Vanilla OS 3 Comes in Hot, Packing ARM64 Support After a Long Wait

My history with Vanilla OS goes back to 2022, when I covered its debut stable release, just a month after I landed this gig at It's FOSS (yapping about open source and Linux).

Since then, my focus has moved to other distros and governance-focused developments, though I did watch it swap its Ubuntu base for a Debian Sid one and, from a distance, then go through an Orchid-themed overhaul in 2024.

I never got around to actually running it later on (or even reviewing it), but that's how things go, I guess.

If you haven't come across it before, Vanilla OS is an immutable, atomic distro built around ABRoot, with Apx facilitating package installation inside containers instead of touching the base system directly.

What's new in Reunion?

Well, if you are someone who is running an ARM64-based setup, be it a single-board computer, laptop, or a fully-fledged workstation that boots UEFI, then you will be glad to know that Vanilla OS now has proper support for the ARM64 platform.

Every package now builds for both ARM64 and x86_64, and the team isn't treating ARM64 as the lesser platform; functionality-wise, it's supposed to match the other one for one.

And, with the exception of NVIDIA builds, most images of Vanilla OS are now bit-for-bit reproducible. That means you can rebuild an image yourself and check it matches what the project ships instead of just blindly trusting what their build infrastructure pumped out.

The desktop-facing upgrades

Powering the release is Linux kernel 7.1.3, which is accompanied by GNOME 50. While we have already talked a fair bit about this desktop environment release across other distro releases, here's a refresher for you.

GDM now runs Wayland only, following through on the X11 removal GNOME backed away earlier. Fractional scaling also graduates out of its experimental flag this time around.

You get a bunch of new default apps too. Ptyxis takes over from Black Box, and its container support is the reason why you should keep an eye out for it.

Papers steps in for Evince with a noticeably more modern PDF reading experience, and Resources replaces GNOME System Monitor, giving you a clean look at what your apps and hardware are actually doing.

Underneath, Apx and VSO now share the same SDK, so config handling, permissions, and logging finally work the same way across both tools instead of each doing its own thing.

Another new addition is Vanilla Continuity, a snapshot-based backup and restore tool that works alongside ABRoot. If configured properly, it can save your home directory, whatever Flatpaks you have installed, and ABRoot's own metadata.

Where those snapshots land is up to you. A local directory works by default, but you can also point Continuity at an external drive it encrypts with LUKS2, or send backups to a remote server over SFTP, FTP, or NFS.

There's also the push for community-built images through a Vanilla-Flavors org on GitHub, which currently hosts the china-image flavor, which caters to people living in mainland China.

If you want to build and maintain your own flavor, the Vanilla OS devs are open to hearing from you.

Download Vanilla OS 3

Images for Vanilla OS 3 'Reunion' are live on the official website right now. Though keep in mind that the download page will make you pick an architecture, then a donation amount.

You can enter "0" as the amount to get the free download link, or you could enter a donation amount greater than zero to directly support the project and then download the ISO.

Vanilla OS 3

At the time of writing, donations were not working via the PayPal integration the project has implemented.

If you're already on Vanilla OS 2 Orchid (v1.4.0), this release will arrive as a normal system upgrade. If it's taking too much time, you can run this to get the release:

abroot upgrade

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This Dev Spent 8 Years on Intel's Clear Linux, Now He's Undoing Its Biggest Mistake

Auke Kok spent eight years working on Clear Linux OS at Intel. Now he thinks he knows exactly what broke it, and he's trying to fix that by himself.

In a LinkedIn post this week, Auke, now a Linux Distribution Architect at Verge.io, introduced The Ur Project. It's a from-scratch Linux distribution build system he's been putting together in whatever spare time he has.

"This project is not a corporate effort," he writes, and as far as I can see, no company, or even foundation for that matter, is backing it. Besides, as you might remember, Intel discontinued Clear Linux OS last year.

Where Clear Linux fell short

Auke has been writing regularly on The Ur Project website about what he thinks went wrong the first time. The biggest one, in his own words, was that they "made it way too hard for the community to contribute."

Simple things, like testing his own code change or carrying out regular incremental development, were needlessly painful, he says.

The release process didn't help either. Mismatched dashboards and a combination of old and new tooling were bolted together just to track daily updates.

Keeping pace with an assortment of daily updates meant stitching together a growing pile of disconnected tools and interfaces, all running on CDN and hardware resources a solo project at this stage will never have access to.

A build system, not a distro

The Ur Project isn't meant to be some shiny new atomic distro, at least not yet. The priority right now is to work on the machinery behind it, not offer a polished product.

Everything runs on Rust, on both the part that builds packages and the part that installs them, sharing the same underlying code.

Packages are defined through what Auke calls recipes, declarative TOML files that describe what a package needs without prescribing exactly how to build it, an idea he compares to "your grandmother's collection of cooking recipes."

Build steps aren't scripted by hand. Instead, a set of probes inspects the source and decides how to compile it. Repositories run on TUF to keep updates signed and verifiable, with content-addressed storage handling deduplication so identical files across packages only get stored once.

Every package carries provenance data documenting how it was built, and Auke says the system is designed so builds can be reproduced bit for bit. The same underlying system can output an OCI container, a live USB image, a cloud image, or a plain installed system.

AI is involved

LLMs are only tasked with writing test suites and build definitions for specific package types.

The build system itself stays deterministic. It runs without any AI agent present, and MCP integrations were deliberately kept out of routine packaging work.

For now, what's available is the website, Auke's regular blog posts on The Ur Project, and the Forgejo instance that hosts the code.

He is currently in the process of figuring out the hosting, letting people actually pull images and repositories. That will take a few more days before it's ready.

Once that's sorted, he plans to publish bootable images for both cloud deployments and live USB use.

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Firefox 155 Adds 2 New Changes for Faster Web Browsing

In a very low-key manner, Firefox 155 has arrived as the latest stable release for the popular privacy-focused web browser.

While there are a range of refinements with this release, we will be focusing on two technical ones that change how Firefox handles webpage loading. Happy Eyeballs v3 and QUIC v2 support for HTTP/3.

Happy Eyeballs v3

Instead of testing one path and hoping it works, Firefox now tries a handful of ways to reach a website side by side and goes with whichever one answers first. There's nothing to turn on and nothing to configure. You'll just notice fewer pages that sit there loading while Firefox figures out how to actually get through to the site.

You see, every time Firefox loads a page, it first has to decide how to reach the server behind it.

And there's often more than one option. An IPv4 address, an IPv6 address, sometimes both, and depending on the site, a choice between HTTP/2 and the newer HTTP/3. Older Firefox versions picked one option and waited to see if it worked before trying the next, so a slow or broken first guess meant the page just sat there without loading.

Happy Eyeballs v3 is what changes that. Firefox fires off connection attempts down several of these paths at once and runs with whichever one lands first, instead of committing to a single guess at the beginning.

Officially, Happy Eyeballs v3 is still an IETF draft rather than a finished RFC, authored by engineers at Apple and Google. Mozilla's own developer notes for this release describe the feature as currently supported on some platforms only, without naming which ones.

QUIC v2 for HTTP/3

QUIC is one of the pieces of tech behind how fast a website loads today, and Firefox 155 adds support for a slightly newer version of it.

What actually matters isn't the new version itself. It's that Firefox can switch to it instantly, skipping the back and forth that normally happens when two computers don't immediately agree on which version to use.

That back-and-forth used to cost an extra trip across the internet and back. Now it doesn't.

HTTP/3 is the newest version of the protocol that moves data between your browser and a website, and runs on QUIC rather than the older TCP. Setting up a secure connection over QUIC takes one round trip instead of several, which is a big part of why HTTP/3 feels quicker on shaky connections.

This is the right time to switch

Firefox with uBlock Origin installed.

Might I add, this is the best time to switch to Firefox. uBlock Origin's full version has been broken on Chrome since Google disabled Manifest V2 back in July 2025, and yesterday Google kicked it out from the Chrome Web Store entirely.

Now, only uBlock Origin Lite remains.

Of course Firefox was never really affected by what Google did, consistently supporting the full uBlock Origin experience without any nerfs in its ad-blocking abilities. You can get its latest release via the Firefox Add-ons portal.

That is not all; the 155 release also has many other new additions, like the Smart Window rollout (limited to users residing in the US, Canada, or France), blocked tracker counts in the address bar, and the ability to reorder containers.

Any of which could cajole you further into switching away from Chrome or any other Chromium-based browser.

Get Firefox 155

Binaries for Firefox 155 are available already, though the official download page hasn't caught up with the new build yet; it should get there shortly. If you can't wait and would rather get this release now, although I would advise waiting.

🚧

Your Linux distribution should offer you the new version pretty soon. It is better to get updates via the official distro method.

Mozilla's FTP mirror already has the 155.0 builds for Linux, Windows, and macOS. Remember to select the correct platform and language options to get the package relevant for your computer's specs.

On my Ubuntu setup, I went with: linux-x86_64 > en-US > firefox-155.0.deb

Firefox 155 (FTP)

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Tether Brings the Apple Continuity Experience to Linux

Zack Bartel is a developer who switched to Linux full time and hasn't really looked back at his earlier daily driver, macOS, as something he would return to. There is, however, an exception.

Continuity.

That is Apple's catch-all term for how iPhone, iPad, and Mac talk to each other. It covers a wide range of small conveniences. Copy something on the iPhone and paste it on the Mac. Start an email on one device and finish it on another. Take a call from the Mac even when the phone is across the room.

But, to no one's surprise, there's a gap in its support. It doesn't officially work on Linux. So Zack built Tether, an open source project that replicates pieces of that experience on Linux without requiring a Mac device.

What does it handle?

Considering an app whose first release was just a few months ago in May, Tether covers a lot of ground already. Clipboard sync, file transfer, OTP autofill, iMessage, SMS, notifications, and contact sync are all live right now, with some of them being in the beta stage of availability.

A background daemon called tetherd handles inter-device communication on Linux, talking to a CLI and a native GTK4 app for pairing. A SwiftUI iPhone app finds the daemon automatically over Bonjour.

Linux has no supported way into iMessage or SMS, and Zack ruled out the common workaround of proxying everything through an actual Mac.

ancs4linux and BlueFerry supplied enough Bluetooth groundwork for him to build his own path, though both are GPL-licensed, so a clean room C++ rewrite kept Tether licensed under MIT.

To secure it all, he built the pairing process on mTLS, so the iPhone and the Linux daemon each have to prove who they are before anything connects. Security sweeps run regularly on the codebase too via Anthropic's Opus and Fable AI models.

Before you say that KDE Connect already does a lot of what Tether does, know that Zack is aware of its existence and calls it great, but points out that the app doesn't cater to the use cases he had in mind.

Get it running

For Arch Linux users, Tether is available via the AUR; you can get it by running the following command:

yay -S tether

If you don't know what Yay is, it is an AUR helper that makes the process of getting packages from the Arch User Repository easy. Though you do have to take note of the risks associated with using the community-run platform.

For other distros, the project's GitHub repo carries .deb and .rpm packages built for recent releases.

On your Apple iPhone, you will have to download the Tether - Linux Companion app from the App Store, which needs your device to be running at least iOS 26.1 or later.

Tether

There's also two extensions that you will need if you want OTP autofill to work properly.

The Firefox extension drops codes into login forms the moment they arrive, and the Thunderbird extension watches your inbox for anything that looks like a verification code and hands it off to the Tether app running on your Linux machine.

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AI Crawlers Are Bleeding The Linux Kernel Repo's Compute Power

If the past few weeks are any indication, more and more open source projects are drawing lines around how much AI they will let into their workflow and codebases before it's too much.

Debian just voted to allow generative AI in project contributions, while Rust adopted a tiered policy that keeps AI mostly out of the actual code. Both of these projects tackled the same underlying question.

And now, Linux, one of the biggest open source projects around, is getting hammered by scrapers, mostly AI-powered ones, sending the same repetitive requests over and over and burning through compute capacity.

Most of it has nothing to do with anyone actually writing code, btw. 🙃

This is absurd

Konstantin Ryabitsev of the Linux Foundation has put out numbers behind a complaint he has been voicing for a while now. Fourteen of git.kernel.org's 90 CPU cores, spread across five nodes, spend every second of every day turning commits into HTML pages.

On the outside, you might think, What's the problem with this? Every commit in Linux's history sits out in the open, is free to clone, and predates the wave of AI tools now scraping it.

You see, all of those characteristics are precisely what makes the repository a "goldmine of learning data." It houses the mainline kernel tree, every stable release branch going back years, dozens of subsystem maintainer trees, and even pre-git history from the BitKeeper era.

What's actually absurd is the way these clankers are going about the scraping business.

Sourced from Konstantin Ryabitsev.

Konstantin ran the numbers on it and found out that a regular clone of linux.git, going through its whole commit history locally, takes about 200 CPU-seconds of server time. Scraping the same 1.48 million commits through cgit's individual pages instead eats up 280 CPU-hours.

📋

Read this if you want to understand how those two CPU time units work.

Do that same scrape across every one of the 922 forks hosted on the server, and the total balloons to 258,160 CPU-hours, something like 4.6 million times more expensive than a single clone.

All that for a worse copy of data that was already free to grab in the first place. 🤭

And that's before counting the separate URLs cgit hands out for every patch, diff, and plain-text view of each commit too, which is what pushes the number of pages a single fork exposes into the quadrillions.

Blocking them turned into an arms race. Fail2Ban and IP bans worked until bots spread across whole subnets. ASN blocks worked too, until millions of residential and mobile IPs took over instead.

Anubis came next, being erected as a proof-of-work wall bots had to solve before getting through. It worked for a while until the clankers started solving increasing levels of challenge difficulty.

Wrapping up the writeup, Konstantin notes that:

However, you should know that out of the total of the 90 cores across 5 geo distributed nodes, there are 14-16 cores that are constantly doing nothing but rendering commits for scrapers.

On average, that's 20% of our entire capacity — except the swarms descend in waves and the actual graph is a lot more spiky than a 20% flatline.

Further speculating that when the AI bubble bursts, the project will quickly see a substantial decrease in the amount of "entities" (his labeling of the scrapers), trying to feed git.kernel.org to their models.

Though he also hopes they "smarten up" and stop scraping their data in the "dumbest way possible."

If you did not know who Konstantin is, he is the director of IT infrastructure security for the Linux Foundation and one of the sysadmins who keeps kernel.org up and running.

AI is inherently greedy

None of this compute waste is unique to git.kernel.org. It is just the clearest example we have got so far.

Feeding a model means burning through cycles on tasks that could be done in a fraction of the time, and right now nobody building these systems seems particularly bothered by that math.

The only approach these systems seem to know is the one your average billionaire runs on, say a fictional one like Carter Pewterschmidt. Already has more than enough. Still wants more. Does not particularly care how it gets there.

Stay on that path long enough, and the greed stops paying for itself. Plenty of companies are already finding out the hard way, and the numbers back it up too.

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Debian AI Vote has Divided the Community

Debian has now officially voted to allow the use of generative AI in project contributions. And as you can expect, such a decision doesn't go well with everyone. There are people who are indifferent to the decision, and then there are people who cannot tolerate anything AI in their favorite open-source projects.

Let's see what Debian has decided.

Debian says yes to "Responsible Use Of Generative AI"

After weeks of discussion and voting, Debian has said yes to AI. While you can read the entire proposal here, it basically comes down to this:

AI tools are permitted for development, maintenance, and documentation work. They can improve contributor productivity when used responsibly.

Every AI-assisted contribution must meet the same quality, correctness, and legal standards as any other. No exemptions.

Contributors must understand, review, test, and where needed, modify AI-generated output before submitting it. The responsibility stays entirely with the human.

Disclosure of AI use is encouraged but not mandatory.

Sharing confidential or security-sensitive information with third-party AI services is explicitly not allowed.

Large-scale automated changes need prior community discussion and human oversight before going through.

A divided community...no surprises

The community is divided yet again. There are people who are dead against anything AI and they have expressed their strong displeasure.

When I shared the news on It's FOSS X account, the comment section was livid. Some were fine, but some called it names like "Linslop", "debAIn", "Slopian" but some even mentioned switching to BSD.

A major outrage came from Debian developer Antoine Le Gonidec who decided to quit the project.

I can not support the current decision of Debian about LLM use, and am no longer willing to be seen as a part of Debian under these new rules. Pretending to have a "neutral stance" when faced with fascism is not neutrality, it’s active collaboration.

Image courtesy Brian Lunduke on X

Say yes to AI...or not?

From GCC to Fedora, almost every open source project out there is creating new guidelines around AI-generated code and contributions.

Some have completely closed the doors on AI. For example, Oracle won't allow AI-generated contributions to OpenJDK. GCC did the same and disallowed any AI-generated code from the compiler code.

But some took a more mild approach towards it. Linux kernel project itself now allows AI-assisted code but with some ifs and buts. Patches that used AI, has to be tagged accordingly. The AI generated code still has to follow GPL 2.0. There are more such rules.

Debian is a big name, a leader in the community, so the decision Debian made is going to set a precedent. It may lead to more projects embracing AI as Debian leads the way.

In my opinion, AI-assisted coding will be (or already is) the new norm, whether we like it or not. The young developers will grow up with them and rely heavily on them. And at some points, projects would have to "compromise" otherwise the number of contributors will drastically reduce as it will start getting difficult to find contributors who could code without any assistance from AI.

This is the same logic applied to increasing use of Rust over C in the Linux kernel. Memory safety aside, the young developers are more comfortable with Rust. Inclusion of Rust means more young contributors, which is good for the long-term future of projects.

The important thing here is that programmers should not handover the reign to AI blindly. They should be able to steer the AI in the right direction and not churn out inefficient, poor quality slop code.

💬 Your turn now. Did Debian do the right thing or not? Share your thoughts in the comments.

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Content Creators Will Love This FOSS Android App

If you are a content creator in 2026, then I am sure you end up juggling through a stack of apps and browser-based tools for the same handful of jobs. One resizes an image, another handles format conversion, a third takes care of watermarks, and a website somewhere tells you the exact pixel dimensions Instagram, X, or YouTube expects.

None of those steps take long on their own, but doing them one after another for every single post does add up.

Image Toolbox is built to make all that easier. It's an Android app that offers resizing, format conversion, watermarking, and platform-accurate exports under one roof.

What can it do for you?

Working under the name "T8RIN," Malik Mukhametzyanov has come up with Image Toolbox, offering it for free without any advertisements inside the app.

For you, as a content creator, the tools that matter the most are batch processing, format conversion, the 61 built-in export presets sized for platforms like Instagram, X, and YouTube, background removal, and watermarking.

The latter, background removal, comes in both automatic and manual modes, with a choice of models including MlKit, U2Net, RMBG, and BiRefNet.

It's built in Kotlin, uses Image Toolbox Libs for shared components, and Coil for image loading. The whole project is released under the Apache License 2.0.

I used it for a while

When you first launch it, Image Toolbox asks you for the usual set of notification and file access-related permissions, along with a dialog that lets you opt out of update checks.

Before I started editing images, I first tweaked the app to my liking.

In the sidebar menu, I noticed that the app had pulled the purple straight off my phone's Dynamic color setting which was nice, and I then turned on "Amoled mode" for getting a deep black-themed interface.

That was followed by a prompt switching of the app to "Dark" mode instead of "System."

Of course there's a lot more buried in there, like separate categories for Layout, Confetti, Vibration, Screen, Text, and Behavior. I left most of it untouched, as the default configuration was good enough for me.

I kept the focus of my testing limited to a few tool entries (among 100+ tools), as many of the smaller tools kept appearing in the same editor.

First up was Single Edit, which I used to play around with an image's quality, dropping the quality slider to 7 percent, and watching it turn into an oil painting with no realism whatsoever.

During that, the file size for the 3.8 MB photo went down to ~128 kb. I used the three buttons right below the preview to fully rotate and mirror the image horizontally too.

Exif editing also lives in the same area, with parameters like capture date, compression, camera make, and camera model all being editable without me needing to leave the app. I changed the camera make and model values, and the changes were reflected successfully when I saved the image.

Though I did go a bit overboard with tone curves, where I pushed the RGB curve too far, and a simple picture of a cloudy day turned into a fever dream. 😅

🖼️

You can check the output here.

It has AI powers

AI Tools was next. I added an image of a clock tower and went with the default/already downloaded U2NetP model that specializes in background removals. I set the output format to be WEBP and clicked on the save button.

It took some time to process the image, but the end result was an image with its background removed. 👇

As you can see above, Image Toolbox gave me a clean output in the requested file format, without any particularly bad artifacting or overcuts. Zoom in, though, and the edges do lack sharpness. I think that's acceptable for a deep learning model that size.

You also have the freedom to opt for heavier, more powerful models if required.

🖼️

You can check the converted (WEBP>PNG) output here.

Finally, I checked out "Presets" under the Resize and Convert tool, which basically showed me the same interface as Single Edit. The horizontally scrollable list had a variety of aspect ratios to fit the various social media platforms.

I picked an image from the gallery and then proceeded to look for the right aspect ratio preset.

I ended up using the 4:5 aspect ratio preset for getting an Instagram-ready image. Though I admit it came out a bit squeezed, the image itself didn't really lose its quality or colors.

🖼️

You can check the output here.

Track usage

Image Toolbox ships with a "Usage Statistics" page, accessible via the chart-looking button at the top. Its job is to quietly track things like app opens, which tools you reach for most, and how much data you've saved along the way.

Don't worry, nothing gets sent to some data mining server; everything stays on the device.

My statistics showed me a total of 3 app opens, 19 tool opens, around 14 MB in data savings, and WEBP as my most-used format.

And if you prefer multitasking, then you can queue up a batch of images and just check the notification panel for updates instead of keeping the app window open.

Install it on Android

The sheer number of tools and settings in here might overwhelm you, though that does come down to personal preference.

For me, there's plenty in this app I'll probably never touch. It makes me want a stripped-down version, something lighter on storage and memory; that would help it run better on older devices too.

There are two main app store listings for Image Toolbox. On F-Droid, it is under Malik's own name, and on the Play Store, it is under the "ProgiX" publisher name.

Both are the same app, but the F-Droid version is a "FOSS" build that ditches Google-related trackers and dependencies, while the Play Store version is a "Market" build that keeps them, including analytics and crash reporting.

Image Toolbox

There's no separate paid plan available here either; the lead dev only asks for donations via the sidebar menu.

You can also download APK files for all the variants of Image Toolbox from the Releases page in the project's GitHub repo.

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