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
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This project is part of:
Hack Week 23
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Is SUSE Trending? Popularity and Developer Sentiment Insight Using Native AI Capabilities by terezacerna
Description
This project aims to explore the popularity and developer sentiment around SUSE and its technologies compared to Red Hat and their technologies. Using publicly available data sources, I will analyze search trends, developer preferences, repository activity, and media presence. The final outcome will be an interactive Power BI dashboard that provides insights into how SUSE is perceived and discussed across the web and among developers.
Goals
- Assess the popularity of SUSE products and brand compared to Red Hat using Google Trends.
- Analyze developer satisfaction and usage trends from the Stack Overflow Developer Survey.
- Use the GitHub API to compare SUSE and Red Hat repositories in terms of stars, forks, contributors, and issue activity.
- Perform sentiment analysis on GitHub issue comments to measure community tone and engagement using built-in Copilot capabilities.
- Perform sentiment analysis on Reddit comments related to SUSE technologies using built-in Copilot capabilities.
- Use Gnews.io to track and compare the volume of news articles mentioning SUSE and Red Hat technologies.
- Test the integration of Copilot (AI) within Power BI for enhanced data analysis and visualization.
- Deliver a comprehensive Power BI report summarizing findings and insights.
- Test the full potential of Power BI, including its AI features and native language Q&A.
Resources
- Google Trends: Web scraping for search popularity data
- Stack Overflow Developer Survey: For technology popularity and satisfaction comparison
- GitHub API: For repository data (stars, forks, contributors, issues, comments).
- Gnews.io API: For article volume and mentions analysis.
- Reddit: SUSE related topics with comments.
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:
- Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
- 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
Update M2Crypto by mcepl
There are couple of projects I work on, which need my attention and putting them to shape:
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.
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/
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.
mgr-ansible-ssh - Intelligent, Lightweight CLI for Distributed Remote Execution by deve5h
Description
By the end of Hack Week, the target will be to deliver a minimal functional version 1 (MVP) of a custom command-line tool named mgr-ansible-ssh (a unified wrapper for BOTH ad-hoc shell & playbooks) that allows operators to:
- Execute arbitrary shell commands on thousand of remote machines simultaneously using Ansible Runner with artifacts saved locally.
- Pass runtime options such as inventory file, remote command string/ playbook execution, parallel forks, limits, dry-run mode, or no-std-ansible-output.
- Leverage existing SSH trust relationships without additional setup.
- Provide a clean, intuitive CLI interface with --help for ease of use. It should provide consistent UX & CI-friendly interface.
- Establish a foundation that can later be extended with advanced features such as logging, grouping, interactive shell mode, safe-command checks, and parallel execution tuning.
The MVP should enable day-to-day operations to efficiently target thousands of machines with a single, consistent interface.
Goals
Primary Goals (MVP):
Build a functional CLI tool (mgr-ansible-ssh) capable of executing shell commands on multiple remote hosts using Ansible Runner. Test the tool across a large distributed environment (1000+ machines) to validate its performance and reliability.
Looking forward to significantly reducing the zypper deployment time across all 351 RMT VM servers in our MLM cluster by eliminating the dependency on the taskomatic service, bringing execution down to a fraction of the current duration. The tool should also support multiple runtime flags, such as:
mgr-ansible-ssh: Remote command execution wrapper using Ansible Runner
Usage: mgr-ansible-ssh [--help] [--version] [--inventory INVENTORY]
[--run RUN] [--playbook PLAYBOOK] [--limit LIMIT]
[--forks FORKS] [--dry-run] [--no-ansible-output]
Required Arguments
--inventory, -i Path to Ansible inventory file to use
Any One of the Arguments Is Required
--run, -r Execute the specified shell command on target hosts
--playbook, -p Execute the specified Ansible playbook on target hosts
Optional Arguments
--help, -h Show the help message and exit
--version, -v Show the version and exit
--limit, -l Limit execution to specific hosts or groups
--forks, -f Number of parallel Ansible forks
--dry-run Run in Ansible check mode (requires -p or --playbook)
--no-ansible-output Suppress Ansible stdout output
Secondary/Stretched Goals (if time permits):
- Add pretty output formatting (success/failure summary per host).
- Implement basic logging of executed commands and results.
- Introduce safety checks for risky commands (shutdown, rm -rf, etc.).
- Package the tool so it can be installed with pip or stored internally.
Resources
Collaboration is welcome from anyone interested in CLI tooling, automation, or distributed systems. Skills that would be particularly valuable include:
- Python especially around CLI dev (argparse, click, rich)
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
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
- 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
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
- Existing Uyuni Test Framework (Cucumber Ruby + Capybara + Selenium)
- My Template for CucumberJS + Playwright in TypeScript
- Started Hackweek Project
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