Description
Start experimenting the generative SUSE-AI chat bot, asking questions on different areas of knowledge or science and possibly analyze the quality of the LLM model response, specific and comparative, checking the answers provided by different LLM models to a same query, using proper quality metrics or tools or methodologies.
Try to define basic guidelines and requirements for quality test automation of AI-generated responses.
First approach of investigation can be based on manual testing: methodologies, findings and data can be useful then to organize valid automated testing.
Goals
- Identify criteria and measuring scales for assessment of a text content.
- Define quality of an answer/text based on defined criteria .
- Identify some knowledge sectors and a proper list of problems/questions per sector.
- Manually run query session and apply evaluation criteria to answers.
- Draft requirements for test automation of AI answers.
Resources
- Announcement of SUSE-AI for Hack Week in Slack
- Openplatform and related 3 LLM models gemma:2b, llama3.1:8b, qwen2.5-coder:3b.
Notes
Foundation models (FMs):
are large deep learning neural networks, trained on massive datasets, that have changed the way data scientists approach machine learning (ML). Rather than develop artificial intelligence (AI) from scratch, data scientists use a foundation model as a starting point to develop ML models that power new applications more quickly and cost-effectively.Large language models (LLMs):
are a category of foundation models pre-trained on immense amounts of data acquiring abilities by learning statistical relationships from vast amounts of text during a self- and semi-supervised training process, making them capable of understanding and generating natural language and other types of content , to perform a wide range of tasks.
LLMs can be used for generative AI (artificial intelligence) to produce content based on input prompts in human language.
Validation of a AI-generated answer is not an easy task to perform, as manually as automated.
An LLM answer text shall contain a given level of informations: correcness, completeness, reasoning description etc.
We shall rely in properly applicable and measurable criteria of validation to get an assessment in a limited amount of time and resources.
This project is part of:
Hack Week 24
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Local AI assistant with optional integrations and mobile companion by livdywan
Description
Setup a local AI assistant for research, brainstorming and proof reading. Look into SurfSense, Open WebUI and possibly alternatives. Explore integration with services like openQA. There should be no cloud dependencies. Mobile phone support or an additional companion app would be a bonus. The goal is not to develop everything from scratch.
User Story
- Allison Average wants a one-click local AI assistent on their openSUSE laptop.
- Ash Awesome wants AI on their phone without an expensive subscription.
Goals
- Evaluate a local SurfSense setup for day to day productivity
- Test opencode for vibe coding and tool calling
Timeline
Day 1
- Took a look at SurfSense and started setting up a local instance.
- Unfortunately the container setup did not work well. Tho this was a great opportunity to learn some new podman commands and refresh my memory on how to recover a corrupted btrfs filesystem.
Day 2
- Due to its sheer size and complexity SurfSense seems to have triggered btrfs fragmentation. Naturally this was not visible in any podman-related errors or in the journal. So this took up much of my second day.
Day 3
- Trying out opencode with Qwen3-Coder and Qwen2.5-Coder.
Day 4
- Context size is a thing, and models are not equally usable for vibe coding.
- Through arduous browsing for ollama models I did find some like
myaniu/qwen2.5-1m:7bwith 1m but even then it is not obvious if they are meant for tool calls.
Day 5
- Whilst trying to make opencode usable I discovered ramalama which worked instantly and very well.
Outcomes
surfsense
I could not easily set this up completely. Maybe in part due to my filesystem issues. Was expecting this to be less of an effort.
opencode
Installing opencode and ollama in my distrobox container along with the following configs worked for me.
When preparing a new project from scratch it is a good idea to start out with a template.
opencode.json
``` {
AI-Powered Unit Test Automation for Agama by joseivanlopez
The Agama project is a multi-language Linux installer that leverages the distinct strengths of several key technologies:
- Rust: Used for the back-end services and the core HTTP API, providing performance and safety.
- TypeScript (React/PatternFly): Powers the modern web user interface (UI), ensuring a consistent and responsive user experience.
- Ruby: Integrates existing, robust YaST libraries (e.g.,
yast-storage-ng) to reuse established functionality.
The Problem: Testing Overhead
Developing and maintaining code across these three languages requires a significant, tedious effort in writing, reviewing, and updating unit tests for each component. This high cost of testing is a drain on developer resources and can slow down the project's evolution.
The Solution: AI-Driven Automation
This project aims to eliminate the manual overhead of unit testing by exploring and integrating AI-driven code generation tools. We will investigate how AI can:
- Automatically generate new unit tests as code is developed.
- Intelligently correct and update existing unit tests when the application code changes.
By automating this crucial but monotonous task, we can free developers to focus on feature implementation and significantly improve the speed and maintainability of the Agama codebase.
Goals
- Proof of Concept: Successfully integrate and demonstrate an authorized AI tool (e.g.,
gemini-cli) to automatically generate unit tests. - Workflow Integration: Define and document a new unit test automation workflow that seamlessly integrates the selected AI tool into the existing Agama development pipeline.
- Knowledge Sharing: Establish a set of best practices for using AI in code generation, sharing the learned expertise with the broader team.
Contribution & Resources
We are seeking contributors interested in AI-powered development and improving developer efficiency. Whether you have previous experience with code generation tools or are eager to learn, your participation is highly valuable.
If you want to dive deep into AI for software quality, please reach out and join the effort!
- Authorized AI Tools: Tools supported by SUSE (e.g.,
gemini-cli) - Focus Areas: Rust, TypeScript, and Ruby components within the Agama project.
Interesting Links
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
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
- Add Github issue support
- Proof the concept/approach by apply the approach on itself using Github issues for tracking and development of new features
- Add support for Bugzilla and Redmine using this approach in the process of doing it. Record a video of it.
- Clean-up and test the implementation and create some documentation
- Create a blog post about this approach
Resources
There is a prototype implementation here. This currently sort of works with JIRA only.
Docs Navigator MCP: SUSE Edition by mackenzie.techdocs
Description
Docs Navigator MCP: SUSE Edition is an AI-powered documentation navigator that makes finding information across SUSE, Rancher, K3s, and RKE2 documentation effortless. Built as a Model Context Protocol (MCP) server, it enables semantic search, intelligent Q&A, and documentation summarization using 100% open-source AI models (no API keys required!). The project also allows you to bring your own keys from Anthropic and Open AI for parallel processing.
Goals
- [ X ] Build functional MCP server with documentation tools
- [ X ] Implement semantic search with vector embeddings
- [ X ] Create user-friendly web interface
- [ X ] Optimize indexing performance (parallel processing)
- [ X ] Add SUSE branding and polish UX
- [ X ] Stretch Goal: Add more documentation sources
- [ X ] Stretch Goal: Implement document change detection for auto-updates
Coming Soon!
- Community Feedback: Test with real users and gather improvement suggestions
Resources
- Repository: Docs Navigator MCP: SUSE Edition GitHub
- UI Demo: Live UI Demo of Docs Navigator MCP: SUSE Edition
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
SUSE Observability MCP server by drutigliano
Description
The idea is to implement the SUSE Observability Model Context Protocol (MCP) Server as a specialized, middle-tier API designed to translate the complex, high-cardinality observability data from StackState (topology, metrics, and events) into highly structured, contextually rich, and LLM-ready snippets.
This MCP Server abstract the StackState APIs. Its primary function is to serve as a Tool/Function Calling target for AI agents. When an AI receives an alert or a user query (e.g., "What caused the outage?"), the AI calls an MCP Server endpoint. The server then fetches the relevant operational facts, summarizes them, normalizes technical identifiers (like URNs and raw metric names) into natural language concepts, and returns a concise JSON or YAML payload. This payload is then injected directly into the LLM's prompt, ensuring the final diagnosis or action is grounded in real-time, accurate SUSE Observability data, effectively minimizing hallucinations.
Goals
- Grounding AI Responses: Ensure that all AI diagnoses, root cause analyses, and action recommendations are strictly based on verifiable, real-time data retrieved from the SUSE Observability StackState platform.
- Simplifying Data Access: Abstract the complexity of StackState's native APIs (e.g., Time Travel, 4T Data Model) into simple, semantic functions that can be easily invoked by LLM tool-calling mechanisms.
- Data Normalization: Convert complex, technical identifiers (like component URNs, raw metric names, and proprietary health states) into standardized, natural language terms that an LLM can easily reason over.
- Enabling Automated Remediation: Define clear, action-oriented MCP endpoints (e.g., execute_runbook) that allow the AI agent to initiate automated operational workflows (e.g., restarts, scaling) after a diagnosis, closing the loop on observability.
Hackweek STEP
- Create a functional MCP endpoint exposing one (or more) tool(s) to answer queries like "What is the health of service X?") by fetching, normalizing, and returning live StackState data in an LLM-ready format.
Scope
- Implement read-only MCP server that can:
- Connect to a live SUSE Observability instance and authenticate (with API token)
- Use tools to fetch data for a specific component URN (e.g., current health state, metrics, possibly topology neighbors, ...).
- Normalize response fields (e.g., URN to "Service Name," health state DEVIATING to "Unhealthy", raw metrics).
- Return the data as a structured JSON payload compliant with the MCP specification.
Deliverables
- MCP Server v0.1 A running Golang MCP server with at least one tool.
- A README.md and a test script (e.g., curl commands or a simple notebook) showing how an AI agent would call the endpoint and the resulting JSON payload.
Outcome A functional and testable API endpoint that proves the core concept: translating complex StackState data into a simple, LLM-ready format. This provides the foundation for developing AI-driven diagnostics and automated remediation.
Resources
- https://www.honeycomb.io/blog/its-the-end-of-observability-as-we-know-it-and-i-feel-fine
- https://www.datadoghq.com/blog/datadog-remote-mcp-server
- https://modelcontextprotocol.io/specification/2025-06-18/index
- https://modelcontextprotocol.io/docs/develop/build-server
Basic implementation
- https://github.com/drutigliano19/suse-observability-mcp-server
Results
Successfully developed and delivered a fully functional SUSE Observability MCP Server that bridges language models with SUSE Observability's operational data. This project demonstrates how AI agents can perform intelligent troubleshooting and root cause analysis using structured access to real-time infrastructure data.
Example execution
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
- Learn something about RAG, LLM, AI.
- Find out if everything works offline as intended.
- 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.
- Be flexible to pivot in any direction, as long as there are new things learned.
Resources
To be found on the fly.
Timeline
Day 1 (of 4)
- Tried out a RAG demo, expanded on feeding it my own data
- Experimented with qwen3-coder to add a persistent chat functionality, and keeping vectors in a pickle file
- Optimizations to keep everything within context window
- Learn and add a bit of PyTest
Day 2
- More experimenting and more data
- Study ChromaDB
- Add a Web UI that works from another computer even though the container sees network is down
Day 3
- The above RAG is working well enough for demonstration purposes.
- Pivot to trying out OpenCode, configuring local Ollama qwen3-coder there, to analyze the RAG demo.
- Figured out how to configure Ollama template to be usable under OpenCode. OpenCode locally is super slow to just running qwen3-coder alone.
Day 4 (final day)
- Battle with OpenCode that was both slow and kept on piling up broken things.
- Call it success as after all the agentic AI was working locally.
- Clean up the mess left behind a bit.
Blog Post
Summarized the findings at blog post.
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;
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
- Add Github issue support
- Proof the concept/approach by apply the approach on itself using Github issues for tracking and development of new features
- Add support for Bugzilla and Redmine using this approach in the process of doing it. Record a video of it.
- Clean-up and test the implementation and create some documentation
- Create a blog post about this approach
Resources
There is a prototype implementation here. This currently sort of works with JIRA only.