Description
Firecracker is an open-source virtualization technology that is purpose-built for creating and managing secure, multi-tenant container and function-based services.
Integrating Firecracker with a CF container runtime will bring security to the underlying Kubernetes cluster and to other apps when using the Cloud Application Platform.
Links (don't share them!)
Looking for hackers with the skills:
This project is part of:
Hack Week 19
Activity
Comments
Be the first to comment!
Similar Projects
SUSE KVM Best Practices - Focus on SAP Workloads and Use Cases by roseswe
Description
SUSE Best Practices around KVM, especially for SAP workloads. Early Google presentation already made from various customer projects and SUSE sources.
Goals
- Complete presentation we can reuse in SUSE Consulting projects
- 2025: Bring it to version 1.00 ready for customers
Resources
KVM (virt-manager) images
SUSE/SAP/KVM Best Practices
- https://documentation.suse.com/en-us/sles/15-SP6/single-html/SLES-virtualization/
- SAP Note 1522993 - "Linux: SAP on SUSE KVM - Kernel-based Virtual Machine" && 2284516 - SAP HANA virtualized on SUSE Linux Enterprise hypervisors https://me.sap.com/notes/2284516
- SUSECon24: [TUTORIAL-1253] Virtualizing SAP workloads with SUSE KVM || https://youtu.be/PTkpRVpX2PM
- SUSE Best Practices for SAP HANA on KVM - https://documentation.suse.com/sbp/sap-15/html/SBP-SLES4SAP-HANAonKVM-SLES15SP4/index.html
Reassess HiFive Premier P550 board (for RISC-V virtualization) by a_faerber
Description
With growing interest in the RISC-V instruction set architecture, we need to re-evaluate ways of building packages for it:
Currently openSUSE OBS is using x86_64 build workers, using QEMU userspace-level (syscall) emulation inside KVM VMs. Occasionally this setup causes build failures, due to timing differences or incomplete emulation. Andreas Schwab and others have collected workarounds in projects like openSUSE:Factory:RISCV to deal with some of those issues.
Ideally we would be using native riscv64 KVM VMs instead. This requires CPUs with the H extension. Two generally available development boards feature the ESWIN 7700X System-on-Chip with SiFive P550 CPUs, HiFive Premier P550 and Milk-V Megrez. We've had access to the HiFive Premier P550 for some time now, but the early version (based on Yocto) had issues with the bootloader, and reportedly later boards were booting to a dracut emergency shell for lack of block device drivers.
Goals
- Update the boot firmware
- Test whether and how far openSUSE Tumbleweed boots
Results
- Boot firmware image 2025.11.00 successfully flashed onto board
- Enables UEFI boot in U-Boot by default
- U-Boot's embedded Flat Device Tree is lacking a timebase-frequency, required for recent (6.16.3) mainline kernels (panic leading to reset, visible via earlycon=sbi)
- Tested eswin/eic7700-hifive-premier-p550.dtb from Ubuntu 2025.11.00 image
- Allows to boot past the above panic, but times out in JeOS image while waiting for block device, dropping to dracut emergency shell
- No devices shown in lsblk -- 6.16 appears to be lacking device drivers still
Resources
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)
Reassess HiFive Premier P550 board (for RISC-V virtualization) by a_faerber
Description
With growing interest in the RISC-V instruction set architecture, we need to re-evaluate ways of building packages for it:
Currently openSUSE OBS is using x86_64 build workers, using QEMU userspace-level (syscall) emulation inside KVM VMs. Occasionally this setup causes build failures, due to timing differences or incomplete emulation. Andreas Schwab and others have collected workarounds in projects like openSUSE:Factory:RISCV to deal with some of those issues.
Ideally we would be using native riscv64 KVM VMs instead. This requires CPUs with the H extension. Two generally available development boards feature the ESWIN 7700X System-on-Chip with SiFive P550 CPUs, HiFive Premier P550 and Milk-V Megrez. We've had access to the HiFive Premier P550 for some time now, but the early version (based on Yocto) had issues with the bootloader, and reportedly later boards were booting to a dracut emergency shell for lack of block device drivers.
Goals
- Update the boot firmware
- Test whether and how far openSUSE Tumbleweed boots
Results
- Boot firmware image 2025.11.00 successfully flashed onto board
- Enables UEFI boot in U-Boot by default
- U-Boot's embedded Flat Device Tree is lacking a timebase-frequency, required for recent (6.16.3) mainline kernels (panic leading to reset, visible via earlycon=sbi)
- Tested eswin/eic7700-hifive-premier-p550.dtb from Ubuntu 2025.11.00 image
- Allows to boot past the above panic, but times out in JeOS image while waiting for block device, dropping to dracut emergency shell
- No devices shown in lsblk -- 6.16 appears to be lacking device drivers still
Resources
Contribute to terraform-provider-libvirt by pinvernizzi
Description
The SUSE Manager (SUMA) teams' main tool for infrastructure automation, Sumaform, largely relies on terraform-provider-libvirt. That provider is also widely used by other teams, both inside and outside SUSE.
It would be good to help the maintainers of this project and give back to the community around it, after all the amazing work that has been already done.
If you're interested in any of infrastructure automation, Terraform, virtualization, tooling development, Go (...) it is also a good chance to learn a bit about them all by putting your hands on an interesting, real-use-case and complex project.
Goals
- Get more familiar with Terraform provider development and libvirt bindings in Go
- Solve some issues and/or implement some features
- Get in touch with the community around the project
Resources
- CONTRIBUTING readme
- Go libvirt library in use by the project
- Terraform plugin development
- "Good first issue" list
SUSE KVM Best Practices - Focus on SAP Workloads and Use Cases by roseswe
Description
SUSE Best Practices around KVM, especially for SAP workloads. Early Google presentation already made from various customer projects and SUSE sources.
Goals
- Complete presentation we can reuse in SUSE Consulting projects
- 2025: Bring it to version 1.00 ready for customers
Resources
KVM (virt-manager) images
SUSE/SAP/KVM Best Practices
- https://documentation.suse.com/en-us/sles/15-SP6/single-html/SLES-virtualization/
- SAP Note 1522993 - "Linux: SAP on SUSE KVM - Kernel-based Virtual Machine" && 2284516 - SAP HANA virtualized on SUSE Linux Enterprise hypervisors https://me.sap.com/notes/2284516
- SUSECon24: [TUTORIAL-1253] Virtualizing SAP workloads with SUSE KVM || https://youtu.be/PTkpRVpX2PM
- SUSE Best Practices for SAP HANA on KVM - https://documentation.suse.com/sbp/sap-15/html/SBP-SLES4SAP-HANAonKVM-SLES15SP4/index.html
SUSE Virtualization (Harvester): VM Import UI flow by wombelix
Description
SUSE Virtualization (Harvester) has a vm-import-controller that allows migrating VMs from VMware and OpenStack, but users need to write manifest files and apply them with kubectl to use it. This project is about adding the missing UI pieces to the harvester-ui-extension, making VM Imports accessible without requiring Kubernetes and YAML knowledge.
VMware and OpenStack admins aren't automatically familiar with Kubernetes and YAML. Implementing the UI part for the VM Import feature makes it easier to use and more accessible. The Harvester Enhancement Proposal (HEP) VM Migration controller included a UI flow implementation in its scope. Issue #2274 received multiple comments that an UI integration would be a nice addition, and issue #4663 was created to request the implementation but eventually stalled.
Right now users need to manually create either VmwareSource or OpenstackSource resources, then write VirtualMachineImport manifests with network mappings and all the other configuration options. Users should be able to do that and track import status through the UI without writing YAML.
Work during the Hack Week will be done in this fork in a branch called suse-hack-week-25, making progress publicly visible and open for contributions. When everything works out and the branch is in good shape, it will be submitted as a pull request to harvester-ui-extension to get it included in the next Harvester release.
Testing will focus on VMware since that's what is available in the lab environment (SUSE Virtualization 1.6 single-node cluster, ESXi 8.0 standalone host). Given that this is about UI and surfacing what the vm-import-controller handles, the implementation should work for OpenStack imports as well.
This project is also a personal challenge to learn vue.js and get familiar with Rancher Extensions development, since harvester-ui-extension is built on that framework.
Goals
- Learn Vue.js and Rancher Extensions fundamentals required to finish the project
- Read and learn from other Rancher UI Extensions code, especially understanding the
harvester-ui-extensioncode base - Understand what the
vm-import-controllerand its CRDs require, identify ready to use components in the Rancher UI Extension API that can be leveraged - Implement UI logic for creating and managing
VmwareSource/OpenstackSourceandVirtualMachineImportresources with all relevant configuration options and credentials - Implemnt UI elements to display
VirtualMachineImportstatus and errors
Resources
HEP and related discussion
- https://github.com/harvester/harvester/blob/master/enhancements/20220726-vm-migration.md
- https://github.com/harvester/harvester/issues/2274
- https://github.com/harvester/harvester/issues/4663
SUSE Virtualization VM Import Documentation
Rancher Extensions Documentation
Rancher UI Plugin Examples
Vue Router Essentials
Vue Router API
Vuex Documentation
Q2Boot - A handy QEMU VM launcher by amanzini
Description
Q2Boot (Qemu Quick Boot) is a command-line tool that wraps QEMU to provide a streamlined experience for launching virtual machines. It automatically configures common settings like KVM acceleration, virtio drivers, and networking while allowing customization through both configuration files and command-line options.
The project originally was a personal utility in D, now recently rewritten in idiomatic Go. It lives at repository https://github.com/ilmanzo/q2boot
Goals
Improve the project, testing with different scenarios , address issues and propose new features. It will benefit of some basic integration testing by providing small sample disk images.
Updates
- Dec 1, 2025 : refactor command line options, added structured logging. Released v0.0.2
- Dec 2, 2025 : added external monitor via telnet option
- Dec 4, 2025 : released v0.0.3 with architecture auto-detection
- Dec 5, 2025 : filing new issues and general polishment. Designing E2E testing
Resources
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
Exploring Modern AI Trends and Kubernetes-Based AI Infrastructure by jluo
Description
Build a solid understanding of the current landscape of Artificial Intelligence and how modern cloud-native technologies—especially Kubernetes—support AI workloads.
Goals
Use Gemini Learning Mode to guide the exploration, surface relevant concepts, and structure the learning journey:
- Gain insight into the latest AI trends, tools, and architectural concepts.
- Understand how Kubernetes and related cloud-native technologies are used in the AI ecosystem (model training, deployment, orchestration, MLOps).
Resources
Red Hat AI Topic Articles
- https://www.redhat.com/en/topics/ai
Kubeflow Documentation
- https://www.kubeflow.org/docs/
Q4 2025 CNCF Technology Landscape Radar report:
- https://www.cncf.io/announcements/2025/11/11/cncf-and-slashdata-report-finds-leading-ai-tools-gaining-adoption-in-cloud-native-ecosystems/
- https://www.cncf.io/wp-content/uploads/2025/11/cncfreporttechradar_111025a.pdf
Agent-to-Agent (A2A) Protocol
- https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/
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! 
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.
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