Project here: https://confluence.suse.com/display/AAI/HackWeek19 Will keep working out of HackWeek as "best effort" personal project to make it evolve and keep learning.

What this project is about?

Data Scientist ofter starts working on their laptop before moving into company resources. As in many other cases they have to solve many challenges by themselves before actually start working on "their stuff". The idea is to build a prototype we will eventually try to evolve in a product that answers the following pre-requisites:

  • Rapid Time to work: I, as Data Scientist or Data Engineer, need to install the playground quickly and be ready to work
  • Everything at the right place: I as Data Scientist or Data Engineer want an easy way to find things and use them
  • No time to waste: I as Data Scientist or Data Engineer want to be able to replicate the model synchronizing it with another infrastructure through a "click and done" model
  • No complexity rule: I as Data Scientist or Data Engineer want to avoid waste time in complex configurations or debug things. Complexity needs to hided to me

Project Team requirements

Because this is a first attempt to prototype I have to ask for some "not official" rules to be applied:

  • Max 7/9 people in the team with a max of 3 Engineers
  • If you apply you have to make yourself available from 10 am to 5 pm CET (if you're on a different time zone you have to consider we'll have a lot of team discussion so could be challenging)
  • This is a 5 days sprint approach where everyone needs to be open, collaborative, bold, creative.

FAQ

  • I'm not an engineer or an expert: Great this project require (possibly) at least 1 person from marketing, sales-engineering, services, support
  • Am I required to code?: No, but you're required to share your ideas and views, while the end goal is to build a prototype (that's why we need a couple of engineers) the scope is to have something to show and demonstrate we may build something useful for the Data Scientist community
  • Woah this seems to be a super serious project: Nah it's a fun experiment to learn how much we may push our limit through rapid prototyping and "be different"
  • So how do I signup?: easy just join the team here on hackweek and/or contact me alessandro.festa@suse.com for further details.

This project is part of:

Hack Week 19

Activity

  • almost 6 years ago: jordimassaguerpla liked this project.
  • almost 6 years ago: rsblendido joined this project.
  • almost 6 years ago: jeffpr joined this project.
  • almost 6 years ago: FSzekely liked this project.
  • almost 6 years ago: bfilho left this project.
  • almost 6 years ago: bfilho joined this project.
  • almost 6 years ago: bfromme liked this project.
  • almost 6 years ago: bfromme joined this project.
  • almost 6 years ago: rsblendido liked this project.
  • almost 6 years ago: gboiko liked this project.
  • almost 6 years ago: afesta added keyword "innovation" to this project.
  • almost 6 years ago: afesta added keyword "projectmanagement" to this project.
  • almost 6 years ago: afesta added keyword "ai" to this project.
  • almost 6 years ago: afesta added keyword "artificial-intelligence" to this project.
  • almost 6 years ago: afesta added keyword "machinelearning" to this project.
  • almost 6 years ago: afesta added keyword "prototype" to this project.
  • almost 6 years ago: afesta added keyword "agile" to this project.
  • almost 6 years ago: afesta liked this project.
  • almost 6 years ago: afesta started this project.
  • almost 6 years ago: afesta originated this project.

  • Comments

    • hennevogel
      almost 6 years ago by hennevogel | Reply

      Can you explain what kind of output you would expect? Like an application? A set of packages? Some IaC description?

      • afesta
        almost 6 years ago by afesta | Reply

        This is something we have to decide during the hack week, usually a prototype based on a target of the challenge decided by the team. If this will be simple artifacts made of a sum of existing items, an application or a set of packages has to be decided. The scope is to foster innovation under a very fast cycle (5 days) and get a result that allows us to learn if: is doable, what we need to address to make it a real product and how long could take. Don't expect huge development or impossible challenges, this is about pure innovation and ideas.. and build a way to demonstrate our idea.

    • afesta
      almost 6 years ago by afesta | Reply

      This is something we have to decide during the hack week, usually a prototype based on a target of the challenge decided by the team. If this will be simple artifacts made of a sum of existing items, an application or a set of packages has to be decided. The scope is to foster innovation under a very fast cycle (5 days) and get a result that allows us to learn if: is doable, what we need to address to make it a real product and how long could take. Don't expect huge development or impossible challenges, this is about pure innovation and ideas.. and build a way to demonstrate our idea.

    • bmwiedemann
      almost 6 years ago by bmwiedemann | Reply

      If you have a need for this project for 2x NVIDIA Tesla T4, 16GB - ping me.

      • afesta
        almost 6 years ago by afesta | Reply

        So cool! To be honest I'll like more to use your brain for the project...willing to give me a chance and have fun for a week with this crazy PM?

    • rsblendido
      almost 6 years ago by rsblendido | Reply

      Is this about Kubeflow?

      • afesta
        almost 6 years ago by afesta | Reply

        Could be. I mean the only "constraint" is that ideally should work on a laptop and Kubeflow works on K8's but if you use something like MLRun you may overcome many challenges. The ultimate goal of the project is to provide Data scientists a playground so that they do not need to learn and install and configure everything but it's easy enough to start from your laptop (and eventually) move it to a server/cloud environment.

    • jeffpr
      almost 6 years ago by jeffpr | Reply

      @afesta : I will be working with you for the SUSEcon demos - just thought I would hop in here when I can.

      • afesta
        almost 6 years ago by afesta | Reply

        Cool!

    Similar Projects

    Multi-agent AI assistant for Linux troubleshooting by doreilly

    Description

    Explore multi-agent architecture as a way to avoid MCP context rot.

    Having one agent with many tools bloats the context with low-level details about tool descriptions, parameter schemas etc which hurts LLM performance. Instead have many specialised agents, each with just the tools it needs for its role. A top level supervisor agent takes the user prompt and delegates to appropriate sub-agents.

    Goals

    Create an AI assistant with some sub-agents that are specialists at troubleshooting Linux subsystems, e.g. systemd, selinux, firewalld etc. The agents can get information from the system by implementing their own tools with simple function calls, or use tools from MCP servers, e.g. a systemd-agent can use tools from systemd-mcp.

    Example prompts/responses:

    user$ the system seems slow
    assistant$ process foo with pid 12345 is using 1000% cpu ...
    
    user$ I can't connect to the apache webserver
    assistant$ the firewall is blocking http ... you can open the port with firewall-cmd --add-port ...
    

    Resources

    Language Python. The Python ADK is more mature than Golang.

    https://google.github.io/adk-docs/

    https://github.com/djoreilly/linux-helper


    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.


    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


    Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0

    Self-Scaling LLM Infrastructure Powered by Rancher

    logo


    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


    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.

    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.


    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


    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

    SUSE Hackweek AI Song Search

    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


    Review SCC team internal development processes by calmeidadeoliveira

    Description

    Continue with the Hackweek 2024, with focus on reviewing existing processes / ways of working and creating workflows:

    Goals

    • Check all the processes from [1] and [3]
    • move them to confluence [4], make comments, corrections, etc.
    • present the result to the SCC team and ask for reviews

    Resources

    [1] https://github.com/SUSE/scc-docs/blob/master/team/workflow/kanban-process.md [2] https://github.com/SUSE/scc-docs/tree/master/team [3] https://github.com/SUSE/scc-docs/tree/master/team/workflow

    Confluence

    [4] https://confluence.suse.com/spaces/scc/pages/1537703975/Processes+and+ways+of+working