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

Bonus

Resources

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

Day 3

Day 4

Day 5

Highlights

Outcomes

opencode

Installing opencode and ollama in my distrobox container along with the following configs worked well enough for me.

When preparing a new project from scratch it is a good idea to start out with a template.

opencode.json

{ "$schema": "https://opencode.ai/config.json", "theme": "catppuccin", "model": "ollama/qwen2.5-coder:1.5b", "provider": { "ollama": { "npm": "[@ai-sdk](/users/ai-sdk)/openai-compatible", "name": "Ollama (local)", "options": { "baseURL": "http://localhost:11434/v1" }, "models": { "qwen2.5-coder:1.5b": { "name": "Qwem2.5-Coder" } } } }, "mcp": { "openqa": { "type": "remote", "enabled": true, "url": "https://openqa.opensuse.org/experimental/mcp", "headers": { "Authorization": "Bearer {env:OPENQA_USER}:{env:OPENQA_APIKEY}:{env:OPENQA_APISECRET}" } }, "gh_grep": { "type": "remote", "url": "https://mcp.grep.app" } } }

The models need to be ollama pulled first, and ollama needs to be serving.

AGENTS.md

When you need to lookup openQA jobs or job groups, use `openqa` tools. If you are unsure how to do something, use `gh_grep` to search code examples from github.

Note: My results varied a lot between models, and also available context length - using something like OLLAMA_CONTEXT_LENGTH=8192 ollama serve & uses twice the amount of RAM ollama allocates by default and gives it more wiggle room.

Looking for hackers with the skills:

ai

This project is part of:

Hack Week 25

Activity

  • 2 days ago: livdywan added keyword "ai" to this project.
  • 2 days ago: livdywan started this project.
  • 7 days ago: rsimai liked this project.
  • 21 days ago: livdywan originated this project.

  • Comments

    Be the first to comment!

    Similar Projects

    Gemini-Powered Systemic Bug Evaluation and Management Assistant by rtsvetkov

    Motivation

    What is the decision critical question which one can ask on a bug? How this question affects the decision on a bug and why?

    Let's make GenAI look on the bug from the systemic point and evaluate what we don't know. Which piece of information is missing to take a decision?

    Description

    To build a tool that takes a raw bug report (including error messages and context) and uses a large language model (LLM) to generate a series of structured, Socratic-style or Systemic questions designed to guide a the integration and development toward the root cause, rather than just providing a direct, potentially incorrect fix.

    Goals

    Set up a Python environment

    Set the environment and get a Gemini API key. 2. Collect 5-10 realistic bug reports (from open-source projects, personal projects, or public forums like Stack Overflow—include the error message and the initial context).

    Build the Dialogue Loop

    1. Write a basic Python script using the Gemini API.
    2. Implement a simple conversational loop: User Input (Bug) -> AI Output (Question) -> User Input (Answer to AI's question) -> AI Output (Next Question). Code Implementation

    Socratic/Systemic Strategy Implementation

    1. Refine the logic to ensure the questions follow a Socratic and Systemic path (e.g., from symptom-> context -> assumptions -> -> critical parts -> ).
    2. Implement Function Calling (an advanced feature of the Gemini API) to suggest specific actions to the user, like "Run a ping test" or "Check the database logs."
    3. Implement Bugzillla call to collect the
    4. Implement Questioning Framework as LLVM pre-conditioning
    5. Define set of instructions
    6. Assemble the Tool

    Resources

    What are Systemic Questions?

    Systemic questions explore the relationships, patterns, and interactions within a system rather than focusing on isolated elements.
    In IT, they help uncover hidden dependencies, feedback loops, assumptions, and side-effects during debugging or architecture analysis.

    Gitlab Project

    gitlab.suse.de/sle-prjmgr/BugDecisionCritical_Question


    Bugzilla goes AI - Phase 1 by nwalter

    Description

    This project, Bugzilla goes AI, aims to boost developer productivity by creating an autonomous AI bug agent during Hackweek. The primary goal is to reduce the time employees spend triaging bugs by integrating Ollama to summarize issues, recommend next steps, and push focused daily reports to a Web Interface.

    Goals

    To reduce employee time spent on Bugzilla by implementing an AI tool that triages and summarizes bug reports, providing actionable recommendations to the team via Web Interface.

    Project Charter

    https://docs.google.com/document/d/1HbAvgrg8T3pd1FIx74nEfCObCljpO77zz5In_Jpw4as/edit?usp=sharing## Description


    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

    References


    The Agentic Rancher Experiment: Do Androids Dream of Electric Cattle? by moio

    Rancher is a beast of a codebase. Let's investigate if the new 2025 generation of GitHub Autonomous Coding Agents and Copilot Workspaces can actually tame it. A GitHub robot mascot trying to lasso a blue bull with a Kubernetes logo tatooed on it


    The Plan

    Create a sandbox GitHub Organization, clone in key Rancher repositories, and let the AI loose to see if it can handle real-world enterprise OSS maintenance - or if it just hallucinates new breeds of Kubernetes resources!

    Specifically, throw "Agentic Coders" some typical tasks in a complex, long-lived open-source project, such as:


    The Grunt Work: generate missing GoDocs, unit tests, and refactorings. Rebase PRs.

    The Complex Stuff: fix actual (historical) bugs and feature requests to see if they can traverse the complexity without (too much) human hand-holding.

    Hunting Down Gaps: find areas lacking in docs, areas of improvement in code, dependency bumps, and so on.


    If time allows, also experiment with Model Context Protocol (MCP) to give agents context on our specific build pipelines and CI/CD logs.

    Why?

    We know AI can write "Hello World." and also moderately complex programs from a green field. But can it rebase a 3-month-old PR with conflicts in rancher/rancher? I want to find the breaking point of current AI agents to determine if and how they can help us to reduce our technical debt, work faster and better. At the same time, find out about pitfalls and shortcomings.

    The Outputs

    ❥ A "State of the Agentic Union" for SUSE engineers, detailing what works, what explodes, and how much coffee we can drink while the robots do the rebasing.

    ❥ Honest, Daily Updates With All the Gory Details


    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 Python web server (e.g., using FastAPI) 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