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.

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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!

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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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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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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

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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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