Project Description

Create a K8s CRD for s3gw.
The operator will be written in Go.
The CRD should in the beginning allow an user to create a bucket.

Goal for this Hackweek

The CRD should in the beginning allow an user to create a bucket.

Project

https://github.com/giubacc/s3gw-operator

Looking for hackers with the skills:

golang kubernetes operator s3gw

This project is part of:

Hack Week 22

Activity

  • about 3 years ago: gbaccini joined this project.
  • about 3 years ago: gbaccini added keyword "golang" to this project.
  • about 3 years ago: gbaccini added keyword "kubernetes" to this project.
  • about 3 years ago: gbaccini added keyword "operator" to this project.
  • about 3 years ago: gbaccini added keyword "s3gw" to this project.
  • about 3 years ago: tdehler started this project.
  • about 3 years ago: gbaccini originated this project.

  • Comments

    Be the first to comment!

    Similar Projects

    Updatecli Autodiscovery supporting WASM plugins by olblak

    Description

    Updatecli is a Golang Update policy engine that allow to write Update policies in YAML manifest. Updatecli already has a plugin ecosystem for common update strategies such as automating Dockerfile or Kubernetes manifest from Git repositories.

    This is what we call autodiscovery where Updatecli generate manifest and apply them dynamically based on some context.

    Obviously, the Updatecli project doesn't accept plugins specific to an organization.

    I saw project using different languages such as python, C#, or JS to generate those manifest.

    It would be great to be able to share and reuse those specific plugins

    During the HackWeek, I'll hang on the Updatecli matrix channel

    https://matrix.to/#/#Updatecli_community:gitter.im

    Goals

    Implement autodiscovery plugins using WASM. I am planning to experiment with https://github.com/extism/extism

    To build a simple WASM autodiscovery plugin and run it from Updatecli

    Resources

    • https://github.com/extism/extism
    • https://github.com/updatecli/updatecli
    • https://www.updatecli.io/docs/core/autodiscovery/
    • https://matrix.to/#/#Updatecli_community:gitter.im


    Create a Cloud-Native policy engine with notifying capabilities to optimize resource usage by gbazzotti

    Description

    The goal of this project is to begin the initial phase of development of an all-in-one Cloud-Native Policy Engine that notifies resource owners when their resources infringe predetermined policies. This was inspired by a current issue in the CES-SRE Team where other solutions seemed to not exactly correspond to the needs of the specific workloads running on the Public Cloud Team space.

    The initial architecture can be checked out on the Repository listed under Resources.

    Among the features that will differ this project from other monitoring/notification systems:

    • Pre-defined sensible policies written at the software-level, avoiding a learning curve by requiring users to write their own policies
    • All-in-one functionality: logging, mailing and all other actions are not required to install any additional plugins/packages
    • Easy account management, being able to parse all required configuration by a single JSON file
    • Eliminate integrations by not requiring metrics to go through a data-agreggator

    Goals

    • Create a minimal working prototype following the workflow specified on the documentation
    • Provide instructions on installation/usage
    • Work on email notifying capabilities

    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.


    go-git: unlocking SHA256-based repository cloning ahead of git v3 by pgomes

    Description

    The go-git library implements the git internals in pure Go, so that any Go application can handle not only Git repositories, but also lower-level primitives (e.g. packfiles, idxfiles, etc) without needing to shell out to the git binary.

    The focus for this Hackweek is to fast track key improvements for the project ahead of the upstream release of Git V3, which may take place at some point next year.

    Goals

    Stretch goals

    Resources

    • https://github.com/go-git/go-git/
    • https://go-git.github.io/docs/


    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


    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/


    Self-Scaling LLM Infrastructure Powered by Rancher by ademicev0

    Self-Scaling LLM Infrastructure Powered by Rancher

    logo


    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


    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:

    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:


    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


    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

    Updates

    1. Training pipeline and datasets
    2. Inference Service py