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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Ansible to Salt integration by vizhestkov

Description

We already have initial integration of Ansible in Salt with the possibility to run playbooks from the salt-master on the salt-minion used as an Ansible Control node.

In this project I want to check if it possible to make Ansible working on the transport of Salt. Basically run playbooks with Ansible through existing established Salt (ZeroMQ) transport and not using ssh at all.

It could be a good solution for the end users to reuse Ansible playbooks or run Ansible modules they got used to with no effort of complex configuration with existing Salt (or Uyuni/SUSE Multi Linux Manager) infrastructure.

Goals

• [v] Prepare the testing environment with Salt and Ansible installed
• [v] Discover Ansible codebase to figure out possible ways of integration
• [v] Create Salt/Uyuni inventory module
• [v] Make basic modules to work with no using separate ssh connection, but reusing existing Salt connection
• [v] Test some most basic playbooks

Resources

GitHub page

Video of the demo

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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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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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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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Exploring Rust's potential: from basics to security by sferracci

Description

This project aims to conduct a focused investigation and practical application of the Rust programming language, with a specific emphasis on its security model. A key component will be identifying and understanding the most common vulnerabilities that can be found in Rust code.

Goals

Achieve a beginner/intermediate level of proficiency in writing Rust code. This will be measured by trying to solve LeetCode problems focusing on common data structures and algorithms. Study Rust vulnerabilities and learning best practices to avoid them.

Resources

Rust book: https://doc.rust-lang.org/book/

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Learn a bit of embedded programming with Rust in a micro:bit v2 by aplanas

Description

micro:bit is a small single board computer with a ARM Cortex-M4 with the FPU extension, with a very constrain amount of memory and a bunch of sensors and leds.

The board is very well documented, with schematics and code for all the features available, so is an excellent platform for learning embedded programming.

Rust is a system programming language that can generate ARM code, and has crates (libraries) to access the micro:bit hardware. There is plenty documentation about how to make small programs that will run in the micro:bit.

Goals

Start learning about embedded programming in Rust, and maybe make some code to the small KS4036F Robot car from keyestudio.

Resources

• micro:bit
• KS4036F
• microbit technical documentation
• schematic
• impl Rust for micro:bit
• Rust Embedded MB2 Discovery Book
• nRF-HAL
• nRF Microbit-v2 BSP (blocking)
• knurling-rs
• C++ microbit codal
• microbit-bsp for Embassy
• Embassy

Diary

Day 1

• Start reading https://mb2.implrust.com/abstraction-layers.html
• Prepare the dev environment (cross compiler, probe-rs)
• Flash first code in the board (blinky led)
• Checking differences between BSP and HAL
• Compile and install a more complex example, with stack protection
• Reading about the simplicity of xtask, as alias for workspace execution
• Reading the CPP code of the official micro:bit libraries. They have a font!

Day 2

• There are multiple BSP for the microbit. One is using async code for non-blocking operations
• Download and study a bit the API for microbit-v2, the nRF official crate
• Take a look of the KS4036F programming, seems that the communication is multiplexed via I2C
• The motor speed can be selected via PWM (pulse with modulation): power it longer (high frequency), and it will increase the speed
• Scrolling some text
• Debug by printing! defmt is a crate that can be used with probe-rs to emit logs
• Start reading input from the board: buttons
• The logo can be touched and detected as a floating point value

Day 3

• A bit confused how to read the float value from a pin

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Looking at Rust if it could be an interesting programming language by jsmeix

Get some basic understanding of Rust security related features from a general point of view.

This Hack Week project is not to learn Rust to become a Rust programmer. This might happen later but it is not the goal of this Hack Week project.

The goal of this Hack Week project is to evaluate if Rust could be an interesting programming language.

An interesting programming language must make it easier to write code that is correct and stays correct when over time others maintain and enhance it than the opposite.

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RMT.rs: High-Performance Registration Path for RMT using Rust by gbasso

Description

The SUSE Repository Mirroring Tool (RMT) is a critical component for managing software updates and subscriptions, especially for our Public Cloud Team (PCT). In a cloud environment, hundreds or even thousands of new SUSE instances (VPS/EC2) can be provisioned simultaneously. Each new instance attempts to register against an RMT server, creating a "thundering herd" scenario.

We have observed that the current RMT server, written in Ruby, faces performance issues under this high-concurrency registration load. This can lead to request overhead, slow registration times, and outright registration failures, delaying the readiness of new cloud instances.

This Hackweek project aims to explore a solution by re-implementing the performance-critical registration path in Rust. The goal is to leverage Rust's high performance, memory safety, and first-class concurrency handling to create an alternative registration endpoint that is fast, reliable, and can gracefully manage massive, simultaneous request spikes.

The new Rust module will be integrated into the existing RMT Ruby application, allowing us to directly compare the performance of both implementations.

Goals

The primary objective is to build and benchmark a high-performance Rust-based alternative for the RMT server registration endpoint.

Key goals for the week:

1. Analyze & Identify: Dive into the SUSE/rmt Ruby codebase to identify and map out the exact critical path for server registration (e.g., controllers, services, database interactions).
2. Develop in Rust: Implement a functionally equivalent version of this registration logic in Rust.
3. Integrate: Explore and implement a method for Ruby/Rust integration to "hot-wire" the new Rust module into the RMT application. This may involve using FFI, or libraries like rb-sys or magnus.
4. Benchmark: Create a benchmarking script (e.g., using k6, ab, or a custom tool) that simulates the high-concurrency registration load from thousands of clients.
5. Compare & Present: Conduct a comparative performance analysis (requests per second, latency, success/error rates, CPU/memory usage) between the original Ruby path and the new Rust path. The deliverable will be this data and a summary of the findings.

Resources

• RMT Source Code (Ruby):
  • https://github.com/SUSE/rmt
• RMT Documentation:
  • https://documentation.suse.com/sles/15-SP7/html/SLES-all/book-rmt.html
• Tooling & Stacks:
  • RMT/Ruby development environment (for running the base RMT)
  • Rust development environment (rustup, cargo)
• Potential Integration Libraries:
  • rb-sys: https://github.com/oxidize-rb/rb-sys
  • Magnus: https://github.com/matsadler/magnus
• Benchmarking Tools:
  • k6 (https://k6.io/)
  • ab (ApacheBench)

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Arcticwolf - A rust based user space NFS server by vcheng

Description

Rust has similar performance to C. Also, have a better async IO module and high integration with io_uring. This project aims to develop a user-space NFS server based on Rust.

Goals

• Get an understanding of how cargo works
• Get an understanding of how XDR was generated with xdrgen
• Create the RUST-based NFS server that supports basic operations like mount/readdir/read/write

Result (2025 Hackweek)

• In progress PR: https://github.com/Vicente-Cheng/arcticwolf/pull/1

Resources

https://github.com/Vicente-Cheng/arcticwolf

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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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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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