When Microsoft decided they wanted to get into the game console market, they were faced with a problem. Everyone knew them as a company that developed computer software, and there was a concern that consumers wouldn’t understand that their new Xbox console was a separate product from their software division. To make sure they got the message though, Microsoft decided to show off a prototype that nobody could mistake for a desktop computer.
The giant gleaming X that shared the stage with Bill Gates and Seamus Blackley at the 2000 Game Developers Conference became the stuff of legend. We now know the machine wasn’t actually a working Xbox, but at the time, it generated enormous buzz. But could it have been a functional console? That’s what [Tito] of Macho Nacho Productions wanted to find out — and the results are nothing short of spectacular.
[zeroshot] designed a simple gimbal to allow the stick to move in two axes, relying primarily on 3D-printed components combined with a smattering of off-the-shelf bearings. For force feedback, an Arduino Micro uses via TMC2208 stepper drivers to control a pair of stepper motors, which can apply force to the stick in each axis via belt-driven pulleys. Meanwhile, the joystick’s position on each axis is tracked via magnetic encoders. The Arduino feeds this data to an attached computer by acting as a USB HID device.
Each year around the end of October we feature plenty of Halloween-related projects, usually involving plastic skeletons and LED lights, or other fun tech for decorations to amuse kids. It’s a highly commercialised festival of pretend horrors which our society is content to wallow in, but beyond the plastic ghosts and skeletons there’s both a history and a subculture of the supernatural and the paranormal which has its own technological quirks. We’re strictly in the realm of the science here at Hackaday so we’re not going to take you ghost hunting, but there’s still an interesting journey to be made through it all.
Today: Fun For Kids. Back Then: Serious Business
English churches abound with marble-carved symbols of death.
Halloween as we know it has its roots in All Hallows Eve, or the day before the remembrance festivals of All Saint’s Day and All Soul’s Day in European Christianity. Though it has adopted a Christian dressing, its many trappings are thought to have their origin in pagan traditions such as for those of us where this is being written, the Gaelic Samhain (pronounced something like “sow-ain”). The boundary between living and dead was thought to be particularly porous at this time of year, hence all the ghosts and other trappings of the season you’ll see today.
Growing up in a small English village as I did, is to be surrounded by the remnants of ancient belief. They survive from an earlier time hundreds of years ago when they were seen as very real indeed, as playground rhymes at the village school or hushed superstitions such as that it would be bad luck to walk around the churchyard in an anticlockwise manner.
As a small child they formed part of the thrills and mild terrors of discovering the world around me, but of course decades later when it was my job to mow the grass and trim the overhanging branches in the same churchyard it mattered little which direction I piloted the Billy Goat. I was definitely surrounded by the mortal remains of a millennium’s worth of my neighbours, but I never had any feeling that they were anything but at peace. Continue reading “The Time Of Year For Things That Go Bump In The Night”→
Has anyone noticed that news stories have gotten shorter and pithier over the past few decades, sometimes seeming like summaries of what you used to peruse? In spite of that, huge numbers of people are relying on large language model (LLM) “AI” tools to get their news in the form of summaries. According to a study by the BBC and European Broadcasting Union, 47% of people find news summaries helpful. Over a third of Britons say they trust LLM summaries, and they probably ought not to, according to the beeb and co.
It’s a problem we’ve discussed before: as OpenAI researchers themselves admit, hallucinations are unavoidable. This more recent BBC-led study took a microscope to LLM summaries in particular, to find out how often and how badly they were tainted by hallucination.
Not all of those errors were considered a big deal, but in 20% of cases (on average) there were “major issues”–though that’s more-or-less independent of which model was being used. If there’s good news here, it’s that those numbers are better than they were when the beeb last performed this exercise earlier in the year. The whole report is worth reading if you’re a toaster-lover interested in the state of the art. (Especially if you want to see if this human-produced summary works better than an LLM-derived one.) If you’re a luddite, by contrast, you can rest easy that your instincts not to trust clanks remains reasonable… for now.
Either way, for the moment, it might be best to restrict the LLM to game dialog, and leave the news to totally-trustworthy humans who never err.
If you haven’t lived underneath a rock for the past decade or so, you will have seen a lot of arguing in the media by prominent figures and their respective fanbases about what the right sensor package is for autonomous vehicles, or ‘self-driving cars’ in popular parlance. As the task here is to effectively replicate what is achieved by the human Mark 1 eyeball and associated processing hardware in the evolutionary layers of patched-together wetware (‘human brain’), it might seem tempting to think that a bunch of modern RGB cameras and a zippy computer system could do the same vision task quite easily.
This is where reality throws a couple of curveballs. Although RGB cameras lack the evolutionary glitches like an inverted image sensor and a big dead spot where the optical nerve punches through said sensor layer, it turns out that the preprocessing performed in the retina, the processing in the visual cortex and analysis in the rest of the brain is really quite good at detecting objects, no doubt helped by millions of years of only those who managed to not get eaten by predators procreating in significant numbers.
Hence the solution of sticking something like a Lidar scanner on a car makes a lot of sense. Not only does this provide advanced details on one’s surroundings, but also isn’t bothered by rain and fog the way an RGB camera is. Having more and better quality information makes subsequent processing easier and more effective, or so it would seem.
These days just about any battery storage solution connected to PV solar or similar uses LiFePO4 (LFP) batteries. The reason for this is obvious: they got a very practical charge and discharge curve that chargers and inverters love, along with a great round trip efficiency. Meanwhile some are claiming that sodium-ion (Na+) batteries would be even better, but this is not borne out by the evidence, with [Will Prowse] testing and tearing down an Na+ battery to prove the point.
The OCV curve for LFP vs Na+ batteries.
The Hysincere brand battery that [Will] has on the test bench claims a nominal voltage of 12 V and a 100 Ah capacity, which all appears to be in place based on the cells found inside. The lower nominal voltage compared to LFP’s 12.8 V is only part of the picture, as can be seen in the OCV curve. Virtually all of LFP’s useful capacity is found in a very narrow voltage band, with only significant excursions when reaching around >98% or <10% of state of charge.
What this means is that with existing chargers and inverters, there is a whole chunk of the Na+ discharge curve that’s impossible to use, and chargers will refuse to charge Na+ batteries that are technically still healthy due to the low cell voltage. In numbers, this means that [Will] got a capacity of 82 Ah out of this particular 100 Ah battery, despite the battery costing twice that of a comparable LFP one.
Yet even after correcting for that, the internal resistance of these Na+ batteries appears to be significantly higher, giving a round trip efficiency of 60 – 92%, which is a far cry from the 95% to 99% of LFP. Until things change here, [Will] doesn’t see much of a future for Na+ beyond perhaps grid-level storage and as a starter battery for very cold climates.
If you cultivate an interest in building radios it’s likely that you’ll at some point make a simple receiver. Perhaps a regenerative receiver, or maybe a direct conversion design, it’ll take a couple of transistors or maybe some simple building-block analogue ICs. More complex designs for analogue radios require far more devices; if you’re embarking on a superhetrodyne receiver in which an oscillator and mixer are used to generate an intermediate frequency then you know it’ll be a hefty project. [VK3YE] is here to explode that assumption, with a working AM broadcast band superhet that uses only two transistors.
It doesn’t get much simpler than this.
A modern portable radio will almost certainly use an all-in-one SDR-based chip, but in the golden age of the transistor radio the first stage of the receiver would be a single transistor that was simultaneously RF amplifier, oscillator, and mixer. The circuit in the video below does this , with a ferrite rod, the familiar red-cored oscillator coil, and a yellow-cored IF transformer filtering out the 455 kHz mixer product between oscillator and signal.
There would normally follow at least one more transistor amplifying the 455 kHz signal, but instead the next device is both a detector and an audio amplifier. Back in the day that would have been a germanium point contact diode, but now the transistor has a pair of 1N4148s in its biasing. We’re guessing this applies a DC bias to counteract the relatively high forward voltage of a silicon diode, but we could be wrong.
We like this radio for its unexpected simplicity and clever design, but also because he’s built it spiderweb-style. We never expected to see a superhet this simple, and even if you have no desire to build a radio we hope you’ll appreciate the ingenuity of using simple transistors to the max.
