Xbox Game Pass has never really worked on Linux beyond laggy cloud streaming, but an open source project called Xodus is trying to change that.

Earlier this week, replying to a thread on Reddit, Paweł Lidwin (imLinguin), project lead of Xodus, confirmed that the hardest parts of running those games on Linux, Xbox authentication, and game downloads are already working.

The project is attempting to reverse engineer Xbox's entire authentication, licensing, and delivery pipeline well enough to run Xbox on PC and Game Pass titles on Linux.

What's Xodus?

Xodus describes itself as "the great gaming migration to Linux," while cautioning people that it is not endorsed by Microsoft and using it comes with risks. You see, modern Xbox PC games run on Microsoft's Game Development Kit, or GDK, and ship as encrypted⁣ MSIXVC packages.

Getting a game running on Linux means handling both.

The project's GitHub page currently houses a handful of repositories that include the main xodus client written in Rust, a forked ntfs library for reading MSIXVC, and xgameruntime, an open source implementation of xgameruntime.dll built for use in Wine.

A companion repo, xgameruntime-docs, documents how that same DLL works internally.

There's also forked copies of Wine and Proton, maintained by the Xodus team, featuring custom tweaks, and xal-rs, an Xbox authentication library forked from OpenXbox.

Keep in mind that there are no binaries available for you to play around with just yet; there's still a lot of work to be done.

So yeah, a First Look at Xodus is still months away. 😆

The devs are surprised

Thanks to that Reddit reply, the project has gotten a wave of press coverage, and it has caught the developers off guard. "Quite unexpected," wrote Paweł on the project's Discord server after Digital Foundry picked it up.

Though the original coverage seems to have been from VideoCardz.com, many of the outlets out there have got a detail wrong. Describing Xodus as a project from the Heroic Games Launcher team is not right, as Paweł is the only Xodus contributor who has also worked on Heroic, not the whole team.

Another contributor, BellezaEmporium, who has been busy tracking this surprise wave of coverage, points out that one of the articles has been quite salty in talking about Xodus, while Olivia (olivi-r) shared some important developmental updates.

She says that:

Progress has been fairly rapid the last few days on the xgameruntime side, XTaskQueue is nearly implemented with a few modes and quirks to sort out.

Currently fixing XUser as it seems the signature generation is a bit messed up 😬

Still need to actually load tickets from xodus into XUser as well, I've been hardcoding mine for the testing, not sure if we're going with the stdio proxy or ipc implemented directly in wine yet.

What now?

If Xodus pulls this off, it closes one of the last major gaps between Linux and Windows gaming. Game libraries on Steam, GOG, and Epic Games already run well through Proton and Heroic, but Xbox PC and Game Pass titles have been locked to Windows or laggy cloud streaming until now.

And, if you ask me, this is the right time for Xbox to make Xodus' job easier by lending a hand, similar to say how Valve has handled the development of Proton while supporting Wine and DXVK upstream. This way, the whole ecosystem benefits, not just Steam.

If Xbox decides to help the project, then they do have many avenues to pursue…

Welcome to the first issue of Local AI Weekly. A lot of It's FOSS readers have been curious about local AI but didn't want it mixed into FOSS Weekly. So here we are. A separate space for people who want to explore AI but the open source ones.

I'll be sharing experiments from my own hardware, open model news, tools worth your attention, and will keep an eye on the AI related news worth knowing. Let's get into it.

🧪 Experiment: Hermes on a Raspberry Pi

Hermes is the buzz of the AI town so I decided to give it a try. But I chose a rather unusual setup. I have the Hermes backend running on a Raspberry Pi and connecting to it via Hermes Desktop on my main machine. So the agent actually runs Pi, and I interact from my computer.

Hermes Desktop also has a voice conversation feature, most AI tools have it these days. Voice AI is shaping up to be the next big thing. Ubuntu 26.10 is already preparing native voice AI support and local tools like Vocalinux are already in development.

Seems like we're not far from AI-based desktop companions you can actually talk to. Think email briefings, task reporting, agent control. Those things are already here, even if in early stages.

🔍 Discover AI tools

Here is a new open source markdown-based knowledge base built for you and your AI agents. It is local-first, git-backed, and ships with a native MCP server, so commercial or local AI agents can read and write your notes directly.

Worth a look if you're building a personal wiki or a shared second brain your coding agents can use across sessions. Still in early stages of development, so expect bugs here and there. I am currently using Tolaria for my personal KB, and this one is my on my weekend activity list.

Another interesting open source AI tool I came across recently is Cleat. It basically runs Claude Code inside a Docker sandbox with one command, so an autonomous agent session can't touch your host system. It shares your Claude auth, edits project files, installs packages, and runs any command inside the container, but stays blocked from your SSH keys, other projects, and the rest of your machine unless you opt in.

The project is fairly new, and I don't see activities on its GitHub repo in the last three weeks. Hope it is not on the road to become an abandonware.

📡 Open Model News

The open model space has had a busy few weeks.

Kimi K3 landed on July 16 from Moonshot AI. It's a 2.8-trillion-parameter Mixture-of-Experts model with a 1M token context window, released under a "Modified MIT license". It's the largest open-weight model ever released, and early benchmarks are putting it within reach of frontier closed models. Running it locally requires serious hardware, but smaller distillations should be here soon.

Around the same time, Inkling was released by Thinking Machines Lab, the startup founded by former OpenAI CTO Mira Murati. It's a 975-billion-parameter multimodal model released under Apache 2.0. The Apache 2.0 choice is significant because it means free commercial use without the usage restrictions that come with some other open licenses, like the modified MIT.

Both are too large to run on most home hardware right now. But these releases matter because quantized versions and smaller distillations typically follow within weeks. Worth keeping an eye on Ollama's model library, even though Ollama is likely offering them on their cloud plan.

🗂 AI Jargon: Quantization

You might have come across the word quantization. It is the process of reducing the 'numerical precision' of a model's weights to make it smaller (and faster). A full-precision model stores each value as a 32-bit or 16-bit float. A quantized model stores them at 8-bit, 4-bit, or even lowre. The model gets smaller so it uses less RAM, and runs faster but that comes at the cost of quality.

Take a look at the tags of any model at Ollama... llama3.1 for example. You'll see names like instruct-q2_K, text-q3_K_S, fp16 etc. Those are quantized. The file size is smaller, an indication that it will need less RAM.

⚡ Quick Tip

When downloading models via Ollama, you can specify the quantization level directly. Instead of ollama pull llama3, try ollama pull llama3:8b-instruct-q4_K_M to get a specific quantized variant. Check the available tags on ollama.com/library for whichever model you're pulling. Just add /tags/ at the end of it.

And we continue...

I'll be honest. The local AI scene is more fragmented than the Linux distro landscape. And not all of us have the same needs. If you're a DevOps person, you might have no interest in AI image restoration tools. If you're a developer, graphics workflows probably don't apply to you.

So I'm going to share my own experiments and exploration. Some of it will be useful to you, some won't. That's fine. You'll likely learn new things and that's the goal.

See you in two week.

My agent harnessing journey started with Nanoclaw. Which is super simple to setup and use. It works for a few simpler tasks through Telegram.

But I wanted something to work on my main computer. Out of all claw like agents, I find Hermes the most suited.

So I installed Hermes on a Raspberry Pi running on Pironman 5 Pro Max. And I installed Hermes desktop on my Asus Zenbook laptop, my primary system.

The advantage is that the Raspberry Pi remains the always-on Hermes machine. I can close Hermes Desktop or shut down the laptop without needing Hermes itself to run on the laptop (for scheduled tasks). Also, Hermes agents won't have unrestricted access to my system. A safer approach, in my opinion. At least, that's the idea I am going with.

The basic architecture is:

Laptop
└── Hermes Desktop
        │
        │ Remote Gateway
        ▼
Raspberry Pi
└── hermes serve
    ├── Agents
    ├── Jobs
    ├── Memory
    └── Tools

Let me show you how you can use the Hermes agent via the remote gateway feature.

Step 1: Start the Hermes Gateway on the Raspberry Pi

Open a terminal on the Raspberry Pi or SSH into it.

You need to add the following in the ~/.hermes/.env file of hermes:

HERMES_DASHBOARD_BASIC_AUTH_USERNAME=admin
HERMES_DASHBOARD_BASIC_AUTH_PASSWORD=YOUR_STRONG_PASSWORD
HERMES_DASHBOARD_BASIC_AUTH_SECRET=YOUR_RANDOM_SECRET

The 'random secret' can be generated with.

openssl rand -base64 32

These credentials will be used from the Hermes desktop. Now start Hermes's backend with:

hermes serve --host 0.0.0.0 --port 9119

You may see a message like this:

Headless backend (hermes serve): web UI disabled — use `hermes dashboard` for the browser UI.

This is normal. hermes serve does not provide a browser interface. It starts the backend that Hermes Desktop connects to.

Keep this process running while testing the connection.

Step 2: Verify that Hermes is listening

Open another terminal tab to access the Raspberry Pi, and run:

ss -ltnp | grep 9119

You should see something containing:

0.0.0.0:9119

This means Hermes is listening for connections on port 9119.

Step 3: Install Hermes Desktop on the pc

Hermes provides an official script for installing the Hermes Desktop:

curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash

During installation, Hermes may ask to choose a terminal backend:

Select terminal backend:

Local
Docker
Modal
SSH
Daytona
...
Keep current (local)

For this setup, you can leave this as:

Keep current (local)

The Terminal Backend setting is separate from the Remote Gateway setting. The gateway is what connects Hermes Desktop to Hermes running on your Raspberry Pi.

I also left out the model selection. Whatever the remote Hermes server uses will be used here, too.

Step 4: Use remote gateway on Hermes

Start Hermes Desktop from the terminal:

hermes desktop

That's the way it runs for the moment. Ironical to run a desktop GUI app from the terminal.

Anyways, inside Hermes Desktop, click on the settings and go to gateway and find the remote gateway option:

In the Remote URL field, enter the address of the device running the Hermes backend with the port 9119:

http://<IP of Pi>:9119

Then save/apply the setting and reconnect.

Hermes Desktop should now connect to the Hermes backend running on your Raspberry Pi.

Hermes Desktop becomes the interface for interacting with the Hermes instance on the Raspberry Pi.

Step 5: Make the gateway permanent

If things are working fine so far, it is time to make things permanent. Because keeping this running on the remote Hermes server is not a wise move.

hermes serve --host 0.0.0.0 --port 9119

Because if I close that terminal or reboot the Raspberry Pi, the gateway will stop.

For an always-on Raspberry Pi setup, running hermes serve as a systemd service works better.

No need to SSH into the Pi and manually launch Hermes every time. It will be automatically start thanks to the systemd service.

Get the Hermes executable path with:

which hermes

And then create the systemd service:

sudo nano /etc/systemd/system/hermes-server.service

Here's the file I used. You should replace the EnnvironmentFile and ExecStart values as per your setup.

[Unit]
Description=Hermes Agent Backend
After=network-online.target
Wants=network-online.target

[Service]
Type=simple
User=pi
EnvironmentFile=/home/pi/.hermes/.env
ExecStart=/home/pi/.local/bin/hermes serve --host 0.0.0.0 --port 9119
Restart=always
RestartSec=5

[Install]
WantedBy=multi-user.target

Once you have saved the service file, run it in this fashion:

sudo systemctl daemon-reload
sudo systemctl enable --now hermes-server

Check the status of the newly created systemd service:

systemctl status hermes-server

Conclusion

I am yet to fully utilize Hermes on desktop with the remote gateway method. Portability could be an issue if I move out of my home network, but even in that case, there are ways to SSH into Raspberry Pi from outside network.

I'll be sharing more of my local AI exploration and experiences. Do subscribe to Local AI Weekly newsletter for that.

The Linux Mint project regularly puts out monthly news that gives us a look at what the developers are working on and where the overall project is headed towards.

Their July update has shown us how the distribution intends to handle kernels going forward, moving the entire job out of the Update Manager and into a different tool altogether.

A new feature?

Yes, and no. Kernel management isn't new, as in the past, Update Manager has let users view and remove individual kernels, but that functionality was removed.

The new implementation sits inside the System Administration tool instead, and it's built to behave the same way whether you're on Linux Mint or LMDE.

Instead of tracking individual versions one at a time, the new "Kernels" page inside System Administration groups multiple kernel versions according to series. Setting up the tool is a one-time choice of you telling it which kernel series matter to you and how deep a backlog to hold onto for each one.

Everything downstream runs on its own after that. The series you're following keeps updating through Update Manager the same way they always have, while anything past your set limit gets thrown out during a scheduled pass once a week.

Manual cleanups are possible too for times when you want to quickly free up some space to accommodate other content. Individual kernels can also be marked "Protected," which keeps them off the chopping block regardless of whether their series is tracked or how old they are.

Before you go looking for this on your own system, know that it isn't live yet.

Linux Mint project lead Clement Lefebvre has said that this is a preview of what's coming to the next Mint release.

Fedora has this already

I understand that Linux Mint has had to reimplement their solution, but Fedora has had this problem solved for years. Its DNF package manager has done this by default the whole time.

The setting behind it is called installonly_limit, and it defaults to 3. Only the three most recent kernel builds stay installed at any given time. Whenever a new kernel comes in through a regular update, DNF removes whatever falls outside that limit as part of the same transaction.

There's no separate scheduled job behind it, unlike Mint's new weekly cleanup. It only runs when you actually update, so a kernel installed outside DNF's normal flow would just sit there untouched.

The two approaches end up solving a similar problem in different ways. Fedora's is a flat, always-on limit tied directly to the update transaction. Mint's new implementation, once it ships, would hand you more direct control instead.

Kernel management wasn't the only thing in Mint's July post. The team also shipped new HWE ISOs for Linux Mint 22.3, this time built on Linux 7.0, and Fcitx5 support lands in Cinnamon, running the same way across Wayland and X11.

For the full rundown, including the new environment variables page and the panel fixes, the original blog is a must-read.

Suggested Read 📖: In related news, Linux Mint now considers Wayland stable enough for inclusion in its next release, due Christmas.

Love them or hate them, rolling release distros have managed to take a slice out of the Linux desktop market share. They provide a bleeding-edge experience that stays quite stable if cared for and bites a user's a** if handled poorly.

CachyOS is one of those options that has been only getting more popular as the days go by. Offering a rebuilt Arch package stack with CPU-specific optimizations and a custom kernel tuned for responsiveness, it is a distro that delivers some good performance.

You might remember late last year, the developers had teased plans for a dedicated Server Edition aimed at NAS setups and hosting providers. Their newest release shows us where some of the work is happening, along with the typical distro-focused improvements.

What's fresh?

The graphical installer sees many new additions, such as Hyprland's Noctalia option swapping SDDM for noctalia-greeter, Cinnamon moving to lightdm-slick-greeter, GNOME gaining gvfs-dnssd, and COSMIC picking up cosmic-monitor.

For the CLI installer, other than the usual housekeeping work of addressing bugs and refactoring the code, there's experimental support for Server Edition installation profiles, marking a key milestone in the new variant's development.

Shelly became CachyOS's default GUI package manager back in April, replacing Octopi, and we found it a promising switch once we spent time with it. With this CachyOS release, Shelly has got its most significant change yet with its v3 iteration.

It has gone from being written in C# to Zig, replacing the managed runtime with native binaries that start faster and use less memory.

Coupled with that is the new onboarding screen that shows up at first run, along with list and grid views for browsing packages, and AUR PKGBUILD previews with build output.

You also get a new Utilities page for syncing the database, cleaning up the cache, and orphaned package removal. Similarly, its CLI version can now search repositories and the AUR from one place, running update checks across the repos, AUR, AppImage, and Flatpak.

Cachy-Update now runs on top of Arch-Update v4.x, with the systray applet ported to Rust. The new --check --enable flag combines two previously separate steps, turning on automated update checks and launching the tray icon together.

Then there's the handheld detection with chwd, which currently works off board name pattern matching, picking up Bulgarian localization, and no longer tripping up when the board_name DMI file is missing.

And finally, we have the Welcome app that had its DNS handling reworked, resolving an issue that kept the speed-test based ranking from actually picking the fastest server.

You can refer to the release announcement blog to know about the smaller changes that we have skipped here.

Get this now

Fresh ISOs for both editions are live now. Grab the "Desktop Edition" for the full CPU-optimized desktop experience, or the "Handheld Edition" if you're running a Steam Deck, ROG Ally, Legion Go, or Legion Go S.

Existing users don't need to reinstall anything to get this release. Just open Shelly from the app launcher and go into the "Update" page. Here, wait a bit for the sync to complete, or click on "Check Again" to pull in updates from the official repositories, AUR, and Flatpak in one go.

If you prefer the terminal, Shelly's CLI can handle the same job with a single command:

shelly

Over the past two years, the AI agent space has gone through wave after wave of "engineering paradigm shifts". From Prompt Engineering to Context Engineering, and on to Harness Engineering, each iteration has focused on making individual agents smarter and more reliable.

However, complex tasks in the real world are never completed by a single person.

For example, multi-disciplinary medical consultations require experts from various fields working in tandem.

Similarly, large-scale software engineering projects demand product managers, frontend engineers, backend developers, and QA testers pushing forward together.

And yet, AI industry is still trying to build an "all-powerful" single agent rather than a well-coordinated team of agents.

This is precisely the problem openJiuwen aims to address.

What is openJiuwen?

openJiuwen is an open-source AI agent platform jointly built by Huawei Laboratories, Huawei Cloud, and the Terminal Xiaoyi team, released under the Apache 2.0 license. Its goal is to help developers build production-grade AI agents and enable large-scale multi-agent collaboration.

In May 2026, the openJiuwen community released a major component: JiuwenSwarm. It marks the first engineering implementation of "Coordination Engineering", moving the focus from "making a single agent smarter" to "making multiple agents collaborate efficiently."

In collaboration with Huawei's Terminal Tablet & PC Product Line, Software Department, and other teams, openJiuwen launched the HarmonyOS PC version of JiuwenSwarm. This means JiuwenSwarm shares the exact same technical DNA with the HarmonyOS ecosystem right from the architectural foundation.

What makes JiuwenSwarm different?

At the core of JiuwenSwarm is the Agent Swarm mechanism, which enables multiple AI agents to autonomously divide labor, dynamically negotiate, and collaborate efficiently.

Below are its key features.

Heterogeneous model routing for different tasks

JiuwenSwarm supports routing members to different models, providing appropriate model capabilities for different roles, thus allocating heavy-reasoning models to complex logic and lightweight models to simple tasks. This reduces system load and improves overall execution quality. Developers simply declare the role-to-model mapping in a configuration file, and the system automatically manages scheduling.

JiuwenSwarm has already achieved seamless integration with platforms like HarmonyOS Xiaoyi and Lark (Feishu). Developers can build JiuwenSwarm-mode agents that are invokable directly via HarmonyOS devices.

Layered memory architecture

Paired with a layered persistent memory system, JiuwenSwarm retains full-dimensional records across sessions, including operation history, enterprise profiles, and policy guidelines. Through a cross-Swarm experience inheritance mechanism, the experience accumulated by one Swarm can be directly inherited by another.

Distributed deployment

JiuwenSwarm supports distributed Agent Swarms. The Leader Agent and swarm members can run across different processes, different nodes, or even entirely different servers. This capability is especially critical for tasks requiring access to isolated environments, such as intranet databases.

This architecture naturally adapts to the multi-device distributed scenarios of HarmonyOS PC. In the HarmonyOS PC "Agent PC" environment, the PC acts as the home base for the Leader Agent, while HarmonyOS devices like phones and tablets act as Teammate Agent nodes, working together without needing extra adaptation.

With the Channels mechanism supporting platform integrations like Xiaoyi out of the box, developers can build multi-agent applications for the HarmonyOS PC ecosystem straight away.

Observability and self-evolution

JiuwenSwarm provides an intuitive interactive interface, helping users track Swarm running states, task progress, and member workloads in real time.

Simultaneously, its experience closed-loop mechanism captures task breakdown paths, role allocation strategies, tool invocation sequences, and result evaluations, converting them into reusable "task templates." The next time a similar task arises, the Swarm can skip the planning phase and jump straight into execution.

In enterprise office scenarios, OfficeClaw has already leveraged JiuwenSwarm to achieve automated collaboration across content generation, document processing, and knowledge search.

Meanwhile, the Swarm Skills Hub has attracted contributions from numerous developers, fostering a virtuous ecosystem loop.

Where it's headed

In openJiuwen's commercialization roadmap, the native HarmonyOS agent system represents one of the largest and strategically most significant deployment cases.

With JiuwenSwarm now open source, developers can directly build multi-agent applications for the HarmonyOS PC ecosystem using this framework.

The project is officially open for the community on GitHub under the Apache 2.0 license.

The Rust project has implemented a formal LLM policy, which pushes people to use LLMs to analyze/think through code, not write it.

With how it is written right now, the policy only covers the core code repo for Rust. It lays out many rules around what's restricted, what's allowed, and a circuit breaker that pauses AI-authored merges once they cross half of all merges in a six-week window.

Just so you know, the core repo hosts the compiler and standard library for the entire language. Every Rust program depends on code that lives here, which makes it a very important repo to moderate.

It's no surprise they banned LLM use the way they did.

An LLM can't write your comments, issues, or PR descriptions under your name. Documentation, safety comments, and compiler diagnostics are all off-limits too. And an LLM review can never substitute for a human one or for reviewing your own code before you post it.

What's allowed is private use. Asking an LLM questions about the codebase needs no disclosure, and neither does privately reviewing your own code, summarizing a PR for yourself, or building personal dev tools with one.

Apart from that, there are a few things that reside in a gray area. Machine translation, trivial fixes made with an LLM, and self-verified bug hunting all sit here, and each one needs to be disclosed.

AI-written code gets the strictest treatment of all. It has to be pre-arranged with a reviewer, unlikely to introduce a bug that breaks Rust's safety guarantees, fully tested, and understood on both sides before it earns the new ai-assisted label.

Jynn Nelson, who wrote up the original policy that the project has now adopted, concluded the announcement by stating that:

This is not the end of the story. One of the goals of the policy is to help us gather data: Are people doing interesting and useful things with LLMs? Are they learning? Are they making repeat contributions? The answers to those questions will help us determine how the policy changes in the future.

A similar move elsewhere

NetworkManager, the default network daemon on most mainstream Linux distributions, has picked up an AI coding assistants policy of its own, recently merging a new section to its CONTRIBUTING.md file.

It reads as follows:

Authors are responsible for 100% of the code they submit. Do not send a patch you cannot explain, and do not send one you have not built and tested yourself. Write your own commit messages and Merge Request descriptions.

Those explain why you are making the change, which is the part a tool cannot know. Respond to review comments yourself. If you cannot discuss your own patch with a reviewer, it will not be merged.

Everything in the Legal section applies unchanged. You are the one certifying that the contribution can be released under LGPL-2.1-or-later. A tool cannot certify that for you.

Large machine-generated Merge Requests that no human has reviewed line by line will be closed.

Josephine Pfeiffer of Red Hat wrote the change and merged it herself on August 7, after maintainer approval. If you read the above, then you can see NetworkManager's version is quite simpler than Rust's tiered system. Right now, it is closer to the human-in-the-loop approach that we have already seen in LLVM's policy.

We are constantly seeing such moves. For instance, Codeberg's members recently voted to ban projects built mostly from AI-generated code, citing server strain and climbing hardware costs.

Time and again, we're getting more evidence that AI use can quickly degrade maintenance-focused work and infrastructure if left unchecked. As usual, the open source ecosystem will continue evolving according to what the use of AI dictates.

I sat down with Bianca Lewis of OpenSearch at Open Source Summit India, right after the project's second OpenSearch Con in the country. Bianca Lewis is the executive director of the OpenSearch Software Foundation.

OpenSearch started life in 2021 as a fork of Elasticsearch, born out of Elastic's move away from an open source license. Five years on, I wanted to know how much of that origin story still holds, and how the project sees itself now that it's an AI data infrastructure layer with its own roadmap and its own community.

We covered a lot of ground: whether OpenSearch is still tethered to Elasticsearch in any way, why ELK Stack remains the more recognizable brand among young developers, how agentic AI fits into the platform, and an uncomfortable but necessary question about what happens when vendor-backed open source projects go proprietary.

Five years in, is OpenSearch still a fork of Elasticsearch, or has it become a product of its own? Does it still pull in any downstream changes from Elastic?

Bianca: That is a great question, and the answer, very simply, is yes, it's an independent project. It's a completely independent project now that began as a fork, as you correctly said, and in the early days the two were very, very similar. Today the focuses and the strengths have become very different, and today they are completely independent projects. I think the only commonality is that users can still use both platforms for similar kinds of use cases.

Explaining OpenSearch to the average Linux user

OpenSearch is enterprise-focused, but how would you explain it to a home labber or self-hoster who's never heard of it?

Bianca: Well, firstly, of course OpenSearch is an AI infrastructure layer for enterprises, but because it's on an open source platform, free and easy to use and adopt, people making new startups, entrepreneurs, developers, and everyone else find it very easy to get started. Download it, it's free to use, all the features are open and available, and they can join very easily a community of thousands and thousands of people to get up and running.

So it's not just enterprise-focused anymore, it's for anyone and everyone. Whoever's requirements fit it, OpenSearch is for them. I don't think it was ever built to just be for the enterprise. The startups of today are the enterprises of tomorrow.

"The startups of today are the enterprises of tomorrow."

The ELK Stack brand problem

ELK Stack is still the more recognizable brand in observability and search. How does OpenSearch compete with that brand recognition among young developers?

Bianca: There is no doubt that Elastic, Elasticsearch, and Logstash and Kibana, which make up ELK, have been around a long time, and that's obviously where the fork came from originally. I hope that after five years of OpenSearch developing as an independent and powerful project — 1.7 billion downloads later, 400 individual companies contributing, more than 3,000 contributors, and 15,000 OpenSearch stack users — the differentiation in young developers' minds is becoming clearer just from the sheer weight of numbers.

I think there are two distinguishing factors. The first is philosophy — how developers want to build their technology stacks conceptually and into the future. Elastic, who owns ELK, even though the license is now back to open source after they closed it, is still owned by Elastic and is open core. That means if a young developer wants to build on Elasticsearch and use the ELK stack, they can use the core version for free, but for any more advanced features or support they're going to have to pay and be tied into Elastic. Developers who want to build on a platform that's fully open, where you get the whole thing for free and can choose whether you want support or hosting when you're ready, have that choice with OpenSearch. It's a different philosophy.

The second layer is that the two platforms have grown along different technological paths. Every platform in this space will say "we're a millisecond faster at this," or "we support this use case better," and that's all true — no platform can honestly say it's the best at everything. But OpenSearch is really good at a lot of different use cases that unify the data layer, and because it's the only alternative that isn't just open core, it's making young developers distinguish OpenSearch, hopefully much faster than ELK or any other platform.

Observability, complexity, and cost

Observability stacks are getting heavier, more expensive, and more complicated. What is OpenSearch doing about ease of setup and cost?

Bianca: Those are two very valid questions, so let me answer them one by one.

On complexity: the advantage of proprietary vendors has always been that on day one it's easy — you buy it, it's off the shelf, it works, and you get support. With an open platform, you build it on day one, and you get the benefit on day two, three, and four because it's customized to your needs and you're not dependent on a vendor. But getting started on day one is a very valid point. Because of that, we've recently launched something called Launchpad, which lets you eailylaunch an observability stack, a search application, or any other OpenSearch application, because OpenSearch isn't just observability, there's also search, AI applications, and security monitoring. All this takes a few minutes, and we encourage developers to ask the community if they get lost, even though the documentation is all there. I believe the ease of setting up OpenSearch has been addressed incredibly well and quickly.

On cost, it's a more complicated question. I can tell you OpenSearch is free, but in real life nothing is ever truly free because you still have to run it on infrastructure and manage it.

What OpenSearch does do is make everything cost-effective. Take observability: in the old days, we had to have it because if something in our infrastructure broke, we had to solve it — we couldn't afford downtime — but nobody wanted to pay for it because it was seen as a cost center.

Today the whole language has changed, because OpenSearch gives you a unified view of the context of your data, which is much more cost-effective than doing log analytics, traces, and metrics separately with tools like Grafana and Prometheus, where everyone looked at the data differently and root-cause analysis took five weeks to put the pieces together.

Today it's all in a single pipeline into OpenSearch — you see it all together, through different lenses in the same view, and you can automate resolutions. You can use models and AI to interrogate that data, automate resolutions, and use your observability stack for more than troubleshooting — for company-wide SLOs, understanding acceptable error rates, and understanding cost.

We're in the token economy now, so we can add cost information into traces and logs and understand accurately how much each service is really costing us. Observability isn't just "pay for troubleshooting" anymore — it's become easier to use and a driver of business imperatives.

AI-agent readiness

How AI-agent-ready is OpenSearch, for someone building a project that leans heavily on agentic AI?

Bianca: In the 3.5 release — we've just released 3.7, so we're two minor versions past that — agentic AI became generally available in OpenSearch, and since then it's become a central theme of how people run their search and observability stacks. I think we're ahead of almost any other platform in the world on fully integrated agentic AI.

As you spin up AI agents, OpenSearch is model-agnostic, so you can use any AI model you want. If it's for a search use case, you can monitor those AI agents with the same platform — monitor the trace of the agent, see which services it's calling, what the costs are, and make sure it's doing the right thing. At scale, when you're running 100,000 queries a minute, we can almost let AI monitor AI for security, safety, and compliance.

AI brings scale and the ability to do things quickly. We don't have to work sequentially anymore. But data without context is just data, it's noise. You need a platform that can recall it and spin up AI agents, you need the data underneath it, and you need the agents to talk to each other to perform tasks but without context, all of that is garbage at scale. OpenSearch has built in some features to address this: short-term agentic memory, so an agent remembers the context of what it was asked over the last week or two, and in 3.7, long-term agentic memory as well, which gives us the context we need to take accurate actions on the data. It's already generally available.

AI data layer vs. traditional search

Is OpenSearch now an AI data layer sitting on top of a traditional search stack, or have the two fully merged?

Bianca: I think ultimately OpenSearch will be looked at in a couple of layers. OpenSearch is the application level, and the application level sits on top of an infrastructure that supports the architecture of the application.

What OpenSearch gives you is that it doesn't tie you into any one vendor's choices. You can have that AI infrastructure layer but choose your own infrastructure. You can self-host, or go to one of the hosted services, like our OpenSearch service on NetApp, Oracle, and AWS and choose whatever fits your model. In terms of features and how things run with agentic AI, we don't really say this works better or worse than that. We say everything works and works really well, and with OpenSearch you retain control over how you want to build it, without being tied into any one approach.

On business-source licensing and the Linux Foundation model

This is a bit of an uncomfortable question, more about the Linux Foundation model in general than OpenSearch specifically. MariaDB, Redis, and Elasticsearch all moved from open source to business-source or server-side licenses at some point, largely to protect their business from hyperscalers who weren't contributing enough but were taking their enterprise customers. Then the Linux Foundation and companies like Amazon fork the original project, as happened with Terraform and OpenTofu, and grow it as the "true" open source version while the original gets left behind. Is there an injustice in that, toward the original projects?

Bianca: I think it's a really great question. I don't ever judge a vendor for deciding to close-source or open-source a project. But without intruding into the particulars of how Elastic or AWS felt at the time, which I can't speak to, I can share a personal viewpoint.

If a vendor is the owner of an open source project, to me that open-core label is simply a go-to-market strategy to get users in and then sell services, support, and enterprise capability. It's not what we'd call vendor-neutral. Building a wide community on a vendor-owned open source project is always tricky, and that's why Mongo, Elastic, Redis, and a few others reached a point where the situation became a threat — the go-to-market had been really successful, they'd built a community, but the biggest customers weren't converting.

I find it interesting that Elastic changed back to an open source license after closing it — because they were losing the community, but they'd closed it because they were losing enterprise customers. They were fine with small players using the free product but wanted the big-money customers. The challenge is that often you want your cake and to eat it too. Once you build your brand on community involvement and then tell the community you're closing it, even if you reopen it later, a lot of the community will just say "thanks, but no thanks."

That's the beauty of the Linux Foundation model. It's a nonprofit that nobody owns, and profit isn't the motive. Yes, there will be cases where hyperscalers fork a project. I think it's important to understand that once they fork it and give it to a nonprofit like the Linux Foundation, they give up ownership. They don't own OpenSearch anymore. They could fork it again and keep something in-house, anyone can always do that, but OpenSearch itself can never be closed now, because the Linux Foundation owns it.

OpenSearch itself can never be closed now, because the people that own it is the Linux Foundation.

It also helps to look at where each company actually makes its money. Take Elastic. They make money selling Elasticsearch licenses, whether self-hosted or on the cloud, so it makes sense for them to gate advanced features behind a license, because they have to survive as a business and answer to shareholders.

Hyperscalers like AWS, Microsoft, Google, IBM, and Oracle make their money from selling compute and infrastructure, not directly from the application. Carrying the burden of growing and innovating an application takes away from their core focus of selling infrastructure, so it makes business sense for them to let the community develop the application while they focus on providing a great service to run it, and put their money where they actually make money.

That's the subtle reason hyperscalers are so eager to support Linux Foundation projects. It lets them focus on where they make money, and it guarantees these projects stay open, available, and well-maintained rather than becoming orphaned.

Ultimately it takes time for a project's original founder to say "now we can let the community manage it." What that actually means is that over the years, instead of maintaining the application with a full-time in-house engineering team, the founder becomes one of the companies contributing to a shared engineering effort. You get all the benefit, but the innovation and cost become shared, and the community retains the choices, which is always good for business.

Certifications

Does OpenSearch have plans for a certification track, like Kubernetes has with the CKA?

Bianca: Yes, we've actually recently announced that. The way we're doing it is quite unique and exciting for the open source world. We've already released the first form of learning content, and in a couple of months we're going to release an OpenSearch competency course that carries a badge of competency you can put on LinkedIn.

On top of that, we're going to do the OpenSearch competency certification proper, and based on that certification, we're doing accreditations for different vendors and companies. The follow-up to that is specialist certifications. Since OpenSearch is used across quite a few use cases, we'll have competencies in observability, search, AI applications, and security monitoring. And to renew your certification, instead of retaking the same test every couple of years, as long as you're advancing to more advanced certifications you keep your original qualifications. We want people to grow and advance within OpenSearch.

The biggest misconception about OpenSearch

What's the one thing that most people get wrong about OpenSearch?

Bianca: There are so many things people get wrong about OpenSearch, but I think the biggest one is not recognizing that, as an AI data infrastructure layer, OpenSearch is the only cloud-native, AI-native platform in the world that's truly open source and vendor-neutral. I don't know if people really understand that.

OpenSearch is the only cloud-native, AI-native platform in the world that's truly open source and vendor-neutral.

Maybe if it were named something like "OpenSearch AI" or "OpenAI Search," it would be more recognizable.

Bianca: Yeah because people often equate it to "how does it compare to Elasticsearch?" But conceptually, you can't really compare the two. They'll say "it's like the AWS service," but that's just a vendor who's a member and provides a service on top of it. The platform runs completely independently, within a community that's truly open source. It's not just an AWS project anymore; it's a truly independent, community-owned observability stack.

OpenSearch's 3.7 release, with long-term agentic memory, is already out, and the project's competency certification track is expected to roll out over the coming months. You can find the project's documentation, downloads, and community channels at opensearch.org.

Illinois has become the latest U.S. state to pull operating systems into the age verification pool. Governor JB Pritzker has signed HB5511, the Children's Online Social Media Safety Act, putting forward a new age verification framework for every device sold or used in the state.

The bill imposes age check on "covered manufacturers," a category that lumps device makers, operating system providers, and app stores together.

Protect the children!

By January 1, 2028, these manufacturers have to build an interface at account setup asking a parent or account holder for the child's birth date. That date is then converted into one of four age brackets: under 13, 13 to 15, 16 to 17, or 18 and up.

The law also stipulates that all transmitted digital signals have to be encrypted.

Applications can then pull that bracket through an API once a user downloads or launches them. Once they get a signal marking a user as a minor, they're legally treated as having "actual knowledge" of that.

Once flagged, apps have to turn on a set of safety settings. Feeds get restricted, profiles get hidden from adult strangers, messages from adults get blocked, precise location gets masked, and notifications go dark between 10 PM and 7 AM.

Parents can override these defaults for their kids, and minors over 16 can do it themselves too.

Apps have until July 1, 2028 to start requesting these signals, and violations can cost up to $50,000 each, enforced by the state Attorney General. Device manufacturers and apps acting in good faith aren't liable if a signal turns out wrong.

Rep. Jennifer Gong-Gershowitz led the bill through the House, with Reps. Margaret Croke and Janet Yang Rohr as chief co-sponsors, and more than a dozen others signed on. Sen. Willie Preston carried it in the Senate alongside Sens. Robert F. Martwick, Mary Edly-Allen, and Adriane Johnson.

It passed 82-27 in the House and 57-0 in the Senate before clearing final concurrence unanimously.

Open source exemption where?

Illinois isn't the first state to try this model. Colorado's SB26-051 and California's AB-1043 both rely on the same OS-level age signal setup; apps request it, and the device answers.

Both of these started off without any concessions for open source platforms (e.g., Linux).

Colorado's SB26-051 originally applied to every operating system and developer without exception. System76 founder Carl Richell worked directly with state lawmakers to change that.

The bill picked up language exempting operating systems and developers distributing software under terms that let anyone copy, redistribute, and modify it freely. It also added a clause blocking platforms from locking down modified versions.

California's AB-1043 had the same gap. Assembly Member Buffy Wicks, the same lawmaker who wrote AB-1043, introduced AB-1856 to fix it. After several rounds of edits, the amended bill redefines "operating system provider" to exclude anyone distributing software under those same open license terms.

Illinois has none of this. HB5511's definitions for "covered manufacturer" and "application store" stay just as broad as these bills were before their amendments.

My two cents

Well, folks, we sure are getting close to someone's wet dream of having a dystopian hellscape where people don't have control over their computers, food, or water.

On the one side, you have these so-called "bipartisan" bills passing through without much hindrance under the guise of protecting children, and then you have the island-class cretins who still haven't been prosecuted.

Virtualization is basically load-bearing infrastructure for any serious data center in 2026. Enterprises lean on it to run more workloads on less hardware, isolate services cleanly, and keep uptime high without buying a rack of extra servers.

Proxmox has built a name for itself in this space over the years with its range of products.

Proxmox Virtual Environment (VE) handles virtualization, Proxmox Backup Server covers data protection, and Proxmox Datacenter Manager ties it all together as a solution for managing distributed infrastructure.

All of it ships as open source software too! 🤌

Now, they have announced a major development for Proxmox VE, their flagship virtualization platform.

Proxmox VE on ARM64

Yep, that's the highlight move. Sitting alongside its existing x86-64 builds are the new ARM64 images that have brought official support for the Arm platform.

Proxmox says the new images follow the same codebase, release lifecycle, and support window as x86-64, while running on Debian 13.5 "Trixie" with Linux kernel 7.0 and additional components like QEMU 11.0, LXC 7.0, and ZFS 2.4.

Likewise, storage gets similar improvements with ZFS 2.4 and Ceph, two of Proxmox VE's core storage options, carrying full feature parity with the x86-64 build.

Though you should know that officially supported hardware is currently limited to the NVIDIA Grace and Vera platforms. Proxmox has also validated the Arm64 build jointly with NVIDIA and Supermicro, testing specifically on Grace Hopper Superchip servers.

Other UEFI-based ARMv9-A hardware gets best-effort support, and Proxmox says ARMv8-A "generally works too." What's not supported is that Raspberry Pi lying on your desk, or really any other devicetree-only single-board computer, like an Orange Pi or a Radxa ROCK board.

Don't think that ARM64 support is stopping at Proxmox VE; the developers are already running internal test builds for their other offerings, with Proxmox Backup Server being one of the examples given.

However, which one gets prioritized depends on their enterprise customers' needs.

Get started

Seeing that ARM64 as a platform has matured so well along the years, and more and more workloads have shifted to this over x86-64, this move doesn't really come as a big surprise to me.

Proxmox didn't ship this as an experiment either. It launched this with full support from day one. This, to me, is a company that saw where the market was headed and adapted accordingly.

Anyhow, Proxmox VE for ARM64 is available now as a free download, either as a bare-metal ISO image or through package repositories alongside the existing x86-64 builds.

Enterprise users get access to the pve-enterprise repository too, same as on x86-64, with Proxmox recommending it for production deployments.

You can grab the Arm64 ISO installer directly, dig into the documentation for setup details, or browse the source code repositories if you want to see what's under the hood.

Suggested Read 📖: Our informative mini-course that covers everything from installing and upgrading Proxmox to using it.

Greg Kroah-Hartman just deleted a Linux driver that hasn't mattered to anyone in years.

The reason for removal wasn't a bug report or a user complaint. It was AI agents poking around dead code and turning up findings nobody asked for.

That sounds like another "AI is reshaping open source" piece. It mostly isn't. This driver was already on its way out, Moxa said so years ago. What changed is the removal finally happening at a time when AI is scraping away at the kernel codebase.

What's happened?

On August 3, GKH sent a patch to the linux-serial mailing list removing drivers/tty/moxa.c entirely. That's the Moxa Intellio family multiport serial driver, code that's been part of the kernel since 1999.

The patch strips out 2,137 lines of driver code, plus the Kconfig and Makefile entries that reference it. In total, 2,148 lines of unused code are gone.

GKH says that:

This is a very old driver, and no known hardware is still around for it, and the company says they do not need it anymore, so let's remove it as the LLMs are starting to poke at it and find "interesting" things which will just waste everyone's time given it's not actually used.

While keeping the door open for anyone who actually still needs it, noting that the driver could return down the line, just built "in a more 'modern' way."

Some context

Moxa isn't some obscure or defunct outfit that used to be cool back in the 90s. It's a Taiwanese company that makes industrial networking gear and serial cards for factories and railway systems.

The Intellio series was an older PCI serial card lineup from their catalog, not a piece of consumer hardware you'd find lying around in people's attics.

While software support was promised through 2023, the hardware itself was discontinued way back in 2017. According to an email Moxa's Ray Chen sent Jiri Slaby back in 2021, the company had no plans to keep the Intellio line alive any longer.

Jiri, who has maintained parts of the kernel's serial driver code for years, floated marking moxa.c as broken or dropping it outright.

Nothing happened until May 2026, when a separate patchset reorganizing the tty subsystem's directory structure brought the topic back up. Crescent Hsieh from Moxa confirmed both the hardware and its software support were fully phased out and even offered to help write the removal patch himself.

That gap isn't unusual on its own; kernel patches sit in queues all the time. What's notable is what finally moved it (AI poking holes).

Where AI comes in

So saying that "AI is deleting kernel code" would be overstating what's actually happened. The Moxa folks had already signed off on removal in May. GKH didn't act until August, three months later.

What the AI did was start poking at 27-year-old code that everyone already agreed was dead, bringing up findings on hardware nobody has left to test against.

If you are used to working with AI agents, then you know that scanning old, unused code doesn't always produce genuinely new work. Sometimes it just produces enough annoyance that maintainers have to act to nip it in the bud.

This change landed minutes before GKH drew a different kind of line, rejecting LLM-generated patches for the kernel's staging tree entirely. So take note of this, while AI isn't writing much of the Linux kernel yet, it's already influencing which parts of it get tossed out.

The entire debate in the Linux and open source world these days is if AI generated code should be used or not.

As Linus told AI critics to fork the project or walk away, GKH took a more surgical approach. AI patches are fine elsewhere in the kernel, but drivers/staging is off limits.

The GNOME extensions review queue is still flooded with AI slop months after the no-AI rule went in. The latest attempt to slow it down is a guide written directly for the bots doing the generating, covering the bad patterns that keep showing up.

Keychron's doing for mice what QMK did for keyboards. ZGM is a GPL-3.0 open source firmware built on Zephyr RTOS, coming first to the G6 HE in Q1 2027. If it takes off, Linux users are the ones who gain most since practically no gaming mice ship with official Linux configuration support.

Another Ubuntu deb is becoming a Snap. hwctl, the CLI tool behind Ubuntu Pro and hardware certification checks, is going Snap-only for better sandboxing and tighter GNOME Control Center integration.

Nine months since the last release and nearly a decade in the 3.4 series, Rhythmbox 3.5.0 has finally arrived. Podcasts have seen the most work, where played episodes are now marked, notifications are better, and there's a new gPodder.net plugin for syncing feeds.

Following up on the LVFS sponsorship story from earlier. NVIDIA has now joined as a premier sponsor, bringing the total to four OEM sponsors and hitting the funding target the project set for itself.

🧠 What We’re Thinking About

GNOME Boxes hasn't looked modern in a long time. The rewrite, in beta now on GNOME Nightly, fixes that, moving to GTK4 and Libadwaita while opting for a Flatpak-only approach.

🧮 Linux Tips, Tutorials, and Learnings

If you use Kitty and haven't touched its Kittens yet, then you are missing out! These are small built-in programs that extend the terminal without needing external tools, covering everything from an interactive theme switcher and font picker to a drop-down terminal, a fuzzy file chooser

If your Linux Mint desktop feels a bit plain, Cinnamon's extension manager provides a set of visual effects worth trying. Effects like burn my windows, wobbly windows, magic lamp, and a desktop cube for workspace switching are available.

A free nine-chapter bash scripting course for beginners, covering variables, arguments, user input, arithmetic, arrays, strings, conditionals, loops, and functions.

👷 AI, Homelab and Hardware Corner

Want to take back control of what you read without an algorithm deciding for you? We have covered ten self-hosted RSS readers, with notes on which ones are actually built for self-hosting and which ones technically work but their own developers don't recommend it.

The list is for self-hosting. For normal desktop usage, we have a separate list of RSS feed readers for Linux. It's a bit old, but most of the apps are still in active development.

✨ Apps and Projects Highlights

While it is yet to arrive on Linux, the terminal-browser is worth a look. It is a real browser that runs inside a terminal.

📽️ Videos for You

You can master AppImage installation by following a few simple steps.

💡 Quick Handy Tip

Transform Dash to Panel into a dynamic, Plank-style dock with ripple hover animations by tweaking a few key extension settings.

First, navigate to the "Position" tab in the Dash to Panel settings. Disable every item on the panel except for the "Taskbar", and change the panel size or length mode to "Dynamic" so it shrinks to fit your active app icons.

Next, open the "Fine-Tune" tab and enable "Keep original gnome-shell top panel." This restores the standard GNOME top bar for your clock, system status icons, and menus while leaving your new dock at the bottom or side.

Finally, switch to the "Style" tab and enable "Animate hovering app icons." Click the settings gear next to this option to choose and adjust your preferred hover effect.

🎋 Fun in the FOSSverse

Do you know what kind of Linux user you are?

Well, it's obvious which one is the winner here. 😉

🗓️ Tech Trivia: IBM launched the System/23 Datamaster on July 28, 1981, just two weeks before the IBM PC. The same engineers built both, and what they learned on the Datamaster helped shape the desktop computer as we know it.

🧑‍🤝‍🧑 From the Community: Pro FOSSer Neville has shared his experiment running Lumina desktop on antiX-26.

For years I thought I had strong opinions on Linux distros. Ubuntu was too heavy or it felt. Arch was too fiddly. Kali was surprisingly practical (don't judge me).

Looking back at nearly a decade of distro hopping, I realize I had the whole thing wrong.

It was never really about the distro. It was about what desktop environment sat on top of it.

My Linux story starts with Mint

The first Linux distribution I ever installed was Linux Mint Cinnamon, around 2017. Linux Mint is what made me think Linux was actually livable. The desktop made sense. Things were where I expected them to be. It was Ubuntu underneath, sure, but that's almost beside the point; what mattered was that the experience felt considered.

I didn't move to Ubuntu until 2019. I ran it for about six months. Then I left.

The reason I told myself at the time was that Ubuntu felt slow on my hardware. I had an i3 first-gen Intel machine. Ubuntu felt sluggish in ways that bothered me. I blamed Ubuntu.

Here's what I discovered later: the culprit wasn't Ubuntu. It was GNOME.

GNOME is a beautiful desktop. I'll give it that. But on modest hardware, it feels less responsive, and even at times, it completely freezes. It shows up in the half-second delays, the slightly-too-late window redraws, and the sense that the machine is working harder than it should be.

XFCE saved my Linux life

The thing that actually kept me on Linux, the desktop environment I kept coming back to, is XFCE.

XFCE doesn't win "most beautiful desktop" polls. But I ran it on that i3 first-gen machine, and it ran smoothly out of the box. I ran it on a Core 2 Duo at some point.

Internet browsing, YouTube, writing articles, Django, and Python development, all of it, without considerable delay. XFCE in that sweet spot between functional and fast is something I haven't found matched elsewhere for low-resource machines.

I did try KDE. More than once, actually, I wanted to like it. I kept going back, hoping something had changed. What I found, every single time, was that a single click could take three to four seconds to get a response. It felt like the desktop was thinking about whether it wanted to comply with my input.

Even recently, a few weeks ago, I tried both CachyOS and EndeavourOS as live USB sessions in a VM, and both now default to KDE. I still felt it. That sluggishness hasn't gone away for me, and I've stopped trying to convince myself otherwise. XFCE remains my benchmark for a desktop that gets out of your way.

My Kali Linux Journey

After six months on Ubuntu, I switched to Kali Linux. And not for the reason you might think.

I had not suddenly become a penetration tester. I didn't have a burning interest in cybersecurity. I switched because Kali ran better on my hardware than Ubuntu did and because I was already comfortable with APT commands from my Mint and Ubuntu days, so the transition cost was essentially zero. I had access to the same package manager, familiar commands, and a lighter desktop.

I chose a distribution built for security professionals over the supposed beginner-friendly option. I chose not because of anything Kali offered that I needed, but because Ubuntu had made itself too heavy for the machine I owned.

The Arch Linux rabbit hole

By mid-2021, I'd been reading about Arch Linux mainly through all the BTW memes. The idea of an "expert's distro" appealed to me.

So I stayed up one night and tried to install plain Arch. I did not finish the installation.

What struck me was running commands to create partition tables and configure Wi-Fi. It was baffling. Not "this is hard, but I'll figure it out." More like: why has someone decided that an operating system installer should work like this?

I tried Fedora after. Then I tried Debian. In both cases, on my ageing hardware, boot times were bad enough that I found myself questioning whether the whole exercise was worth it. With Debian, I faced an internet connectivity issue as well.

The thing that drew me to Arch was Pacman and leanness. I kept asking myself: why can't the installation be as simple as Ubuntu's, but give me the system I actually want?

That's when I found EndeavourOS. It required no manual commands to connect to Wi-Fi. No writing partition tables by hand. Just select a disk, set your timezone, keyboard layout, and hostname, and you're done.

And when I booted in for the first time, I found the default looks fascinating. The default terminal: black background, blue accents, immediately distinctive. The default wallpaper was something I actually liked enough not to replace it, which almost never happens.

I used EndeavourOS for four-five years, I think.

What those "Arch" years looked like

I want to be honest about this stretch, because people who recommend Arch-based systems sometimes skip over the rough parts.

I hit the initramfs wall three or four times. That's the experience of booting your machine and being dropped into a minimal emergency shell with no desktop, no obvious path forward, just a blinking cursor.

The first time it happened, I didn't know recovery was possible. I reinstalled the entire system. Only later did I learn to boot from a live EndeavourOS USB, use arch-chroot to get inside the broken system, and rebuild the kernel from there. The culprit was usually a problematic package upgrade.

I also ran into keyring mismatches more than once, that particular class of Arch-ecosystem errors where package signatures don't line up, and installations fail with cryptic messages. The fix I settled on:

sudo pacman -Syu archlinux-keyring endeavouros-keyring

I'm sharing them because this is the actual texture of running an Arch-based system. It's manageable. I managed it. But a beginner who hits the initramfs wall on their third week of Linux use is probably not going to calmly research arch-chroot. They're going to conclude that Linux is unstable, and they were right not to trust it.

AntiX chapter and the limits of anti-systemd

Around mid-2024, I discovered AntiX Linux and ran it alongside EndeavourOS for roughly two years. It taught me a lot about IceWM as a window manager. For someone with a potato-tier hardware, old machines that most distributions have quietly given up on, AntiX is remarkable.

But I hit the systemd wall eventually. AntiX deliberately avoids systemd, which is philosophically interesting but practically limiting. A growing range of applications that assume systemd exists simply don't work cleanly. And I'll be honest: the AntiX GUI is not appealing. It's functional, and it works. But there's a visual coarseness to it.

After two years, I started thinking about moving on.

Returning to Kali for real this time

Now I'm back on Kali, but the circumstances are completely different. I really have an interest in cybersecurity, particularly bug bounty work. I'm running a Dell laptop with an i5 8th gen and 12GB of RAM now.

Two months in, I've had one issue worth documenting. Kali ships Firefox ESR, and if you want to remove it, the system objects, because Firefox ESR is part of the kali-xfce4 metapackage, and removing it wants to take other things with it.

The workaround I found: install the equivs package, use it to create a dummy Debian package with the same name as the dependency you want to satisfy, install that dummy, and the system believes the dependency is met. It's inelegant, but functional.

Where I am now: leaving XFCE for window managers

A lot is happening in the Linux desktop world around Wayland, Hyprland, and tiling window managers. The thing that excites me about this space, and I've come to believe this more firmly the longer I've used Linux, is the plain-text configuration model.

Your entire environment lives in text files you can read, version-control, back up, and restore. That's a kind of ownership over your system that no GUI settings panel can replicate.

I spent this past couple of months trying several options: Niri, Sway, and Scroll WM. I eventually settled on Mango WM as my main environment. I know, calling it a desktop environment feels technically wrong, but it's what I live in. The configuration philosophy suits how I think about my system.

That means I'm saying goodbye to XFCE after years of loyalty. Not because XFCE failed me, but because I've moved somewhere new.

Desktop environment, not distro

Looking back at all of it, I did not like Mint just because it's Mint. I felt comfortable with Cinnamon. I did not dislike Ubuntu because it's Ubuntu. It's because GNOME demanded more of my old hardware than the hardware could give.

Every distro-hop I've listed here, from Kali to Arch to AntiX to Mango WM, was really a hop between desktop environments and window managers wearing a different distro's name. The package manager changed. The installation experience changed.

What actually decided whether I stayed or left was always what sat on top: how fast it responded, how much RAM it wanted, whether I could read its configuration in a text file, how it felt to use.

The desktop environment mattered more than the distro.

If you're picking your first Linux setup, this is the question worth asking before you pick a distro: what desktop environment does it ship, and does that match your hardware and your preference?

Office suites have changed a lot since the first ones landed on desktops decades ago. What began as basic tools for typing letters and totaling numbers has turned into sprawling software loaded with templates, cloud sync, and now, AI.

It's 2026, and that last part has split the office suite market into two camps.

Microsoft has leaned into pushing AI everywhere, with Copilot showing up across Word, Excel, and PowerPoint whether you want it there or not. Others have gone the calmer route, building AI in as something you can turn on, not something forced.

GenOffice is a new offering from MainFunc.ai that strongly focuses on AI, putting it in the first camp, featuring a range of editors that one might expect from a modern office suite.

GenOffice: A quick look

Built by a single developer equipped with $10,000 worth of tokens (for AI), GenOffice bundles four editors, Docs, Sheets, Slides, and PDF, into one Electron-based desktop app.

For .docx work, Docs lets you draft a section, restyle a paragraph, or rewrite it for a different reader from a simple prompt, with the AI working directly inside the document rather than a side panel.

Sheets, for .xlsx files, runs on Univer, an open source spreadsheet engine, paired with an in-house Rust sidecar for reading and writing files.

Presentations run through Slides, which builds .pptx decks from a single prompt, picking a layout, writing the copy, and dropping in data cards as needed.

Rounding out the office suite is PDF, built on the open source pdf.js and pdf-lib libraries, letting you read long documents and ask questions about specific passages without you needing to switch between apps.

It's quite obvious that AI runs through every one of these editors. All of that is made possible via Genspark's Super Agent, which is treated as a core part of the app rather than a chat window tacked on the side.

Opening, editing, and saving files costs nothing, but AI features draw from Genspark's credit system, tying them to an account rather than a local model.

That matters, because GenOffice doesn't appear to have its own privacy policy. AI features run through Genspark's service, so anything you send them falls under Genspark's terms and privacy policy instead.

That policy states text fed to the AI features gets passed to third-party providers, including OpenAI, Anthropic, Google, xAI, and ElevenLabs, and that no API key is stored on your device.

Currently, GenOffice is available only for Windows (x64) and macOS on Apple Silicon. Intel Macs are not supported, and as you've probably guessed, neither is Linux.

Where's the Linux build?

I did find an open issue (very likely AI-generated) that mentions a native Linux build, filed by a community member. It explains how shipping GenOffice for Linux isn't as simple as it sounds.

As of writing, there was no reply from the lead developer of GenOffice, so I wouldn't hold my breath for a Linux build anytime soon.

If you still want to try GenOffice, you can grab installers for Windows and macOS from the official website or go through the source code on GitHub to see if you can get a Linux build running yourself.

Suggested Read 📖: 6 Best Open Source Alternatives to Microsoft Office for Linux

Greg Kroah-Hartman's "clanker" branch started as his own experiment, where an AI-assisted fuzzing tool turned up real bugs in the ksmbd and SMB code. These days, he isn't the only one using it, as other contributors have started sending in patches of their own, tagged the same way.

So when he posted a new AI policy a few days ago, restricting use of AI, it wasn't him turning against a tool he's comfortable with. It was a restriction that applied to one part of the kernel, the one place he doesn't want AI anywhere near the code.

A new rule

In a post to the linux-staging mailing list, GKH said he's now proactively rejecting LLM-generated patches for drivers/staging, pointing to an "onslaught" of them showing up lately.

His reasoning comes down to what staging is actually for. The subsystem exists primarily as a training ground, full of relatively simple cleanups and API changes that let newcomers learn the submission process.

The project already skips "fix all the coding style issues" patches generated by automated tools because clearing out that mess would eliminate the reason staging exists. GKH argues an LLM doing the same cleanup work defeats its purpose while calling out wannabe tricksters by saying that:

And yes, it is VERY obvious when people submit LLM-generated patches, so don't think that just not disclosing the use of them will allow you to "get away" with anything here. The goal is for people to be able to learn, not to try to "trick" a maintainer. If anyone is determined to have deliberately "tricked" us, well, consider this your warning ahead of time...

There's an exception, a strict one that too. A genuine security fix found by an LLM can still be submitted, but only if the submitter has tested it on the actual hardware the driver targets and can convincingly explain how testing was carried out.

A line drawn while Linus makes peace

This move lands right as Linus Torvalds is coming around on AI. He's spent years shifting his stance, and in a recent mailing list post he made it clear to critics that Linux "is not one of those anti-AI projects," telling them to fork the project or walk away if they disagreed.

GKH's approach to drivers/staging fits inside the AI Coding Assistants policy, where he isn't undoing what Linus signed off on but rather focusing on a narrower scope.

He is treating this subsystem as a gym, one where developers are supposed to show up and lift the weights themselves, not have an AI do the reps and hand them the result.

That's the line GKH has drawn. AI can be used to carry out work elsewhere in the kernel, but in a subsystem built for people still learning, he'd rather they build it the hard way first.

💬 What do you think of GKH's decision? Will it help newcomers?