Description

Based on my other hackweek project, SUSE Edge Image Builder's Json Schema I would like to build also a MCP to be able to generate EIB config files the AI way.

Realistically I don't think I'll be able to have something consumable at the end of this hackweek but at least I would like to start exploring MCPs, the difference between an API and MCP, etc.

Goals

Familiarize myself with MCPs
Unrealistic: Have an MCP that can generate an EIB config file

Resources

https://hackweek.opensuse.org/25/projects/suse-edge-image-builder-json-schema
Anything else :)

Result

https://github.com/e-minguez/eib-mcp

I've extensively used antigravity and its agent mode to code this. This heavily uses https://hackweek.opensuse.org/25/projects/suse-edge-image-builder-json-schema for the MCP to be built.

I've ended up learning a lot of things about "prompting", json schemas in general, some golang, MCPs and AI in general :)

Example:

Generate an Edge Image Builder configuration for an ISO image based on slmicro-6.2.iso, targeting x86_64 architecture. The output name should be 'my-edge-image' and it should install to /dev/sda. It should deploy a 3 nodes kubernetes cluster with nodes names "node1", "node2" and "node3" as: * hostname: node1, IP: 1.1.1.1, role: initializer * hostname: node2, IP: 1.1.1.2, role: agent * hostname: node3, IP: 1.1.1.3, role: agent The kubernetes version should be k3s 1.33.4-k3s1 and it should deploy a cert-manager helm chart (the latest one available according to https://cert-manager.io/docs/installation/helm/). It should create a user called "suse" with password "suse" and set ntp to "foo.ntp.org". The VIP address for the API should be 1.2.3.4

Generates:

apiVersion: "1.0" image: arch: x86_64 baseImage: slmicro-6.2.iso imageType: iso outputImageName: my-edge-image kubernetes: helm: charts: - name: cert-manager repositoryName: jetstack version: v1.19.1 repositories: - name: jetstack url: https://charts.jetstack.io network: apiVIP: 1.2.3.4 nodes: - hostname: node1 type: server - hostname: node2 type: agent - hostname: node3 type: agent version: 1.33.4+k3s1 operatingSystem: isoConfiguration: installDevice: /dev/sda time: ntp: servers: - foo.ntp.org timezone: UTC users: - encryptedPassword: $2a$10$x9Z/vnOEeWhjAR.1RHNDk.Lsdg44cCIXjBvwkSRXek9rbufRubjli username: suse

NOTE: The format is ok but I cannot make hackweek.opensuse.org to render it properly :D ¯\_(ツ)_/¯

Looking for hackers with the skills:

mcpserver edge edge-image-builder mcp ai vibecoding

This project is part of:

Hack Week 25

Activity

about 1 month ago: varelarg liked this project.

about 2 months ago: eminguez liked this project.

about 2 months ago: eminguez added keyword "vibecoding" to this project.

about 2 months ago: eminguez added keyword "mcpserver" to this project.

about 2 months ago: eminguez added keyword "edge" to this project.

about 2 months ago: eminguez added keyword "edge-image-builder" to this project.

about 2 months ago: eminguez added keyword "mcp" to this project.

about 2 months ago: eminguez added keyword "ai" to this project.

about 2 months ago: eminguez started this project.

about 2 months ago: eminguez originated this project.

Comments

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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.

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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

Write a basic Python script using the Gemini API.
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

Refine the logic to ensure the questions follow a Socratic and Systemic path (e.g., from symptom-> context -> assumptions -> -> critical parts -> ).
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."
Implement Bugzillla call to collect the
Implement Questioning Framework as LLVM pre-conditioning
Define set of instructions
Assemble the Tool

Resources

What are Systemic Questions?

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Gitlab Project

gitlab.suse.de/sle-prjmgr/BugDecisionCritical_Question

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:7b with 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

``` {

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.

Kubernetes-Based ML Lifecycle Automation by lmiranda

Description

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The pipeline will automate the lifecycle of a machine learning model, including:

Data ingestion/collection
Model training as a Kubernetes Job
Model artifact storage in an S3-compatible registry (e.g. Minio)
A Go-based deployment controller that automatically deploys new model versions to Kubernetes using Rancher
A lightweight inference service that loads and serves the latest model
Monitoring of model performance and service health through Prometheus/Grafana

The outcome is a working prototype of an MLOps workflow that demonstrates how AI workloads can be trained, versioned, deployed, and monitored using the Kubernetes ecosystem.

Goals

By the end of Hack Week, the project should:

Produce a fully functional ML pipeline running on Kubernetes with:
- Data collection job
- Training job container
- Storage and versioning of trained models
- Automated deployment of new model versions
- Model inference API service
- Basic monitoring dashboards
Showcase a Go-based deployment automation component, which scans the model registry and automatically generates & applies Kubernetes manifests for new model versions.
Enable continuous improvement by making the system modular and extensible (e.g., additional models, metrics, autoscaling, or drift detection can be added later).
Prepare a short demo explaining the end-to-end process and how new models flow through the system.

Resources

Project Repository

Updates

Training pipeline and datasets
Inference Service py

vibecoding

SUSE Edge Image Builder json schema by eminguez

Description

I'll make use of AI tools for this so I'd learn more about vibe coding, agents, etc.

Goals

Learn about json schemas
Try to implement something that can take the EIB source code and output an initial json schema definition
Create a PR for EIB to be adopted
Learn more about AI tools and how those can help on similar projects.

Resources

json-schema.org
suse-edge/edge-image-builder
Any AI tool that can help me!

Result

Pull Request created! https://github.com/suse-edge/edge-image-builder/pull/821

I've ended up learning a few things about "prompting", json schemas in general, some golang and AI in general :)