A tinkerer, Kathy Korevec, has made ZigZag available under the Apache 2.0 license. She says that the entire app was built with AI, using ChatGPT, and her goal was to get software made to her specs, similar to the Adobe suite, but with MCP support built in.
That MCP support is a built-in server that lets AI agents draw and edit in the same window.
Her post about this on Hacker News has already managed to reach top 10 status (over a 48-hour period), and she has been answering questions there about how this was built and which machines it supports.
ZigZag: Why develop this?
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This was built around a Brother Skitch PP1 embroidery machine that she says cost her around $300.
You see, the Skitch needs Brother's Artspira app to operate. While the app is free and works with the machine, a paid plan called Artspira+ adds useful extras such as digitizing images into embroidery patterns and creating cross-stitch patterns.
Kathy says the free version is limited in colors and tools, and she believes some of the clip art is reserved for subscribers. She also guesses Brother sold the machine at a loss at first because of the subscription.
💭
That last bit checks out, seeing what many hardware manufacturers are doing.
ZigZag imports SVG art and can trace PNG and JPEG images into shapes. It covers 11 stitch types, including fill, satin, and appliqué, with a preview of the stitches being shown in the editor.
As for the MCP server, it lets an AI agent inspect the open design, add and edit objects and text, trace images, preview the stitches, and export files. It can also control a Skitch device, and each action counts as one undo step.
This is not something new.
Open source embroidery tools existed before ZigZag. People have brought up projects like Ink/Stitch, an Inkscape extension, and Respira already lets people send designs to the Skitch without Artspira.
The project also credits Respira for the Bluetooth protocol and sewing flow. Kathy says she reverse-engineered the protocol herself and that her agent used Respira to verify it.
The README covers installation, Bluetooth pairing, and the MCP server, and it advises against firmware updates on the Skitch. Linux users can follow its steps for building ZigZag from source with Rust.
The Brother Skitch PP1 is the only machine ZigZag sends designs to over Bluetooth, and Artspira needs to be closed first since the Skitch can only talk to one app at a time.
For other machines, ZigZag can export PES, DST, EXP, JEF, or VP3 files.
Traditional operating systems are designed primarily to manage computing resources, including CPU scheduling, memory allocation, storage, networking, and application execution.
Agentic AI introduces another layer of complexity: infrastructure must also support the lifecycle of intelligent agents.
This includes agent deployment, task scheduling, execution isolation, context management, memory retrieval, tool invocation, observability, and recovery.
openEuler is embracing the shift toward agentic infrastructure and evolving its OS capabilities to support the development, deployment, execution, coordination, and optimization of agents across heterogeneous computing environments.
AI agents are moving beyond conversation to execute code, call tools, interact with applications, and coordinate multi-step workflows. As these workloads scale, infrastructure faces a new set of challenges: secure execution, fast environment provisioning, efficient context reuse, persistent memory, and elastic resource management.
Traditional infrastructure was not designed around these requirements. The next evolution of the operating system must go beyond managing processes and hardware resources to support the full lifecycle of agent execution.
This is the direction behind openEuler's agentic OS: building an open, secure, and efficient software stack that connects operating system capabilities with agent runtimes, AI frameworks, data services, and heterogeneous computing resources.
The infrastructure is designed to operate across diverse computing architectures, including general-purpose processors such as x86, Arm, RISC-V, and LoongArch, as well as AI accelerators from multiple vendors. This heterogeneous approach is important because enterprise AI deployments rarely operate on a single hardware architecture or accelerator platform.
The architecture is centered on two layers: Agent Kernel, which provides foundational system-level capabilities, and Agent Service, which exposes reusable services such as secure sandboxing, memory, session management, and Tool Gateway. Around this foundation, complementary infrastructure technologies—including distributed caching and serverless data services—help support the performance, scalability, and data requirements of agent workloads.
Rather than placing every capability inside the OS kernel, the approach combines system-level optimization with reusable infrastructure services and an open ecosystem. Five technical pillars are central to this architecture.
Five Pillars of Agentic Infrastructure
1. Sandbox: Secure, Scalable Agent Execution
Agents need isolated environments to execute generated code, invoke tools, and interact with external systems. These environments must start quickly, scale to high concurrency, and recover reliably when tasks are interrupted.
openEuler brings together lightweight virtualization, snapshot-based recovery, resource prewarming, and high-density scheduling to address these requirements. Technologies such as StratoVirt and the Conch runtime help create and manage sandbox environments, while orchestration capabilities support large-scale deployment.
According to openEuler's performance tests, sandbox startup latency can be as low as 20 ms, with a density of more than 2,000 sandboxes. In a separate large-scale concurrency test, the platform launched 100,000 sandboxes within 10 seconds. These results demonstrate the potential for fast provisioning and high-density agent execution at scale.
2. KV Cache: Making Inference Context Reusable
In multi-turn agent workflows, repeated inference often involves substantial overlap in context. Recomputing or repeatedly transferring the same attention states wastes accelerator memory and increases time to first token (TTFT).
openEuler's KV cache approach combines multi-tier caching, global indexing, NUMA-aware placement, and high-speed cross-node data transfer. With technologies including LMCache and openYuanrong, KV cache can be reused across instances and accessed closer to compute resources.
Agent-aware scheduling takes this further: by mapping agent context to KV cache chunks, the system can predict reuse and proactively coordinate prefetching and eviction.
The performance gains are significant: inference throughput increases by 1.5x–3x, while time to first token (TTFT) drops by 30%–70% in targeted scenarios.
3. Memory Agent: Giving Agents Persistent Context
KV cache accelerates inference by reusing computed states. Agent memory addresses a different challenge: helping agents retain and retrieve useful knowledge across turns, tasks, and sessions.
openEuler's memory infrastructure combines memory extraction and orchestration frameworks with hybrid retrieval and storage capabilities. Solutions involving OpenViking, oGMemory, and openGauss support structured context management, vector and full-text retrieval, and knowledge sharing across agents.
This enables agents to retrieve relevant information when needed instead of repeatedly processing lengthy histories. It also supports shared knowledge accumulation in multi-agent workflows.
By retrieving only the context an agent needs, the system can cut token costs by 30%–70% and improve task success rates by 1.5×–3× in targeted scenarios.
4. Serverless Database: Enabling Elastic Data Services for AI Agents
Beyond the core Agent Kernel and Agent Service layers, data infrastructure plays an important supporting role in agentic workloads. Serverless database capabilities provide the elastic, branchable data environments needed by agents that create, modify, compare, and roll back data across dynamic workflows.
Built on openGauss, this approach combines elastic compute provisioning with copy-on-write storage and database branching. Multiple branches can share baseline data while maintaining independent changes. Point-in-time recovery, branch comparison, and merging further support flexible data workflows. The result is a more dynamic data foundation for AI agents, where database environments can be provisioned, modified, and recovered as workflows evolve.
The solution is designed for rapid, elastic data provisioning, with millisecond-level instance startup and second-level database branch creation and rollback. These capabilities, enabled by compute elasticity and copy-on-write storage, allow agents to create and manage isolated data environments with minimal overhead.
5. An Open, Heterogeneous Ecosystem
Agentic infrastructure cannot depend on a single model, framework, or hardware platform. It must connect diverse components while making their resources manageable and accessible.
openEuler's ecosystem brings together agent frameworks, inference engines, orchestration platforms, memory systems, and heterogeneous processors and accelerators. Through collaboration with projects and technologies such as Kubernetes, openFuyao, vLLM, MindSpore, and openGauss, it aims to support different agent workloads across diverse computing environments.
The objective is not simply compatibility. It is to provide a consistent infrastructure foundation on which developers and ecosystem partners can build, deploy, and scale agent applications.
From an Operating System to an Agentic Infrastructure Foundation
The transition to Agentic AI is not simply another change in application development. It is reshaping the infrastructure requirements of enterprise computing.
As agents become more autonomous, infrastructure must support more than model inference. It must provide secure execution, efficient data movement, persistent memory, elastic data services, and reliable coordination across increasingly complex workloads.
openEuler's agentic infrastructure initiative addresses these requirements through five interconnected technology areas: an open infrastructure vision, high-performance sandbox execution, distributed KV cache acceleration, persistent agent memory, and serverless databases.
Each addresses a distinct bottleneck in agent deployment and execution. Together, they represent an approach to making agent capabilities more scalable, reusable, and manageable across heterogeneous computing environments.
Sandbox: Secure and recoverable agent execution.
KV Cache: Efficient reuse of inference context.
Memory Agent: Persistent context and knowledge retrieval.
Serverless Data Services: Elastic and branchable data infrastructure.
Open Ecosystem: Integration across AI frameworks, orchestration platforms, and heterogeneous hardware.
Together, they point to a broader evolution in the role of the operating system. As AI agents become more autonomous and workloads increasingly dynamic, infrastructure must do more than allocate compute resources: it must help agents execute safely, operate efficiently, retain context, and scale reliably.
First a quick appeal. If you use Google, please add It's FOSS as a preferred source. This way, you won't miss the reliable and trusting tutorials and news from It's FOSS in the noise of AI-generated content.
Speaking of AI, Asahi Linux, the project behind Linux on Apple Silicon Macs, is moving every repository from MIT to GPL v2 or later. The team says people were lifting unreleased code and rewriting it with AI to hide its origin. That hints at Omarchy but DHH told us in an interview that he really doesn't care about this license move.
Firefox 160 can send a full tab, or just a box you draw around part of a page, into a Mini Window. We tested it and shared this new feature.
Closing a kitty session no longer means losing your layout. The 0.50 release can save every window, tab, and scrollback at logout and restore them at the next launch, once you enable the setting.
Hate Chromium? Domicile is a Rust-based Wayland compositor that draws your entire desktop as a web page using a patched Chromium build ☠️. It only runs on x86_64 machines for now.
Nautilus, or Files, GNOME's file manager, may soon get a neater way to browse long folders. Developers are testing optional section headers that sort files into groups, though this is an early build, so things could change.
Synaptics has sent a cease-and-desist to developer Mike Lothian over Vino, his open source DisplayLink driver. The letters claim patent infringement, and Mike has had to pull the repository from public view.
The company behind GameOS, Playtron, is now KORA Systems. Its new desktop OS, also called KORA, puts AI models and agents right into a Linux base, with a launch planned for later in 2027.
This edition of FOSS Weekly is supported by LabEx.
LabEx offers interactive, hands-on learning experience on Linux, devops and cybersecurity. There are labs and challenges to practice your learnings straight into the 'playground' offered by LabEx.
Not sure which backup tool to pick? Déjà Dup suits GNOME desktops with almost no setup, while Kopia and Restic handle cloud storage for those who want more control. UrBackup covers fleets of machines.
📆 Trivia Bit: Firefox 1.0 arrived on November 9, 2004, as Mozilla’s open source alternative to Internet Explorer. It came with tabbed browsing, a built-in pop-up blocker, and phishing protection, and was released for Windows, Mac OS X, and Linux.
Agent Name Service (ANS) now has a home at the Linux Foundation. The launch happened at KubeCon North America in Salt Lake City, and you might remember that the foundation first announced its plans for the project way back in June.
GoDaddy, Cloudflare, Salesforce, and DigiCert are among the organizations now involved with ANS, bringing the total number up to eleven.
One of the early deployments of ANS has been in AGNTCY, another project under the foundation, where its directory of AI agents can now check an agent’s ANS identity.
Announcing the move, Jim Zemlin, CEO of the Linux Foundation, had this to share:
Autonomous AI agents are transacting and operating across global systems, but they can't succeed without verifiable identity.
Under the neutral governance of the Linux Foundation, ANS provides a common foundation for trust that the entire industry can adopt, implement and improve together.
An explainer
Platforms identify AI agents in their own ways, and a service that receives a request from one has no standardized way to confirm who the agent represents.
ANS tackles this by linking each agent to a domain name its operator owns, the same way a website is tied to its domain. Each version of an agent gets its own name, such as ans://v1.4.2.travel.example.com.
To register an agent, the operator first has to prove it controls that domain. Certificates are then issued for the agent, and every registration goes into a public log that anyone can read.
ANS also publishes details about each agent in DNS, the internet's address book, and other systems can then look them up with tools they already use.
The main DNS records use a format from a separate project called DNS-AID. ANS adds one record of its own that points to the agent's entry in the public log.
When an agent sends a request, the receiving system can check that its registration exists, that it has not been revoked, and that the agent really holds the key tied to it.
ANS only covers who the agent is. Deciding what it can do is left to the receiving system, and platforms have to add the check themselves.
The setup behind ANS
A technical steering committee now oversees the project. It starts with five seats, held by three people from GoDaddy, Ken Huang of DistributedApps.ai, and Michael Kantor of Hashgraph Online (HOL).
That lineup is only a starting point. The first election has to be held within 12 months, and after it, the charter prevents any one company from holding half or more of the seats (to ensure vendor neutrality, I assume).
The charter also mentions that meetings are meant to be open to the public and that discussions and decisions should be visible to everyone.
As for all the code in the project, the Go, Rust, and Java libraries currently use the MIT license, but new code contributed from now on has to use the Apache 2.0 license.
Some lore info
ANS began in 2025 as a draft sent to the IETF, the internet standards body, and a follow-up came in October. Drafts like these have no formal standing at the IETF and are not endorsed by it.
At present, the Linux Foundation calls the specification formalized. The reference code and a local demo are on the project's GitHub page, and the specifications are on ansinfo.ai.
Before we dive into the matter, you first have to understand what DisplayLink is. It is a proprietary graphics transport technology that lets computers connect to one or more external displays over USB, using docks or adapters built around DisplayLink chips.
It works by compressing display data on the host computer and sending it to a chip in the connected device. The chip then decodes it for output to monitors through interfaces such as HDMI or DisplayPort.
On Linux, this support can rub some people the wrong way due to its closed-source nature. That is where Mike Lothian's (FireBurn) Vino comes in as an open alternative.
Unfortunately, Synaptics owns DisplayLink and has sent Mike cease-and-desist letters alleging that he infringed its DisplayLink patents with his project.
What's Vino?
Vino is a driver that lets the Linux kernel talk to DisplayLink docks directly. It is meant to replace DisplayLink's closed-source software and the open source EVDI kernel module that sits beside it.
Mike wrote it with help from Claude Code, and the first version reached the kernel mailing list in June, unfinished. He had faced a communication issue between the dock and the driver and asked other developers for help.
In early July (courtesy of LWN.net), he emailed a Synaptics employee for guidance and mentioned his Rust rewrite of EVDI. About two weeks later, a different employee replied.
That reply said his Rust rewrite of EVDI was not a thing of concern. Rather, the concern was the DisplayLink protocol and codec, which Synaptics believes are covered by its patents.
Lawyers get involved
A cease-and-desist letter followed on August 17. It told Mike to stop development and withdraw anything he had submitted to maintainers and hosting sites, with a signed confirmation due by August 28.
An appendix listed over 125 patents without saying which applied. Mike posted a working version 3 of Vino on August 26 and asked Synaptics the next day which claims they had in mind.
The answer came in October as claim charts. They map one claim from each of two patents, GB2484736 and GB2485613, to his source code.
The October 5 letter said Synaptics would seek $25,000 for the cost of preparing those charts if Mike missed an October 11 deadline, with the figure rising by at least $25,000 each week.
Mike replied on October 6 (message 8) with an offer to pause public work on the encoding code the charts point to if Synaptics paused the deadline and the weekly charge. He also asked about licensing that code for non-commercial use.
On October 7, Synaptics said it would grant no patent license or promise not to sue and set October 18 as a final deadline.
💭
What I can see so far is a very aggressive patent infringement witch hunt against a lone open source developer who was quite open to communication.
Synaptics took about three weeks to answer his request for specifics, then followed with a potentially life-ruining $25,000 demand that compounds weekly.
Clean room design quirks
Mike describes Vino as a clean-room driver and says he reverse engineered it from captured USB traffic and vendor programs.
If you didn't know, in a clean-room recreation, one party studies the original implementation, then writes a description, and a separate party builds the new code from that description alone.
It is meant to act as a defense against copyright claims because it shows nothing was copied. Synaptics’ July reply argues that clean-room or independently written code can still infringe a patent. 🤷
The timing
Vino also lands in a busy stretch for AI-assisted reverse engineering, from smart TV cleanups to office suite clones. Legal pressure has reached other AI-built projects too.
Mike has already taken down the Vino repository in response to the demands, and Synaptics has not made a public statement so far.
Only time will tell what happens to the work, and I hope someone forked it before it went offline, as that would give others a good starting point to build on.
Claude Code users are hitting "safety checks" in the middle of decompiling things. Some Codex users got warnings and account restrictions for poking at their own smart TVs. 404 Media calls it a crackdown on the coolest thing people were doing with AI and I agree with them.
This is precisely the reason why local AI and open source/weight models are going to be the next big thing for people who want control over their data and computer.
So this edition is about who gets to say 'no' on your machine.
🐧 AI at the core of new Linux distro
Remember Playtron, the Linux gaming OS from Cyanogen co-founder Kirt McMaster? It rebranded as Kora in July 2025 and has now unveiled Kora OS, a Linux desktop pitched at people who primarily use AI.
Here's the catch. Kora OS will be free when it ships in 2027, but the money comes from subscriptions that bundle AI models and agent services. Cortex and the desktop interface are proprietary. So it's Linux-based, not open, and only "local when possible".
Kora won't be the only one. Omarchy is already there and so do openKylin and openEuler. There will be more distros offering AI-first opearting systems.
🔐 Agent security
You know that "Yes, and don't ask again" option in Claude Code? It saves an allow rule in .claude/settings.local.json inside your project. Sometimes that rule is the exact command you approved, password or token included, in plain text.
The bug report about this goes back to August 2025. A bot closed it as "not planned" after 60 days of no activity. So the same tool that won't help you decompile your own TV firmware will happily remember your production database password.
🔍 Discover AI tools
Memory Poisoning Lab is an MIT-licensed teaching lab for memory poisoning attacks on AI agents. You replay six scenarios, try defenses and see what repair actually restores. It uses a scripted agent and synthetic data, so it runs offline with no API keys. There's a browser demo too.
TurboDiffusion is a video generation acceleration framework that can speed up end-to-end diffusion generation upto 100 times on a single RTX 5090.
Ram-Altman relates to last week's edition. Since Sam Altman was unhappy with consumers not fully utilizing their RAMS, someone created a bash script you run from cron. Any day your Linux box has more than 10% of its RAM available, it mails Sam Altman a note. It's silly but awesome 😄
🗂 AI jargon: Abliteration
In AI, Abliteration is a way to remove the refusal behaviour from an open-weight model without retraining it. Researchers have found that refusals are typically tied to a single "direction" inside the model's activations. Find that direction, subtract it from the weights, and the model mostly stops saying no.
That's why you see "abliterated" versions of popular models on Hugging Face. You see, with open weights LLMS, the guardrails are yours to keep or remove. With a cloud model, they're someone else's call.
😂 Meme
In case you didn't know already, NVIDIA CEO Jensen Huang is accused of dating a Chinese spy.
Kirt McMaster founded Playtron in 2022, and the company went on to build GameOS, a Linux-based operating system for gaming PCs. It now goes by KORA Systems Inc. and is launching a new AI-focused desktop operating system.
The OS shares the KORA name, with GameOS living inside it as GameFrame, a dedicated mode for turning the system into a game console. We received a press release regarding this weeks before the launch, so this write-up might be missing some updates.
🚧
A major component of this OS is not open source.
KORA, explained
KORA is a desktop OS developed on a Linux base, with AI models and agents built right into the system. Don't think that it is an app or a layer over another one; it's a standalone offering.
The company says AI workloads on macOS and Windows stay boxed into a single app or tab, with those having no view of the hardware underneath. KORA is built on the idea that AI belongs inside the OS.
The desktop is organized around neural workspaces, where each one keeps a project or client apart from the rest, and the AI only sees the workspace that is open. Files in it also keep their own version history.
Each workspace doubles as a sandbox too. The user sets what files, devices, AI tools, and online services every app and agent can access, while AppArmor enforces it all.
Search in the neural file system works on meaning, so a person or an agent can find a file by what it contains.
Its memory system builds context from the email, contacts, calendar, and cloud files the user connects. Local models run that analysis on the device, and data goes to the cloud only with the user's approval.
How open is it?
Cortex is the engine behind the models, and it spreads AI work over the CPU, GPU, and NPU, then passes some of it to other computers on the network.
Local models run by default, and cloud ones only join when a task needs them. KORA speculates that local models will handle roughly seven in ten of the calls it manages in 2027.
They also say that models from any outside lab can be integrated, and a chat can move from its own agent to Codex or Claude Code with its context intact. A planned SDK would also let third-party apps and agents tap into projects, memory, search, and email.
Sadly, Cortex is not open source. :(
Still, the wider ecosystem is open, and developers will get the same building blocks KORA's own apps use, such as workspaces, file versioning, and collaboration.
You mentioned Qualcomm?
Yes! Qualcomm engineers worked alongside KORA for nearly a year to bring a Linux desktop to the Snapdragon X2 Elite platform.
Though Qualcomm's wider Linux effort for X2 laptops is still early. Its own developer preview targets kernel developers and not end users, and the drivers meant to open the NPU to local AI are still being pushed upstream.
Even so, KORA says the PCs can run a language model with 26 billion parameters locally, with HUMAIN, the Saudi AI company, working with KORA on an enterprise version.
That version is called HUMAIN OS, with the first OEM PCs expected to arrive preloaded with it in 2027, while KORA itself follows later that year.
So, yes, for now, there is no ISO to download in case you got too excited.
Some thoughts
If you look closely, they are going at it differently than Omarchy, which began in June 2025 as a ready-made setup of Arch Linux and Hyprland that has since landed millions in backing for its foundation.
Omarchy's Quattro release lets users pick from several third-party agents, while KORA builds its own agent into the system and lets others plug in their models. They are basically taking different routes to chase the same bet.
Both seem convinced that AI agents are where the desktop market is headed.
