Rust is a systems programming language from Mozilla. It has stronger safety guarantees than Go, and is well suited to working on cloud native infrastructure.

Most Kubernetes development is focused in Go, and it would be great to have something like https://github.com/kubernetes/client-go in Rust.

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rust kubernetes

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Hack Week 15

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almost 9 years ago: robdaemon added keyword "rust" to this project.

almost 9 years ago: robdaemon added keyword "kubernetes" to this project.

almost 9 years ago: robdaemon originated this project.

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rust

Build a terminal user-interface (TUI) for Agama by IGonzalezSosa

Description

Officially, Agama offers two different user interfaces. On the one hand, we have the web-based interface, which is the one you see when you run the installation media. On the other hand, we have a command-line interface. In both cases, you can use them using a remote system, either using a browser or the agama CLI.

We would expect most of the cases to be covered by this approach. However, if you cannot use the web-based interface and, for some reason, you cannot access the system through the network, your only option is to use the CLI. This interface offers a mechanism to modify Agama's configuration using an editor (vim, by default), but perhaps you might want to have a more user-friendly way.

Goals

The main goal of this project is to built a minimal terminal user-interface for Agama. This interface will allow the user to install the system providing just a few settings (selecting a product, a storage device and a user password). Then it should report the installation progress.

Resources

https://agama-project.github.io/
https://ratatui.rs/

Conclusions

We have summarized our conclusions in a pull request. It includes screenshots ;-) We did not implement all the features we wanted, but we learn a lot during the process. We know that, if needed, we could write a TUI for Agama and we have an idea about how to build it. Good enough.

Exploring Rust's potential: from basics to security by sferracci

Description

This project aims to conduct a focused investigation and practical application of the Rust programming language, with a specific emphasis on its security model. A key component will be identifying and understanding the most common vulnerabilities that can be found in Rust code.

Goals

Achieve a beginner/intermediate level of proficiency in writing Rust code. This will be measured by trying to solve LeetCode problems focusing on common data structures and algorithms. Study Rust vulnerabilities and learning best practices to avoid them.

Resources

Rust book: https://doc.rust-lang.org/book/

AI-Powered Unit Test Automation for Agama by joseivanlopez

The Agama project is a multi-language Linux installer that leverages the distinct strengths of several key technologies:

Rust: Used for the back-end services and the core HTTP API, providing performance and safety.
TypeScript (React/PatternFly): Powers the modern web user interface (UI), ensuring a consistent and responsive user experience.
Ruby: Integrates existing, robust YaST libraries (e.g., yast-storage-ng) to reuse established functionality.

The Problem: Testing Overhead

Developing and maintaining code across these three languages requires a significant, tedious effort in writing, reviewing, and updating unit tests for each component. This high cost of testing is a drain on developer resources and can slow down the project's evolution.

The Solution: AI-Driven Automation

This project aims to eliminate the manual overhead of unit testing by exploring and integrating AI-driven code generation tools. We will investigate how AI can:

Automatically generate new unit tests as code is developed.
Intelligently correct and update existing unit tests when the application code changes.

By automating this crucial but monotonous task, we can free developers to focus on feature implementation and significantly improve the speed and maintainability of the Agama codebase.

Goals

Proof of Concept: Successfully integrate and demonstrate an authorized AI tool (e.g., gemini-cli) to automatically generate unit tests.
Workflow Integration: Define and document a new unit test automation workflow that seamlessly integrates the selected AI tool into the existing Agama development pipeline.
Knowledge Sharing: Establish a set of best practices for using AI in code generation, sharing the learned expertise with the broader team.

Contribution & Resources

We are seeking contributors interested in AI-powered development and improving developer efficiency. Whether you have previous experience with code generation tools or are eager to learn, your participation is highly valuable.

If you want to dive deep into AI for software quality, please reach out and join the effort!

Authorized AI Tools: Tools supported by SUSE (e.g., gemini-cli)
Focus Areas: Rust, TypeScript, and Ruby components within the Agama project.

Interesting Links

goose

Looking at Rust if it could be an interesting programming language by jsmeix

Get some basic understanding of Rust security related features from a general point of view.

This Hack Week project is not to learn Rust to become a Rust programmer. This might happen later but it is not the goal of this Hack Week project.

The goal of this Hack Week project is to evaluate if Rust could be an interesting programming language.

An interesting programming language must make it easier to write code that is correct and stays correct when over time others maintain and enhance it than the opposite.

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

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/

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

Updates

Training pipeline and datasets
Inference Service py

OpenPlatform Self-Service Portal by tmuntan1

Description

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

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:

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!