Mysterious Files PH

This unusual clock by [Moritz v. Sivers] looks like a holographic dial surrounded by an LED ring, but that turns out to not be the case. What appears to be a ring of LEDs is in fact a second hologram. There are LEDs but they are tucked out of the way, and not directly visible. The result is a very unusual clock that really isn’t what it appears to be.

The face of the clock is a reflection hologram of a numbered spiral that serves as a dial. A single LED – the only one visibly mounted – illuminates this hologram from the front in order to produce the sort of holographic image most of us are familiar with, creating a sense of depth.

The lights around the circumference are another matter. What looks like a ring of LEDs serving as clock hands is actually a transmission hologram made of sixty separate exposures. By illuminating this hologram at just the right angle with LEDs (which are mounted behind the visible area), it is possible to selectively address each of those sixty exposures. The result is something that really looks like there are lit LEDs where there are in fact none.

[Moritz] actually made two clocks in this fashion. The larger green one shown here, and a smaller red version which makes some of the operating principles a bit more obvious on account of its simpler construction.

If it all sounds a bit wild or you would like to see it in action, check out the video (embedded below) which not only showcases the entire operation and assembly but also demonstrates the depth of planning and careful execution that goes into multi-exposure of a holographic plate.

[Moritz v. Sivers] is no stranger to making unusual clocks. In fact, this analog holographic clock is a direct successor to his holographic 7-segment display clock. And don’t miss the caustic clock, nor his lenticular clock.

Debugging an application crash can oftentimes feel like you’re an intrepid detective in a grimy noir detective story, tasked with figuring out the sordid details behind an ugly crime. Slogging through scarce clues and vapid hints, you find yourself down in the dumps, contemplating the deeper meaning of life and  the true nature of man, before hitting that eureka moment and cracking the case. One might say that this makes for a good game idea, and [Jonathan] would agree with that notion, thus creating the Fatal Core Dump game.

Details can be found in the (spoiler-rich) blog post on how the game was conceived and implemented. The premise of the game is that of an inexplicable airlock failure on an asteroid mining station, with you being the engineer tasked to figure out whether it was ‘just a glitch’ or that something more sinister was afoot. Although an RPG-style game was also considered, ultimately that proved to be a massive challenge with RPG Maker, resulting in this more barebones game, making it arguably more realistic.

Suffice it to say that this game is not designed to be a cheap copy of real debugging, but the real deal. You’re expected to be very comfortable with C, GDB, core dump analysis, x86_64 ASM, Linux binary runtime details and more. At the end you should be able to tell whether it was just a silly mistake made by an under-caffeinated developer years prior, or a malicious attack that exploited or introduced some weakness in the code.

If you want to have a poke at the code behind the game, perhaps to feel inspired to make your own take on this genre, you can take a look at the GitHub project.

Alzheimer’s disease remains a frustratingly difficult condition to manage for the millions of patients affected worldwide and their families. The cause of the disease is still not properly understood, and by the time memory loss and cognitive decline become apparent, the underlying brain pathology has often been quietly building for decades.

Soon, though it may be possible to diagnose impending Alzheimer’s disease ahead of time, before symptoms have taken hold. New research suggests this could be achieved through a simple blood draw, providing clinicians and patients precious time to manage the condition and plan ahead.

Early Warning

A hallmark of Alzheimer’s disease is the buildup of amyloid and tau protein in the brain. Despite decades of research, the protein’s precise role in the disease remains somewhat unclear. Typically, Alzheimer’s disease is diagnosed by symptoms like memory loss and cognitive decline, with later investigation revealing the presence of elevated levels of these proteins in the brain. This investigation often involves brain imaging or invasive and painful spinal fluid tests.

However, researchers have developed a new predictive model that suggests it’s possible to estimate when a person will begin showing Alzheimer’s symptoms, based on a similar marker. The key ingredient is another protein called p-tau217, which circulates in the blood plasma and mirrors the slow, steady accumulation of amyloid and tau protein within the brain tissues.

Earlier research had previously established that p-tau217 levels tended to track alongside the rise of amyloid and tau buildup in the brain. This indicated that the level of p-tau217 in the blood could be a proxy for how far along the disease process has progressed. This allows the estimation of how many years remain before symptoms emerge for a given patient, with the research study suggesting this could be as specific as a margin of three or four years.

The research study drew on data from 603 older adults enrolled in two long-running studies — the Knight Alzheimer Disease Research Center at WashU Medicine and the multi-site Alzheimer’s Disease Neuroimaging Initiative. Plasma p-tau217 was measured using simple blood draws. One of the more interesting findings concerns age. The model revealed that the interval between elevated p-tau217 levels and symptom onset isn’t fixed. A person whose levels first ticked upwards at the age 60 might not develop noticeable cognitive problems for another two decades. But if that same elevation appeared at age 80, symptoms tended to follow within about eleven years. It suggests that younger brains appear to tolerate Alzheimer’s-related pathology for longer, while older brains are less able to withstand the protein buildup once the process gets underway.

Right now, p-tau217 testing is primarily used to help confirm an Alzheimer’s diagnosis in patients who are already experiencing cognitive trouble. It isn’t recommended for screening asymptomatic individuals outside of research settings. However, using p-tau217 as a predictive marker has obvious potential. If clinical trials for preventive Alzheimer’s drugs could enroll participants based on a predicted timeline for symptom onset, rather than waiting years for decline to actually happen, those trials could become dramatically shorter and cheaper to run, and  more efficient in general. Perhaps more importantly, it has the potential to give patients a better understanding of what lies ahead, allowing them to plan ahead before cognitive symptoms become an unmanageable imposition on their life.

The research team has made their model development code publicly available on Github and created a web-based tool that lets other researchers explore the clock models in detail. Looking ahead, they note that combining p-tau217 with other blood-based biomarkers linked to cognitive decline could sharpen predictions even further. It’s still early days, but a future where a routine blood test could serve as an early-warning system for Alzheimer’s is looking more plausible than it did a few years ago.

Who among us does not have a plethora of mains-powered devices on their workbench, and a consequent mess of power strips to run them all? [Jeroen Brinkman] made his more controllable with a multi-way switch box.

At first sight it’s a bank of toggle switches, one for each socket. But this is far more than a wiring job, because of course there are a couple of microcontrollers involved, and each of those switches ultimately controls a relay. There are also status LEDs for each socket, and a master switch to bring them all down. Arduino code is provided, so you can build one too if you want to.

We like the idea of a handy power strip controller, and especially the master switch with the inherent state memory provided by the switches. This could find a home on a Hackaday bench, and we suspect on many others too. It’s by no means the first power strip with brains we’ve seen, but most others have been aimed at the home instead.

What hardware hacker doesn’t have a soft spot for transparent cases? While they may have fallen out of mainstream favor, they have an undeniable appeal to anyone with an interest in electronic or mechanical devices. Which is why the Orbigator built by [wyojustin] stands out among similar desktop orbital trackers we’ve seen.

Conceptually, it’s very similar to the International Space Station tracking lamp that [Will Dana] built in 2025. In fact, [wyojustin] cites it specifically as one of the inspirations for this project. But unlike that build, which saw a small model of the ISS moving across the surface of the globe, a transparent globe is rotated around the internal mechanism. This not only looks gorgeous, but solves a key problem in [Will]’s design — that is, there’s no trailing servo wiring that needs to be kept track of.

For anyone who wants an Orbigator of their own, [wyojustin] has done a fantastic job of documenting the hardware and software aspects of the build, and all the relevant files are available in the project’s GitHub repository.

The 3D printable components have been created with OpenSCAD, the firmware responsible for calculating the current position of the ISS on the Raspberry Pi Pico 2 is written in MicroPython, and the PCB was designed in KiCad. Incidentally, we noticed that Hackaday alum [Anool Mahidharia] appears to have been lending a hand with the board design.

As much as we love these polished orbital trackers, we’ve seen far more approachable builds if you don’t need something so elaborate. If you’re more interested in keeping an eye out for planes and can get your hands on a pan-and-tilt security camera, it’s even easier.

While it might not be comprehensive, [Bret.dk] recently posted a retrospective titled “Every Single Board Computer I Tested in 2025.” The post covers 15 boards from 8 different companies. The cheapest board was $42, but the high-end topped out at $590.

We like the structure of the post. The boards are grouped in an under $50 category, another group for $50-100, and a final group for everything north of $100. Then there’s some analysis of what RAM prices are doing to the market, and commentary about CIX P1, Qualcomm, RISC-V, and more.

You get the idea that the post is only summarizing experiences with each board, and, for the intended purpose, that’s probably a good thing. On the other hand, many of the boards have full reviews linked, so be sure to check them out if you want more details. The Arduino Q didn’t fare well in review, nor did the BeagleBoard Green Eco. But the surprise was newcomer CIX. Their SoC powers two entries, one from Radaxa and the other from Orange Pi. In both cases, the performance of these was surprisingly good. There are some concerns with tooling and a few hiccups with things like power consumption, but if those were fixed, the CIX chips could be showing up more often.

[Bret’s] post is very informative. We’d be interested to hear whether you disagree with any of his assessments or have a favorite SBC that didn’t make his list. Let us know in the comments. Of course, there are other boards out there, but you can see that development tools and support often differentiate products more than just raw computing power.

A friend of mine has been a software developer for most of the last five decades, and has worked with everything from 1960s mainframes to the machines of today. She recently tried AI coding tools to see what all the fuss is about, as a helper to her extensive coding experience rather than as a zero-work vibe coding tool. Her reaction stuck with me; she referenced her grandfather who had been born in rural America in the closing years of the nineteenth century, and recalled him describing the first time he saw an automobile.

Après Nous, Le Krach

We are living amid a wave of AI slop and unreasonable hype so it’s an easy win to dunk on LLMs, but as the whole thing climbs towards the peak of inflated expectations on the Gartner hype cycle perhaps it’s time to look forward. The current AI hype is inevitably going to crash and burn, but what comes afterwards? The long tail of the plateau of productivity will contain those applications in which LLMs are a success, but what will they be? We have yet to hack together a working crystal ball, but perhaps it’s still time to gaze into the future.

To most of the population, AI, which for them mostly means ChatGPT, is a magic tool that can write stuff for them, and make them look smart when they’re not asking it to draw a picture of a cat doing something human. It has replaced a search engine for many people, and become a confidante to many others to the extent that the phrase “Chatbot psychosis” has entered the lexicon.

Having a tool that can write anything you ask it to has of course unleashed that AI slop; whether it’s a useless web page or an equally useless report at your employer, we’re all acquiring the skill of spotting fake content. There are some people who have predicted the demise of human writers as a result, but though the chatbots can do a pretty good job of copying a writer’s style I do not share that view. By the time we’ve reached that long plateau, there will be an enhanced value in content written by meatbags because the consumer will have evolved a hair-trigger response to slop, so rest assured, Hackaday will not succumb.

If I have a prediction for those chatbots it will mirror previous booms and crashes; that the circular economic illusion between chipmakers and AI companies will inevitably derail, and like search engines in the early 2000s, most of them will not survive.

Ah, I See You’re A Waffle Man, Then

My software developer friend sees an LLM as a productivity aid in her coding to be something with a future, but where do I as a writer and Hackaday scribe see them going? It’s something I’ve given quite some thought to, and my conclusion is one that is much less all-encompassing. The privacy aspect of sharing your innermost thoughts, business decisions, or whatever other valuable stuff with a third party will inevitably catch up with the LLM industry, whether it’s through an unscrupulous data sharing deal or an LLM revealing things it shouldn’t to others. I thus think that the most ubiquitous LLMs in our future will be ones that are much more local, with less reliance on those power-hungry datacentres. I can’t predict all their applications, but I’m going to give a couple of examples in the here and now which have caught my attention.

The first example comes from my experience outside Hackaday, over a long career in the publishing and documentation industry, Many organisations have huge libraries of information on their intranets which is commercially sensitive enough that it can’t leave the site for processing by external AI company. Imagine documentation, product specifications, and the like. There’s already a thriving industry of intranet search and retrieval products in this space, and the AI companies naturally want a piece of it too. I can see a future in which a local LLM equivalent of those old yellow Google Search rack servers provides an intelligent interface to those troves of data, without the danger of leaks, or of going off piste.

The second comes from both a 1980s British TV sit-com, and from the LLM projects we’re starting to see here at Hackaday. In short, I think that appliances you can talk to will find their way into the consumer market, and nowhere will be safe from the Red Dwarf Talkie Toaster.

Jokes about maniacal kitchen appliances aside, we are now at the point at which the latest Raspberry Pi can just about run a functioning speech-based chatbot. Given a few years more microprocessor and microcontroller development, and the current cost, of a Pi with the accelerator board, will drop to a few dollars for a high-end microcontroller to do the same task.

I see it as inevitable that there will be a class of chip that will be offered out of the box with some kind of LLM capability, and that in no time the most unlikely of appliances will have personalities. It will inevitably be annoying, but out of that will come a few that might be useful.

So along with my software developer friend I’ve tried to move beyond my writer’s disdain for the very obvious negative side of the LLM bubble, and look ahead to a future when using a chatbot is no longer thought to make you look smart. In a few years time an LLM will be one of those things that’s just there, and what form will it take? Like that early-20th-century American who looked at a car and saw it was going to have an impact on the future I know I’m looking at something that’s going to remain with me whether I like it or not. I’ve speculated on how that might happen in a couple of ways above, but what about you? Are the agents which are the darling of the AI crowd at the moment going to take over our lives? Or will it be something else? As always, the comments are below.