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320 Articles

Automatically Remove AI Features From Windows 11

December 18, 2025 by 64 Comments

It seems like a fair assessment to state that the many ‘AI’ features that Microsoft added to Windows 11 are at least somewhat controversial. Unsurprisingly, this has led many to wonder about disabling or outright removing these features, with [zoicware]’s ‘Remove Windows AI’ project on GitHub trying to automate this process as much as reasonably possible.

All you need to use it is your Windows 11-afflicted system running at least 25H2 and the PowerShell script. The script is naturally run with Administrator privileges as it has to do some manipulating of the Windows Registry and prevent Windows Update from undoing many of the changes. There is also a GUI for those who prefer to just flick a few switches in a UI instead of running console commands.

Among the things that can be disabled automatically are the disabling of Copilot, Recall, AI Actions, and other integrations in applications like Edge, Paint, etc. The reinstallation of removed packages is inhibited by a custom package. For the ‘features’ that cannot be disabled automatically, there is a list of where to toggle those to ‘off’.

Naturally, since Windows 11 is a moving target, it can be rough to keep a script like this up to date, but it seems to be a good start at least for anyone who finds themselves stuck on Windows 11 with no love for Microsoft’s ‘AI’ adventures. For the other features, there are also Winaero Tweaker and Open-Shell, with the latter in particular bringing back the much more usable Windows 2000-style start menu, free of ads and other nonsense.

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Hackaday Links: December 7, 2025

December 7, 2025 by 17 Comments

We stumbled upon a story this week that really raised our eyebrows and made us wonder if we were missing something. The gist of the story is that U.S. Secretary of Energy Chris Wright, who has degrees in both electrical and mechanical engineering, has floated the idea of using the nation’s fleet of emergency backup generators to reduce the need to build the dozens of new power plants needed to fuel the AI data center building binge. The full story looks to be a Bloomberg exclusive and thus behind a paywall — hey, you don’t get to be a centibillionaire by giving stuff away, you know — so we might be missing some vital details, but this sounds pretty stupid to us.

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So Long, Firefox, Part One

November 20, 2025 by 153 Comments

It’s likely that Hackaday readers have among them a greater than average number of people who can name one special thing they did on September 23rd, 2002. On that day a new web browser was released, Phoenix version 0.1, and it was a lightweight browser-only derivative of the hugely bloated Mozilla suite. Renamed a few times to become Firefox, it rose to challenge the once-mighty Microsoft Internet Explorer, only to in turn be overtaken by Google’s Chrome.

Now in 2025 it’s a minority browser with an estimated market share just over 2%, and it’s safe to say that Mozilla’s take on AI and the use of advertising data has put them at odds with many of us who’ve kept the faith since that September day 23 years ago. Over the last few months I’ve been actively chasing alternatives, and it’s with sadness that in November 2025, I can finally say I’m Firefox-free.

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Kubernetes Cluster Goes Mobile In Pet Carrier

November 18, 2025 by 7 Comments

There’s been a bit of a virtualization revolution going on for the last decade or so, where tools like Docker and LXC have made it possible to quickly deploy server applications without worrying much about dependency issues. Of course as these tools got adopted we needed more tools to scale them easily. Enter Kubernetes, a container orchestration platform that normally herds fleets of microservices in sprawling cloud architectures, but it turns out it’s perfectly happy running on a tiny computer stuffed in a cat carrier.

This was a build for the recent Kubecon in Atlanta, and the project’s creator [Justin] wanted it to have an AI angle to it since the core compute in the backpack is an NVIDIA DGX Spark. When someone scans the QR code, the backpack takes a picture and then runs it through a two-node cluster on the Spark running a local AI model that stylizes the picture and sends it back to the user. Only the AI workload runs on the Spark; [Justin] also is using a LattePanda to handle most of everything else rather than host everything on the Spark.

To get power for the mobile cluster [Justin] is using a small power bank, and with that it gets around three hours of use before it needs to be recharged. Originally it was planned to work on the WiFi at the conference as well but this was unreliable and he switched to using a USB tether to his phone. It was a big hit with the conference goers though, with people using it around every ten minutes while he had it on his back. Of course you don’t need a fancy NVIDIA product to run a portable kubernetes cluster. You can always use a few old phones to run one as well.

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Hackaday Links: November 16, 2025

November 16, 2025 by 14 Comments

We make no claims to be an expert on anything, but we do know that rule number one of working with big, expensive, mission-critical equipment is: Don’t break the big, expensive, mission-critical equipment. Unfortunately, though, that’s just what happened to the Deep Space Network’s 70-meter dish antenna at Goldstone, California. NASA announced the outage this week, but the accident that damaged the dish occurred much earlier, in mid-September. DSS-14, as the antenna is known, is a vital part of the Deep Space Network, which uses huge antennas at three sites (Goldstone, Madrid, and Canberra) to stay in touch with satellites and probes from the Moon to the edge of the solar system. The three sites are located roughly 120 degrees apart on the globe, which gives the network full coverage of the sky regardless of the local time.

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“AI, Make Me A Degree Certificate”

November 10, 2025 by 139 Comments

One of the fun things about writing for Hackaday is that it takes you to the places where our community hang out. I was in a hackerspace in a university town the other evening, busily chasing my end of month deadline as no doubt were my colleagues at the time too. In there were a couple of others, a member who’s an electronic engineering student at one of the local universities, and one of their friends from the same course. They were working on the hardware side of a group project, a web-connected device which with a team of several other students, and they were creating from sensor to server to screen.

I have a lot of respect for my friend’s engineering abilities, I won’t name them but they’ve done a bunch of really accomplished projects, and some of them have even been featured here by my colleagues. They are already a very competent engineer indeed, and when in time they receive the bit of paper to prove it, they will go far. The other student was immediately apparent as being cut from the same cloth, as people say in hackerspaces, “one of us”.

They were making great progress with the hardware and low-level software while they were there, but I was saddened at their lament over their colleagues. In particular it seemed they had a real problem with vibe coding: they estimated that only a small percentage of their classmates could code by hand as they did, and the result was a lot of impenetrable code that looked good, but often simply didn’t work.

I came away wondering not how AI could be used to generate such poor quality work, but how on earth this could be viewed as acceptable in a university.
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Expert Systems: The Dawn Of AI

October 29, 2025 by 19 Comments

We’ll be honest. If you had told us a few decades ago we’d teach computers to do what we want, it would work some of the time, and you wouldn’t really be able to explain or predict exactly what it was going to do, we’d have thought you were crazy. Why not just get a person? But the dream of AI goes back to the earliest days of computers or even further, if you count Samuel Butler’s letter from 1863 musing on machines evolving into life, a theme he would revisit in the 1872 book Erewhon.

Of course, early real-life AI was nothing like you wanted. Eliza seemed pretty conversational, but you could quickly confuse the program. Hexapawn learned how to play an extremely simplified version of chess, but you could just as easily teach it to lose.

But the real AI work that looked promising was the field of expert systems. Unlike our current AI friends, expert systems were highly predictable. Of course, like any computer program, they could be wrong, but if they were, you could figure out why.

Experts?

As the name implies, expert systems drew from human experts. In theory, a specialized person known as a “knowledge engineer” would work with a human expert to distill his or her knowledge down to an essential form that the computer could handle.

This could range from the simple to the fiendishly complex, and if you think it was hard to do well, you aren’t wrong. Before getting into details, an example will help you follow how it works.

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