2026-04-09 04:00:11
Modern computers use dynamic RAM, a technology that allows very compact bits in return for having to refresh for about 400 nanoseconds every 3-4 microseconds. But what if you couldn’t afford even such a tiny holdup? [LaurieWired] goes into excruciating detail about how to avoid this delay.
But first, why do we care? It once again comes down to high-frequency trading; a couple nanoseconds of latency can be the difference between winning or losing a buy order. You likely miss all the caches and need to fetch data from the remote land of main memory. And if you get unlucky, you’ll be waiting on that price for a precious 400+ nanoseconds! [Laurie] explains all the problems faced in trying to avoid this penalty; you try to get a copy of the data on two independent refresh timers. That’s easier said than done; not only does the operating system hide the physical addresses from you, but the memory controllers themselves also scramble the addresses to the underlying RAM!
For the real computer architecture nerds, there’s a lot more to it, and [Laurie] goes over it in meticulous detail in the video after the break.
Thanks to [Keith Olson] for the tip!
2026-04-09 02:30:56
Odds are, if you like neon lights, you’re not thrilled with the LED faux-“neon” strips that are supposed to replace them. They’ve got their advantages, but the light quality of RGB LEDs lacks something compared to the emission spectrum of nobel gas, at least to purists. On the other hand, you cannot create an animation by bending glass tubes, like [David Hamp-Gonsalves] has demonstrated with his Neon Animated Eye.
Back in the day, you’d have needed dozens of tubes for a flickery animation, but [David] figured that since these LED strips are flexible, why not flex them? He’s using addressable LEDs — WS2812s, specifically — so activating and deactivating the pupil of the eye is easy-peasy. Opening and closing the lid is accomplished with a geared motor driven by a TB6612 driver turning a barrel cam. The ends of the stiff LED strip being brought together and pulled apart result in the blinking effect here, but as [David] points out you’re hardly limited that specific motion. There’s a whole world of Tron-like glowing animatronics that can be created with this technique. Code and STLs are available on GitHub, though, if you want to replicate the eye exactly.
[David] says he’d like to see this in a storefront someday, but given that fatigue life is a thing, it might be something to keep in your back pocket for seasonal displays like Christmas and Halloween rather than something that’s going to run 24/7. On the other hand, if you’re careful about limiting flexion and which faux-neon strip you buy, you might be able to create an animation that can last for years.
This is hardly the first time we’ve seen these faux-neon strips , but it is the first time we’ve seen them animated. We can’t help but think the Hauntimator software we featured before would be a good paring with this hack.
2026-04-08 23:30:40
2001: A Space Odyssey not only pushed the boundaries of filmmaking, but introduced us to one of the most enduring villains in all of media. The HAL 9000 artificial intelligence was human-like but inhuman, a singular uncanny red light on a wall, tasked not only with control of a spaceship and its inner workings but also with being a companion for its occupants. It’s gone on to be the inspiration and basis of many projects around here, where it is generally given much less scope than control of a space ship and instead is tasked with something like monitoring air quality in a home.
Called the PAL 8000 by its creator [Arnov], this uses a Raspberry Pi Pico 2 at its core which monitors a volatile organic compound (VOC) sensor to take air quality measurements. The device features a custom 3D printed enclosure with glowing LEDs and plays contextual audio responses based on air quality levels, completing the HAL 9000 theme. The project also includes a local web dashboard which reports on its data, allowing users to see information in real time rather than relying on HAL’s voice reports alone.
For those looking to build other HAL-inspired projects, [Arnov] has made many of the printing files available on the project’s site. It’s a well-polished build faithful to the source material and could be a great addition to any home automation system for many other tasks beyond air quality monitoring. Perhaps something like a more general-purpose voice assistant, minus the megalomania.
2026-04-08 22:00:39
It’s odd being a technology writer in 2026, because around you are many people who will tell you that your craft is outdated. Like the manufacturers of buggy-whips at the turn of the twentieth century, the automobile (in the form of large language model AI) is on the market, and your business will soon be an anachronism. Adapt or go extinct, they tell you. It’s an argument I’ve found myself facing a few times over the last year in my wandering existence, and it’s forced me to think about it. What are the reasons everyone is excited about AI and are those reasons valid, what is there to be scared of, and what are the real reasons people should be excited about it?
I’ll start by repeating my tale from a few weeks ago when I asked readers what AI applications would survive when the hype is over. The reaction of a friend with decades of software experience on trying an AI coding helper 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. I agree with her that this has the potential to be a transformative technology, and while it’s entertaining to make fun of its shortcomings as I did three years ago when the idea of what we now call vibe coding first appeared, it’s already making itself useful in some applications. Simply dismissing it is no longer appropriate, but equally, drinking freely of the Kool-Aid seems like joining yet another hype bandwagon that will inevitably derail. A middle way has to be found.
It’s likely many of us will over the last couple of years met a Guy In A Suit who’s got a little too excited about ChatGPT. I think guys like him are motivated by several things; he’s impressed with that LLM because it appears really smart to him, he’s used it to make himself appear smart to other people so it’s made him feel smarter than the engineer who’s pointing out his flaws, he thinks it’s a magic bullet that can do lots of work for him and either save or make him lots of money, and perhaps most importantly, he’s scared witless of missing out on the Next Big Thing.
It’s easy to take pot-shots at those motivations even it it won’t make you popular. His feeling smart will last only as long as the moment he gets it that everyone else has the same thing, or perhaps until it leads him astray into a calamitous decision. Meanwhile there’s a good chance the magic bullet will go the way that wholesale outsourcing of software development did twenty years ago, as an over reliance on something-for-nothing work will generate far more other work to fix its problems. But while those pot-shots weaken some arguments they aren’t perhaps the crushing blows one might imagine they are. LLMs have their uses, however annoying that may be if you’re sick to death of low-value slop.
Perhaps more worthy of examination is the fear of missing out, because that’s a more fundamental motivation. We all want to be among the Cool Kids, Hackaday readers having the latest tech toys before everyone else are not immune to this. And when you have convinced yourself that the alternative to being one of the Cool Kids is being the commercial equivalent of a buggy-whip salesman circa 1920, it assumes an extra urgency. It’s time to look at a perennial favourite, the Gartner Hype Cycle, for inspiration. Just where on a Gartner Hype Cycle curve do you have to be, to miss out?
On the left of the graph is the steep slope towards the Peak of Inflated Expectation. This is the part we most associate with tech bubbles; as an example we might point to the dotcom boom during its most intensive period in 1997 or 1998. If you pick the moment of the peak or indeed the downward slope towards the Trough of Disillusionment to jump in, then it’s obvious you have missed out. But how far back down the upward slope do you have to be to have not missed out? I’d contend that it’s much earlier, to use our dotcom boom analogy: if you weren’t in the game by 1996, perhaps you were too late. Transposing to the AI boom of today, has our Guy In A Suit already missed the boat without realising it?
It pains me when I see people newly excited by AI in 2026 for the reasons listed above. To them they’re valid, but having lived and worked through so many other booms and subsequent crashes driven by similar ideas about those technologies I know how the next year or so will go. I think there are many other valid reasons to be excited here, but they lie elsewhere on the Gartner graph. Back to the dotcom boom, the whole thing was driven by sometimes outright crazy ideas surrounding e-commerce, yet it would be social media a decade later that would make many of the huge players we have today. Could someone have made Facebook in 1996? Possibly, but if anyone thought of it at that point, it seems they didn’t do it. If Guy In A Suit is looking for something to be excited about, he should be polishing his crystal balls and looking ahead to the right hand side of the Gartner graph in a decade’s time, not running with the herd.
Returning to my first paragraph and whether a writer will inevitably join the buggy-whip salesmen, I remain rather optimistic that they won’t. Hackaday is meat-based for good reason, but more generally I’m watching the consumer develop a hair-trigger response to slop. I’m certain that there will be a space for machine-generated content in the future whether we like it or not, but I’m equally sure that in my line at least, a human input will retain some value.
Having considered Guy In A Suit and then myself, perhaps it’s time to talk about you, the Hackaday reader. We probably have more AI-skeptics among us than can be found in the general public and I consider myself in part among them, but for all that skepticism I think we should channel it into seeking out the interesting things rather than turning our backs on it. I’ve mentioned the AI-based coding helpers as an example where our community has found some benefit, and as I’ve mentioned before I think that the ability to run a useful LLM locally on commodity hardware delivers huge potential over a cloud data-slurper. If we don’t believe in it, at least we should be like Fox Mulder, and want to believe.
Where are you on that continuum?
2026-04-08 19:00:01
“Once, men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them.” — so said [Frank Herbert] in his magnum opus, Dune, or rather in the OC Bible that made up part of the book’s rich worldbuilding. A recent study demonstrating “cognitive surrender” in large language model (LLM) users, as reported in Ars Technica, is going to add more fuel to that Butlerian fire.
Cognitive surrender is, in short, exactly what [Herbert] was warning of: giving over your thinking to machines. In the study, people were asked a series of questions, and — except for the necessary “brain-only” control group — given access to a rigged LLM to help them answer. It was rigged in that it would give wrong answers 50% of the time, which while higher than most LLMs, only a difference in degree, not in kind. Hallucination is unavoidable; here it was just made controllably frequent for the sake of the study.
The hallucinations in the study were errors that the participants should have been able to see through, if they’d thought about the answers. Eighty percent of the time, they did not. That is to say: presented with an obviously wrong answer from the machine, only in 20% of cases did the participants bother to question it. The remainder were experiencing what the researchers dubbed “cognitive surrender”: they turned their thinking over to the machines. There’s a lot more meat to this than we can summarize here, of course, but the whole paper is available free for your perusal.
Giving over thinking to machines is nothing new, of course; it’s probably been a couple decades since the first person drove into a lake on faulty GPS directions, for example. One might even argue that since LLMs are correct much more than 50% of the time, it is statistically wise to listen to them. In that case, however, one might be encouraged to read Dune.
Thanks to [Monika] for the tip!
2026-04-08 16:00:56
In a move that’s no doubt going to upset and confuse many, Espressif has released its newest microcontroller — the ESP32-S31. The confusing part here is that the ESP32-S series was always the one based on Tensilica Xtensa LX7 cores, while the ESP32-C series was the one using RISC-V cores.
That said, if one looks at it as a beefier -S3 MCU it does have some appealing upgrades. The most obvious improvements are with the use of WiFi 6, as well as Bluetooth Classic and LE 5.4, including LE Audio. There is also Thread and Zigbee support for those who are into such things.
The Ethernet MAC got a bump from the 100 Mbit RMII MAC in previous MCUs and is now gigabit-rated, while the number of GPIO is significantly higher at 60 instead of 45 on the -S3. On the RAM side, things are mostly the same, except for DDR PSRAM support, with octal SPI offering up to 250 MHz compared to 80 MHz on the -S3.
On the CPU side the up-to-320 MHz RISC-V cores are likely to be about as powerful as the 240 MHz LX7 cores in the -S3, based on the ESP32-C series performance in terms of IPC. Overall it does seem like a pretty nice MCU, it’s just confusing that it doesn’t use LX7 cores with the series it was put into. When this MCU will be available for sale doesn’t seem to be known yet, with only samples available to select customers.
