Set Up an Ephemeral Uyuni Instance by mbussolotto

Description

To test, check, and verify the latest changes in the master branch, we want to easily set up an ephemeral environment.

Goals

• Create an ephemeral environment manually

• Create an ephemeral environment automatically

  Resources

• https://github.com/uyuni-project/uyuni

• https://www.uyuni-project.org/uyuni-docs/en/uyuni/index.html

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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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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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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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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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Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios

Description

Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.

This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.

The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.

Goals

By the end of Hack Week, we aim to have a single, working Python script that:

1. Connects to Prometheus and executes a query to fetch detailed test failure history.
2. Processes the raw data into a format suitable for the Gemini API.
3. Successfully calls the Gemini API with the data and a clear prompt.
4. Parses the AI's response to extract a simple list of flaky tests.
5. Saves the list to a JSON file that can be displayed in Grafana.
6. New panel in our Dashboard listing the Flaky tests

Resources

• Jenkins Prometheus Exporter: https://github.com/uyuni-project/jenkins-exporter/
• Data Source: Our internal Prometheus server.
• Key Metric: jenkins_build_test_case_failure_age{jobname, buildid, suite, case, status, failedsince}.
• Existing Query for Reference: count by (suite) (max_over_time(jenkins_build_test_case_failure_age{status=~"FAILED|REGRESSION", jobname="$jobname"}[$__range])).
• AI Model: The Google Gemini API.
• Example about how to interact with Gemini API: https://github.com/srbarrios/FailTale/
• Visualization: Our internal Grafana Dashboard.
• Internal IaC: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring

Outcome

• Jenkins Flaky Test Detector: https://github.com/srbarrios/jenkins-flaky-tests-detector and its container
• IaC on MLM Team: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring/jenkinsflakytestsdetector?reftype=heads, https://gitlab.suse.de/galaxy/infrastructure/-/blob/master/srv/salt/monitoring/grafana/dashboards/flaky-tests.json?ref_type=heads, and others.
• Grafana Dashboard: https://grafana.mgr.suse.de/d/flaky-tests/flaky-tests-detection @ @ text

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Try out Neovim Plugins supporting AI Providers by enavarro_suse

Description

Experiment with several Neovim plugins that integrate AI model providers such as Gemini and Ollama.

Goals

Evaluate how these plugins enhance the development workflow, how they differ in capabilities, and how smoothly they integrate into Neovim for day-to-day coding tasks.

Resources

• Neovim 0.11.5
• AI-enabled Neovim plugins:
  • avante.nvim: https://github.com/yetone/avante.nvim
  • Gp.nvim: https://github.com/Robitx/gp.nvim
  • parrot.nvim: https://github.com/frankroeder/parrot.nvim
  • gemini.nvim: https://dotfyle.com/plugins/kiddos/gemini.nvim
  • ...
• Accounts or API keys for AI model providers.
• Local model serving setup (e.g., Ollama)
• Test projects or codebases for practical evaluation:
  • OBS: https://build.opensuse.org/
  • OBS blog and landing page: https://openbuildservice.org/
  • ...

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Multi-agent AI assistant for Linux troubleshooting by doreilly

Description

Explore multi-agent architecture as a way to avoid MCP context rot.

Having one agent with many tools bloats the context with low-level details about tool descriptions, parameter schemas etc which hurts LLM performance. Instead have many specialised agents, each with just the tools it needs for its role. A top level supervisor agent takes the user prompt and delegates to appropriate sub-agents.

Goals

Create an AI assistant with some sub-agents that are specialists at troubleshooting Linux subsystems, e.g. systemd, selinux, firewalld etc. The agents can get information from the system by implementing their own tools with simple function calls, or use tools from MCP servers, e.g. a systemd-agent can use tools from systemd-mcp.

Example prompts/responses:

user$ the system seems slow
assistant$ process foo with pid 12345 is using 1000% cpu ...

user$ I can't connect to the apache webserver
assistant$ the firewall is blocking http ... you can open the port with firewall-cmd --add-port ...

Resources

Language Python. The Python ADK is more mature than Golang.

https://google.github.io/adk-docs/

https://github.com/djoreilly/linux-helper

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issuefs: FUSE filesystem representing issues (e.g. JIRA) for the use with AI agents code-assistants by llansky3

Description

Creating a FUSE filesystem (issuefs) that mounts issues from various ticketing systems (Github, Jira, Bugzilla, Redmine) as files to your local file system.

And why this is good idea?

• User can use favorite command line tools to view and search the tickets from various sources
• User can use AI agents capabilities from your favorite IDE or cli to ask question about the issues, project or functionality while providing relevant tickets as context without extra work.
• User can use it during development of the new features when you let the AI agent to jump start the solution. The issuefs will give the AI agent the context (AI agents just read few more files) about the bug or requested features. No need for copying and pasting issues to user prompt or by using extra MCP tools to access the issues. These you can still do but this approach is on purpose different.

Goals

1. Add Github issue support
2. Proof the concept/approach by apply the approach on itself using Github issues for tracking and development of new features
3. Add support for Bugzilla and Redmine using this approach in the process of doing it. Record a video of it.
4. Clean-up and test the implementation and create some documentation
5. Create a blog post about this approach

Resources

There is a prototype implementation here. This currently sort of works with JIRA only.

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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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MCP Trace Suite by r1chard-lyu

Description

This project plans to create an MCP Trace Suite, a system that consolidates commonly used Linux debugging tools such as bpftrace, perf, and ftrace.

The suite is implemented as an MCP Server. This architecture allows an AI agent to leverage the server to diagnose Linux issues and perform targeted system debugging by remotely executing and retrieving tracing data from these powerful tools.

• Repo: https://github.com/r1chard-lyu/systracesuite
• Demo: Slides

Goals

1. Build an MCP Server that can integrate various Linux debugging and tracing tools, including bpftrace, perf, ftrace, strace, and others, with support for future expansion of additional tools.

2. Perform testing by intentionally creating bugs or issues that impact system performance, allowing an AI agent to analyze the root cause and identify the underlying problem.

Resources

• Gemini CLI: https://geminicli.com/
• eBPF: https://ebpf.io/
• bpftrace: https://github.com/bpftrace/bpftrace/
• perf: https://perfwiki.github.io/main/
• ftrace: https://github.com/r1chard-lyu/tracium/

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"what is it" file and directory analysis via MCP and local LLM, for console and KDE by rsimai

Description

Users sometimes wonder what files or directories they find on their local PC are good for. If they can't determine from the filename or metadata, there should an easy way to quickly analyze the content and at least guess the meaning. An LLM could help with that, through the use of a filesystem MCP and to-text-converters for typical file types. Ideally this is integrated into the desktop environment but works as well from a console. All data is processed locally or "on premise", no artifacts remain or leave the system.

Goals

• The user can run a command from the console, to check on a file or directory
• The filemanager contains the "analyze" feature within the context menu
• The local LLM could serve for other use cases where privacy matters

TBD

• Find or write capable one-shot and interactive MCP client
• Find or write simple+secure file access MCP server
• Create local LLM service with appropriate footprint, containerized
• Shell command with options
• KDE integration (Dolphin)
• Package
• Document

Resources

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Multi-agent AI assistant for Linux troubleshooting by doreilly

Description

Explore multi-agent architecture as a way to avoid MCP context rot.

Having one agent with many tools bloats the context with low-level details about tool descriptions, parameter schemas etc which hurts LLM performance. Instead have many specialised agents, each with just the tools it needs for its role. A top level supervisor agent takes the user prompt and delegates to appropriate sub-agents.

Goals

Create an AI assistant with some sub-agents that are specialists at troubleshooting Linux subsystems, e.g. systemd, selinux, firewalld etc. The agents can get information from the system by implementing their own tools with simple function calls, or use tools from MCP servers, e.g. a systemd-agent can use tools from systemd-mcp.

Example prompts/responses:

user$ the system seems slow
assistant$ process foo with pid 12345 is using 1000% cpu ...

user$ I can't connect to the apache webserver
assistant$ the firewall is blocking http ... you can open the port with firewall-cmd --add-port ...

Resources

Language Python. The Python ADK is more mature than Golang.

https://google.github.io/adk-docs/

https://github.com/djoreilly/linux-helper

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Try to use AI and MCP for ACPI table analysis by joeyli

Description

Try to use AI and MCP if they can help with ACPI table analysis.

Goals

It's not easy for looking at ACPI tables even it be disassemble to ASL. I want to learn AI and MCP in Hackweek 25 to see if they can help ACPI table analysis.

Resources

Any resources about AI and MCP.

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Song Search with CLAP by gcolangiuli

Description

Contrastive Language-Audio Pretraining (CLAP) is an open-source library that enables the training of a neural network on both Audio and Text descriptions, making it possible to search for Audio using a Text input. Several pre-trained models for song search are already available on huggingface

Goals

Evaluate how CLAP can be used for song searching and determine which types of queries yield the best results by developing a Minimum Viable Product (MVP) in Python. Based on the results of this MVP, future steps could include:

• Music Tagging;
• Free text search;
• Integration with an LLM (for example, with MCP or the OpenAI API) for music suggestions based on your own library.

The code for this project will be entirely written using AI to better explore and demonstrate AI capabilities.

Result

In this MVP we implemented:

• Async Song Analysis with Clap model
• Free Text Search of the songs
• Similar song search based on vector representation
• Containerised version with web interface

We also documented what went well and what can be improved in the use of AI.

You can have a look at the result here:

• Result Presentation
• hw25-song-search Github repo

Future implementation can be related to performance improvement and stability of the analysis.

References

• CLAP: The main model being researched;
• huggingface: Pre-trained models for CLAP;
• Free Music Archive: Creative Commons songs that can be used for testing;

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Enable more features in mcp-server-uyuni by j_renner

Description

I would like to contribute to mcp-server-uyuni, the MCP server for Uyuni / Multi-Linux Manager) exposing additional features as tools. There is lots of relevant features to be found throughout the API, for example:

• System operations and infos
• System groups
• Maintenance windows
• Ansible
• Reporting
• ...

At the end of the week I managed to enable basic system group operations:

• List all system groups visible to the user
• Create new system groups
• List systems assigned to a group
• Add and remove systems from groups

Goals

• Set up test environment locally with the MCP server and client + a recent MLM server [DONE]
• Identify features and use cases offering a benefit with limited effort required for enablement [DONE]
• Create a PR to the repo [DONE]

Resources

• MCP Server Uyuni
• MLM API Docs

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