Project Description
Create a K8s CRD for s3gw.
The operator will be written in Go.
The CRD should in the beginning allow an user to create a bucket.
Goal for this Hackweek
The CRD should in the beginning allow an user to create a bucket.
Project
https://github.com/giubacc/s3gw-operator
Looking for hackers with the skills:
This project is part of:
Hack Week 22
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terraform-provider-feilong by e_bischoff
Project Description
People need to test operating systems and applications on s390 platform. While this is straightforward with KVM, this is very difficult with z/VM.
IBM Cloud Infrastructure Center (ICIC) harnesses the Feilong API, but you can use Feilong without installing ICIC(see this schema).
What about writing a terraform Feilong provider, just like we have the terraform libvirt provider? That would allow to transparently call Feilong from your main.tf files to deploy and destroy resources on your z/VM system.
Goal for Hackweek 23
I would like to be able to easily deploy and provision VMs automatically on a z/VM system, in a way that people might enjoy even outside of SUSE.
My technical preference is to write a terraform provider plugin, as it is the approach that involves the least software components for our deployments, while remaining clean, and compatible with our existing development infrastructure.
Goals for Hackweek 24
Feilong provider works and is used internally by SUSE Manager team. Let's push it forward!
Let's add support for fiberchannel disks and multipath.
Goals for Hackweek 25
Modernization, maturity, and maintenance: support for SLES 16 and openTofu, new API calls, fixes...
Resources
Outcome
Rewrite Distrobox in go (POC) by fabriziosestito
Description
Rewriting Distrobox in Go.
Main benefits:
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SUSE Health Check Tools by roseswe
SUSE HC Tools Overview
A collection of tools written in Bash or Go 1.24++ to make life easier with handling of a bunch of tar.xz balls created by supportconfig.
Background: For SUSE HC we receive a bunch of supportconfig tar balls to check them for misconfiguration, areas for improvement or future changes.
Main focus on these HC are High Availability (pacemaker), SLES itself and SAP workloads, esp. around the SUSE best practices.
Goals
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- Adding new collectors
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Resources
csv2xls* example.sh go.mod listprodids.txt sumtext* trails.go README.md csv2xls.go exceltest.go go.sum m.sh* sumtext.go vercheck.py* config.ini csvfiles/ getrpm* listprodids* rpmdate.sh* sumxls* verdriver* credtest.go example.py getrpm.go listprodids.go sccfixer.sh* sumxls.go verdriver.go
docollall.sh* extracthtml.go gethostnamectl* go.sum numastat.go cpuvul* extractcluster.go firmwarebug* gethostnamectl.go m.sh* numastattest.go cpuvul.go extracthtml* firmwarebug.go go.mod numastat* xtr_cib.sh*
$ getrpm -r pacemaker
>> Product ID: 2795 (SUSE Linux Enterprise Server for SAP Applications 15 SP7 x86_64), RPM Name:
+--------------+----------------------------+--------+--------------+--------------------+
| Package Name | Version | Arch | Release | Repository |
+--------------+----------------------------+--------+--------------+--------------------+
| pacemaker | 2.1.10+20250718.fdf796ebc8 | x86_64 | 150700.3.3.1 | sle-ha/15.7/x86_64 |
| pacemaker | 2.1.9+20250410.471584e6a2 | x86_64 | 150700.1.9 | sle-ha/15.7/x86_64 |
+--------------+----------------------------+--------+--------------+--------------------+
Total packages found: 2
Create a Cloud-Native policy engine with notifying capabilities to optimize resource usage by gbazzotti
Description
The goal of this project is to begin the initial phase of development of an all-in-one Cloud-Native Policy Engine that notifies resource owners when their resources infringe predetermined policies. This was inspired by a current issue in the CES-SRE Team where other solutions seemed to not exactly correspond to the needs of the specific workloads running on the Public Cloud Team space.
The initial architecture can be checked out on the Repository listed under Resources.
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A CLI for Harvester by mohamed.belgaied
Harvester does not officially come with a CLI tool, the user is supposed to interact with Harvester mostly through the UI. Though it is theoretically possible to use kubectl to interact with Harvester, the manipulation of Kubevirt YAML objects is absolutely not user friendly. Inspired by tools like multipass from Canonical to easily and rapidly create one of multiple VMs, I began the development of Harvester CLI. Currently, it works but Harvester CLI needs some love to be up-to-date with Harvester v1.0.2 and needs some bug fixes and improvements as well.
Project Description
Harvester CLI is a command line interface tool written in Go, designed to simplify interfacing with a Harvester cluster as a user. It is especially useful for testing purposes as you can easily and rapidly create VMs in Harvester by providing a simple command such as:
harvester vm create my-vm --count 5
to create 5 VMs named my-vm-01 to my-vm-05.
Harvester CLI is functional but needs a number of improvements: up-to-date functionality with Harvester v1.0.2 (some minor issues right now), modifying the default behaviour to create an opensuse VM instead of an ubuntu VM, solve some bugs, etc.
Github Repo for Harvester CLI: https://github.com/belgaied2/harvester-cli
Done in previous Hackweeks
- Create a Github actions pipeline to automatically integrate Harvester CLI to Homebrew repositories: DONE
- Automatically package Harvester CLI for OpenSUSE / Redhat RPMs or DEBs: DONE
Goal for this Hackweek
The goal for this Hackweek is to bring Harvester CLI up-to-speed with latest Harvester versions (v1.3.X and v1.4.X), and improve the code quality as well as implement some simple features and bug fixes.
Some nice additions might be: * Improve handling of namespaced objects * Add features, such as network management or Load Balancer creation ? * Add more unit tests and, why not, e2e tests * Improve CI * Improve the overall code quality * Test the program and create issues for it
Issue list is here: https://github.com/belgaied2/harvester-cli/issues
Resources
The project is written in Go, and using client-go the Kubernetes Go Client libraries to communicate with the Harvester API (which is Kubernetes in fact).
Welcome contributions are:
- Testing it and creating issues
- Documentation
- Go code improvement
What you might learn
Harvester CLI might be interesting to you if you want to learn more about:
- GitHub Actions
- Harvester as a SUSE Product
- Go programming language
- Kubernetes API
- Kubevirt API objects (Manipulating VMs and VM Configuration in Kubernetes using Kubevirt)
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
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. 
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 CONCLUSION!!!
A
State of the Union
document was compiled to summarize lessons learned this week. For more gory details, just read on the diary below!
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
A CLI for Harvester by mohamed.belgaied
Harvester does not officially come with a CLI tool, the user is supposed to interact with Harvester mostly through the UI. Though it is theoretically possible to use kubectl to interact with Harvester, the manipulation of Kubevirt YAML objects is absolutely not user friendly. Inspired by tools like multipass from Canonical to easily and rapidly create one of multiple VMs, I began the development of Harvester CLI. Currently, it works but Harvester CLI needs some love to be up-to-date with Harvester v1.0.2 and needs some bug fixes and improvements as well.
Project Description
Harvester CLI is a command line interface tool written in Go, designed to simplify interfacing with a Harvester cluster as a user. It is especially useful for testing purposes as you can easily and rapidly create VMs in Harvester by providing a simple command such as:
harvester vm create my-vm --count 5
to create 5 VMs named my-vm-01 to my-vm-05.
Harvester CLI is functional but needs a number of improvements: up-to-date functionality with Harvester v1.0.2 (some minor issues right now), modifying the default behaviour to create an opensuse VM instead of an ubuntu VM, solve some bugs, etc.
Github Repo for Harvester CLI: https://github.com/belgaied2/harvester-cli
Done in previous Hackweeks
- Create a Github actions pipeline to automatically integrate Harvester CLI to Homebrew repositories: DONE
- Automatically package Harvester CLI for OpenSUSE / Redhat RPMs or DEBs: DONE
Goal for this Hackweek
The goal for this Hackweek is to bring Harvester CLI up-to-speed with latest Harvester versions (v1.3.X and v1.4.X), and improve the code quality as well as implement some simple features and bug fixes.
Some nice additions might be: * Improve handling of namespaced objects * Add features, such as network management or Load Balancer creation ? * Add more unit tests and, why not, e2e tests * Improve CI * Improve the overall code quality * Test the program and create issues for it
Issue list is here: https://github.com/belgaied2/harvester-cli/issues
Resources
The project is written in Go, and using client-go the Kubernetes Go Client libraries to communicate with the Harvester API (which is Kubernetes in fact).
Welcome contributions are:
- Testing it and creating issues
- Documentation
- Go code improvement
What you might learn
Harvester CLI might be interesting to you if you want to learn more about:
- GitHub Actions
- Harvester as a SUSE Product
- Go programming language
- Kubernetes API
- Kubevirt API objects (Manipulating VMs and VM Configuration in Kubernetes using Kubevirt)
Preparing KubeVirtBMC for project transfer to the KubeVirt organization by zchang
Description
KubeVirtBMC is preparing to transfer the project to the KubeVirt organization. One requirement is to enhance the modeling design's security. The current v1alpha1 API (the VirtualMachineBMC CRD) was designed during the proof-of-concept stage. It's immature and inherently insecure due to its cross-namespace object references, exposing security concerns from an RBAC perspective.
The other long-awaited feature is the ability to mount virtual media so that virtual machines can boot from remote ISO images.
Goals
- Deliver the v1beta1 API and its corresponding controller implementation
- Enable the Redfish virtual media mount function for KubeVirt virtual machines
Resources
- The KubeVirtBMC repo: https://github.com/starbops/kubevirtbmc
- The new v1beta1 API: https://github.com/starbops/kubevirtbmc/issues/83
- Redfish virtual media mount: https://github.com/starbops/kubevirtbmc/issues/44
