This is mostly a learning activity for myself, others may benefit from documentation.
Project Description
Practical setup of a k3s HA cluster
Goal for this Hackweek
Understand the concept, get the cluster up and running workloads. Create documentation that others can follow.
Resources
Use my workstation, or other available hardware. Probably utilize MicroOS.
Looking for hackers with the skills:
This project is part of:
Hack Week 22
Activity
Comments
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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
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! 
Cluster API Provider for Harvester by rcase
Project Description
The Cluster API "infrastructure provider" for Harvester, also named CAPHV, makes it possible to use Harvester with Cluster API. This enables people and organisations to create Kubernetes clusters running on VMs created by Harvester using a declarative spec.
The project has been bootstrapped in HackWeek 23, and its code is available here.
Work done in HackWeek 2023
- Have a early working version of the provider available on Rancher Sandbox : *DONE *
- Demonstrated the created cluster can be imported using Rancher Turtles: DONE
- Stretch goal - demonstrate using the new provider with CAPRKE2: DONE and the templates are available on the repo
DONE in HackWeek 24:
- Add more Unit Tests
- Improve Status Conditions for some phases
- Add cloud provider config generation
- Testing with Harvester v1.3.2
- Template improvements
- Issues creation
DONE in 2025 (out of Hackweek)
- Support of ClusterClass
- Add to
clusterctlcommunity providers, you can add it directly withclusterctl - Testing on newer versions of Harvester v1.4.X and v1.5.X
- Support for
clusterctl generate cluster ... - Improve Status Conditions to reflect current state of Infrastructure
- Improve CI (some bugs for release creation)
Goals for HackWeek 2025
- FIRST and FOREMOST, any topic is important to you
- Add e2e testing
- Certify the provider for Rancher Turtles
- Add Machine pool labeling
- Add PCI-e passthrough capabilities.
- Other improvement suggestions are welcome!
Thanks to @isim and Dominic Giebert for their contributions!
Resources
Looking for help from anyone interested in Cluster API (CAPI) or who wants to learn more about Harvester.
This will be an infrastructure provider for Cluster API. Some background reading for the CAPI aspect:
OpenPlatform Self-Service Portal by tmuntan1
Description
In SUSE IT, we developed an internal developer platform for our engineers using SUSE technologies such as RKE2, SUSE Virtualization, and Rancher. While it works well for our existing users, the onboarding process could be better.
To improve our customer experience, I would like to build a self-service portal to make it easy for people to accomplish common actions. To get started, I would have the portal create Jira SD tickets for our customers to have better information in our tickets, but eventually I want to add automation to reduce our workload.
Goals
- Build a frontend website (Angular) that helps customers create Jira SD tickets.
- Build a backend (Rust with Axum) for the backend, which would do all the hard work for the frontend.
Resources (SUSE VPN only)
- development site: https://ui-dev.openplatform.suse.com/login?returnUrl=%2Fopenplatform%2Fforms
- https://gitlab.suse.de/itpe/core/open-platform/op-portal/backend
- https://gitlab.suse.de/itpe/core/open-platform/op-portal/frontend
Rancher/k8s Trouble-Maker by tonyhansen
Project Description
When studying for my RHCSA, I found trouble-maker, which is a program that breaks a Linux OS and requires you to fix it. I want to create something similar for Rancher/k8s that can allow for troubleshooting an unknown environment.
Goals for Hackweek 25
- Update to modern Rancher and verify that existing tests still work
- Change testing logic to populate secrets instead of requiring a secondary script
- Add new tests
Goals for Hackweek 24 (Complete)
- Create a basic framework for creating Rancher/k8s cluster lab environments as needed for the Break/Fix
- Create at least 5 modules that can be applied to the cluster and require troubleshooting
Resources
- https://github.com/celidon/rancher-troublemaker
- https://github.com/rancher/terraform-provider-rancher2
- https://github.com/rancher/tf-rancher-up
- https://github.com/rancher/quickstart
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
Advent of Code: The Diaries by amanzini
Description
It was the Night Before Compile Time ...
Hackweek 25 (December 1-5) perfectly coincides with the first five days of Advent of Code 2025. This project will leverage this overlap to participate in the event in real-time.
To add a layer of challenge and exploration (in the true spirit of Hackweek), the puzzles will be solved using a non-mainstream, modern language like Ruby, D, Crystal, Gleam or Zig.
The primary project intent is not just simply to solve the puzzles, but to exercise result sharing and documentation. I'd create a public-facing repository documenting the process. This involves treating each day's puzzle as a mini-project: solving it, then documenting the solution with detailed write-ups, analysis of the language's performance and ergonomics, and visualizations.
|
\ ' /
-- (*) --
>*<
>0<@<
>>>@<<*
>@>*<0<<<
>*>>@<<<@<<
>@>>0<<<*<<@<
>*>>0<<@<<<@<<<
>@>>*<<@<>*<<0<*<
\*/ >0>>*<<@<>0><<*<@<<
___\\U//___ >*>>@><0<<*>>@><*<0<<
|\\ | | \\| >@>>0<*<0>>@<<0<<<*<@<<
| \\| | _(UU)_ >((*))_>0><*<0><@<<<0<*<
|\ \| || / //||.*.*.*.|>>@<<*<<@>><0<<<
|\\_|_|&&_// ||*.*.*.*|_\\db//_
""""|'.'.'.|~~|.*.*.*| ____|_
|'.'.'.| ^^^^^^|____|>>>>>>|
~~~~~~~~ '""""`------'
------------------------------------------------
This ASCII pic can be found at
https://asciiart.website/art/1831
Goals
Code, Docs, and Memes: An AoC Story
Have fun!
Involve more people, play together
Solve Days 1-5: Successfully solve both parts of the Advent of Code 2025 puzzles for Days 1-5 using the chosen non-mainstream language.
Daily Documentation & Language Review: Publish a detailed write-up for each day. This documentation will include the solution analysis, the chosen algorithm, and specific commentary on the language's ergonomics, performance, and standard library for the given task.