Mozilla has a new product called Firefox Enterprise. Which is basically Firefox but built around the needs of private and government organizations.

The idea is simple. If staff uses a browser for work, organizations should be able to control its settings, protect company data, and decide where its management system runs.

Because a lot of work happens directly in the browser. And that's also where the common security issues occur as well. Securing is both a priority and a challenge for organizations. That's where this 'on-prem' version of Firefox comes in.

What is Firefox Enterprise?

Think of it as Firefox with a central control panel for your IT team. Instead of setting up each browser separately, administrators can manage browser rules and access across employee devices.

Firefox has supported business deployments for years, including policy management and Extended Support Release (ESR) versions. This new offering adds a self-hosted management platform, extra security controls, and professional support.

For example, your IT team can decide which extensions employees can use. It can also control browser connections and telemetry, and allow, restrict, or disable AI tools.

There are controls to warn you or block certain actions involving sensitive data, based on the website involved. Security teams can export browser events to their monitoring tools, while a Lockdown Mode adds restrictions for high-risk browsing.

Mozilla says organizations can keep their existing login providers, device management tools, and security monitoring systems. Firefox Enterprise is designed to work alongside them.

The browser still uses Mozilla's Gecko engine. That gives organizations an alternative to browsers built on Google's Chromium technology.

Professional support is included with Firefox Enterprise. If your organization uses regular Firefox, you can buy support separately. Mozilla hasn't listed public prices on the product page, so you'll need to contact its sales team.

Thunderbird Enterprise is coming, too

Mozilla is also working on Thunderbird Enterprise. It plans to let organizations manage email, calendars, and contacts from the same Firefox Enterprise console.

That could make this more useful if an organization already uses both applications. However, Thunderbird Enterprise is still in development, and the announcement doesn't give a release date.

A good fit for Europe's push for digital independence

Mozilla's initial sales focus is France, Germany, Austria, and Switzerland, with more markets planned. Its announcement puts Europe's push for digital sovereignty at the center of the launch.

In simple terms, that's about having more control over your technology and data, rather than relying on a few large suppliers.

I think supporters of that movement will welcome this approach. Running the management console on your own servers gives you more control over where management data and policies live. Using a separate browser engine also helps avoid depending on the same technology everywhere.

Mozilla says the browser is open source, but doesn't confirm whether the management console is open source too. Self-hosted doesn't automatically mean open source.

Another way for Mozilla to earn money

This also gives Mozilla another way to bring in revenue: selling enterprise services and support.

Mozilla still depends on Google through its search partnerships. If Firefox Enterprise attracts paying customers, that income could help reduce Mozilla's dependence on Google.

Of course, it won't replace search revenue overnight, and we don't yet know how much this business focused product will earn.

Still, I find the direction encouraging. Organizations get more control over their browsers, and Mozilla gets a chance to earn more money from the software it already builds. That's a win-win for everyone involved, right?

Remember Ubuntu’s convergence dream? One operating system across your desktop, tablet, and phone, with apps that adapt to whichever screen you’re using.

Canonical pursued that vision with Unity 8 and Ubuntu Phone before pulling its investment in 2017. Now, a new project called Luma wants to tackle a similar ambition.

It’s early days, and only a desktop build is available to download. But its plans go well beyond becoming another desktop Linux distro.

The project's founder, Nick (nmcmil), has worked with a small group of people to achieve this, with the group answering to no shareholders or investors and taking all the key project-related decisions themselves.

When I tested it, it was built on Fedora 44 and ran a recent version of the Linux kernel. The team does say that they have reworked enough of the stack that it doesn't count as a typical distro.

Their blog on AI also points out that the distro features AI-assisted contributions, with contributors being held responsible for any work that carries their name.

Luma: Overview ⭐

The desktop is called Prairie, and the team describes it as a hard fork of GNOME. Its goal is to provide a shell, windows, fonts, and apps that all look and feel the same.

Apps are built with GTK and Libadwaita, with the project shipping LumaUI as a toolkit outside developers can use to make their software feel at home on the system. Luma’s original code is licensed under Apache 2.0, and anything taken from upstream retains its original license.

The distro comes with pre-installed apps built for it; these include Depot as the app store, Viola as the browser, Charlie as the email client, and Tide for any music needs, alongside the self-explanatory Filer, Notes, Photos, and Messages.

If you opt for the Creative Collection, you get access to Canvas for design, Focus for RAW photo editing, Grid for spreadsheets, Stage for slides, Write for documents, Reel for video edits, and Sessions for audio mixing.

You also get Ari, the built-in AI assistant that's meant to work with local models, so everything can stay on your own hardware.

Package support is covered too. I installed apps through DNF5, Flatpak, and Snap on the October 11 nightly, and all three worked.

Valet takes care of AppImage, DEB, EXE, and APK files, which is how Windows and Android apps are meant to sit alongside native ones. It also tells you who made an app and what it can access before you install it.

Android support comes from an optional subsystem based on Waydroid, and compatibility varies by app. Luma is meant to be one system across desktop, tablet, and phone, so apps adjust their layout and controls to the screen size and to touch or mouse input.

There's also the optional Luma Connect app that handles sync for apps, settings, and files across devices.

For Smartphones Too!

Luma also wants to be your smartphone's OS. The team names the Fairphone 6 and the Google Pixel 9 family as its targets and says it plans to support more phones and tablets.

With what I can see from the screenshots they shared with us, the interface looks like it's on par with modern Android and iOS offerings, while sticking to a very measured approach for the colors and elements it employs.

The Luma folks are directing people to their hub's bounty board to vote for the devices they want and pledge money towards getting them supported.

Test it Now

Luma's download page lists a single 7.2 GB x86_64 nightly ISO as the recommended way to try the distro out. It needs a 64-bit Intel or AMD processor with at least two cores running at 2 GHz or faster, 4 GB of RAM, and 40 GB of storage, with an SSD preferred.

An ARM64 build is yet to make an appearance here, so people on that platform will have to wait a bit. Similarly, creative apps like Grid, Stage, Reel, Sessions, and Canvas are also set for a later arrival.

For most of its life, Linux has been built around one simple assumption. There's a box; it has CPUs, memory, and devices, and the kernel manages them. Clusters exist, of course, but they've mostly been a problem for the layers above the OS, i.e., schedulers, orchestrators, distributed frameworks.

AI infrastructure wants more. Today's training and inference setups bind dozens of CPUs, NPUs, GPUs, and memory pools so tightly together that they behave less like a group of servers and more like a single but very large computer. And then come AI agents, which don't just run a model but spin up tools, sandboxes, and long-running sessions on the fly.

That was the core of the keynote Xinwei Hu, Chairman of the openEuler Technical Committee, gave at Open Source Summit Europe 2026 in Prague.

Let's see how openEuler is innovating Linux around agentic scaling.

The problem: the server is no longer the unit

openEuler's keynote slide describes the shift as moving "beyond the box," from a single host to a SuperPoD. A SuperPoD is a tightly interconnected system where compute, memory, and accelerators across many nodes are linked and expected to work as one pool of resources.

That creates trouble the classic OS was never designed for. Memory you need may sit on another node, a fault on one machine can ripple into others, and moving data between hosts and accelerators becomes a bottleneck. And somebody has to manage the whole thing.

Hu grouped openEuler's goals for this into three buckets: ease of use, reliability, and performance.

SuperPoD OS: making a rack look like a server

The ease-of-use goal is ambitious. openEuler wants runtimes and SDKs that work out of the box on SuperPoD hardware, and system administration that is assisted by an agent, so that you can manage a SuperPoD "like a single server."

Reliability is of upmost importance here. According to Hu, openEuler can notify other nodes of a node failure with a latency of 500 ms or less, and can notify individual threads when shared memory develops a fault. It also aims to isolate faults so they don't spread across processes on the same host or across nodes. There's also "fault storm suppression," a fast response to remote memory faults so that a flood of SEA errors doesn't hang the system.

On performance, the Hu's keynote cites two figures. UMDK, which openEuler describes as a unified heterogeneous interconnect infrastructure for SuperPoDs, cuts RPC latency by 20%. An asynchronous pipeline plus unified memory addressing cuts RH2D (Remote Host-to-Device) latency in KV cache operations by 20%.

All of this sits on UnifiedBus, a hardware interconnect protocol in the same family as NVLink, RDMA, and CXL. openEuler's architecture slides show two layers built on top of it. The UB OS Component adds device management, memory pooling, inter-node communication, and virtualization to existing OS components. The UB Service Core adds higher-level services (memory, communication, I/O, a resource engine, virtualization, serverless, and a compiler and runtime).

The part I find most practical is how applications reach this. General-purpose apps keep using the standard POSIX API with transparent calls, while AI applications can use the UB Service Core API directly. If an app needs a memory pool, the UBS Engine finds which servers can spare memory, exports it from them, and imports it into the local machine.

We covered the UnifiedBus foundations in more depth earlier if you want the background.

Where mainline Linux stands

openEuler isn't alone. Mainline Linux has been working on the memory side of the same problem for a few years, mostly through CXL, an open interconnect standard that lets servers attach or share memory beyond what sits on the motherboard.

The idea is to present far memory as just another NUMA (Non-Uniform Memory Access) node, so most applications don't need to change.

That's roughly the philosophy behind openEuler's transparent POSIX path. The difference is scope. Mainline mostly deals with extra memory attached to one host, while SuperPoD OS tries to coordinate memory, devices, and faults across many hosts at once, on a Huawei-originated interconnect rather than a vendor-neutral standard for now.

Agentic Scaling: an OS for workloads that think in loops

The second half of the keynote moves from hardware to how AI itself is scaling. Hu's slide traced three phases. First came "big is better," where intelligence is fixed during pre-training. Then came test-time scaling, where models get smarter through post-training and spending more compute at inference time. The third phase is Agentic Scaling: building new system architecture around the way agents actually behave, while keeping costs under control.

The pitch is aimed squarely at open-weight models (models like DeepSeek, Qwen, and Llama, whose weights you can download and run yourself). openEuler's argument is that this ecosystem won't reach the frontier by chasing raw model size. Instead, it should use the control you get from running models locally to build intelligence at the system level.

Here's what it means. An agent doesn't just call a model once. It runs tools, executes generated code, keeps memory across long sessions, and spawns many short-lived tasks in parallel. Each of those is an OS problem: isolation, startup time, memory management, and recovery.

openEuler's agentic infrastructure slides tackle three of them.

Sandboxes. Agents running code need isolated, disposable environments. openEuler's sandbox stack works with Firecracker, StratoVirt, gVisor, and Cloud Hypervisor, and is compatible with Kubernetes and E2B APIs. Using prewarmed pools and snapshot recovery, the project claims cold starts down to 20 ms (a 70% to 95% reduction), over 2,000 concurrent sandboxes, and task recovery times cut by more than 50%.

KV cache. The KV cache (the model's short-term working memory for a conversation) grows fast with long contexts and many users. openEuler's approach spreads it over four tiers: HBM on the accelerator, local DRAM, a shared DRAM pool across nodes via UnifiedBus, and SSD or object storage for cold data. Combined with a global index that lets one instance reuse cache built by another, the project claims a 30% to 70% drop in time to first token and 1.5x to 3x higher throughput.

Agent memory. For long-term agent memory, openEuler showed oGMemory, built on the openGauss database with vector, full-text, and graph search. The slide claims it halves token overhead and improves task completion by 20%.

What this means for Linux

I think the most important takeaway is that the definition of "the machine" an OS manages is changing, and openEuler is one of the few distros building for that openly.

There's a fair caveat, though. UnifiedBus and the TaiShan and Atlas hardware platforms are closely associated with Huawei's technology ecosystem, and much of the SuperPoD stack is optimized for these platforms.

The real test is how much of this work flows upstream into mainline Linux and plays well with vendor-neutral standards like CXL, rather than staying an openEuler-specific feature. Given that openEuler already counts itself among the top contributors to the Linux kernel, that's the thing worth watching.

Either way, the questions Hu raised (how an OS handles memory it doesn't physically own, faults it didn't cause, and workloads that spawn themselves) are ones every Linux distribution will have to answer soon.

Qiusheng Wu has put out the first release of RustGIS, a new open source GIS application with an announcement on X. v0.1.0 is available right now for anyone to use, along with a second release, v0.2.0, that has rolled out close on the heels of the first one.

The tool's ribbon-based interface will be familiar to you if you have used a modern office suite recently, and the tool itself allows you to analyze map data in various ways.

The release notes for v0.1.0 mention that it comes in 15 interface languages, has support for over 1,000 tools from the GeoLibre and Whitebox suites, and the editing tools cover feature templates and snapping, along with domains and attribute rules.

So, let's check out what RustGIS has to offer. 👇

Who is it for?

GIS stands for geographic information system, which is software for working with data tied to places on Earth. A GIS stacks layers such as roads, rivers, and population on top of each other, matched by location.

Working with those layers in RustGIS starts with analysis tools such as buffer and clip, plus SQL-based selection. Guided workflows cover jobs like viewshed analysis and finding the shortest path. The v0.2.0 release added SQL queries through DuckDB Spatial too.

The app opens common files like GeoJSON, Shapefile, GeoPackage, and GeoParquet, while large files are streamed. You can also open GDAL formats such as File Geodatabase, GPX, and DXF if you install ogr2ogr.

There's AI assistance too! 🤖

The AI Assistant is a chat pane that answers questions about the data or runs commands on the map. Undoing a change takes one step, and commands that touch files or the network wait for approval.

The command line can also render maps and chain tools, while an MCP server lets agents such as Claude Code and Cursor run RustGIS commands and generate maps.

Download RustGIS

If you are looking to try it out, RustGIS is suitable for terrain and hydrology work and for anyone who wants to execute repeatable workflows from the command line. Newcomers might also find the assistant's plain-language prompts easier than wading through the menus.

The newest release at the time of writing was v0.2.0. You can visit the downloads portal to grab binaries for Linux, Windows, and macOS. If you prefer a web-based solution, RustGIS is also offered as a WebApp.

The project hosts the source code for this on GitHub, and it also has a comparison table that shows how RustGIS fares compared to other tools like ArcGIS Pro and QGIS.

ONLYOFFICE is rolling out AI tools across every editor in its suite, but the open source versions do not have them built in. The company says the choice came in response to user feedback, making it so that those versions would require separate AI tool installations.

If you didn't know, they have a few main offerings. Docs is the editor suite that you self-host or integrate into other platforms, while the Desktop Editors bring the editors to Linux, Windows, and macOS, and DocSpace facilitates room-based collaboration.

There's also the Workspace bundle of web apps and mobile apps for iOS and Android.

With the ONLYOFFICE Docs 10.0 release, they are introducing a wide range of fixes, as well as modular apps that should help make your workflow easier.

🆕 ONLYOFFICE Docs 10.0: What's New?

ONLYOFFICE Docs now has a home screen with an Discover Apps category that lists four pre-installed free apps: Files, Rooms, Forms, and AI agents. Each of these is an optional modular app that you use when the need arises.

In short, Files handles storing, organizing, and sharing files, Rooms is for collaborating with other people, Forms is for building forms and analyzing responses, and AI agents can be used to deploy agents for automating tasks and answering questions.

That last one supports MCP servers and can be used for extended reasoning, search, and personalized knowledge base use cases.

What about the editors?

Let's begin with the PDF Editor. The Redact tab now has a "Remove hidden information" option that clears metadata before you share a file, and the Comment tab offers a signature option, with automatic table recognition being available for the Edit Text function.

In the Document Editor, a new "Keep with next" feature has been added to the advanced settings for paragraphs that stops a heading from being separated from its content, including in tables and block content.

Inline objects such as graphical formulas behave more reliably in right-to-left documents, and line-based cropping trims content that runs past the line height. Horizontal lines, which was introduced with Docs 9.4, also get its own settings window.

For collaborative sessions, a new "Follow/Stop" option appears in the list of users editing a file. If you click on it, your mouse's cursor will take you to that person's cursor; clicking on "Stop following" ends tracking.

The Spreadsheet Editor features speed-ups, usability tweaks, and formula aids. Large workbooks should feel quicker, as styles and data are now stored separately and fewer cells are processed.

Copy and cut actions now draw a dynamic outline over the selected cells for quick verification glances, sheet backgrounds are kept intact when files are saved and reopened, and function constant hints appear as you type.

Similarly, for the Presentation Editor, you can now export presentations as a GIF or video file, while the View tab gives you access to a bunch of new views that include the Outline View, Reading View, Notes Page, Notes Master, and Handout Master.

The print settings are broader too, with notes, handouts, and outlines all being printable from the File tab.

The AI bits

Each editor has a dedicated AI Tools tab that launches the AI chat, with common actions preloaded as prompts alongside any you save yourself.

Text documents can be summarized, rewritten, translated, and proofread, with a table of contents, title, and formatting added on request. Presentations get outlines, slide titles, speaker notes, and visual ideas.

In a PDF, the AI can summarize in one click, answer questions, draft emails, and redact confidential details. In spreadsheets, it can tidy messy data, write and explain formulas, spot trends, and build forecasts and Pivot Tables.

For one-off jobs such as changing tone, there are tools such as Quick AI actions and AI assistants that keep running while you edit. Though these are only offered in the Document Editor for now.

If you go the cloud way, you will have to top up a wallet to get access to the ONLYOFFICE AI service, which handles model configuration and deployment for you.

For self-hosting, you will be able to use any popular cloud-based AI service or a local one through tools like Ollama.

📥 Download ONLYOFFICE Docs 10.0

The cloud version of ONLYOFFICE Docs 10.0 is available now through DocSpace, and a free account is enough to try the editors on the platform. If you were looking to host it, then you will have to wait a little longer as the update is still rolling out for self-hosted solutions.

On the other hand, if you are an individual user running Linux or a walled garden platform, then you could opt for the Desktop Editors.

For more details on this release, you can refer to the changelog and the official blog post.

Suggested Read 📖: LibreOffice doubles down on its no AI stance.

Linus Torvalds has a new branch in his own kernel repository named noref-filemap-read-with-llm.

It holds an eight-patch series built on Linux 7.3-rc6 plus fixes, and its commit message gives no kernel release target for merging it.

While the commit message is long and technical, the change itself is easy to describe.

What he changed

When many threads read the same file, the kernel serves them from the page cache, its in-memory copy of recently read data. Today, each read takes a hold of the cached data and releases it afterwards.

Every thread updates the same counter to do so, resulting in the threads tripping over each other. The issue dates to 2024, when Matthew Wilcox flagged it, citing a customer's workload.

Linus' series of patches skips that step. To make that safe, memory that leaves the cache is not handed out for reuse until a short period has passed, so a reader can copy data straight out without holding anything.

The cost is that more memory sits idle for a moment.

The payoff, measured on his own laptop with some security protections switched off, is that eight threads reading one file went from about 10 million reads per second to between 30 and 33 million.

Linus ran the measurements himself, but for the rest, the commit message credits Claude Code, working under his direction. That includes a draft of the message itself, the code and test programs, and the review behind its list of known problems.

This is not the first time he has used AI on kernel work. In August, he used it to help fix a bug in the Intel Xe graphics driver.

What he said in Prague

Linus spoke with his long-time friend, Dirk Hohndel, the head of Ericsson Software Technology, at Open Source Summit Europe in Prague on Friday.

ZDNet's Steven Vaughan-Nichols, who was in attendance, quotes Linus, saying, "I actually really like using AI."

He said he used to think AI was not very good but has changed his mind. In his view, it works well as long as it is treated as a tool, and it makes programming more enjoyable for him.

His own example was a guitar pedal project with an interface he had written in C. He wanted a more modern look and tried AI for it, saying, "I use AI to do the things that I'm bad at."

He also defended vibe coding as "a wonderful way to find joy in programming," mainly for newcomers, and says that while AI is improving the code base, the load it puts on maintainers has become a real problem.

This again shows how much Linus’ stance has changed when it comes to AI. In 2024, he called the AI industry "90 percent marketing and 10 percent reality." Now, he is actively using it for kernel work.

Via: Phoronix

Suggested Read 📖: Gentoo needs your help for sponsoring its infrastructure.

Most Linux distributions are still figuring out where AI fits. Some are even blocking AI-based contributions while others are shipping AI tools in the system itself.

openEuler is doing something more comprehensive. It is embracing AI completely. From its development workflow to the operating system it offers to end users, AI is omnipresent. The aim is to move towards an AI-first ecosystem to give users an "AgentOS".

The project is putting AI into three places at once: the community's own plumbing, the workstation you develop on, and the operating system itself (so that it can be used by AI agents and harness like OpenClaw and Hermes).

openEuler laid out this roadmap at its Developer Day 2026, and the focus is on improving developer experience (DX). That's a nice term to use, actually.

I haven't run any of this hands-on yet, so what follows comes from openEuler's material and public repositories. Feel free to check them out.

AI inside the community's own infrastructure

Before openEuler asks you to trust AI in your OS, it's putting AI to work on its own code. And I think this matters most for contributors, specially because it makes their job and life easier.

The openEuler community is adding AI-assisted review to its CI pipelines, starting with kernel patches. CI (continuous integration) builds and tests every patch before merging, so this means that AI checks on the code before a maintainer sees it. AI agents are also generating package spec files, the recipes that turn source code into RPM packages, which is exactly the tedious work that keeps new packagers away.

Vulnerability handling is where the work is most concrete. According to the community's April 2026 monthly report, the infrastructure SIG built a CVE repair agent team with 6 roles, from architect and reviewer to QA and a CI watcher.

It automates the path from parsing a CVE issue to submitting a fix PR, and when a build fails, it learns from the EulerMaker logs and updates its own rules. openEuler says this "significantly" shortened the repair cycle but hasn't shared figures.

MCP and agent infrastructure

The second piece is data. Community information is scattered across SIG pages, CVE advisories, and download mirrors. openEuler is using the Model Context Protocol, or MCP (an open standard that lets AI assistants call external tools and data), to give easier access to all this data.

The clearest example is the openEuler Portal MCP Server. Plug it into Claude Code, Cursor, or Cline, and you can ask "who maintains this SIG?" or "which CVEs affect this version?" in plain language.

Though the strategic focus is on where agents run. openEuler is building agentic sandboxes (isolated environments where an agent can execute code without touching the rest of your system) on a hardened security base. The goal is to give you a reliable place to run and operate agents, and we'll see how that works with Conch below.

A workstation that keeps up with AI

If you've tried running a recent AI agent on an enterprise distro, you probably have faced this problem. The agent wants Python 3.12 or Node.js 22, and your stable release ships something much older.

DevStation, openEuler's developer desktop, now has an 'Innovation Edition' to fix that. It tracks the master branch and pulls in updated software every 2 weeks instead of every 6 or 12 months, with Python 3.12+ and Node.js 22+ as the baseline.

It builds on what we covered earlier, when DevStation in openEuler 24.03 LTS SP4 shipped an MCP-based assistant that changes system settings from natural language. The idea now is broader: keep AI software perpetually updatable and make the community infrastructure friendly to both developers and agents.

Evolving toward AgentOS

openEuler has started evolving toward AgentOS, blending AI directly with OS capabilities. Hu Xinwei, chairman of the openEuler Technical Committee, describes it as a "production-grade AgentOS" built to tackle usability, security, and cost.

It comes in layers: Agent App, Agent Service, and Agent Kernel. The app layer isn't a walled garden either, since openEuler's architecture lists Claude Code and OpenClaw alongside its own agents. So, you are not restricted for choices here.

There are four core capabilities.

1. An intelligent hub

You talk to AgentOS through the PolyMind GUI or a TUI in the terminal. It quickly pulls in community-grade skill modules.

openEuler also stresses that memory use stays low even on long-running tasks. The xiaoO repository, which calls itself "the intelligence hub of AgentOS," points to layered memory and context compression as the reason.

2. End-to-end security protection

AgentOS builds an active defense network on behavior analysis and eBPF monitoring. eBPF is a Linux kernel technology that lets small, verified programs watch file access, network calls, and process launches with very little overhead.

In practice, the OS watches what an agent actually does rather than trusting what it says it will do. For something that runs shell commands on your behalf, that's the right instinct.

3. The Conch sandbox engine

Conch gives each agent a strongly isolated place to run using lightweight virtual machines. According to an openEuler deep dive, it restores snapshots of the filesystem, VM memory, and VM state together, so a sandbox resumes from a saved point instead of booting from scratch.

That's how openEuler gets startup times on the order of 100 milliseconds. You get VM-level isolation at close to container speed, which is the trade-off every agent platform is chasing.

4. Extreme token-efficiency optimization

Tokens cost money and time, whether you pay an API provider or run models locally. openEuler offers one-click integration of acceleration components like X-LITE (a lightweight inference runtime from its GVirt project) and LMCache, which reuses the model's cached work on repeated context.

openEuler says these improve TTFT (time to first token, how long before the answer starts) and TPOT (time per output token, how fast it streams) by 20% to 30%. Agents make many model calls in a row, so those gains add up quickly.

openEuler is racing ahead with AI

If you contribute to openEuler (or any other distro), you'll feel the community-side AI first: AI review on kernel patches, generated spec files, and automated CVE fix PRs. Less manual work, and humans can still check what the agents submit.

If you need a cutting edge system for your AI agents, the DevStation Innovation Edition is the one to watch. A 2-week update cycle removes the most common reason to avoid an enterprise-grade distro for agent work.

AgentOS is interesting too. Sandboxing, eBPF monitoring, and token efficiency are the real problems anyone running agents in production hits. Conch, xiaoO, PolyMind, and the Portal MCP server are all publicly available on their respective repos.

Apart from being AI-native, openEuler is also doing the rather less fancy work of using AI on its own maintenance first. It will be worth watching for published numbers, especially CVE repair times and Conch in real workloads. Stay tuned for that.

A Fixes tag in a kernel patch points to the commit that introduced the bug, but the last commit to touch a buggy line may only have moved the code to a new file.

Sasha Levin, who co-maintains the stable and long-term support kernel trees, wants AI coding assistants to verify which commit introduced a bug before assigning it a Fixes: tag.

His two-patch series went to the workflows and ksummit mailing lists last week, where he has proposed a new agents directory in the kernel tree to hold the skill that does it.

The new directory

The first patch adds an agents/ directory at the top of the kernel source tree for skills, prompts, and other resources meant for coding assistants. It also adds a MAINTAINERS entry covering the directory.

Each skill gets its own folder under agents/skills/, with a SKILL.md file as its entry point, written in the Agent Skills format.

Assistants are told to read a resource directly from its path in the repository, and links inside a skill are relative to that skill's folder. A new section in the kernel's coding assistants document is meant to describe this layout.

The find-fixes skill, which arrives with the second patch, would be the first one in the directory.

Why do Fixes tags go wrong?

Levin says the series traces back to a question from Ted Ts'o about incorrect tags in stable tree work. He pointed to LLM workflows that help find the right commit.

Ted later explained that the request came from a talk with Hannes Reinecke, who estimated that 15 to 20 percent of Fixes tags are wrong.

That matters beyond bookkeeping, since the tag is how stable maintainers and distributions work out which kernels need a given fix. A wrong one can send a fix to the wrong branches or leave a bug unpatched.

The new find-fixes skill reads Git history and returns a proposed tag with its evidence, while leaving the commit itself untouched.

The skill's guide.rst file says no single clue settles which commit a Fixes tag should name, not even an existing tag. The agent explains how the bug fails, traces candidate commits, and compares each against its parent.

It separates the commit that added the code, the one that made it defective, and any later change that only exposed the flaw.

For backported fixes, it checks whether the bug came from the original change or from how that change was adapted for an older kernel. Merges, reverts, and incomplete histories get their own handling.

When it cannot establish the origin, it returns no tag, lists the candidates and what is missing, and the fix stays a draft until someone resolves it.

Still a work in progress

The second patch would add a section to the kernel's coding assistants document saying an assistant must follow the guide before finalizing a bug fix or its commit message and when reviewing an existing tag.

The document's step for committing a fix would ask for a verified tag, where it now asks only for a Fixes tag. Keep in mind that the skill would not write commit messages, apply the tag, or decide whether a fix belongs in stable.

The series follows Levin's earlier push for the kernel's AI coding assistants policy, which we covered in April, and his proposal for a kernel killswitch.

Where the files should live is still open. The introductory patch message says he is unsure whether they belong under Documentation or in a separate agents directory at the root.

Greg Kroah-Hartman asked whether the series would fix tools skipping the kernel's guidance because an AGENTS.md file is missing. Sasha replied that this is a separate issue for the symlink patch.

There are plenty of online PDF tools that let you edit documents without installing anything on your computer. You just open a website, upload your file, make the changes, and download the edited document.

But there is a catch. You are handing your files over to a third-party service, and you may not always know how it handles your data. Some services may have questionable privacy practices, while others are outright malicious.

In fact, the FBI has warned people about fraudulent online file converters that steal sensitive information and spread malware in the past.

These tools scrape uploaded files for personal details, banking information, email addresses, and passwords, making them particularly risky when dealing with confidential work documents that should've never left an organization's secure environment.

BentoPDF is an open source alternative that advertises itself as a privacy-first tool that processes files directly in your browser instead of uploading them to a server, helping avoid some of the aforementioned privacy risks associated with online PDF tools.

BentoPDF: For Your PDF Needs

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Created by Alam, a developer out of India, BentoPDF traces its origins to an internal tool that was being used for merging and splitting PDFs. Soon after its creation, the lead dev decided to share his work with the world via Reddit, and a few weeks later the code was live on GitHub.

BentoPDF uses WebAssembly, which lets heavy programs run right inside your browser. Its OCR tool, for instance, runs on Tesseract.js, a port of the Tesseract OCR engine. That way, the processing happens on your computer, and nothing is uploaded to a server.

The project is dual-licensed, where the code is provided under the AGPL-3.0, making it free to use and open for anyone looking to fork it. A separate commercial license is available for closed-source use and costs $79 as a one-time purchase.

Alam also publishes two other open source projects, Hyper Compress and Kura. The first is a PDF compression engine, while the latter converts PDFs to standards like PDF/A and PDF/UA.

Anyhow, coming back to the PDF editor, I took it for a test run on Vivaldi, a Chromium-based browser, covering a few tools to see how it holds up in everyday use.

Editing PDFs

The Edit PDF Text tool shows each block of text in the document with a dashed outline, and picking one brings up the formatting sidebar, which houses the usual editing controls.

It has buttons for tweaking the font, size, colors, alignment, and character spacing, along with bold, italic, underline, and strikethrough formatting.

I could also make it fetch all the fonts installed on my Linux system using the System fonts button, and the info button at the bottom was useful for glancing over the metadata of a document.

Once done editing, exporting the file is only a matter of clicking the "Export" button.

The PDF Editor is mainly meant for annotation work, with tools for highlighting, strikethrough, ink, text insertion, and comments. You pick the relevant tool and drag your cursor over a piece of text to apply it.

Adding watermarks to documents is also possible via the Add Watermark tool, which should come in handy for protecting your work or marking a document as sensitive. It offers a lot of flexibility, letting you choose between a text or image type watermark, a single or tiled layout, and granular controls for the pages, text, and visibility.

Common keyboard shortcuts like the ones for copy (Ctrl+C), paste (Ctrl+V), undo (Ctrl+Z), and redo (Ctrl+Y) also work well here.

Easy OCRs

Sometimes a PDF is nothing more than a set of page images, like a scanned report or a document full of charts. You cannot search through it or copy anything out of it because there is no text for your PDF viewer to find.

The solution to that problem is to run OCR on it, which converts the pages into machine-readable text.

OCR PDF handles this in BentoPDF by letting you upload a file, select the relevant language(s) found in the document, and start the process. During my test, I used a two-page brochure for a motorcycle I like to window shop for and left the advanced settings untouched.

It took a while to process the pages, but the output was mostly accurate. Only the very small, lossy text at the bottom of the document tripped it up. I could copy everything to the clipboard or export it as a .txt or searchable PDF file.

File Conversions

BentoPDF has a range of file conversion tools available, and I tested out two of them. PDF to JPG converts each page of a PDF into a JPG image, and using it is quite simple. You upload a PDF file, set the image quality with a slider (the pre-set value is 90%), and click "Convert."

Images to PDF does the reverse, and it accepts an array of formats beyond JPG, including PNG, BMP, HEIC, and PSD.

There are plenty more conversion tools on offer, covering formats like ODP, CSV, EPUB, JSON, WebP, and Markdown, among others.

Quick Compressions

Usually, when handling PDF files that need to be sent via email, its file size matters a lot. Popular email services like Gmail, Proton Mail, Outlook.com, and Yahoo Mail cap attachments at around 20 to 25 MB.

To tackle that, the Compress PDF tool offers two algorithms, "Condense", which is the recommended one, and "Photon," that's tailored for photo-heavy PDFs. Alongside them are four compression levels that run from "Light" to "Extreme.".

For checking it out, I stuck with Condense and only changed the compression level, going with Extreme to see how small the file could could get.

It took quite a while to process, but BentoPDF brought my 120.4 MB PDF down to 28.5 MB, a 76.3% reduction. That is still a little over the usual email limits, but for a 214-page document, it's a good result.

The text and images stayed very readable too, and I only noticed the loss in quality when I zoomed into the images.

Start Editing

There's a lot more BentoPDF has to offer than what I have covered above. Just scroll down on the homepage to see its full list of 100+ PDF tools. There are some security-focused ones that I am sure many of you will like.

If you are looking for the source code, then you should visit the project's GitHub repository, which also contains details on the tech stack that powers BentoPDF.

Long-standing open source projects won't receive new DigitalOcean infrastructure credits after the company quietly ended its support program in a disruptive move that has affected many open source projects. Though they did have enough money to fund Omarchy. 🤷

I cannot confirm, but it could be related. Gentoo Linux is seeking new infrastructure sponsors after several long-term backers withdrew support. The project accepts donated machines, colocation space, virtual machines, physical servers, or funds.

Music copyright group IFPI has submitted yt-dlp, a general-purpose command-line downloader, to the EU's piracy watchlist, lumping it with dedicated ripping services. They named four maintainers while claiming the tool fuels copyright harm.

AI-generated spam flooded Google's open source bug bounty program, prompting the company to temporarily pause product vulnerability submissions. Supply chain reports are still welcome, with them teasing a revamped program by Q1 2027.

Siemens silently deleted the OpenRadioss repository, but AGPL licensing has saved it. Rocky Linux co-founder Brian Clemens forked it as OpenCourant within days; it's already shipping binaries for Linux and Windows.

Red Hat's Lightwell flips the security script. Instead of waiting for upstream patches, it backports fixes directly to production Java libraries. Having cleared 400 vulnerabilities, its on track to grow.

Sequoia PGP arrives in Ubuntu 26.10 as a modern Rust-based OpenPGP tool that sits alongside GnuPG. Both implementations coexist for now, but the plan is to eventually phase out the latter.

Tuta Mail now has an app in the Nextcloud App Store, and a separate Nextcloud plugin in Tuta lets users upload email attachments to Nextcloud and embed video conference links in calendar events.

This edition of FOSS Weekly is supported by Karakeep, an open source bookmark manager.

🧠 What We’re Thinking About

Someone has used AI to create clones of Adobe Photoshop and other tools. I would not keep my hopes high. The project is in a very early stage of development, so there are many functions missing and the user experience is not close to the original. It will also be worth seeing if the project gets development a few months down the line. Because AI has made it easier to create software, so many people create something and then forget about maintaining it.

Carl Dong, a former Bitcoin Core contributor, explains why he built Obscura around distrust rather than promises.

🐧 Are You Playing Tuxdle?

Tuxdle is the Linux terms guessing game that I created two weeks ago. I am delighted to see that this tiny puzzle game gained a niche following of daily active users. After all, this is a fun way to keep your general Linux knoweldge in check.

🧮 Linux Tips, Tutorials, and Learnings

GitHub Desktop isn't officially available for Ubuntu, yet a community fork fills the gap. While it hasn't received a release recently, the project worked well to bring that app to my Linux setup.

S-TUI is a Python tool that monitors CPU frequency, temperature, and power use in your terminal. It can stress-test your processor and detect thermal throttling, with customizable display options and mouse support for navigation.

There is this handy tool that lets you get all kinds of system information in the terminal. Very helpful to know what's on your computer.

👷 AI, Homelab and Hardware Corner

ORICO's X50 is a sleek Thunderbolt 5 SSD enclosure that works seamlessly on Linux without requiring any prior configuration.

Then there's Raspberry Pi, who have finally released the Debian 13-based Raspberry Pi Desktop for PCs and Macs.

✨ Apps and Projects Highlights

If your gaming needs have been affected by the absurd increase in prices for memory, storage, and GPU, why not give cloud game streaming a try? NVIDIA already offers a native client for GeForce NOW on Ubuntu.

📽️ Videos for You

In the latest video, learn to make the command output in Linux terminal look pretty. It could also be useful in various cases when you have to vioew csv or code files.

💡 Quick Handy Tip

In the Nemo file manager, pressing F3 opens an extra pane to view two folders side-by-side, making it easy to drag, drop, copy, or move files between different directories without switching windows.

If you find the keyboard shortcut inconvenient, you can add a dedicated toggle button to your interface by going to Edit → Preferences and opening the "Toolbar" tab. Here, you can enable/disable the Extra Pane option. When enabled, it adds a split-window icon to your top toolbar, letting you control the view with a single click.

🎋 Fun in the FOSSverse

Do you use Arch, btw? Can you solve our Pacman command quiz?

And here I thought Windows were only useful for ventilating a room. 🤭

🗓️ Tech Trivia: Did you know? On September 30, 1980, Xerox, Intel, and DEC published the "Blue Book" as the first Ethernet specification. Ethernet had already been running at Xerox PARC for years, but this document helped turn a lab experiment into the networking technology we still use today.

🧑‍🤝‍🧑 From the Community: You might have heard about the Blanc Browser by now. It's creator has made an appearance in the forum, replying to an inquisitive thread by one of our Pro FOSSers, Bill.

Plenty of open source projects keep going on goodwill alone, with contributors putting in the hours and expecting nothing back. While others lean on outside support like cash, donated hardware, or someone else covering the hosting bill.

This kind of support rarely gets much attention, but these needs are some of the most foundational aspects of any project that's serious about growing.

Gentoo falls in the second group. The source-based Linux distribution, built around the Portage package manager, runs largely on sponsored infrastructure, as it can only manage to maintain a few vital machines.

Sadly, many sponsors that had backed Gentoo all these years have decided to stop making contributions. The project is now looking for replacements and has put out a call for infrastructure sponsors.

Currently, its sponsors page lists names such as Oregon State University's Open Source Lab, CDN77, HP, and Hetzner, but it may not yet reflect the dropouts.

Gentoo's search for sponsors

There are five ways to help, as laid out on the sponsorship page of Gentoo's wiki, with preference being given to supporters who share the project's vision of software that respects choice and stays open to user-driven changes.

The first way is to accept donated machines for the project's hosting locations in the US. They are not asking for much; anything good enough to handle server-focused tasks in today's time is a good offering for them to use.

Next is Colocation, a type of physical hosting. The sponsor would provide rack space, power, and connectivity, while Gentoo finds the hardware to put in it. Capacity outside the US is their most urgent need at the moment, though offers from the US are welcome too.

Third are virtual machines, with a minimum of 2 vCPUs, 4GB of RAM, and 25GB of storage. The project says that it is leaning more on the cloud wherever it can, but many of its services are monolithic, and that Kubernetes would suit them poorly.

The fourth way is full-package hosted physical servers. The wiki gives two examples: a root server like the ones Hetzner offers, or a sponsor's older or unused servers with Gentoo getting full remote access.

In that second case, the sponsor would still own and manage the hardware, whereas Gentoo would take care of the operating system and remote management, only asking for advance notice before any migration events.

The last is plain old money. Any donated funds would go toward paying for colocation and hosting.

A call to act

Gentoo is far from the only project that needs this kind of backing.

Take LVFS, the firmware update service for Linux. It leaned heavily on the Linux Foundation and Red Hat, with Framework and the Open Source Firmware Foundation pitching in $10,000 a year.

Then Lenovo and Dell joined at the Premier tier, at $100,000 a year each, along with HP and NVIDIA, who followed soon after. I would call that a win for LVFS and a sign that sponsors are out there for projects that go looking.

Maybe your organization has spare rack space, or you have money lying around and want it to do some good. Either way, someone has to host the servers behind projects like this, and Gentoo's infrastructure team is available at infra@gentoo.org for any infrastructure sponsorship-related queries.

Suggested Read 📖: Google has shut down part of its Open Source Bounty Program thanks to AI.

How does an operating system that isn't proprietary or powered by the Linux kernel manage to run Docker, you ask? Well, it would need features like namespaces and cgroups.

That's exactly what Vinix did, adding its own implementations of those plus overlayfs and seccomp, allowing the stock Alpine Linux Docker engine to run on ARM64.

It also runs QEMU 9.1.2, which can boot a second Vinix inside a window of the first, and for gaming, it can run DOOM, a DOOM 3 demo, Gothic II's demo via OpenGothic, and Minecraft Java Edition.

There are still some limitations.

For instance, the Docker support lacks bridge networking and iptables rules, and QEMU runs on software emulation for now. Minecraft runs in interpreter mode on one CPU core, and DOOM 3 manages about 13 FPS under software rendering.

Yet another OS?

Vinix exists to give MacBooks a fast, minimal, open source OS, with the project's motivation being tied to macOS getting slower and bloated with every passing release. They are initially targeting Apple Silicon devices to eventually cover the whole M-series lineup, though only the M1 is supported right now.

While Linux users have little reason to switch to it, Vinix aims for a setup without systemd and a kernel small enough for one person to read through.

Beyond that, nothing runs in the background unless you start it, and the system sticks to a single init system, desktop, package manager, and binary format. The kernel handles memory manually instead of using a garbage collector, so a cleanup pass can't interrupt a system call or the compositor.

All of it runs on a V programming language foundation, which was picked for its fast compiles and small, readable design. The project doubles as a testbed for bare metal programming in V too!

The security model of the OS is modeled after OpenBSD, with pledge and unveil available, though apps must opt in, and user memory can't be both writable and executable by default. Verifying the bootloader, kernel, and root filesystem isn't supported yet.

It runs Linux apps

At its core, Vinix is a monolithic kernel booted through Limine, with its drivers built into the image. Most of it is V, alongside some C and assembly.

It features a native desktop built with V UI2 that renders through the framebuffer, with X.org and Hyprland sessions as options on ARM64. The developers claim that the OS takes up 50 MB of RAM after boot and roughly 1 GB of disk once installed.

Alpine Linux, the distro behind that Docker engine from earlier, is lightweight and security-focused, and it's built around musl and BusyBox. Vinix targets its aarch64 packages.

Thanks to that, Vinix is able to run Linux apps because it answers the system calls they make, plus the ELF format and musl behavior they rely on, and those binaries run natively, without a VM or an emulator.

New applications can be installed with pkg, a command-line tool that pulls packages from Alpine's aarch64 repositories. Apps like Chromium, GIMP, Sublime Text, and Gnumeric are known to work, as is Wine, the widely adopted compatibility layer.

The catch is that this is limited to ARM64 and selected packages. Software that relies on a Linux kernel feature Vinix doesn't have yet may not run properly.

Additionally, support for the M3, M4, and M5 is coming soon, while GPU drivers are still a work in progress.

Who's behind it?

Vinix's history spans many years, with the first commits on GitHub dating back to March 2021 and the original kernel core being the work of Mintsuki, the creator of Limine.

Development then paused for around two years before Alexander Medvednikov, the author of V, took over as maintainer.

Mintsuki's core covered the physical and virtual memory managers and the scheduler, but since the handover, Vinix has gained storage, networking, input, and graphics support, along with a Unix-style userspace and the Alpine compatibility we saw earlier.

Get Vinix

You will find that the official website offers a .dmg installer file for M1 Macs, whereas the project repository hosts two distinct ISOs, one for ARM64 (targeted at QEMU and VirtualBox on Apple Silicon Macs) and the other for x86_64 (targeted at Intel and AMD systems).

If you want to test it on a virtual machine, the requirements are quite modest. Vinix needs at least 4 GB of memory and 4 GB or more storage space. Building from source is also an option.

Since 2022, Google's Open Source Software Vulnerability Reward Program (OSS VRP) has been the path for outside researchers to get paid for reporting security flaws in the company's open source code, including projects like Flutter, Angular, Go, and Fuchsia.

With an announcement on X, they have now decided to discontinue the product-facing side of it.

They are calling the change temporary, while supply chain reports remain open and anything submitted before October 1 stays unaffected.

What's closed, what's not?

Product vulnerabilities are bugs in the projects themselves, like a failing HTML sanitizer, memory corruption issues in file format parsers, or insecure code examples in documentation.

The updated rules do not limit the change to a specific project tier, as they just won't be accepting any reports related to this.

Supply chain reports, on the other hand, cover vulnerabilities in how the software is built and shipped. Exposed package manager credentials used to publish build artifacts is one case the rules page lists. It remains unaffected by this change.

There's also an exception for some Google Cloud repositories. If a bug there affects a Cloud product, Google may still accept the report, but through its Cloud VRP.

Why did it come to this?

Back in March, a post on the Bug Hunters blog from Google engineers said AI-generated reports were flooding the program. Some were serving up hallucinated information, while others were flagging legit coding errors that had little to no impact on the security posture of the targeted project.

Google's first response was to raise the bar on memory corruption reports for its two top project tiers. Researchers had to either reproduce the bug through an existing OSS-Fuzz fuzz target or point to a patch that maintainers had already merged.

An update to the same post the following month went further. The standard and low-priority tiers, OT2 and OT3, stopped offering rewards or credit for product vulnerabilities and other security issues. The top supply chain reward for OT2 projects also fell to $3,133.70.

Supply chain reports still pay, from $500 on OT2 projects up to $31,337 on flagship ones.

As an alternative, Google points to the Patch Rewards Program, which pays between $100 and $15,000, though only for patches that have stayed in a project for a month without being reverted.

An open question

Google has not said when or in what form product vulnerability reports will return. Their announcement only promises an update in the first quarter of 2027 while they continue reworking that part of the program.

There's also a loose end. At the time of writing, the OSS VRP page still lists reward ranges for product vulnerabilities, up to $7,500. Though, as you saw earlier, the rules page for it has already been updated, so it shouldn't be long before this is addressed.

Suggested Read 📖: cURL gets rid of its bug bounty program due to AI.

Lightwell is Red Hat and IBM's response to a specific problem in enterprise open source security. Vulnerabilities sit in production library versions that upstream maintainers haven't patched and, in some cases, won't.

More than 90% of enterprise application code traces back to open source or third-party libraries, per figures Red Hat cites, and a typical enterprise codebase carries over 500 known vulnerabilities at any given time.

They say that attacks on known vulnerabilities arrive, on average, a week before any patch exists.

Lightwell's approach is to bypass that timeline. Rather than waiting for upstream maintainers to push fixes to the library versions enterprises are actually running, it backports those fixes directly, delivering them through secure package repositories.

Red Hat reached out recently to share how far it has come.

400 down, more to come

To date, Lightwell has already managed to clear 400 previously unknown vulnerabilities across foundational Java libraries, going beyond reported CVEs and contributing fixes upstream in line with responsible disclosure protocols.

The primary target so far has been organizations running Java environments with pinned dependency versions that can't be safely updated. Lightwell patches those in place, leaving the pinned version intact.

Coverage is also set to expand beyond Java, with Python, JavaScript, and .NET on the roadmap. Each will follow the same approach, with fixes backported to the versions already in production and applicable patches being contributed upstream.

The Clearinghouse opens up

Then there's Clearinghouse Premier, which has so far operated on restrictive terms. Before today, it was reserved for a pre-selected group of organizations in critical infrastructure sectors.

That restriction is now lifted, as it has reached general availability, which means any enterprise can sign up for Clearinghouse directly without waiting on an infrastructure designation to clear access.

You see, Lightwell runs across two access tiers. The Lightwell Network, which reached general availability in July as a self-service subscription open to any organization. It provides access to the backported patches and their compliance documentation.

What Clearinghouse Premier offers is more tailored. Organizations can specify which vulnerabilities matter most to their environment, get early notice before issues go public, and know exactly when fixes will arrive.

As a whole, Lightwell sits within IBM and Red Hat's $5 billion commitment to open source security, with both companies pointing to AI-assisted tooling raising the stakes on older, unpatched open source dependencies.

That stance is further strengthened by Gunnar Hellekson, Vice President and General Manager for Lightwell at Red Hat, who stated that:

AI agents shifted the threat landscape overnight, exploiting old dependencies at machine speed. They do not care if a codebase is ten years old or otherwise considered stable, because one small crack is all it takes to chain an attack together.

If you are interested in what's being offered, a closer look before committing is possible via Red Hat's technical demo, which will walk you through the patching workflow.

The globally recognized tech and engineering company has quietly shut down the OpenRadioss project at the start of this month, keeping the GitHub page around but completely removing the repository.

I sound complainy because usually when an open project is discontinued, the maintainers put it into a read-only, public archive state, which allows other developers to learn and fork off it.

OpenRadioss was the open source release of Radioss, a finite element solver Altair Engineering developed for crash testing, blast response, impact analysis, and other structure loading scenarios.

They had released it in September 2022, drawing in a multinational community of researchers, software developers, and industry contributors who extended the solver and built upon the code.

Siemens, which acquired Altair last year, kept the repo and its contents publicly available for more than a year after the acquisition before abruptly deleting it and redirecting people to its transition page for Simcenter Radioss.

A fork appears

Brian Clemens, the co-founder of Rocky Linux has already got an OpenRadioss fork up and running, which carries the full OpenRadioss commit history, ships under the same GNU AGPLv3 license, and is hosted on GitHub.

It's called OpenCourant, named after mathematician Richard Courant and the Courant-Friedrichs-Lewy (CFL) condition, a stability criterion central to solvers like this one.

In fact, this fork owes its existence to the AGPL. You see, Siemens is legally within its rights to take down the repository, but the license applying to every commit gave anyone the right to fork, distribute, and build on the code.

It's just sad to see how abruptly the takedown was carried out; makes you think they didn't want people to get a heads-up.

Also worth noting is that OpenCourant is an independent community project, and it is actively looking for past OpenRadioss contributors to pitch in.

What's already shipping?

Initially, the upstream build pipeline that depended on proprietary Siemens infrastructure could not be transferred. That was rebuilt from scratch, and the results appear promising.

Just days since its inception, the project is already distributing Linux and Windows x86_64 packages.

And before that, a closed-source hm_reader input-reader binary that was never committed to the git history disappeared along with the upstream OpenRadioss repository.

Thankfully, an unnamed community member who had kept a copy came forward, the OpenCourant team independently verified it, and it now ships in every build. Though they are still looking for specific release archives to start work on ARM64 support and improve current platform compatibility.

If you are interested in OpenCourant, then its package options include the Starter and Engine offerings in single and double precision, SMP builds, and converters for animation files and time history data.

You can download the latest builds from the official downloads page.

If you get lost, the INSTALL.md file can be a good resource, though keep in mind it is still the OpenRadioss version, as the OpenCourant team has yet to update it to reflect the new project.