It’s no big secret at this point that we are looking at the final years of the two venerable Voyager spacecraft. With their power supplies diminishing and their 1970s-era electronics still giving it all they got, their teams are working miracles to stretch out the time to the final goodbye for as long as possible. Despite these efforts Voyager 1 went silent for more than 24 hours on October 18 and 19, with the team still trying to figure out what happened.

The most recent change to Voyager 1 was the same ‘big bang’ switching of multiple instruments to lower-power versions, while keeping an eye on keeping the electronics and fuel at reasonable temperatures. This should give both spacecraft a few more years before power reserves fall below usable levels. Whether this switch is related to this communication blackout is currently unknown.

From the diagnostic data that the team obtained after communications were restored it’s clear that a group of thrusters went offline with backup units kicking in, indicating that the onboard computer detected some kind anomaly with the former. Whether this is a recurrence of the clogged fuel lines that has been observed before will have to be investigated.

We still fervently hope that both Voyagers can make it into the 2030s before both of them will fall silent forever. Final goodbyes are never easy, but we are most grateful to the Voyager teams over the decades for going above and beyond to make sure that literally everything has been attempted before those fateful days arrive.

Thanks to [Mark Stevens] for the tip.

[Roni Bandini]’s JEV WiFi Analyzer is an interesting experiment that glues together a single-board Linux computer, a USB WiFi adapter, and a specialized AI model to create a new kind of automated tool that passively monitors WiFi activity to measure and profile coverage, congestion, and suggest possible optimizations.

Here’s how it works: a Python program connects to WiFi, cycles through available channels, and uses scapy to gather details like signal strength, number of clients, WiFi frames per second, and similar data about each given channel. That data is fed to Jev, a highly efficient sort of decision-making AI model responsible for interpreting that data on an ongoing basis.

Briefly, Jev is a new kind of AI that is completely unable to generate text or images. It outputs only numbers representing bounded decisions. We covered these decision-making models earlier, including locally-installed versions. Jev is cheap enough to throw at a variety of problems, but it’s also practical to run your own model on your own hardware.

In [Roni]’s analyzer, Python gathers the raw numbers and Jev takes care of classifying the WiFi network’s coverage, congestion level, and basic remedial actions if applicable. There’s a brief video embedded below (Spanish only) that shows the Jev WiFi Analyzer in action.

The AI isn’t making particularly deep analyses, but it doesn’t take much to imagine Jev taking a more active role like deciding which channels to spend more (or less) time monitoring. Using a decision-making model in this way is interesting, and makes us wonder if similar models integrated with other kinds of hardware is a branch of projects waiting to happen.

In just under a week from today, almost 700 hackers will be getting together in Pasadena, CA for three days of scheming, soldering, and socializing. If you’re able to join us, you absolutely should – there is no substitute for attending a hacker convention in person.

But even if you can’t make it to Supercon, you’re not shut out. We have an absolutely killer slate of talks, and we’ll be streaming at least one stage’s worth during the event. Watching a good talk isn’t just entertainment, but it can plant new seeds of possibility in your brain, or give you new perspectives on something that you thought you already knew. A super talk can launch a thousand hacks.

It’s a funny part of our culture, that even though we all have the Internet where the printed word and the video reign supreme, many of our best ideas are still transmitted the old fashioned way: one person who has thought deeply about a topic gets up in front of a room and lets loose. Maybe it’s because presentations require so much preparation that they’re so powerful? I don’t know, but I do know that I’ve seen a number of Supercon (and other!) talks that have literally changed the way I see the world. A good presentation sticks with you.

Hackaday strives to be the equivalent of the newspaper of record for everything going on in our scene. If there is a cool hack out there, we want to showcase it so that everyone can draw from it. In just exactly the same vein, we are stoked and honored to be the stage for the next round of great talks. Whether you’re there in the audience live with us, or watching along at home, and whichever of the topics you’re interested in, we hope that the signal gets out to you.

Sometimes an STL file needs a bit of editing, but if one doesn’t already know how to use a mesh editor it’s unlikely that one has the time to sit down and learn it in the middle of a project. Happily, that’s where [Prusa]’s five-minute guide to editing STL files directly will come in handy.

The editor of choice is Blender, a fantastic piece of open source software that can be intimidating because it’s got one of those interfaces in which clicking around with an explorer’s heart just isn’t going to get you very far. Most guides start at learning Blender from scratch, assuming one will be using it to create new 3D models. This guide instead focuses on importing and modifying existing models rather than trying to teach the entire interface. For all the apparent complexity, Blender’s underlying functions are actually quite straightforward and simple to use once one knows the right approach.

Having to make changes to an STL file is something most folks have grimaced inwardly about at least once when 3D printing. Certainly, STL files can be imported into CAD programs like FreeCAD, but editing them that way is always a bit of a square peg in a round hole.

A guide like this is a valuable reference for the next time an STL file needs some changes, but the time has not yet come to sit down with a gallon of coffee and learn Blender from scratch.

They may not deliver the highest efficiency, but absorption refrigerators have earned a persistent place in camper vans and industrial plants thanks to a few distinct advantages: they can operate using waste heat alone as an energy source, and they can be quite reliable due to their lack of moving parts. It can be a bit hard to understand their working principles, though, which is why [Ben Krasnow] built a transparent Einstein-Szilard refrigerator.

Most absorption refrigerators use ammonia and water as their working fluids; since the ammonia gas needs to condense at room temperature, the refrigerator requires pressures above what can be comfortably held in a glass or plastic enclosure. The Einstein-Szilard design, by contrast, uses a third liquid as its refrigerant. A strong solution of ammonia in water is separated by boiling, and the ammonia gas induces the refrigerant to evaporate by decreasing its partial pressure. The refrigerant and ammonia gasses then mix with water, in which the ammonia dissolves, leaving the refrigerant vapor to condense and start the cycle again. The newly reformed water-ammonia solution flows back into the boiler.

The refrigerant is usually butane, but since [Ben] was powering this pressurized system with an open flame, he opted instead for nonflammable perfluoropentane. The refrigerator body itself was 3D printed in clear resin, with a copper block transferring heat from the flame to the boiler chamber; ammonia solutions do dissolve copper, so this isn’t a long-term solution, but it works for a demonstration. The single internal path of the refrigerator presented some difficulties: since almost no pressure builds up between different parts of the system, a bubble in the wrong place can easily airlock it. In the end, [Ben] managed to get it working well enough to reach five degrees Celsius below the ambient temperature. Conveniently, he notes that, for alcohol at a sufficiently high concentration, you could cool your drink by burning a portion of it.

Ammonia-based refrigerators are a tough build for most hackers, but we have seen a salt-water-based absorption refrigerator before. For more less-known refrigerators, check out our article on strange cooling methods.

Thanks to [spacedog] for the tip!

The Godox C100 is a cheap camera that’s best described as a toy, its party trick being a large clear viewfinder with a head-up-display light meter. It’s by no means a high quality device, being little more than a dashcam in a fancy case, but like many others of its kind it offers opportunity for the camera hacker. [Holofermes] has one, and has given it a suite of patches that, among other things, convert it to a USB webcam and give it a Pi Zero dock.

It takes the standard firmware and adds the patches on top, so while they’re installed it can only act as USB drive and webcam, if a return to camera is desired then the original firmware must be flashed. There are some details on the hardware, such as that ti runs FreeRTOS on an Arm architecture with 16 megabytes of memory. The sensor has a 1920 by 1080 pixel resolution, which is a step up from the upscaled VGA of may such cameras.

Many novelty cameras are not as hackable as this one, which makes it interesting. We hope that more camera hackers will turn their attention to it. It’s not the first cheap camera we’ve taken a look at.

Those in the virtual reality scene might be interested in [Prohurtz]’s add-on for the Bigscreen Beyond VR headset. BSB-SLAM adds SLAM (Simultaneous Localization and Mapping) tracking to the headset by attaching a sensor to the front of the headset, and providing the software to tie the two together. As a result, the headset can be fully tracked in 3D space with no need for external base stations. It essentially adds inside-out tracking as a DIY add-on.

Just to be clear, that doesn’t mean the headset becomes wireless. The Beyond connects to a PC, essentially acting as a display, and that part is the same. But it does mean the headset is no longer tied to having to work in a room with base stations pre-installed.

[Prohurtz]’s solution does this by adding a small off-the-shelf Xvisio XR50 board, USB cable, and 3D-printed mount to the unit. The headset works as normal, just with some extra software needed to use the sensor data in lieu of the base stations.

As far as sensors go the Xvisio XR50 is a bit spendy, but the fact that a small and lightweight SLAM sensor that’s capable of delivering positioning data suitable for tracking a headset in near real-time can be bought online and so readily adapted is pretty interesting.

[Prohurtz] got the idea working back in 2025 but it’s now at the point where one can order a part, print an adapter, and be off to the races with a software package.

This isn’t [Prohurtz]’s first time with VR hardware, either. The EyeTrackVR eye tracking add-on is also their work, combining largely off-the-shelf hardware into an effective DIY solution for VR use.