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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You Can Now Run Multiple Linux Kernels on One Machine at Once

Multikernel Technologies has put out mklinux v7.0-mk2, the first public release of the multikernel Linux tree it's been building since last year.

The basic premise of the project is that one physical server can run several independent Linux kernels side by side, each with its own dedicated CPUs and memory. So, the same box could run a database on one kernel and a GPU training job on another without either kernel touching the other's resources.

Don't worry, it isn't some obscure copy but rather a direct fork of the Linux kernel with a bunch of patches added that give it such a high level of parallel play.

How it works

One kernel runs as the host, controlling the machine's CPUs, memory, and PCI devices. When it spins up a new kernel instance, that instance gets its own dedicated slice of hardware and boots straight onto it using Linux's kexec mechanism, running natively rather than under a hypervisor.

The host tracks all of this through device tree overlays under /sys/fs/multikernel/, and it can shift hardware between running instances while everything stays up.

That's a different setup from both VMs and containers. A VM still puts a hypervisor and a host kernel between the app and the hardware. A container skips the hypervisor, but every tenant on the machine shares one kernel.

Multikernel avoids both problems. Each instance is a full, independent kernel running on its own dedicated cores. Modern NICs and SSDs can hand out separate hardware queues too, so an instance gets exclusive access to a slice of the device, not just a slice of the CPU.

This app-kernel and device-kernel split is the pattern the company documents for the architecture generally, dedicating one instance purely to drivers and I/O so the app-facing kernels never see a device interrupt at all.

This release only supports x86_64 hardware, though Multikernel Technologies says the architecture-specific code is already modular enough for other ports to follow later.

Who's this for?

Multikernel Technologies already sells three products based on this kernel tree, and what they're pitched as says plenty about who this is actually for. Private Cloud is sold as a way to consolidate workloads on bare metal without a hypervisor.

Sandbox is marketed for running AI agents in their own kernel with direct GPU access, and LiveUpdate promises to patch a running kernel without taking the system down.

Every one of those pitches targets infrastructure and SRE teams running their own hardware, not desktop users.

The company's own site names even more targeted use-cases beyond those three, ranging from high core count ARM servers to RISC-V systems built for one kernel per cluster on heterogeneous SoCs.

OEMs looking to bake Multikernel into an embedded device are also welcome.

If you remember, Cong Wang, Multikernel's founder and CEO, first pitched this architecture in an RFC posted to the Linux kernel mailing list back in September 2025. It's great to see how far this project has come.

There's more to it than what I have covered so far. You can read the release announcement to dive into the comprehensive benchmark numbers shared by Cong.

And here's the source code for your perusal. 👇

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LibreOffice 26.8 Doubles Down on No AI, Right After Collabora Welcomed It

TDF and Collabora have been at odds since April, when TDF's Membership Committee kicked out every Collabora staffer and partner in one move, with more than 30 people dropped.

That didn't stop Collabora from shipping a range of new releases, with Collabora Office 26.04 being the most recent one, where AI was introduced as an opt-in feature, letting users pick a model and bring their own credentials.

Now, LibreOffice, and by extension, The Document Foundation, has put its rejection of AI at the very forefront with the LibreOffice 26.8 release, going as far as calling it out in the release announcement title itself.

🆕 LibreOffice 26.8: What's New?

There's no AI in LibreOffice 26.8. Nothing about the suite talks to a remote server, and none of it needs an internet connection to work.

This isn't an oversight; it's a promise. TDF says that if your work can't leave the building for legal or privacy reasons, the only way to prove that later (during an audit, for example) is if the software never gave it anywhere to go.

Anyhow, coming back to the editors. 👇

Writer now detects paragraph direction automatically when you open or paste a document, end-of-line spacing follows the paragraph's direction instead of the reading direction, and object resize controls play well with RTL and vertical CJK documents.

It also gets a way to see the baseline grid during editing, and the grid itself now aligns correctly inside text frames. A new Draft View for stripping out the headers, footers, and page margins for distraction-free writing is available too.

Calc picks up the same RTL detection we saw earlier. Typing right-to-left text into an empty cell sets its direction automatically instead of requiring a manual toggle.

With additional improvements like Calculated Fields for pivot tables and a "Shuffle" command for scrambling cell order within a selected range.

Similarly, Math gets new named functions and operators built for two west African writing systems, N'Ko and Adlam, which cover the Manding languages and Fulani, plus left-pointing vector arrows for formulas written right to left.

From left to right: Writer, Impress, and Colored Notebookbars.

Chart has basic support for reading and writing back the newer chart formats (chartex) that show up in Microsoft's OOXML files, covering the box-and-whisker, funnel, Pareto, sunburst, treemap, and waterfall types.

Impress and Draw can mix page sizes within one file instead of forcing every slide or page to match, and Impress also picks up presentation sections, letting you group slides under a name instead of scrolling through one long list.

And generally speaking, Calc, Writer, Impress, and Draw now each carry their own background tint in the Notebookbar (the ribbon-style interface on top), so you can tell which app you're in without reading the title bar.

There's also a new Paragraph Composer that balances word spacing across a whole paragraph instead of judging each line by itself, fixing the uneven look that comes from justifying one line at a time.

⚙️ How to Install/Upgrade?

You will usually find LibreOffice already installed on popular Linux distributions, and it is only a matter of time before this release shows up as part of a routine system upgrade.

If you can't wait, then downloading the DEB or RPM package is the quickest way to get this release installed. Alternatively, you could also opt for the Flatpak, but know that it receives updates a bit late.

LibreOffice 26.8

Refer to the release notes if you want to see the new changes in action.

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