An Almost Invisible Desktop

When you’re putting together a computer workstation, what would you say is the cleanest setup? Wireless mouse and keyboard? Super-discrete cable management? How about no visible keeb, no visible mouse, and no obvious display?

That’s what [Basically Homeless] was going for. Utilizing a Flexispot E7 electronically raisable standing desk, an ASUS laptop, and some other off-the-shelf parts, this project is taking the idea of decluttering to the extreme, with no visible peripherals and no visible wires.

There was clearly a lot of learning and much painful experimentation involved, and the guy kind of glazed over how a keyboard was embedded in the desk surface. By forming a thin layer of resin in-plane with the desk surface, and mounting the keyboard just below, followed by lots of careful fettling of the openings meant the keys could be depressed. By not standing proud of the surface, the keys were practically invisible when painted. After all, you need that tactile feedback, and a projection keeb just isn’t right.

ChatGPT-inspired machine learning mouse emulator

Moving on, never mind an ultralight gaming mouse, how about a zero-gram mouse? Well, this is a bit of a cheat, as they mounted a depth-sensing camera inside a light fitting above the desk, and built a ChatGPT-designed machine-learning model to act as a hand-tracking HID device. Nice idea, but we don’t see the code.

The laptop chassis had its display removed and was embedded into the bottom of the desk, along with the supporting power supplies, a couple of fans, and a projector. To create a ‘floating’ display, a piece of transparent plastic was treated to a coating of Lux labs “ClearBright” transparent display film, which allows the image from the projector to be scattered and observed with sufficient clarity to be usable as a PC display. We have to admit, it looks a bit gimmicky, but playing Minecraft on this setup looks a whole lotta fun.

Many of the floating displays we’ve covered tend to be for clocks (after all timepieces are important) like this sweet HUD hack.

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The Glitch That Brought Down Japan’s Lunar Lander

When a computer crashes, it usually doesn’t leave debris. But when a computer happens to be descending towards the lunar surface and glitches out, that’s a very different story. Turns out that’s what happened on April 26th, as the Japanese Hakuto-R Lunar lander made its mark on the Moon…by crashing into it. [Scott Manley] dove in to try and understand the software bug that caused an otherwise flawless mission to go splat.

The lander began the descent sequence as expected at 100 km above the surface. However, as it descended, the altitude sensor reported the altitude as much lower than it was. It thought it was at zero altitude once it reached about 5 km above the surface. Confused by the fact it hadn’t yet detected physical contact with the surface, the craft continued to slowly descend until it ran out of fuel and plunged to the surface.

Ultimately it all came down to sensor fusion. The lander merges several noisy sensors, such as accelerometers, gyroscopes, and radar, into one cohesive source of truth. The craft passed over a particularly large cliff that caused the radar altimeter to suddenly spike up 3 km. Like good filtering software, the craft reasons that the sensor must be getting spurious data and filters it out. It was now just estimating its altitude by looking at its acceleration. As anyone who has tried to track an object through space using just gyros and accelerometers alone can attest, errors accumulate, and suddenly you’re not where you think you are.

We know what you’re thinking: surely they would have run landing simulations to catch errors like these? Ironically they did, it’s just that after the simulations were run, the landing site for Hakuto-R was changed. Unfortunately, nobody thought to re-run the simulations, and now the Moon has a new lawn ornament,

We’ve previously written about why lunar landings are so hard. While knowing what led to the crash will hopefully prevent a similar fate for future missions, the reality is that remotely landing a robot on a dusty world without the help of GPS is fiendishly difficult and likely will be for some time.

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Bike Rides Played Back Via Aircraft Altitude Indicator

Any good bike ride should have a big climb to push your fitness, and a nice descent for the joy of careening down at high speed. [Glen Akins] has been recording his altitude during his mountain biking expeditions, and has now built a way to play them back on an aircraft altitude indicator.

A Python script is used to parse a recorded GPX file, which stores position and elevation data captured from a GPS device during [Glen]’s rides. The elevation data is then output to a Raspberry Pi Pico, which drives a set of three Microchip MCP4802 DACs and three TI OPA584 op-amps in order to create the necessary 400 Hz AC waveforms to drive the aircraft altitude indicator. One DAC and op-amp are used to generate 400 Hz AC to simply power the device, while the other two are used to generate synchro signals to actually drive the dial as needed. The maths involved is worth checking out, particularly if you’re into old-school instrumentation from the 20th century.

We’ve seen similar tinkering efforts from [Glen] before, too.

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The Apple Silicon That Never Was

Over Apple’s decades-long history, they have been quick to adapt to new processor technology when they see an opportunity. Their switch from PowerPC to Intel in the early 2000s made Apple machines more accessible to the wider PC world who was already accustomed to using x86 processors, and a decade earlier they moved from Motorola 68000 processors to take advantage of the scalability, power-per-watt, and performance of the PowerPC platform. They’ve recently made the switch to their own in-house silicon, but, as reported by [The Chip Letter], this wasn’t the first time they attempted to design their own chips from the ground up rather than using chips from other companies like Motorola or Intel.

In the mid 1980s, Apple was already looking to move away from the Motorola 68000 for performance reasons, and part of the reason it took so long to make the switch is that in the intervening years they launched Project Aquarius to attempt to design their own silicon. As the article linked above explains, they needed a large amount of computing power to get this done and purchased a Cray X-MP/48 supercomputer to help, as well as assigning a large number of engineers and designers to see the project through to the finish. A critical error was made, though, when they decided to build their design around a stack architecture rather than a RISC. Eventually they switched to a RISC design, though, but the project still had struggled to ever get a prototype working. Eventually the entire project was scrapped and the company eventually moved on to PowerPC, but not without a tremendous loss of time and money.

Interestingly enough, another team were designing their own architecture at about the same time and ended up creating what would eventually become the modern day ARM architecture, which Apple was involved with and currently licenses to build their M1 and M2 chips as well as their mobile processors. It was only by accident that Apple didn’t decide on a RISC design in time for their personal computers. The computing world might look a lot different today if Apple hadn’t languished in the early 00s as the ultimate result of their failure to develop a competitive system in the mid 80s. Apple’s distance from PowerPC now doesn’t mean that architecture has been completely abandoned, though.

Thanks to [Stephen] for the tip!

ADATA SSD Gets Liquid Cooling, But Not Everyone’s Convinced

Solid-state drives (SSDs) were a step change in performance when it came to computer storage. They offered incredibly fast seek times by virtue of dispensing with solid rust for silicon instead. Now, some companies have started pushing the limits to the extent that their drives supposedly need liquid cooling, as reported by The Register.

The device in question is the ADATA Project NeonStorm, which pairs a PCIe 5.0 SSD with RGB LEDs, a liquid cooling reservoir and radiator, and a cooling fan. The company is light on details, but it’s clearly excited about its storage products becoming the latest piece of high-end gamer jewelry.

Notably though, not everyone’s jumping on the bandwagon. Speaking to The Register, Jon Tanguy from Crucial indicated that while the company has noted modern SSDs running hotter, it doesn’t yet see a need for active cooling. In their case, heatsinks have proven enough. He notes that NAND flash used in SSDs actually operates best at 60 to 70 C. However, going beyond 80 C risks damage and most drives will shutdown or throttle access at this point.

Realistically, you probably don’t need to liquid cool your SSDs, even if you’ve got the latest and greatest models. However, if you want the most tricked out gaming machine on Twitch, there’s plenty of products out there that will happily separate you from your money.

Software Driving Hardware

We were talking about [Christopher Barnatt]’s very insightful analysis of what the future holds for the Raspberry Pi single board computers on the Podcast. On the one hand, they’re becoming such competent computers that they are beginning to compete with lightweight desktop machines, instead of just being a hacker curiosity.

On the other hand, especially given the shortage and the increase in price that has come with the Pi’s expanding memory endowments, a lot of people who would “just throw in a Raspberry Pi” are starting to think more carefully about their options. Five years ago, this would have meant looking into what you could whip together on an Arduino-based platform, either on actual Arduino hardware or on an ESP8266 or similar, but that’s a very different beast from a programmer’s perspective. Working with microcontrollers used to be very different from working with even the smallest Linux machines.

These days, there is no shortage of microcontrollers that have enough memory – both flash and RAM – to support a higher-level environment like MicroPython. And if you think about it, MicroPython brings to the microcontrollers a lot of what people were using a Raspberry Pi for in projects anyway: a friendly interactive programming environment that was free of the compile-here, flash-there debug cycle. If you’re happy coding Python on a single-board Linux computer, you’ll be more or less happy coding in MicroPython or Circuit Python on a microcontroller.

And what this leaves us with, as hackers, is a fantastic spectrum of choices. Where before there was a hard edge between programming C on an 8-bit PIC or an AVR and working with something that had a full Linux operating system like a Pi, it’s all blurry now. And as the Pis, the Jetson, and all the other Linux SBCs are blurring the boundary with more traditional computers as they all become more competent and gain more computer-like peripherals. Nowadays your choice is much freer, and the hardware landscape more fluid. You don’t have to let software development concerns drive your hardware choices, and we think that’s a great thing.

Zelda Guardian Sculpture Tracks Humans And Pets Via Camera

In The Legend of Zelda: Breath of the Wild Guardians are a primitive form of sentry turret that tracks the player with a watchful eye. Inspired by this, [npentrel] decided to whip up one of his own in the real world.

The build relies on a Raspberry Pi kitted out with its usual camera for machine vision purposes. It uses the Viam robot toolkit, which runs a machine learning model to detect pets and humans on the camera feed. The guardian then tracks any pets or humans that show up by turning its head, and thus the camera, with a servo controlled by a PWM signal via the Raspberry Pi’s GPIO pins. It’s all wrapped up in a nicely-decorated 3D printed model that really does look like something straight out of Breath of the Wild.

Sentry projects are a great way to learn about electronics, mechanics, and image processing techniques. It’s funny to see how advanced and complicated these projects were fifteen years ago, compared to how easy they are today with modern machine learning libraries. How times change!