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.

---

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)

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Extracting, converting and importing VMs from Nutanix into SUSE Virtualization by emendonca

Description

The idea is to delve into understanding Nutanix AHV internals on how it stores and runs VMs, and how to extract them in an automated way for importing into a KVM-compatible hypervisor, like SUSE Virtualization/Harvester. The final product will be not only be documentation, but a working prototype that can be used to automate the process.

Goals

1) document how to create a simple lab with NutaniX AHV community edition 2) determine the basic elements we need to interact with 3) determine what are the best paths to grab the images through, balancing speed and complexity 4) document possible issues and create a roadmap for tackling them 4) should we adapt an existing solution or implement a new one? 5) implement the solution!

Resources

Similar project I created: https://github.com/doccaz/vm-import-ui Nutanix AHV forums Nutanix technical bulletins

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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.

---

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)

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Play with the userfaultfd(2) system call and download on demand using HTTP Range Requests with Golang by rbranco

Description

The userfaultfd(2) is a cool system call to handle page faults in user-space. This should allow me to list the contents of an ISO or similar archive without downloading the whole thing. The userfaultfd(2) part can also be done in theory with the PROT_NONE mprotect + SIGSEGV trick, for complete Unix portability, though reportedly being slower.

Goals

1. Create my own library for userfaultfd(2) in Golang.
2. Create my own library for HTTP Range Requests.
3. Complete portability with Unix.
4. Benchmarks.
5. Contribute some tests to LTP.

Resources

1. https://docs.kernel.org/admin-guide/mm/userfaultfd.html
2. https://www.cons.org/cracauer/cracauer-userfaultfd.html

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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

• Overall improvement of the tools
• Adding new collectors
• Add support for SLES16

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

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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

• The links included above
• https://chatmail.at/doc/relay/
• https://delta.chat/en/help
• Project repo -> https://codeberg.org/EndShittification/containerized-chatmail-relay

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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.

---

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

1. The AI agent should expose a new ws/autocomplete endpoint to proxy autocomplete messages to the LLM.
2. The UI extension should be able to display prompt suggestions and allow users to apply the autocomplete to the Prompt via keyboard shortcuts.

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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

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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.

---

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

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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

1. Deliver the v1beta1 API and its corresponding controller implementation
2. 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

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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:

1. 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

2. Showcase a Go-based deployment automation component, which scans the model registry and automatically generates & applies Kubernetes manifests for new model versions.

3. Enable continuous improvement by making the system modular and extensible (e.g., additional models, metrics, autoscaling, or drift detection can be added later).

4. Prepare a short demo explaining the end-to-end process and how new models flow through the system.

Resources

Project Repository

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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

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Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0

Self-Scaling LLM Infrastructure Powered by Rancher

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Description

The Problem

Running LLMs can get expensive and complex pretty quickly.

Today there are typically two choices:

1. Use cloud APIs like OpenAI or Anthropic. Easy to start with, but costs add up at scale.
2. 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

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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

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