Rewrite Distrobox in go (POC) by fabriziosestito

Description

Rewriting Distrobox in Go.

Main benefits:

• Easier to maintain and to test
• Adapter pattern for different container backends (LXC, systemd-nspawn, etc.)

Goals

• Build a minimal starting point with core commands
• Keep the CLI interface compatible: existing users shouldn't notice any difference
• Use a clean Go architecture with adapters for different container backends
• Keep dependencies minimal and binary size small
• Benchmark against the original shell script

Resources

• Upstream project: https://github.com/89luca89/distrobox/
• Distrobox site: https://distrobox.it/
• ArchWiki: https://wiki.archlinux.org/title/Distrobox

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Technical talks at universities by agamez

Description

This project aims to empower the next generation of tech professionals by offering hands-on workshops on containerization and Kubernetes, with a strong focus on open-source technologies. By providing practical experience with these cutting-edge tools and fostering a deep understanding of open-source principles, we aim to bridge the gap between academia and industry.

For now, the scope is limited to Spanish universities, since we already have the contacts and have started some conversations.

Goals

• Technical Skill Development: equip students with the fundamental knowledge and skills to build, deploy, and manage containerized applications using open-source tools like Kubernetes.
• Open-Source Mindset: foster a passion for open-source software, encouraging students to contribute to open-source projects and collaborate with the global developer community.
• Career Readiness: prepare students for industry-relevant roles by exposing them to real-world use cases, best practices, and open-source in companies.

Resources

• Instructors: experienced open-source professionals with deep knowledge of containerization and Kubernetes.
• SUSE Expertise: leverage SUSE's expertise in open-source technologies to provide insights into industry trends and best practices.

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Extended private brain - RAG my own scripts and data into offline LLM AI by tjyrinki_suse

Description

For purely studying purposes, I'd like to find out if I could teach an LLM some of my own accumulated knowledge, to use it as a sort of extended brain.

I might use qwen3-coder or something similar as a starting point.

Everything would be done 100% offline without network available to the container, since I prefer to see when network is needed, and make it so it's never needed (other than initial downloads).

Goals

1. Learn something about RAG, LLM, AI.
2. Find out if everything works offline as intended.
3. As an end result have a new way to access my own existing know-how, but so that I can query the wisdom in them.
4. Be flexible to pivot in any direction, as long as there are new things learned.

Resources

To be found on the fly.

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MCP Server for SCC by digitaltomm

Description

Provide an MCP Server implementation for customers to access data on scc.suse.com via MCP protocol. Similar to the organization APIs, this can expose to customers data about their subscriptions, orders, systems and products. Authentication should be done by organization credentials, similar to what needs to be provided to RMT/MLM. Customers can connect to the SCC MCP server from their own MCP-compatible client and Large Language Model (LLM), so no third party is involved.

Goals

We want to demonstrate a proof of concept to connect to the SCC MCP server with any AI agent, like gemini-cli, copilot or Claude desktop. Enabling the user to ask questions regarding their SCC inventory, like "When do I need to re-new my SLES subscription", "Do I have active systems running on unsupported operating systems?".

Milestones

[ ] Basic MCP API setup
[ ] MCP endpoints
  [ ] Products / Repositories
  [ ] Subscriptions / Orders 
  [ ] Systems
[ ] Document usage with VSCode Copilot, Claude Desktop, Gemini CLI

Resources

• fast_mcp library for Rails
• MCP test client

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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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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:

1. Connects to Prometheus and executes a query to fetch detailed test failure history.
2. Processes the raw data into a format suitable for the Gemini API.
3. Successfully calls the Gemini API with the data and a clear prompt.
4. Parses the AI's response to extract a simple list of flaky tests.
5. Saves the list to a JSON file that can be displayed in Grafana.
6. 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

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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.

Resources

• CLAP: The main model being researched;
• huggingface: Pre-trained models for CLAP;
• Free Music Archive: Creative Commons songs that can be used for testing;
• Colab: To be used as the development environment;
• hw25-song-search: Github repo of the project.

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