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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Background Coding Agent by mmanno

Description

I had only bad experiences with AI one-shots. However, monitoring agent work closely and interfering often did result in productivity gains.

Now, other companies are using agents in pipelines. That makes sense to me, just like CI, we want to offload work to pipelines: Our engineering teams are consistently slowed down by "toil": low-impact, repetitive maintenance tasks. A simple linter rule change, a dependency bump, rebasing patch-sets on top of newer releases or API deprecation requires dozens of manual PRs, draining time from feature development.

So far we have been writing deterministic, script-based automation for these tasks. And it turns out to be a common trap. These scripts are brittle, complex, and become a massive maintenance burden themselves.

Can we make prompts and workflows smart enough to succeed at background coding?

Goals

We will build a platform that allows engineers to execute complex code transformations using prompts.

By automating this toil, we accelerate large-scale migrations and allow teams to focus on high-value work.

Our platform will consist of three main components:

• "Change" Definition: Engineers will define a transformation as a simple, declarative manifest:
  
  • The target repositories.
    
  • A wrapper to run a "coding agent", e.g., "gemini-cli".
    
  • The task as a natural language prompt.
    
• "Change" Management Service: A central service that orchestrates the jobs. It will receive Change definitions and be responsible for the job lifecycle.
  
• Execution Runners: We could use existing sandboxed CI runners (like GitHub/GitLab runners) to execute each job or spawn a container.

MVP

• Define the Change manifest format.
  
• Build the core Management Service that can accept and queue a Change.
• Connect management service and runners, dynamically dispatch jobs to runners.
• Create a basic runner script that can run a hard-coded prompt against a test repo and open a PR.
  

Stretch Goals:

• Multi-layered approach, Workflow Agents trigger Coding Agents:
  
  1. Workflow Agent: Gather information about the task interactively from the user.
     
  2. Coding Agent: Once the interactive agent has refined the task into a clear prompt, it hands this prompt off to the "coding agent." This background agent is responsible for executing the task and producing the actual pull request.
     
• Use MCP:
  
  1. Workflow Agent gathers context information from Slack, Github, etc.
     
  2. Workflow Agent triggers a Coding Agent.
     
• Create a "Standard Task" library with reliable prompts.
  1. Rebasing rancher-monitoring to a new version of kube-prom-stack
     
  2. Update charts to use new images
     
  3. Apply changes to comply with a new linter
     
  4. Bump complex Go dependencies, like k8s modules
     
  5. Backport pull requests to other branches
• Add “review agents” that review the generated PR.
  

See also

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Explore LLM evaluation metrics by thbertoldi

Description

Learn the best practices for evaluating LLM performance with an open-source framework such as DeepEval.

Goals

Curate the knowledge learned during practice and present it to colleagues.

-> Maybe publish a blog post on SUSE's blog?

Resources

https://deepeval.com

https://docs.pactflow.io/docs/bi-directional-contract-testing

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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)
• More fun to learn jujutsu
• Play more with Gemini, how much it help (or not).
• Perhaps, also (just slightly related), help to fix vis to work with LuaJIT, particularly to make vis-lspc working.

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Try AI training with ROCm and LoRA by bmwiedemann

Description

I want to setup a Radeon RX 9600 XT 16 GB at home with ROCm on Slowroll.

Goals

I want to test how fast AI inference can get with the GPU and if I can use LoRA to re-train an existing free model for some task.

Resources

• https://rocm.docs.amd.com/en/latest/compatibility/compatibility-matrix.html
• https://build.opensuse.org/project/show/science:GPU:ROCm
• https://src.opensuse.org/ROCm/
• https://www.suse.com/c/lora-fine-tuning-llms-for-text-classification/

Results

got inference working with llama.cpp:

export LLAMACPP_ROCM_ARCH=gfx1200
HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$LLAMACPP_ROCM_ARCH \
-DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \
-Dhipblas_DIR=/usr/lib64/cmake/hipblaslt/ \
&& cmake --build build --config Release -j8
m=models/gpt-oss-20b-mxfp4.gguf
cd $P/llama.cpp && build/bin/llama-server --model $m --threads 8 --port 8005 --host 0.0.0.0 --device ROCm0 --n-gpu-layers 999

Without the --device option it faulted. Maybe because my APU also appears there?

I updated/fixed various related packages: https://src.opensuse.org/ROCm/rocm-examples/pulls/1 https://src.opensuse.org/ROCm/hipblaslt/pulls/1 SR 1320959

benchmark

I benchmarked inference with llama.cpp + gpt-oss-20b-mxfp4.gguf and ROCm offloading to a Radeon RX 9060 XT 16GB. I varied the number of layers that went to the GPU:

• 0 layers 14.49 tokens/s (8 CPU cores)
• 9 layers 17.79 tokens/s 34% VRAM
• 15 layers 22.39 tokens/s 51% VRAM
• 20 layers 27.49 tokens/s 64% VRAM
• 24 layers 41.18 tokens/s 74% VRAM
• 25+ layers 86.63 tokens/s 75% VRAM (only 200% CPU load)

So there is a significant performance-boost if the whole model fits into the GPU's VRAM.

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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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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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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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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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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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