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

---

Create a page with all devel:languages:perl packages and their versions by tinita

Description

Perl projects now live in git: https://src.opensuse.org/perl

It would be useful to have an easy way to check which version of which perl module is in devel:languages:perl. Also we have meta overrides and patches for various modules, and it would be good to have them at a central place, so it is easier to lookup, and we can share with other vendors.

I did some initial data dump here a while ago: https://github.com/perlpunk/cpan-meta

But I never had the time to automate this.

I can also use the data to check if there are necessary updates (currently it uses data from download.opensuse.org, so there is some delay and it depends on building).

Goals

• Have a script that updates a central repository (e.g. https://src.opensuse.org/perl/_metadata) with metadata by looking at https://src.opensuse.org/perl/_ObsPrj (check if there are any changes from the last run)
• Create a HTML page with the list of packages (use Javascript and some table library to make it easily searchable)

Resources

Results

• https://perlpunk.github.io/opensuse-perl-meta/
• https://github.com/perlpunk/opensuse-perl-meta

Day 1

• First part of the code which retrieves data from https://src.opensuse.org/perl/_ObsPrj with submodules and creates a YAML and a JSON file.
• Repo: https://github.com/perlpunk/opensuse-perl-meta
• Also a first version of the HTML is live: https://perlpunk.github.io/opensuse-perl-meta/

Day 2

• HTML Page has now links to src.opensuse.org and the date of the last update, plus a short info at the top
• Code is now 100% covered by tests: https://app.codecov.io/gh/perlpunk/opensuse-perl-meta
• I used the modern perl class feature, which makes perl classes even nicer and shorter. See example
• Tests
  • I tried out the mocking feature of the modern Test2::V0 library which provides call tracking. See example
  • I tried out comparing data structures with the new Test2::V0 library. It let's you compare parts of the structure with the like function, which only compares the date that is mentioned in the expected data. example

Day 3

• Added various things to the table
  • Dependencies column
  • Show popup with info for cpanspec, patches and dependencies
  • Added last date / commit to the data export.

Plan: With the added date / commit we can now daily check _ObsPrj for changes and only fetch the data for changed packages.

Day 4

---

Switch software-o-o to store repomd in a database by hennevogel

Description

The openSUSE Software portal is a web app to explore binary packages of openSUSE distributions. Kind of like an package manager / app store.

https://software.opensuse.org/

This app has been around forever (August 2007) and it's architecture is a bit brittle. It acts as a frontend to the OBS distributions and published binary search APIs, calculates and caches a lot of stuff in memory and needs code changes nearly every openSUSE release to keep up.

As you can imagine, it's a heavy user of the OBS API, especially when caches are cold.

Goals

I want to change the app to cache repomod data in a (postgres) database structure

• Distributions have many Repositories
• Repositories have many Packages
• Packages have many Patches

The UI workflows will be as following

• As an admin I setup Distribution and it's repositories
• As an admin I sync all repositories repomd files into to the database
• As a user I browse a Distribution by category
• As a user I search for Package of a Distribution in it's Repositories
• As a user I extend the search to Package build on OBS for this Distribution

This has a couple of pro's:

• Less traffic on the OBS API as the usual Packages are inside the database
• Easier base to add features to this page. Like comments, ratings, openSUSE specific screenshots etc.
• Separating the Distribution package search from searching through OBS will hopefully make more clear for newbies that enabling extra repositories is kind of dangerous.

And one con:

• You can't search for packages build for foreign distributions with this app anymore (although we could consume their repomd etc. but I doubt we have the audience on an opensuse.org domain...)

TODO

• Introduce a PG database
• Add clockworkd as scheduler and delayed_job as ActiveJob backend
• Introduce ActiveStorage
• Build initial data model
• Introduce repomd to database sync
  • Adapt repomd sync to Leap 16.0 repomod layout changes (single arch, no update repo)
  • Make repomd sync idempotent
• Introduce database search
• Setup foreman to run rails s and rake jobs:workoff
• Adapt UI
  • Build Category Browsing
  • Build Admin Distribution CRUD interface

---

Improvements to osc (especially with regards to the Git workflow) by mcepl

Description

There is plenty of hacking on osc, where we could spent some fun time. I would like to see a solution for https://github.com/openSUSE/osc/issues/2006 (which is sufficiently non-serious, that it could be part of HackWeek project).

---

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.

---

GenAI-Powered Systemic Bug Evaluation and Management Assistant by rtsvetkov

Motivation

What is the decision critical question which one can ask on a bug? How this question affects the decision on a bug and why?

Let's make GenAI look on the bug from the systemic point and evaluate what we don't know. Which piece of information is missing to take a decision?

Description

To build a tool that takes a raw bug report (including error messages and context) and uses a large language model (LLM) to generate a series of structured, Socratic-style or Systemic questions designed to guide a the integration and development toward the root cause, rather than just providing a direct, potentially incorrect fix.

Goals

Set up a Python environment

Set the environment and get a Gemini API key. 2. Collect 5-10 realistic bug reports (from open-source projects, personal projects, or public forums like Stack Overflow—include the error message and the initial context).

Build the Dialogue Loop

1. Write a basic Python script using the Gemini API.
2. Implement a simple conversational loop: User Input (Bug) -> AI Output (Question) -> User Input (Answer to AI's question) -> AI Output (Next Question). Code Implementation

Socratic/Systemic Strategy Implementation

1. Refine the logic to ensure the questions follow a Socratic and Systemic path (e.g., from symptom-> context -> assumptions -> -> critical parts -> ).
2. Implement Function Calling (an advanced feature of the Gemini API) to suggest specific actions to the user, like "Run a ping test" or "Check the database logs."
3. Implement Bugzillla call to collect the
4. Implement Questioning Framework as LLVM pre-conditioning
5. Define set of instructions
6. Assemble the Tool

Resources

What are Systemic Questions?

Systemic questions explore the relationships, patterns, and interactions within a system rather than focusing on isolated elements.
In IT, they help uncover hidden dependencies, feedback loops, assumptions, and side-effects during debugging or architecture analysis.

Gitlab Project

gitlab.suse.de/sle-prjmgr/BugDecisionCritical_Question

---

Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0

Self-Scaling LLM Infrastructure Powered by Rancher

---

Description

The Problem

Running LLMs can get expensive and complex pretty quickly.

Today there are typically two choices:

1. Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
2. Self-host everything - set up Kubernetes, figure out GPU scheduling, handle scaling, manage model serving... it's a lot of work.

What if there was a middle ground?

What if infrastructure scaled itself instead of making you scale it?

Can we use existing Rancher capabilities like CAPI, autoscaling, and GitOps to make this simpler instead of building everything from scratch?

Project Repository: github.com/alexander-demicev/llmserverless

---

What This Project Does

A key feature is hybrid deployment: requests can be routed based on complexity or privacy needs. Simple or low-sensitivity queries can use public APIs (like OpenAI), while complex or private requests are handled in-house on local infrastructure. This flexibility allows balancing cost, privacy, and performance - using cloud for routine tasks and on-premises resources for sensitive or demanding workloads.

A complete, self-scaling LLM infrastructure that:

• Scales to zero when idle (no idle costs)
• Scales up automatically when requests come in
• Adds more nodes when needed, removes them when demand drops
• Runs on any infrastructure - laptop, bare metal, or cloud

Think of it as "serverless for LLMs" - focus on building, the infrastructure handles itself.

How It Works

A combination of open source tools working together:

Flow:

• Users interact with OpenWebUI (chat interface)
• Requests go to LiteLLM Gateway
• LiteLLM routes requests to:
  • Ollama (Knative) for local model inference (auto-scales pods)
  • Or cloud APIs for fallback

---

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/

---

Backporting patches using LLM by jankara

Description

Backporting Linux kernel fixes (either for CVE issues or as part of general git-fixes workflow) is boring and mostly mechanical work (dealing with changes in context, renamed variables, new helper functions etc.). The idea of this project is to explore usage of LLM for backporting Linux kernel commits to SUSE kernels using LLM.

Goals

• Create safe environment allowing LLM to run and backport patches without exposing the whole filesystem to it (for privacy and security reasons).
• Write prompt that will guide LLM through the backporting process. Fine tune it based on experimental results.
• Explore success rate of LLMs when backporting various patches.

Resources

• Docker
• Gemini CLI

Repository

Current version of the container with some instructions for use are at: https://gitlab.suse.de/jankara/gemini-cli-backporter

---

GenAI-Powered Systemic Bug Evaluation and Management Assistant by rtsvetkov

Motivation

What is the decision critical question which one can ask on a bug? How this question affects the decision on a bug and why?

Let's make GenAI look on the bug from the systemic point and evaluate what we don't know. Which piece of information is missing to take a decision?

Description

To build a tool that takes a raw bug report (including error messages and context) and uses a large language model (LLM) to generate a series of structured, Socratic-style or Systemic questions designed to guide a the integration and development toward the root cause, rather than just providing a direct, potentially incorrect fix.

Goals

Set up a Python environment

Set the environment and get a Gemini API key. 2. Collect 5-10 realistic bug reports (from open-source projects, personal projects, or public forums like Stack Overflow—include the error message and the initial context).

Build the Dialogue Loop

1. Write a basic Python script using the Gemini API.
2. Implement a simple conversational loop: User Input (Bug) -> AI Output (Question) -> User Input (Answer to AI's question) -> AI Output (Next Question). Code Implementation

Socratic/Systemic Strategy Implementation

1. Refine the logic to ensure the questions follow a Socratic and Systemic path (e.g., from symptom-> context -> assumptions -> -> critical parts -> ).
2. Implement Function Calling (an advanced feature of the Gemini API) to suggest specific actions to the user, like "Run a ping test" or "Check the database logs."
3. Implement Bugzillla call to collect the
4. Implement Questioning Framework as LLVM pre-conditioning
5. Define set of instructions
6. Assemble the Tool

Resources

What are Systemic Questions?

Systemic questions explore the relationships, patterns, and interactions within a system rather than focusing on isolated elements.
In IT, they help uncover hidden dependencies, feedback loops, assumptions, and side-effects during debugging or architecture analysis.

Gitlab Project

gitlab.suse.de/sle-prjmgr/BugDecisionCritical_Question

---

Minimal neovim LSP setup, without any external plugin. by wqu_suse

Description

Neovim is getting more and more built-in features, from LSP client, snippet to auto-completion.

Now it's possible to built a neovim IDE environment, with built-in lsp, snippet and auto-completion, without any external plugin.

Goals

Use a minimal init.lua only, without any nvim package manager nor external plugin, to build an IDE environment, which can:

• Use LSP to do context aware context lookup
• Auto-complete function name, parameter list etc
• Support multiple LSP servers for different languages

Linux kernel and btrfs-progs will be used as the example projects.

Resources

https://github.com/adam900710/nvimsimpleconfig

---