Project Description
Generate a personalized avatar artwork images by fine-tuning stable diffusion on personal pictures
Goal for this Hackweek
Get a new fancy and unique avatar!
Resources
- https://huggingface.co/docs/diffusers/using-diffusers/sdxl
- https://huggingface.co/docs/diffusers/training/dreambooth
- https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sdxl.md
- https://civitai.com/models/133005/juggernaut-xl?modelVersionId=198530
Looking for hackers with the skills:
This project is part of:
Hack Week 23
Activity
Comments
-
-
about 2 years ago by STorresi | Reply
These are generated after a bespoke LoRA training using DreamBooth over the JuggernautXL model, which in turn is based on SDXL 1.0.
As you can see, hands are still tricky (a known issue of diffusion models, apparently), but I didn't try inpainting and img2img fine-tuning, which are supposed to be the go-to way to solve small issues like that. I must say the overall experience was quite painful due to the hardware requirements of SDXL and the amount of memory leaks in pytorch. A high-end consumer grade GPU like an NVIDIA 4080 with 16GB of VRAM often wasn't enough and ran OOM.
Similar Projects
Liz - Prompt autocomplete by ftorchia
Description
Liz is the Rancher AI assistant for cluster operations.
Goals
We want to help users when sending new messages to Liz, by adding an autocomplete feature to complete their requests based on the context.
Example:
- User prompt: "Can you show me the list of p"
- Autocomplete suggestion: "Can you show me the list of p...od in local cluster?"
Example:
- User prompt: "Show me the logs of #rancher-"
- Chat console: It shows a drop-down widget, next to the # character, with the list of available pod names starting with "rancher-".
Technical Overview
- The AI agent should expose a new ws/autocomplete endpoint to proxy autocomplete messages to the LLM.
- The UI extension should be able to display prompt suggestions and allow users to apply the autocomplete to the Prompt via keyboard shortcuts.
Resources
SUSE Edge Image Builder MCP by eminguez
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
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
Kubernetes-Based ML Lifecycle Automation by lmiranda
Description
This project aims to build a complete end-to-end Machine Learning pipeline running entirely on Kubernetes, using Go, and containerized ML components.
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
Updates
- Training pipeline and datasets
- Inference Service py
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
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