RMT.rs: High-Performance Registration Path for RMT using Rust by gbasso

Description

The SUSE Repository Mirroring Tool (RMT) is a critical component for managing software updates and subscriptions, especially for our Public Cloud Team (PCT). In a cloud environment, hundreds or even thousands of new SUSE instances (VPS/EC2) can be provisioned simultaneously. Each new instance attempts to register against an RMT server, creating a "thundering herd" scenario.

We have observed that the current RMT server, written in Ruby, faces performance issues under this high-concurrency registration load. This can lead to request overhead, slow registration times, and outright registration failures, delaying the readiness of new cloud instances.

This Hackweek project aims to explore a solution by re-implementing the performance-critical registration path in Rust. The goal is to leverage Rust's high performance, memory safety, and first-class concurrency handling to create an alternative registration endpoint that is fast, reliable, and can gracefully manage massive, simultaneous request spikes.

The new Rust module will be integrated into the existing RMT Ruby application, allowing us to directly compare the performance of both implementations.

Goals

The primary objective is to build and benchmark a high-performance Rust-based alternative for the RMT server registration endpoint.

Key goals for the week:

1. Analyze & Identify: Dive into the SUSE/rmt Ruby codebase to identify and map out the exact critical path for server registration (e.g., controllers, services, database interactions).
2. Develop in Rust: Implement a functionally equivalent version of this registration logic in Rust.
3. Integrate: Explore and implement a method for Ruby/Rust integration to "hot-wire" the new Rust module into the RMT application. This may involve using FFI, or libraries like rb-sys or magnus.
4. Benchmark: Create a benchmarking script (e.g., using k6, ab, or a custom tool) that simulates the high-concurrency registration load from thousands of clients.
5. Compare & Present: Conduct a comparative performance analysis (requests per second, latency, success/error rates, CPU/memory usage) between the original Ruby path and the new Rust path. The deliverable will be this data and a summary of the findings.

Resources

• RMT Source Code (Ruby):
  • https://github.com/SUSE/rmt
• RMT Documentation:
  • https://documentation.suse.com/sles/15-SP7/html/SLES-all/book-rmt.html
• Tooling & Stacks:
  • RMT/Ruby development environment (for running the base RMT)
  • Rust development environment (rustup, cargo)
• Potential Integration Libraries:
  • rb-sys: https://github.com/oxidize-rb/rb-sys
  • Magnus: https://github.com/matsadler/magnus
• Benchmarking Tools:
  • k6 (https://k6.io/)
  • ab (ApacheBench)

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Learn how to use the Relm4 Rust GUI crate by xiaoguang_wang

Relm4 is based on gtk4-rs and compatible with libadwaita. The gtk4-rs crate provides all the tools necessary to develop applications. Building on this foundation, Relm4 makes developing more idiomatic, simpler, and faster.

https://github.com/Relm4/Relm4

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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:

1. Automatically generate new unit tests as code is developed.
2. 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

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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.

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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/

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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

Updates

1. Training pipeline and datasets
2. Inference Service py

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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:

• Cluster infrastructure provider contract
• Machine infrastructure provider contract

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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/

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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 (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

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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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