Project Description

The goal is to have a language model, that is able to answer technical questions on Uyuni. Uyuni documentation is too large for in-context processing, so finetuning is the way to go.

Goal for this Hackweek

Finetune a model based on llama-2-7b.

Resources

github repo

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ai uyuni

This project is part of:

Hack Week 23

Activity

over 2 years ago: nadvornik added keyword "ai" to this project.

over 2 years ago: nadvornik added keyword "uyuni" to this project.

over 2 years ago: nadvornik originated this project.

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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

Update M2Crypto by mcepl

There are couple of projects I work on, which need my attention and putting them to shape:

M2Crypto

Goal for this Hackweek

Put M2Crypto into better shape (most issues closed, all pull requests processed)
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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:

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]
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Create a PR to the repo [DONE]

Resources

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.

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!

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:

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;

uyuni

Enable more features in mcp-server-uyuni by j_renner

Description

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

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

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.

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

Resources

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:

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

Set Uyuni to manage edge clusters at scale by RDiasMateus

Description

Prepare a Poc on how to use MLM to manage edge clusters. Those cluster are normally equal across each location, and we have a large number of them.

The goal is to produce a set of sets/best practices/scripts to help users manage this kind of setup.

Goals

step 1: Manual set-up

Goal: Have a running application in k3s and be able to update it using System Update Controler (SUC)

Deploy Micro 6.2 machine
Deploy k3s - single node
- https://docs.k3s.io/quick-start
Build/find a simple web application (static page)
- Build/find a helmchart to deploy the application
Deploy the application on the k3s cluster
Install App updates through helm update
Install OS updates using MLM

step 2: Automate day 1

Goal: Trigger the application deployment and update from MLM

Salt states For application (with static data)
- Deploy the application helmchart, if not present
- install app updates through helmchart parameters
Link it to GIT
- Define how to link the state to the machines (based in some pillar data? Using configuration channels by importing the state? Naming convention?)
- Use git update to trigger helmchart app update
Recurrent state applying configuration channel?

step 3: Multi-node cluster

Goal: Use SUC to update a multi-node cluster.

Create a multi-node cluster
Deploy application
- call the helm update/install only on control plane?
Install App updates through helm update
Prepare a SUC for OS update (k3s also? How?)
- https://github.com/rancher/system-upgrade-controller
- https://documentation.suse.com/cloudnative/k3s/latest/en/upgrades/automated.html
- Update/deploy the SUC?
- Update/deploy the SUC CRD with the update procedure