Project Description

We know that Kubernetes clusters need control planes but running them in dedicated VMs might not be always efficient, instead, we can run them as pods within the management cluster. This project aims to solve the following problems:

Reduce the cost of provisioning control planes
Provide a declarative API for control plane management
Centralize management of control planes and decouple them from workers
Provide k3s clusters as a service on any infrastructure.

Goal for this Hackweek

Look into existing solutions like https://github.com/clastix/kamaji
Figure out if any can be reused for managing K3S
Investigate what changes need to be done in K3S in order to support this use-case
Build an API that allows provisioning and configuring of K3S control planes, see https://github.com/zawachte/cluster-api-k3s/ for generating k3s configuration using K8S API.

Resources

No project repository for now, all hacking will be done in these forks: https://github.com/alexander-demicev/kamaji https://github.com/alexander-demicev/k3s

What was achieved during hack week?

I was able to deploy k3s in a pod using experimental agentless feature https://docs.k3s.io/advanced#running-agentless-servers-experimental, meaning the server will not run kubelet, container runtime, or CNI
It was possible to deploy an external etcd and connect agentless servers to it https://docs.k3s.io/installation/ha#2-launch-server-nodes
I managed to run and connect a worker node to control planes running in pods

What wasn't done during hack week?

After some investigation I came to conclusion that kamaji might be reused but with some changes to its codebase as we are plugging our kubernetes distro
I was working on POC operator based on CAPI k3s provider https://github.com/zawachte/cluster-api-k3s/, the operator would manage pod deployments instead of CAPI machines but one week is not enough to get it working

Join this project Leave this project

Looking for hackers with the skills:

rancher containers kubernetes edge k3s go

This project is part of:

Hack Week 22

Activity

almost 3 years ago: flonnegren liked this project.

almost 3 years ago: fgiudici liked this project.

almost 3 years ago: paulgonin liked this project.

almost 3 years ago: ademicev0 started this project.

almost 3 years ago: ademicev0 added keyword "rancher" to this project.

almost 3 years ago: ademicev0 added keyword "containers" to this project.

almost 3 years ago: ademicev0 added keyword "kubernetes" to this project.

almost 3 years ago: ademicev0 added keyword "edge" to this project.

almost 3 years ago: ademicev0 added keyword "k3s" to this project.

almost 3 years ago: ademicev0 added keyword "go" to this project.

almost 3 years ago: ademicev0 originated this project.

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rancher

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)

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

GitHub repository

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 clusterctl community providers, you can add it directly with clusterctl
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:

Rancher Cluster Lifecycle Visualizer by jferraz

Description

Rancher’s v2 provisioning system represents each downstream cluster with several Kubernetes custom resources across multiple API groups, such as clusters.provisioning.cattle.io and clusters.management.cattle.io. Understanding why a cluster is stuck in states like "Provisioning", "Updating", or "Unavailable" often requires jumping between these resources, reading conditions, and correlating them with agent connectivity and known failure modes. This project will build a Cluster Lifecycle Visualizer: a small, read-only controller that runs in the Rancher management cluster and generates a single, human-friendly view per cluster. It will watch Rancher cluster CRDs, derive a simplified lifecycle phase, keep a history of phase transitions from installation time onward, and attach a short, actionable recommendation string that hints at what the operator should check or do next.

Goals

Provide a compact lifecycle summary for each Rancher-managed cluster (e.g. Provisioning, WaitingForClusterAgent, Active, Updating, Error) derived from provisioning.cattle.io/v1 Cluster and management.cattle.io/v3 Cluster status and conditions.
Maintain a phase history for each cluster, allowing operators to see how its state evolved over time since the visualizer was installed.
Attach a recommended action to the current phase using a small ruleset based on common Rancher failure modes (for example, cluster agent not connected, cluster still stabilizing after an upgrade, or generic error states), to improve the day-to-day debugging experience.
Deliver an easy-to-install, read-only component (single YAML or small Helm chart) that Rancher users can deploy to their management cluster and inspect via kubectl get/describe, without UI changes or direct access to downstream clusters.
Use idiomatic Go, wrangler, and Rancher APIs.

Resources

Rancher Manager documentation on RKE2 and K3s cluster configuration and provisioning flows.
Rancher API Go types for provisioning.cattle.io/v1 and management.cattle.io/v3 (from the rancher/rancher repository or published Go packages).
Existing Rancher architecture docs and internal notes about cluster provisioning, cluster agents, and node agents.
A local Rancher management cluster (k3s or RKE2) with a few test downstream clusters to validate phase detection, history tracking, and recommendations.

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 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

containers

Help Create A Chat Control Resistant Turnkey Chatmail/Deltachat Relay Stack - Rootless Podman Compose, OpenSUSE BCI, Hardened, & SELinux by 3nd5h1771fy

Description

The Mission: Decentralized & Sovereign Messaging

FYI: If you have never heard of "Chatmail", you can visit their site here, but simply put it can be thought of as the underlying protocol/platform decentralized messengers like DeltaChat use for their communications. Do not confuse it with the honeypot looking non-opensource paid for prodect with better seo that directs you to chatmailsecure(dot)com

In an era of increasing centralized surveillance by unaccountable bad actors (aka BigTech), "Chat Control," and the erosion of digital privacy, the need for sovereign communication infrastructure is critical. Chatmail is a pioneering initiative that bridges the gap between classic email and modern instant messaging, offering metadata-minimized, end-to-end encrypted (E2EE) communication that is interoperable and open.

However, unless you are a seasoned sysadmin, the current recommended deployment method of a Chatmail relay is rigid, fragile, difficult to properly secure, and effectively takes over the entire host the "relay" is deployed on.

Why This Matters

A simple, host agnostic, reproducible deployment lowers the entry cost for anyone wanting to run a privacy‑preserving, decentralized messaging relay. In an era of perpetually resurrected chat‑control legislation threats, EU digital‑sovereignty drives, and many dangers of using big‑tech messaging platforms (Apple iMessage, WhatsApp, FB Messenger, Instagram, SMS, Google Messages, etc...) for any type of communication, providing an easy‑to‑use alternative empowers:

Censorship resistance - No single entity controls the relay; operators can spin up new nodes quickly.
Surveillance mitigation - End‑to‑end OpenPGP encryption ensures relay operators never see plaintext.
Digital sovereignty - Communities can host their own infrastructure under local jurisdiction, aligning with national data‑policy goals.

By turning the Chatmail relay into a plug‑and‑play container stack, we enable broader adoption, foster a resilient messaging fabric, and give developers, activists, and hobbyists a concrete tool to defend privacy online.

Goals

As I indicated earlier, this project aims to drastically simplify the deployment of Chatmail relay. By converting this architecture into a portable, containerized stack using Podman and OpenSUSE base container images, we can allow anyone to deploy their own censorship-resistant, privacy-preserving communications node in minutes.

Our goal for Hack Week: package every component into containers built on openSUSE/MicroOS base images, initially orchestrated with a single container-compose.yml (podman-compose compatible). The stack will:

Run on any host that supports Podman (including optimizations and enhancements for SELinux‑enabled systems).
Allow network decoupling by refactoring configurations to move from file-system constrained Unix sockets to internal TCP networking, allowing containers achieve stricter isolation.
Utilize Enhanced Security with SELinux by using purpose built utilities such as udica we can quickly generate custom SELinux policies for the container stack, ensuring strict confinement superior to standard/typical Docker deployments.
Allow the use of bind or remote mounted volumes for shared data (/var/vmail, DKIM keys, TLS certs, etc.).
Replace the local DNS server requirement with a remote DNS‑provider API for DKIM/TXT record publishing.

By delivering a turnkey, host agnostic, reproducible deployment, we lower the barrier for individuals and small communities to launch their own chatmail relays, fostering a decentralized, censorship‑resistant messaging ecosystem that can serve DeltaChat users and/or future services adopting this protocol

Resources

Technical talks at universities by agamez

Description

This project aims to empower the next generation of tech professionals by offering hands-on workshops on containerization and Kubernetes, with a strong focus on open-source technologies. By providing practical experience with these cutting-edge tools and fostering a deep understanding of open-source principles, we aim to bridge the gap between academia and industry.

For now, the scope is limited to Spanish universities, since we already have the contacts and have started some conversations.

Goals

Technical Skill Development: equip students with the fundamental knowledge and skills to build, deploy, and manage containerized applications using open-source tools like Kubernetes.
Open-Source Mindset: foster a passion for open-source software, encouraging students to contribute to open-source projects and collaborate with the global developer community.
Career Readiness: prepare students for industry-relevant roles by exposing them to real-world use cases, best practices, and open-source in companies.

Resources

Instructors: experienced open-source professionals with deep knowledge of containerization and Kubernetes.
SUSE Expertise: leverage SUSE's expertise in open-source technologies to provide insights into industry trends and best practices.

Rewrite Distrobox in go (POC) by fabriziosestito

Description

Rewriting Distrobox in Go.

Main benefits:

Easier to maintain and to test
Adapter pattern for different container backends (LXC, systemd-nspawn, etc.)

Goals

Build a minimal starting point with core commands
Keep the CLI interface compatible: existing users shouldn't notice any difference
Use a clean Go architecture with adapters for different container backends
Keep dependencies minimal and binary size small
Benchmark against the original shell script

Resources

Upstream project: https://github.com/89luca89/distrobox/
Distrobox site: https://distrobox.it/
ArchWiki: https://wiki.archlinux.org/title/Distrobox

kubernetes

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

A CLI for Harvester by mohamed.belgaied

Project Description

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

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)

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

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

Project Repository

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

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?

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

edge

SUSE Edge Image Builder json schema by eminguez

Description

Current SUSE Edge Image Builder tool doesn't provide a json schema (yes, I know EIB uses yaml but it seems JSON Schema can be used to validate YAML documents yay!) that defines the configuration file syntax, values, etc.

Having a json schema will make integrations straightforward, as once the json schema is in place, it can be used as the interface for other tools to consume and generate EIB definition files (like TUI wizards, web UIs, etc.)

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 extensively used gemini via the VScode "gemini code assist" plugin but I found it not too good... my workstation froze for minutes using it... I have a pretty beefy macbook pro M2 and AFAIK the model is being executed on the cloud... so I basically spent a few days fighting with it... Then I switched to antigravity and its agent mode... and it worked much better.

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

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

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

Set Uyuni to manage edge clusters at scale by RDiasMateus

Description

Prepare a Poc on how to use MLM to manage edge clusters. Those cluster are normally equal across each location, and we have a large number of them.

The goal is to produce a set of sets/best practices/scripts to help users manage this kind of setup.

Goals

step 1: Manual set-up

Goal: Have a running application in k3s and be able to update it using System Update Controler (SUC)

Deploy Micro 6.2 machine
Deploy k3s - single node
- https://docs.k3s.io/quick-start
Build/find a simple web application (static page)
- Build/find a helmchart to deploy the application
Deploy the application on the k3s cluster
Install App updates through helm update
Install OS updates using MLM

step 2: Automate day 1

Goal: Trigger the application deployment and update from MLM

Salt states For application (with static data)
- Deploy the application helmchart, if not present
- install app updates through helmchart parameters
Link it to GIT
- Define how to link the state to the machines (based in some pillar data? Using configuration channels by importing the state? Naming convention?)
- Use git update to trigger helmchart app update
Recurrent state applying configuration channel?

step 3: Multi-node cluster

Goal: Use SUC to update a multi-node cluster.

Create a multi-node cluster
Deploy application
- call the helm update/install only on control plane?
Install App updates through helm update
Prepare a SUC for OS update (k3s also? How?)
- https://github.com/rancher/system-upgrade-controller
- https://documentation.suse.com/cloudnative/k3s/latest/en/upgrades/automated.html
- Update/deploy the SUC?
- Update/deploy the SUC CRD with the update procedure

go

Cluster API Provider for Harvester by rcase

Project Description

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 clusterctl community providers, you can add it directly with clusterctl
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:

Help Create A Chat Control Resistant Turnkey Chatmail/Deltachat Relay Stack - Rootless Podman Compose, OpenSUSE BCI, Hardened, & SELinux by 3nd5h1771fy

Description

The Mission: Decentralized & Sovereign Messaging

Why This Matters

Censorship resistance - No single entity controls the relay; operators can spin up new nodes quickly.
Surveillance mitigation - End‑to‑end OpenPGP encryption ensures relay operators never see plaintext.
Digital sovereignty - Communities can host their own infrastructure under local jurisdiction, aligning with national data‑policy goals.

Goals

Run on any host that supports Podman (including optimizations and enhancements for SELinux‑enabled systems).
Allow network decoupling by refactoring configurations to move from file-system constrained Unix sockets to internal TCP networking, allowing containers achieve stricter isolation.
Utilize Enhanced Security with SELinux by using purpose built utilities such as udica we can quickly generate custom SELinux policies for the container stack, ensuring strict confinement superior to standard/typical Docker deployments.
Allow the use of bind or remote mounted volumes for shared data (/var/vmail, DKIM keys, TLS certs, etc.).
Replace the local DNS server requirement with a remote DNS‑provider API for DKIM/TXT record publishing.

Resources

Create a go module to wrap happy-compta.fr by cbosdonnat

Description

https://happy-compta.fr is a tool for french work councils simple book keeping. While it does the job, it has no API to work with and it is tedious to enter loads of operations.

Goals

Write a go client module to be used as an API to programmatically manipulate the tool.

Writing an example tool to load data from a CSV file would be good too.

Add support for todo.sr.ht to git-bug by mcepl

Description

I am a big fan of distributed issue tracking and the best (and possibly) only credible such issue tracker is now git-bug. It has bridges to another centralized issue trackers, so user can download (and modify) issues on GitHub, GitLab, Launchpad, Jira). I am also a fan of SourceHut, which has its own issue tracker, so I would like it bridge the two. Alas, I don’t know much about Go programming language (which the git-bug is written) and absolutely nothing about GraphQL (which todo.sr.ht uses for communication). AI to the rescue. I would like to vibe code (and eventually debug and make functional) bridge to the SourceHut issue tracker.

Goals

Functional fix for https://github.com/git-bug/git-bug/issues/1024

Resources

anybody how actually understands how GraphQL and authentication on SourceHut (OAuth2) works

A CLI for Harvester by mohamed.belgaied

Project Description

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

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)