dark projects aren’t that uncommon. You can even get glow-in-the-dark PLA filament. However, those common glowing items require a charge from light, and the glow fades very quickly. [Ogrinz Labs] wasn’t satisfied with that. His “Night Blossom” 3D-printed flower glows using radioactive tritium and will continue to glow for decades.
Tritium vials are available and often show up in watches for nighttime visibility. The glow doesn’t actually come directly from the radioactive tritium (an isotope of hydrogen). Instead, the radioactive particles excite phosphor, which glows in the visible spectrum.
Once you have the vials, it is easy to understand how to finish off the project. The flower contains some long tubes inside each petal. There are also a few tiny vials in the center. The whole assembly goes together with glue.
DietPi recently released version 9.1, which among other changes includes new images for the Raspberry Pi 5, Radxa Rock 4 SE and NanoPi R5S/R5C & 6. The Radxa Rock 4 SE image was necessary because the Rock 4’s RK3399 SoC is subtly different from the RK3399-T’s SoC in terms of memory support, which prevents a Rock 4 image from booting on the Rock 4 SE. Meanwhile the Raspberry Pi 5 image is all new and still a bit rough around the edges, with features like the changing of the resolution and camera module support not working yet. These new images are all available for testing.
We covered DietPi previously with their 8.12 release, along with the reasons why you might want to use DietPi over Armbian and Raspberry Pi OS. Essentially DietPi’s main focus is on performance combined with a small installed size, with the included configuration tools and the setup allowing for many more features to be tweaked than you usually find. If the performance improvements, lower RAM usage and faster boot times seen with the Raspberry Pi 4 holds up, then DietPi can just give the Raspberry Pi 5 a nice little boost, while saving power in the process.
The news doesn’t go long without some kind of superconductor announcement these days. Unfortunately, these come in several categories: materials that require warmer temperatures than previous materials but still require cryogenic cooling, materials that require very high pressures, or materials that, on closer examination, aren’t really superconductors. But it is clear the holy grail is a superconducting material that works at reasonable temperatures in ambient temperature. Most people call that a room-temperature superconductor, but the reality is you really want an “ordinary temperature and pressure superconductor,” but that’s a mouthful.
In the Hackaday bunker, we’ve been kicking around what we will do when the day comes that someone nails it. It isn’t like we have a bunch of unfinished projects that we need superconductors to complete. Other than making it easier to float magnets, what are we going to do with a room-temperature superconductor? Continue reading “Ask Hackaday: What If You Did Have A Room Temperature Superconductor?”→
Pinokio is billed as an autonomous virtual computer, which could mean anything really, but don’t click away just yet, because this is one heck of a project. AI enthusiast [cocktail peanut] (and other undisclosed contributors) has created a browser-style application which enables a virtual Unix-like environment to be embedded, regardless of the host architecture. A discover page loads up registered applications from GitHub, allowing a one-click install process, which is ‘simply’ a JSON file describing the dependencies and execution flow. The idea is rather than manually running commands and satisfying dependencies, it’s all wrapped up for you, enabling a one-click to download and install everything needed to run the application.
But what applications? we hear you ask, AI ones. Lots of them. The main driver seems to be to use the Pinokio hosting environment to enable easy deployment of AI applications, directly onto your machine. One click to install the app, then another one to download models, and whatever is needed, from the likes of HuggingFace and friends. A final click to launch the app, and a browser window opens, giving you a web UI to control the locally running AI backend. Continue reading “On-click Install local AI Applications Using Pinokio”→
