Uyuni Saltboot rework by oholecek

Description

When Uyuni switched over to the containerized proxies we had to abandon salt based saltboot infrastructure we had before. Uyuni already had integration with a Cobbler provisioning server and saltboot infra was re-implemented on top of this Cobbler integration.

What was not obvious from the start was that Cobbler, having all it's features, woefully slow when dealing with saltboot size environments. We did some improvements in performance, introduced transactions, and generally tried to make this setup usable. However the underlying slowness remained.

Goals

This project is not something trying to invent new things, it is just finally implementing saltboot infrastructure directly with the Uyuni server core.

Instead of generating grub and pxelinux configurations by Cobbler for all thousands of systems and branches, we will provide a GET access point to retrieve grub or pxelinux file during the boot:

/saltboot/group/grub/$fqdn and similar for systems /saltboot/system/grub/$mac

Next we adapt our tftpd translator to query these points when asked for default or mac based config.

Lastly similar thing needs to be done on our apache server when HTTP UEFI boot is used.

Resources

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Enhance setup wizard for Uyuni by PSuarezHernandez

Description

This project wants to enhance the intial setup on Uyuni after its installation, so it's easier for a user to start using with it.

Uyuni currently uses "uyuni-tools" (mgradm) as the installation entrypoint, to trigger the installation of Uyuni in the given host, but does not really perform an initial setup, for instance:

• user creation
• adding products / channels
• generating bootstrap repos
• create activation keys
• ...

Goals

• Provide initial setup wizard as part of mgradm uyuni installation

Resources

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Uyuni read-only replica by cbosdonnat

Description

For now, there is no possible HA setup for Uyuni. The idea is to explore setting up a read-only shadow instance of an Uyuni and make it as useful as possible.

Possible things to look at:

• live sync of the database, probably using the WAL. Some of the tables may have to be skipped or some features disabled on the RO instance (taskomatic, PXT sessions…)
• Can we use a load balancer that routes read-only queries to either instance and the other to the RW one? For example, packages or PXE data can be served by both, the API GET requests too. The rest would be RW.

Goals

• Prepare a document explaining how to do it.
• PR with the needed code changes to support it

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mgr-ansible-ssh - Intelligent, Lightweight CLI for Distributed Remote Execution by deve5h

Description

By the end of Hack Week, the target will be to deliver a minimal functional version 1 (MVP) of a custom command-line tool named mgr-ansible-ssh (a unified wrapper for BOTH ad-hoc shell & playbooks) that allows operators to:

1. Execute arbitrary shell commands on thousand of remote machines simultaneously using Ansible Runner with artifacts saved locally.
2. Pass runtime options such as inventory file, remote command string/ playbook execution, parallel forks, limits, dry-run mode, or no-std-ansible-output.
3. Leverage existing SSH trust relationships without additional setup.
4. Provide a clean, intuitive CLI interface with --help for ease of use. It should provide consistent UX & CI-friendly interface.
5. Establish a foundation that can later be extended with advanced features such as logging, grouping, interactive shell mode, safe-command checks, and parallel execution tuning.

The MVP should enable day-to-day operations to efficiently target thousands of machines with a single, consistent interface.

Goals

Primary Goals (MVP):

Build a functional CLI tool (mgr-ansible-ssh) capable of executing shell commands on multiple remote hosts using Ansible Runner. Test the tool across a large distributed environment (1000+ machines) to validate its performance and reliability.

Looking forward to significantly reducing the zypper deployment time across all 351 RMT VM servers in our MLM cluster by eliminating the dependency on the taskomatic service, bringing execution down to a fraction of the current duration. The tool should also support multiple runtime flags, such as:

mgr-ansible-ssh: Remote command execution wrapper using Ansible Runner

Usage: mgr-ansible-ssh [--help] [--version] [--inventory INVENTORY]
                   [--run RUN] [--playbook PLAYBOOK] [--limit LIMIT]
                   [--forks FORKS] [--dry-run] [--no-ansible-output]

Required Arguments
--inventory, -i      Path to Ansible inventory file to use

Any One of the Arguments Is Required
--run, -r            Execute the specified shell command on target hosts
--playbook, -p       Execute the specified Ansible playbook on target hosts

Optional Arguments
--help, -h           Show the help message and exit
--version, -v        Show the version and exit
--limit, -l          Limit execution to specific hosts or groups
--forks, -f          Number of parallel Ansible forks
--dry-run            Run in Ansible check mode (requires -p or --playbook)
--no-ansible-output  Suppress Ansible stdout output

Secondary/Stretched Goals (if time permits):

1. Add pretty output formatting (success/failure summary per host).
2. Implement basic logging of executed commands and results.
3. Introduce safety checks for risky commands (shutdown, rm -rf, etc.).
4. Package the tool so it can be installed with pip or stored internally.

Resources

Collaboration is welcome from anyone interested in CLI tooling, automation, or distributed systems. Skills that would be particularly valuable include:

1. Python especially around CLI dev (argparse, click, rich)

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Move Uyuni Test Framework from Selenium to Playwright + AI by oscar-barrios

Description

This project aims to migrate the existing Uyuni Test Framework from Selenium to Playwright. The move will improve the stability, speed, and maintainability of our end-to-end tests by leveraging Playwright's modern features. We'll be rewriting the current Selenium code in Ruby to Playwright code in TypeScript, which includes updating the test framework runner, step definitions, and configurations. This is also necessary because we're moving from Cucumber Ruby to CucumberJS.

If you're still curious about the AI in the title, it was just a way to grab your attention. Thanks for your understanding.

Nah, let's be honest AI helped a lot to vibe code a good part of the Ruby methods of the Test framework, moving them to Typescript, along with the migration from Capybara to Playwright. I've been using "Cline" as plugin for WebStorm IDE, using Gemini API behind it.

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Goals

• Migrate Core tests including Onboarding of clients
• Improve test reliabillity: Measure and confirm a significant reduction of flakiness.
• Implement a robust framework: Establish a well-structured and reusable Playwright test framework using the CucumberJS

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Resources

• Existing Uyuni Test Framework (Cucumber Ruby + Capybara + Selenium)
• My Template for CucumberJS + Playwright in TypeScript
• Started Hackweek Project

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The Agentic Rancher Experiment: Do Androids Dream of Electric Cattle? by moio

Rancher is a beast of a codebase. Let's investigate if the new 2025 generation of GitHub Autonomous Coding Agents and Copilot Workspaces can actually tame it.

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The Plan

Create a sandbox GitHub Organization, clone in key Rancher repositories, and let the AI loose to see if it can handle real-world enterprise OSS maintenance - or if it just hallucinates new breeds of Kubernetes resources!

Specifically, throw "Agentic Coders" some typical tasks in a complex, long-lived open-source project, such as:

❥ The Grunt Work: generate missing GoDocs, unit tests, and refactorings. Rebase PRs.

❥ The Complex Stuff: fix actual (historical) bugs and feature requests to see if they can traverse the complexity without (too much) human hand-holding.

❥ Hunting Down Gaps: find areas lacking in docs, areas of improvement in code, dependency bumps, and so on.

If time allows, also experiment with Model Context Protocol (MCP) to give agents context on our specific build pipelines and CI/CD logs.

Why?

We know AI can write "Hello World." and also moderately complex programs from a green field. But can it rebase a 3-month-old PR with conflicts in rancher/rancher? I want to find the breaking point of current AI agents to determine if and how they can help us to reduce our technical debt, work faster and better. At the same time, find out about pitfalls and shortcomings.

The CONCLUSION!!!

A State of the Union document was compiled to summarize lessons learned this week. For more gory details, just read on the diary below!

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SUSE Edge Image Builder MCP by eminguez

Description

Based on my other hackweek project, SUSE Edge Image Builder's Json Schema I would like to build also a MCP to be able to generate EIB config files the AI way.

Realistically I don't think I'll be able to have something consumable at the end of this hackweek but at least I would like to start exploring MCPs, the difference between an API and MCP, etc.

Goals

• Familiarize myself with MCPs
• Unrealistic: Have an MCP that can generate an EIB config file

Resources

• https://hackweek.opensuse.org/25/projects/suse-edge-image-builder-json-schema
• Anything else :)

Result

https://github.com/e-minguez/eib-mcp

I've extensively used antigravity and its agent mode to code this. This heavily uses https://hackweek.opensuse.org/25/projects/suse-edge-image-builder-json-schema for the MCP to be built.

I've ended up learning a lot of things about "prompting", json schemas in general, some golang, MCPs and AI in general :)

Example:

Generate an Edge Image Builder configuration for an ISO image based on slmicro-6.2.iso, targeting x86_64 architecture. The output name should be 'my-edge-image' and it should install to /dev/sda. It should deploy a 3 nodes kubernetes cluster with nodes names "node1", "node2" and "node3" as: * hostname: node1, IP: 1.1.1.1, role: initializer * hostname: node2, IP: 1.1.1.2, role: agent * hostname: node3, IP: 1.1.1.3, role: agent The kubernetes version should be k3s 1.33.4-k3s1 and it should deploy a cert-manager helm chart (the latest one available according to https://cert-manager.io/docs/installation/helm/). It should create a user called "suse" with password "suse" and set ntp to "foo.ntp.org". The VIP address for the API should be 1.2.3.4

Generates:

``` apiVersion: "1.0" image: arch: x86_64 baseImage: slmicro-6.2.iso imageType: iso outputImageName: my-edge-image kubernetes: helm: charts: - name: cert-manager repositoryName: jetstack

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Uyuni Health-check Grafana AI Troubleshooter by ygutierrez

Description

This project explores the feasibility of using the open-source Grafana LLM plugin to enhance the Uyuni Health-check tool with LLM capabilities. The idea is to integrate a chat-based "AI Troubleshooter" directly into existing dashboards, allowing users to ask natural-language questions about errors, anomalies, or performance issues.

Goals

• Investigate if and how the grafana-llm-app plug-in can be used within the Uyuni Health-check tool.
• Investigate if this plug-in can be used to query LLMs for troubleshooting scenarios.
• Evaluate support for local LLMs and external APIs through the plugin.
• Evaluate if and how the Uyuni MCP server could be integrated as another source of information.

Resources

Grafana LMM plug-in

Uyuni Health-check

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AI-Powered Unit Test Automation for Agama by joseivanlopez

The Agama project is a multi-language Linux installer that leverages the distinct strengths of several key technologies:

• Rust: Used for the back-end services and the core HTTP API, providing performance and safety.
• TypeScript (React/PatternFly): Powers the modern web user interface (UI), ensuring a consistent and responsive user experience.
• Ruby: Integrates existing, robust YaST libraries (e.g., yast-storage-ng) to reuse established functionality.

The Problem: Testing Overhead

Developing and maintaining code across these three languages requires a significant, tedious effort in writing, reviewing, and updating unit tests for each component. This high cost of testing is a drain on developer resources and can slow down the project's evolution.

The Solution: AI-Driven Automation

This project aims to eliminate the manual overhead of unit testing by exploring and integrating AI-driven code generation tools. We will investigate how AI can:

1. Automatically generate new unit tests as code is developed.
2. Intelligently correct and update existing unit tests when the application code changes.

By automating this crucial but monotonous task, we can free developers to focus on feature implementation and significantly improve the speed and maintainability of the Agama codebase.

Goals

• Proof of Concept: Successfully integrate and demonstrate an authorized AI tool (e.g., gemini-cli) to automatically generate unit tests.
• Workflow Integration: Define and document a new unit test automation workflow that seamlessly integrates the selected AI tool into the existing Agama development pipeline.
• Knowledge Sharing: Establish a set of best practices for using AI in code generation, sharing the learned expertise with the broader team.

Contribution & Resources

We are seeking contributors interested in AI-powered development and improving developer efficiency. Whether you have previous experience with code generation tools or are eager to learn, your participation is highly valuable.

If you want to dive deep into AI for software quality, please reach out and join the effort!

• Authorized AI Tools: Tools supported by SUSE (e.g., gemini-cli)
• Focus Areas: Rust, TypeScript, and Ruby components within the Agama project.

Interesting Links

• goose

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Exploring Modern AI Trends and Kubernetes-Based AI Infrastructure by jluo

Description

Build a solid understanding of the current landscape of Artificial Intelligence and how modern cloud-native technologies—especially Kubernetes—support AI workloads.

Goals

Use Gemini Learning Mode to guide the exploration, surface relevant concepts, and structure the learning journey:

• Gain insight into the latest AI trends, tools, and architectural concepts.
• Understand how Kubernetes and related cloud-native technologies are used in the AI ecosystem (model training, deployment, orchestration, MLOps).

Resources

• Red Hat AI Topic Articles

  • https://www.redhat.com/en/topics/ai

• Kubeflow Documentation

  • https://www.kubeflow.org/docs/

• Q4 2025 CNCF Technology Landscape Radar report:

  • https://www.cncf.io/announcements/2025/11/11/cncf-and-slashdata-report-finds-leading-ai-tools-gaining-adoption-in-cloud-native-ecosystems/
  • https://www.cncf.io/wp-content/uploads/2025/11/cncfreporttechradar_111025a.pdf

• Agent-to-Agent (A2A) Protocol

  • https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/

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Multimachine on-prem test with opentofu, ansible and Robot Framework by apappas

Description

A long time ago I explored using the Robot Framework for testing. A big deficiency over our openQA setup is that bringing up and configuring the connection to a test machine is out of scope.

Nowadays we have a way¹ to deploy SUTs outside openqa, but we only use if for cloud tests in conjuction with openqa. Using knowledge gained from that project I am going to try to create a test scenario that replicates an openqa test but this time including the deployment and setup of the SUT.

Goals

Create a simple multimachine test scenario with the support server and SUT all created by the robot framework.

Resources

1. https://github.com/SUSE/qe-sap-deployment
2. terraform-libvirt-provider

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openQA tests needles elaboration using AI image recognition by mdati

Description

In the openQA test framework, to identify the status of a target SUT image, a screenshots of GUI or CLI-terminal images, the needles framework scans the many pictures in its repository, having associated a given set of tags (strings), selecting specific smaller parts of each available image. For the needles management actually we need to keep stored many screenshots, variants of GUI and CLI-terminal images, eachone accompanied by a dedicated set of data references (json).

A smarter framework, using image recognition based on AI or other image elaborations tools, nowadays widely available, could improve the matching process and hopefully reduce time and errors, during the images verification and detection process.

Goals

Main scope of this idea is to match a "graphical" image of the console or GUI status of a running openQA test, an image of a shell console or application-GUI screenshot, using less time and resources and with less errors in data preparation and use, than the actual openQA needles framework; that is:

• having a given SUT (system under test) GUI or CLI-terminal screenshot, with a local distribution of pixels or text commands related to a running test status,
• we want to identify a desired target, e.g. a screen image status or data/commands context,
  • based on AI/ML-pretrained archives containing object or other proper elaboration tools,
  • possibly able to identify also object not present in the archive, i.e. by means of AI/ML mechanisms.
• the matching result should be then adapted to continue working in the openQA test, likewise and in place of the same result that would have been produced by the original openQA needles framework.
• We expect an improvement of the matching-time(less time), reliability of the expected result(less error) and simplification of archive maintenance in adding/removing objects(smaller DB and less actions).

Hackweek POC:

Main steps

• Phase 1 - Plan
  • study the available tools
  • prepare a plan for the process to build
• Phase 2 - Implement
  • write and build a draft application
• Phase 3 - Data
  • prepare the data archive from a subset of needles
  • initialize/pre-train the base archive
  • select a screenshot from the subset, removing/changing some part
• Phase 4 - Test
  • run the POC application
  • expect the image type is identified in a good %.

Resources

First step of this project is quite identification of useful resources for the scope; some possibilities are:

• SUSE AI and other ML tools (i.e. Tensorflow)
• Tools able to manage images
• RPA test tools (like i.e. Robot framework)
  
• other.

Project references

• Repository: openqa-needles-AI-driven

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Testing and adding GNU/Linux distributions on Uyuni by juliogonzalezgil

Join the Gitter channel! https://gitter.im/uyuni-project/hackweek

Uyuni is a configuration and infrastructure management tool that saves you time and headaches when you have to manage and update tens, hundreds or even thousands of machines. It also manages configuration, can run audits, build image containers, monitor and much more!

Currently there are a few distributions that are completely untested on Uyuni or SUSE Manager (AFAIK) or just not tested since a long time, and could be interesting knowing how hard would be working with them and, if possible, fix whatever is broken.

For newcomers, the easiest distributions are those based on DEB or RPM packages. Distributions with other package formats are doable, but will require adapting the Python and Java code to be able to sync and analyze such packages (and if salt does not support those packages, it will need changes as well). So if you want a distribution with other packages, make sure you are comfortable handling such changes.

No developer experience? No worries! We had non-developers contributors in the past, and we are ready to help as long as you are willing to learn. If you don't want to code at all, you can also help us preparing the documentation after someone else has the initial code ready, or you could also help with testing :-)

The idea is testing Salt (including bootstrapping with bootstrap script) and Salt-ssh clients

To consider that a distribution has basic support, we should cover at least (points 3-6 are to be tested for both salt minions and salt ssh minions):

1. Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
2. Onboarding (salt minion from UI, salt minion from bootstrap scritp, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)
3. Package management (install, remove, update...)
4. Patching
5. Applying any basic salt state (including a formula)
6. Salt remote commands
7. Bonus point: Java part for product identification, and monitoring enablement
8. Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
9. Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
10. Bonus point: testsuite enablement (https://github.com/uyuni-project/uyuni/tree/master/testsuite)

If something is breaking: we can try to fix it, but the main idea is research how supported it is right now. Beyond that it's up to each project member how much to hack :-)

• If you don't have knowledge about some of the steps: ask the team
• If you still don't know what to do: switch to another distribution and keep testing.

This card is for EVERYONE, not just developers. Seriously! We had people from other teams helping that were not developers, and added support for Debian and new SUSE Linux Enterprise and openSUSE Leap versions :-)

In progress/done for Hack Week 25

Guide

We started writin a Guide: Adding a new client GNU Linux distribution to Uyuni at https://github.com/uyuni-project/uyuni/wiki/Guide:-Adding-a-new-client-GNU-Linux-distribution-to-Uyuni, to make things easier for everyone, specially those not too familiar wht Uyuni or not technical.

openSUSE Leap 16.0

The distribution will all love!

https://en.opensuse.org/openSUSE:Roadmap#DRAFTScheduleforLeap16.0

Curent Status We started last year, it's complete now for Hack Week 25! :-D

• [W] Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file) NOTE: Done, client tools for SLMicro6 are using as those for SLE16.0/openSUSE Leap 16.0 are not available yet
• [W] Onboarding (salt minion from UI, salt minion from bootstrap scritp, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)
• [W] Package management (install, remove, update...). Works, even reboot requirement detection

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