[Charmed Labs] are responsible for bringing numerous open-source hardware products to fruition over the years, and their latest device is an adorably small robotic camera platform called Goby, currently crowdfunding for its initial release. Goby has a few really clever design features and delivers a capable (and hackable) platform for under 100 USD.

Goby embraces its small size, delivering what its creators dub “tinypresence” — or the feeling of being there, but on a very small scale. Cardboard courses, LEGO arenas, or even tabletop gaming scenery hits different when experienced from a first-person perspective. Goby is entirely reprogrammable with nothing more than a USB cable and the Arduino IDE, while costing less than most Arduino starter kits.

One of the physical features we really like is the tail-like articulated caster at the rear. Flexing this pivots Goby up or down (and can even flip Goby completely over), allowing one to pan and tilt the view without needing to mount the camera on a gimbal. It also comes into play for recharging; Goby simply moves over the disc-shaped charger and pivots down to make contact.

At Goby‘s heart is an ESP32-S3 and OmniVision OV2640 camera sensor streaming a live video feed (and driving controls) with WebRTC. Fitting the WebRTC stack onto an ESP32 wasn’t easy, but opens up possibilities beyond just media streaming.

Goby is set up to make launching an encrypted connection as easy as sharing a URL or scanning a QR code. The link is negotiated between bot and client with the initial help of an external server, and once a peer-to-peer connection is established, the server’s job is done and it is out of the picture. [Charmed Labs]’s code for this functionality — named BitBang — is in beta and destined for an open release as well. While BitBang is being used here to make it effortless to access Goby remotely, it’s more broadly intended to make web access for any ESP32-based device easier to implement.

As far as tiny remote camera platforms go, it might not be as small as rebuilding a Hot Wheels car into a micro RC platform, but it’s definitely more accessible and probably cheaper, to boot. Check it out at the Kickstarter (see the first link in this post) and watch it in action in the video, embedded just below the page break.

[Geoffrey Litt] shows that getting an effective digital assistant that’s tailored to one’s own needs just needs a little DIY, and thanks to the kinds of tools that are available today, it doesn’t even have to be particularly complex. Meet Stevens, the AI assistant who provides the family with useful daily briefs. The back end? Little more than one SQLite table and a few cron jobs.

Every day, Stevens sends a daily brief via Telegram that includes calendar events, appointments, weather notes, reminders, and even a fun fact for the day. Stevens isn’t just send-only, either. Users can add new entries or ask questions about items through Telegram.

It’s rudimentary, but [Geoffrey] already finds it far more useful than Siri. This is unsurprising, as it has been astutely observed that big tech’s digital assistants are designed to serve their makers rather than their users. Besides, it’s also fun to have the freedom to give an assistant its own personality, something existing offerings sorely lack.

Architecture-wise, the assistant has a notebook (the single SQLite table) that gets populated with entries. These entries come from things like reading family members’ Google calendars, pulling data from a public weather API, processing delivery notices from the post office, and Telegram conversations. With a notebook of such entries (along with a date the entry is expected to be relevant), generating a daily brief is simple. After all, LLMs (Large Language Models) are amazingly good at handling and formatting natural language. That’s something even a locally-installed LLM can do with ease.

[Geoffrey] says that even this simple architecture is super useful, and it’s not even a particularly complex system. He encourages anyone who’s interested to check out his project, and see for themselves how useful even a minimally-informed assistant can be when it’s designed with ones’ own needs in mind.

With all the attention on LLMs (Large Language Models) and image generators lately, it’s nice to see some of the more niche and unusual applications of machine learning. GARF (Generalizeable 3D reAssembly for Real-world Fractures) is one such project.

GARF may play fast and loose with acronym formation, but it certainly knows how to be picky when it counts. Its whole job is to look at the pieces of a broken object and accurately figure out how to fit the pieces back together, even if there are some missing bits or the edges aren’t clean.

Efficiently and accurately figuring out how to re-assemble different pieces into a whole is not a trivial task. One may think it can in theory be brute-forced, but the complexity of such a job rapidly becomes immense. That’s where machine learning methods come in, as researchers created a system that can do exactly that. It addresses the challenge of generalizing from a synthetic data set (in which computer-generated objects are broken and analyzed for training) and successfully applying it to the kinds of highly complex breakage patterns that are seen in real-world objects like bones, recovered archaeological artifacts, and more.

The system is essentially a highly adept 3D puzzle solver, but an entirely different beast from something like this jigsaw puzzle solving pick-and-place robot. Instead of working on flat pieces with clean, predictable edges it handles 3D scanned fragments with complex break patterns even if the edges are imperfect, or there are missing pieces.

GARF is exactly the kind of software framework that is worth keeping in the back of one’s mind just in case it comes in handy some day. The GitHub repository contains the code (although at this moment the custom dataset is not yet uploaded) but there is also a demo available for the curious.

Incomplete JSON (such as from a log that terminates unexpectedly) doesn’t parse cleanly, which means anything that usually prints JSON nicely, won’t. Frustration with this is what led [Simon Willison] to make The Incomplete JSON pretty printer, a single-purpose web tool that pretty-prints JSON regardless of whether it’s complete or not.

Making a tool to solve a particular issue is a fantastic application of software, but in this case it also is a good lead-in to some thoughts [Simon] has to share about vibe coding. The incomplete JSON printer is a perfect example of vibe coding, being the product of [Simon] directing an LLM to iteratively create a tool and not looking at the actual code once.

[Simon] shares that the term “vibe coding” was first used in a social media post by [Andrej Karpathy], who we’ve seen shared a “hello world” of GPT-based LLMs as well as how to train one in pure C, both of which are the product of a deep understanding of the subject (and fantastically educational) so he certainly knows how things work.

Anyway, [Andrej] had a very specific idea he was describing with vibe coding: that of engaging with the tool in almost a state of flow for something like a weekend project, just focused on iterating one’s way to what they want without fussing the details. Why? Because doing so is new, engaging, and fun.

Since then, vibe coding as a term seems to get used to refer to any and all AI-assisted coding, a subject on which folks have quite a few thoughts (many of which were eagerly shared on a recent Ask Hackaday on the subject).

Of course human oversight is critical to a solid and reliable development workflow. But not all software is the same. In the case of the Incomplete JSON Pretty Printer, [Simon] really doesn’t care what the code actually looks like. He got it made in a short amount of time, the tool does exactly what he wants, and it’s hard to imagine the stakes being any lower. To [Simon], however the LMM decided to do things is fine, and there’s a place for that.

Most robots depend on controlled environments, because the real world is hard to get around in. The smaller the robot, the bigger this problem because little wheels (or legs) can take only little steps. One way around that is MIT’s latest one-legged hopping robot, which sports a set of four insect-like wings on its top end and can quickly pogo-hop its way across different terrain with ease.

The wings aren’t for flying in the usual sense. They provide lift, but also help the tiny device steer itself so that its hops land precisely. Earlier incarnations of one-legged hopping robots (like this one) accomplished this with propellers and electric motors, but traditional motors are a non-starter on a device that weighs less than a paperclip.

Right now, this little winged hopper is not completely self-contained (power and control systems are off-board) but running it as a tethered unit allows researchers to test and evaluate different, minimalistic ways for a machine to move around efficiently. And efficiency is the whole goal of going in this direction.

Certainly tiny flying drones already exist and get about in the real world just fine. But if one wants to shed mass, ditch conventional motors, and reduce cost and power consumption, this tiny winged hopping machine is one way to do it. And it can even carry payloads! The payloads are tiny, of course, but being able to haul around ten times one’s own weight and still function reliably is an impressive feat.

You can watch it in action in the video embedded just below the page break. Once you’ve watched that, we’d like to remind you that novel locomotion isn’t just the domain of hopping robots. Tiny robots with explosive joints is just as wild as it sounds.

Underwater robots face many challenges, not least of which is how to move around. ZodiAq is a prototype underwater soft robot (link is to research paper) that takes an unusual approach to this problem: multiple flexible appendages. The result is a pretty unconventional-looking device that can not only get around effectively, but can do so without disturbing marine life.

ZodiAq sports a soft flexible appendage from each of its twelve faces, but they aren’t articulated like you might think. Despite this, the device can crawl and swim.

Each soft appendage is connected to a motor, which rotates the attached appendage. This low-frequency but high-torque rotation, combined with the fact that each appendage has a 45° bend to it, has each acting as a rotor. Rotation of the appendages acts on the surrounding fluid, generating thrust. When used together in the right way, these appendages allow the unit to move in a perfectly controllable manner.

This locomotion method is directly inspired by the swimming gait of bacterial flagella, which the paper mentions are regarded as the only example of a biological “wheel”.

How fast can it go? The prototype covers a distance of two body lengths every fifteen seconds. True, it’s no speed demon compared to a propeller, but it doesn’t disturb marine life or environments as it moves around. This method of movement has a lot going for it. It’s adaptable and doesn’t use all twelve appendages at once; so there’s redundancy built in. If some get damaged or go missing, it can still move, just slower.

ZodiAq‘s design strikes us as a very accessible concept, should any aspiring marine robot hackers wish to give it a shot. We’ve seen other highly innovative and beautiful underwater designs as well, like body-length undulating fins and articulated soft arms.

We do notice that since it lacks a “front” — it might be a challenge to decide how to mount something like a camera. If you have any ideas, share them in the comments.

[Antoine Pirrone] and [Grégoire Passault] are making a DIY miniature re-imagining of Disney’s BDX droid design, and while it’s still early, there is definitely a lot of progress to see. Known as the Open Duck Mini v2 and coming in at a little over 40 cm tall, the project is expected to have a total cost of around 400 USD.

Bipedal robots are uncommon, and back in the day they were downright rare. One reason is that the state of controlled falling that makes up a walking gait isn’t exactly a plug-and-play feature.

Walking robots are much more common now, but gait control for legged robots is still a big design hurdle. This goes double for bipeds. That brings us to one of the interesting things about the Open Duck Mini v2: computer simulation of the design is playing a big role in bringing the project into reality.

It’s a work in progress but the repository collects all the design details and resources you could want, including CAD files, code, current bill of materials, and links to a Discord community. Hardware-wise, the main work is being done with very accessible parts: Raspberry Pi Zero 2W, fairly ordinary hobby servos, and an BNO055-based absolute orientation IMU.

So, how far along is the project? Open Duck Mini v2 is already waddling nicely and can remain impressively stable when shoved! (A “testing purposes” shove, anyway. Not a “kid being kinda mean to your robot” shove.)

Check out the videos to see it in action, and if you end up making your own, we want to hear about it, so remember to send us a tip!

We’ve seen plenty of motor projects, but [Jeremy]’s DIY Tubular Linear Motor is a really neat variety of stepper motor in a format we certainly don’t see every day. It started as a design experiment in making a DIY reduced noise, gearless actuator and you can see the result here.

Here’s how it works: the cylindrical section contains permanent magnets, and it slides back and forth through the center of a row of coils depending on how those coils are energized. In a way, it’s what one would get by unrolling a typical rotary stepper motor. The result is a gearless (and very quiet) linear actuator that controls like a stepper motor.

While a tubular linear motor is at its heart a pretty straightforward concept, [Jeremy] found very little information on how to actually go about making one from scratch. [Jeremy] acknowledges he’s no expert when it comes to motor design or assembly, but he didn’t let that stop him from iterating on the concept (which included figuring out optimal coil design and magnet spacing and orientation) until he was satisfied. We love to see this kind of learning process centered around exploring an idea.

We’ve seen DIY linear motors embedded in PCBs and even seen them pressed into service as model train tracks, but this is the first time we can recall seeing a tubular format.

Watch it in action in the short video embedded below, and dive into the project log that describes how it works for added detail.

As everyone knows, no matter how many drill bits one owns, one inevitably needs a size that isn’t on hand. Well, if you ever find yourself needing to drill a hole that’s precisely 13 mm, here’s a trick from [AvE] to keep in mind for doing it with a 1/2″ bit. It’s a hack that only works in certain circumstances, but hey, it just may come in handy some day.

So the first step in making a 13 mm hole is to drill a hole with a 1/2″ bit. That’s easy enough. Once that’s done, fold a few layers of tinfoil over into a small square and lay it over the hole. Then put the drill bit onto the foil, denting it into the hole (but not puncturing it) with the tip, and drill at a slow speed until the foil wraps itself around the bit like a sheath and works itself into the hole. The foil enlarges the drill bit slightly and — as long as the material being drilled cooperates — resizes the hole a tiny bit bigger in the process. The basic idea can work with just about any drill bit.

It’s much easier demonstrated than described, so watch it in action in the video around the 2:40 mark which will make it all very clear.

It’s not the most elegant nor the most accurate method (the hole in the video actually ends up closer to 13.4 mm) but it’s still something worth keeping in the mental toolbox. Just file it away along with laying your 3D printer on its side to deal with tricky overhangs.

[ombates] shares a step-by-step method for making a conductive bio-string from scratch, no fancy equipment required. She demonstrates using it to create a decorative top with touch-sensitive parts, controlling animations on an RGB LED pendant. To top it off, it’s even biodegradable!

The string is an alginate-based bioplastic that can be made at home and is shaped in a way that it can be woven or knitted. Alginate comes primarily from seaweed, and it gels in the presence of calcium ions. [ombates] relies on this to make a goopy mixture that, once extruded into a calcium chloride bath, forms a thin rubbery length that can be dried into the strings you see here. By adding carbon to the mixture, the resulting string is darkened in color and also conductive.

There’s no details on what the actual resistance of a segment of this string can be expected to measure, but while it might not be suitable to use as wiring it is certainly conductive enough to act as a touch sensor in a manner similar to the banana synthesizer. It would similarly be compatible with a Makey Makey (the original and incredibly popular hardware board for turning household objects into touch sensors.)

What you see here is [ombates]’ wearable demonstration, using the white (non-conductive) string interwoven with dark (conductive) portions connected to an Adafruit Circuit Playground board mounted as an LED pendant, with the conductive parts used as touch sensors.

Alginate is sometimes used to make dental molds and while alginate molds lose their dimensional accuracy as they dry out, for this string that’s not really a concern. If you give it a try, visit our tip line to let us know how it turned out!

[Simon] wrote in to let us know about DingPong, his handheld portable Pong console. There’s a bit more to it than meets the eye, however. Consider for a moment that back in the 1970s playing Pong required a considerable amount of equipment, not least of which was dedicated electronics and a CRT monitor. What was huge (in more than one way) in the 70s has been shrunk down to handheld, and implemented almost entirely on modern e-waste in the process.

DingPong is housed in an old video doorbell unit (hence the name) and the screen is a Sony Video Watchman, a portable TV from 1982 with an amazing 4-inch CRT whose guts [Simon] embeds into the enclosure. Nearly everything in the build is either salvaged, or scrounged from the junk bin. Components are in close-enough values, and power comes from nameless lithium-ion batteries that are past their prime but still good enough to provide about an hour of runtime. The paddle controllers? Two pots (again, of not-quite-the-right values) sticking out the sides of the unit, one for each player.

At the heart of DingPong one will not find any flavor of Arduino, Raspberry Pi, or ESP32. Rather, it’s built around an AY-3-8500 “Ball & paddle” (aka ‘Pong’) integrated circuit from 1977, which means DingPong plays the real thing!

We have seen Pong played on a Sony Watchman before, and we’ve also seen a vintage Pong console brought back to life, but we’re pretty sure this is the first time we’ve seen a Sony Watchman running Pong off a chip straight from the 70s. Watch it in action in the video (in German), embedded below.

Cloudflare has gotten more active in its efforts to identify and block unauthorized bots and AI crawlers that don’t respect boundaries. Their solution? AI Labyrinth, which uses generative AI to efficiently create a diverse maze of data as a defensive measure.

This is an evolution of efforts to thwart bots and AI scrapers that don’t respect things like “no crawl” directives, which accounts for an ever-growing amount of traffic. Last year we saw Cloudflare step up their game in identifying and blocking such activity, but the whole thing is akin to an arms race. Those intent on hoovering up all the data they can are constantly shifting tactics in response to mitigations, and simply identifying bad actors with honeypots and blocking them doesn’t really do the job any more. In fact, blocking requests mainly just alerts the baddies to the fact they’ve been identified.

Instead of blocking requests, Cloudflare goes in the other direction and creates an all-you-can-eat sprawl of linked AI-generated content, luring crawlers into wasting their time and resources as they happily process an endless buffet of diverse facts unrelated to the site being crawled, all while Cloudflare learns as much about them as possible.

That’s an important point: the content generated by the Labyrinth might be pointless and irrelevant, but it isn’t nonsense. After all, the content generated by the Labyrinth can plausibly end up in training data, and fraudulent data would essentially be increasing the amount of misinformation online as a side effect. For that reason, the human-looking data making up the Labyrinth isn’t wrong, it’s just useless.

It’s certainly a clever method of dealing with crawlers, but the way things are going it’ll probably be rendered obsolete sooner rather than later, as the next move in the arms race gets made.

Do you like high-detail 3D models intended for resin printing, but wish you could more easily print them on a filament-based FDM printer? Good news, because [Jacob] of Painted4Combat shared a tool he created to make 3D models meant for resin printers — the kind popular with tabletop gamers — easier to port to FDM. It comes in the form of a Blender add-on called Resin2FDM. Intrigued, but wary of your own lack of experience with Blender? No problem, because he also made a video that walks you through the whole thing step-by-step.

3D models intended for resin printing aren’t actually any different, format-wise, from models intended for FDM printers. The differences all come down to the features of the model and how well the printer can execute them. Resin printing is very different from FDM, so printing a model on the “wrong” type of printer will often have disappointing results. Let’s look at why that is, to better understand what makes [Jacob]’s tool so useful.

Rafts and a forest of thin tree-like supports are common in resin printing. In the tabletop gaming scene, many models come pre-supported for convenience. A fair bit of work goes into optimizing the orientation of everything for best printed results, but the benefits don’t carry directly over to FDM.

For one thing, supports for resin prints are usually too small for an FDM printer to properly execute — they tend to be very thin and very tall, which is probably the least favorable shape for FDM printing. In addition, contact points where each support tapers down to a small point that connects to the model are especially troublesome; FDM slicer software will often simply consider those features too small to bother trying to print. Supports that work on a resin printer tend to be too small or too weak to be effective on FDM, even with a 0.2 mm nozzle.

To solve this, [Jacob]’s tool allows one to separate the model itself from the support structure. Once that is done, the tool further allows one to tweak the nest of supports, thickening them up just enough to successfully print on an FDM printer, while leaving the main model unchanged. The result is a support structure that prints well via FDM, allowing the model itself to come out nicely, with a minimum of alterations to the original.

Resin2FDM is available in two versions, the Lite version is free and an advanced version with more features is available to [Jacob]’s Patreon subscribers. The video (embedded below) covers everything from installation to use, and includes some general tips for best results. Check it out if you’re interested in how [Jacob] solved this problem, and keep it in mind for the next time you run across a pre-supported model intended for resin printing that you wish you could print with FDM.

[Simon Willison] has put together a list of how, exactly, one goes about using a large language models (LLM) to help write code. If you have wondered just what the workflow and techniques look like, give it a read. It’s full of examples, strategies, and useful tips for effectively using AI assistants like ChatGPT, Claude, and others to do useful programming work.

It’s a very practical document, with [Simon] emphasizing realistic expectations and the importance of managing context (both in terms of giving the LLM direction, as well as the model’s context in terms of being mindful of how much the LLM can fit in its ‘head’ at once.) It is useful to picture an LLM as a capable and obedient but over-confident programming intern or assistant, albeit one that never gets bored or annoyed. Useful work can be done, but testing is crucial and human oversight simply cannot be automated away.

Even if one has no interest in using LLMs to help in writing production code, there’s still a lot of useful work they can do to speed up the process of software development in general, especially when learning. They can help research options, interactively explore unfamiliar codebases, or prototype ideas quickly. [Simon] provides useful strategies for all these, and more.

If you have wondered how exactly glorified chatbots can meaningfully help with software development, [Simon]’s writeup hopefully gives you some new ideas. And if this is is all leaving you curious about how exactly LLMs work, in the time it takes to enjoy a warm coffee you can learn how they do what they do, no math required.

Popular Science has an excellent article on how Josephine Cochrane transformed how dishes are cleaned by inventing an automated dish washing machine and obtaining a patent in 1886. Dishwashers had been attempted before, but hers was the first with the revolutionary idea of using water pressure to clean dishes placed in wire racks, rather than relying on some sort of physical scrubber. The very first KitchenAid household dishwashers were based on her machines, making modern dishwashers direct descendants of her original design.

It wasn’t an overnight success. Josephine faced many hurdles. Saying it was difficult for a woman to start a venture or do business during this period of history doesn’t do justice to just how many barriers existed, even discounting the fact that her late husband was something we would today recognize as a violent alcoholic. One who left her little money and many debts upon his death, to boot.

She was nevertheless able to focus on developing her machine, and eventually hired mechanic George Butters to help create a prototype. The two of them working in near secrecy because a man being seen regularly visiting her home was simply asking for trouble. Then there were all the challenges of launching a product in a business world that had little place for a woman. One can sense the weight of it all in a quote from Josephine (shared in a write-up by the USPTO) in which she says “If I knew all I know today when I began to put the dishwasher on the market, I never would have had the courage to start.”

But Josephine persevered and her invention made a stir at the 1893 World’s Fair in Chicago, winning an award and mesmerizing onlookers. Not only was it invented by a woman, but her dishwashers were used by restaurants on-site to clean tens of thousands of dishes, day in and day out. Her marvelous machine was not yet a household device, but restaurants, hotels, colleges, and hospitals all saw the benefits and lined up to place orders.

Early machines were highly effective, but they were not the affordable, standard household appliances they are today. There certainly existed a household demand for her machine — dishwashing was a tedious chore that no one enjoyed — but household dishwashing was a task primarily done by women. Women did not control purchasing decisions, and it was difficult for men of the time (who did not spend theirs washing dishes) to be motivated about the benefits. The device was expensive, but it did away with a tremendous amount of labor. Surely the price was justified? Yet women themselves — the ones who would benefit the most — were often not on board. Josephine reflected that many women did not yet seem to think of their own time and comfort as having intrinsic value.

Josephine Cochrane ran a highly successful business and continued to refine her designs. She died in 1913 and it wasn’t until the 1950s that dishwashers — direct descendants of her original design — truly started to become popular with the general public.

Nowadays, dishwashers are such a solved problem that not only are they a feature in an instructive engineering story, but we rarely see anyone building one (though it has happened.)

We have Josephine Cochrane to thank for that. Not just her intellect and ingenuity in coming up with it, but the fact that she persevered enough to bring her creation over the finish line.

[Ken Shirriff] has been sharing a really low-level look at Intel’s Pentium (1993) processor. The Pentium’s architecture was highly innovative in many ways, and one of [Ken]’s most recent discoveries is that it contains a complex circuit — containing around 9,000 transistors — whose sole purpose is to multiply specifically by three. Why does such an apparently simple operation require such a complex circuit? And why this particular operation, and not something else?

Let’s back up a little to put this all into context. One of the feathers in the Pentium’s cap was its Floating Point Unit (FPU) which was capable of much faster floating point operations than any of its predecessors. [Ken] dove into reverse-engineering the FPU earlier this year and a close-up look at the Pentium’s silicon die shows that the FPU occupies a significant chunk of it. Of the FPU, nearly half is dedicated to performing multiplications and a comparatively small but quite significant section of that is specifically for multiplying a number by three. [Ken] calls it the x3 circuit.

Why does the multiplier section of the FPU in the Pentium processor have such specialized (and complex) functionality for such an apparently simple operation? It comes down to how the Pentium multiplies numbers.

Multiplying two 64-bit numbers is done in base-8 (octal), which ultimately requires fewer operations than doing so in base-2 (binary). Instead of handling each bit separately (as in binary multiplication), three bits of the multiplier get handled at a time, requiring fewer shifts and additions overall. But the downside is that multiplying by three must be handled as a special case.

[Ken] gives an excellent explanation of exactly how all that works (which is also an explanation of the radix-8 Booth’s algorithm) but it boils down to this: there are numerous shortcuts for multiplying numbers (multiplying by two is the same as shifting left by 1 bit, for example) but multiplying by three is the only one that doesn’t have a tidy shortcut. In addition, because the result of multiplying by three is involved in numerous other shortcuts (x5 is really x8 minus x3 for example) it must also be done very quickly to avoid dragging down those other operations. Straightforward binary multiplication is too slow. Hence the reason for giving it so much dedicated attention.

[Ken] goes into considerable detail on how exactly this is done, and it involves carry lookaheads as a key element to saving time. He also points out that this specific piece of functionality used more transistors than an entire Z80 microprocessor. And if that is not a wild enough idea for you, then how about the fact that the Z80 has a new OS available?

Lucid dreaming is the state of becoming aware one is dreaming while still being within the dream. To what end? That awareness may allow one to influence the dream itself, and the possibilities of that are obvious and compelling enough that plenty of clever and curious people have formed some sort of interest in this direction. Now there are some indications that VR might be a useful tool in helping people achieve lucid dreaming.

The research paper (Virtual reality training of lucid dreaming) is far from laying out a conclusive roadmap, but there’s enough there to make the case that VR is at least worth a look as a serious tool in the quest for lucid dreaming.

One method of using VR in this way hinges on the idea that engaging in immersive VR content can create mild dissociative experiences, and this can help guide and encourage users to perform “reality checks”. VR can help such reality checks become second nature (or at least more familiar and natural), which may help one to become aware of a dream state when it occurs.

Another method uses VR as a way to induce a mental state that is more conducive to lucid dreaming. As mentioned, engaging in immersive VR can induce mild dissociative experiences, so VR slowly guides one into a more receptive state before falling asleep. Since sleeping in VR is absolutely a thing, perhaps an enterprising hacker with a healthy curiosity in lucid dreaming might be inspired to experiment with combining them.

We’ve covered plenty of lucid dreaming hacks over the years and there’s even been serious effort at enabling communication from within a dreaming state. If you ask us, that’s something just begging to be combined with VR.

[Nicholas LaBonte] shows off a Cyberdeck Handheld that demonstrates just how good something can look when care and attention goes into the design and fabrication. He wanted to make something that blended cyberpunk and nautical aesthetics with a compact and elegant design, and we think he absolutely succeeded.

On the inside is a Raspberry Pi and an RTL-SDR. The back of the unit is machined from hardwood, and sports a bronze heat sink for the Raspberry Pi. The front has a prominent red PSP joystick for mouse input and a custom keyboard. The keyboard is especially interesting. On the inside it’s a custom PCB with tactile switches and a ATmega32U4 running QMK firmware — a popular choice for DIY keyboards — and presents to the host as a regular USB HID device.

How did he make those slick-looking keys? It’s actually a single plate that sits between the front panel and the switches themselves. [Nicholas] used a sheet of polymer with a faux-aluminum look to it and machined it down, leaving metal-looking keys with engraved symbols as tabs in a single panel. It looks really good, although [Nicholas] already has some ideas about improving it.

On the right side is the power button and charging port, and astute readers may spot that the power button is where a double-stack of USB ports would normally be on a Raspberry Pi 5. [Nicholas] removed the physical connectors, saving some space and connecting the USB ports internally to the keyboard and SDR.

As mentioned, [Nicholas] is already full of ideas for improvements. The bronze heat sink isn’t as effective as he’d like, the SDR could use some extra shielding, and the sounds the keyboard ends up making could use some work. Believe it or not, there’s still room to spare inside the unit and he’d maybe like to figure out a way to add a camera, GPS receiver, or maybe a 4G modem. We can’t wait! Get a good look for yourself in the video, embedded below.

Holography is about capturing 3D data from a scene, and being able to reconstruct that scene — preferably in high fidelity. Holography is not a new idea, but engaging in it is not exactly a point-and-shoot affair. One needs coherent light for a start, and it generally only gets touchier from there. But now researchers describe a new kind of holographic camera that can capture a scene better and faster than ever. How much better? The camera goes from scene capture to reconstructed output in under 30 milliseconds, and does it using plain old incoherent light.

The new camera is a two-part affair: acquisition, and calculation. Acquisition consists of a camera with a custom electrically-driven liquid lens design that captures a focal stack of a scene within 15 ms. The back end is a deep learning neural network system (FS-Net) which accepts the camera data and computes a high-fidelity RGB hologram of the scene in about 13 ms.  How good are the results? They beat other methods, and reconstruction of the scene using the data looks really, really good.

One might wonder what makes this different from, say, a 3D scene captured by a stereoscopic camera, or with an RGB depth camera (like the now-discontinued Intel RealSense). Those methods capture 2D imagery from a single perspective, combined with depth data to give an understanding of a scene’s physical layout.

Holography by contrast captures a scene’s wavefront information, which is to say it captures not just where light is coming from, but how it bends and interferes. This information can be used to optically reconstruct a scene in a way data from other sources cannot; for example allowing one to shift perspective and focus.

Being able to capture holographic data in such a way significantly lowers the bar for development and experimentation in holography — something that’s traditionally been tricky to pull off for the home gamer.