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.

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Mammuthus - The NFS-Ganesha inside Kubernetes controller by vcheng

Description

As the user-space NFS provider, the NFS-Ganesha is wieldy use with serval projects. e.g. Longhorn/Rook. We want to create the Kubernetes Controller to make configuring NFS-Ganesha easy. This controller will let users configure NFS-Ganesha through different backends like VFS/CephFS.

Goals

1. Create NFS-Ganesha Package on OBS: nfs-ganesha5, nfs-ganesha6
2. Create NFS-Ganesha Container Image on OBS: Image
3. Create a Kubernetes controller for NFS-Ganesha and support the VFS configuration on demand. Mammuthus

Resources

NFS-Ganesha

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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?

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
• Cluster Autoscaler scales nodes up/down as needed
• Fleet keeps everything in sync via GitOps

Goals

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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

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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

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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.

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Play with the userfaultfd(2) system call and download on demand using HTTP Range Requests with Golang by rbranco

Description

The userfaultfd(2) is a cool system call to handle page faults in user-space. This should allow me to list the contents of an ISO or similar archive without downloading the whole thing. The userfaultfd(2) part can also be done in theory with the PROT_NONE mprotect + SIGSEGV trick, for complete Unix portability, though reportedly being slower.

Goals

1. Create my own library for userfaultfd(2) in Golang.
2. Create my own library for HTTP Range Requests.
3. Complete portability with Unix.
4. Benchmarks.
5. Contribute some tests to LTP.

Resources

1. https://docs.kernel.org/admin-guide/mm/userfaultfd.html
2. https://github.com/loopholelabs/userfaultfd-go
3. https://github.com/DHowett/ranger
4. https://www.cons.org/cracauer/cracauer-userfaultfd.html

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SUSE Health Check Tools by roseswe

SUSE HC Tools Overview

A collection of tools written in Bash or Go 1.24++ to make life easier with handling of a bunch of tar.xz balls created by supportconfig.

Background: For SUSE HC we receive a bunch of supportconfig tar balls to check them for misconfiguration, areas for improvement or future changes.

Main focus on these HC are High Availability (pacemaker), SLES itself and SAP workloads, esp. around the SUSE best practices.

Goals

• Overall improvement of the tools
• Adding new collectors
• Add support for SLES16

Resources

csv2xls* example.sh go.mod listprodids.txt sumtext* trails.go README.md csv2xls.go exceltest.go go.sum m.sh* sumtext.go vercheck.py* config.ini csvfiles/ getrpm* listprodids* rpmdate.sh* sumxls* verdriver* credtest.go example.py getrpm.go listprodids.go sccfixer.sh* sumxls.go verdriver.go

docollall.sh* extracthtml.go gethostnamectl* go.sum numastat.go cpuvul* extractcluster.go firmwarebug* gethostnamectl.go m.sh* numastattest.go cpuvul.go extracthtml* firmwarebug.go go.mod numastat* xtr_cib.sh*

$ getrpm -r pacemaker >> Product ID: 2795 (SUSE Linux Enterprise Server for SAP Applications 15 SP7 x86_64), RPM Name: +--------------+----------------------------+--------+--------------+--------------------+ | Package Name | Version | Arch | Release | Repository | +--------------+----------------------------+--------+--------------+--------------------+ | pacemaker | 2.1.10+20250718.fdf796ebc8 | x86_64 | 150700.3.3.1 | sle-ha/15.7/x86_64 | | pacemaker | 2.1.9+20250410.471584e6a2 | x86_64 | 150700.1.9 | sle-ha/15.7/x86_64 | +--------------+----------------------------+--------+--------------+--------------------+ Total packages found: 2

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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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Add support for todo.sr.ht to git-bug by mcepl

Description

I am a big fan of distributed issue tracking and the best (and possibly) only credible such issue tracker is now git-bug. It has bridges to another centralized issue trackers, so user can download (and modify) issues on GitHub, GitLab, Launchpad, Jira). I am also a fan of SourceHut, which has its own issue tracker, so I would like it bridge the two. Alas, I don’t know much about Go programming language (which the git-bug is written) and absolutely nothing about GraphQL (which todo.sr.ht uses for communication). AI to the rescue. I would like to vibe code (and eventually debug and make functional) bridge to the SourceHut issue tracker.

Goals

Functional fix for https://github.com/git-bug/git-bug/issues/1024

Resources

• anybody how actually understands how GraphQL and authentication on SourceHut (OAuth2) works

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SUSE Observability MCP server by drutigliano

Description

The idea is to implement the SUSE Observability Model Context Protocol (MCP) Server as a specialized, middle-tier API designed to translate the complex, high-cardinality observability data from StackState (topology, metrics, and events) into highly structured, contextually rich, and LLM-ready snippets.

This MCP Server abstract the StackState APIs. Its primary function is to serve as a Tool/Function Calling target for AI agents. When an AI receives an alert or a user query (e.g., "What caused the outage?"), the AI calls an MCP Server endpoint. The server then fetches the relevant operational facts, summarizes them, normalizes technical identifiers (like URNs and raw metric names) into natural language concepts, and returns a concise JSON or YAML payload. This payload is then injected directly into the LLM's prompt, ensuring the final diagnosis or action is grounded in real-time, accurate SUSE Observability data, effectively minimizing hallucinations.

Goals

• Grounding AI Responses: Ensure that all AI diagnoses, root cause analyses, and action recommendations are strictly based on verifiable, real-time data retrieved from the SUSE Observability StackState platform.
• Simplifying Data Access: Abstract the complexity of StackState's native APIs (e.g., Time Travel, 4T Data Model) into simple, semantic functions that can be easily invoked by LLM tool-calling mechanisms.
• Data Normalization: Convert complex, technical identifiers (like component URNs, raw metric names, and proprietary health states) into standardized, natural language terms that an LLM can easily reason over.
• Enabling Automated Remediation: Define clear, action-oriented MCP endpoints (e.g., execute_runbook) that allow the AI agent to initiate automated operational workflows (e.g., restarts, scaling) after a diagnosis, closing the loop on observability.

 Hackweek STEP

• Create a functional MCP endpoint exposing one (or more) tool(s) to answer queries like "What is the health of service X?") by fetching, normalizing, and returning live StackState data in an LLM-ready format.

 Scope

• Implement read-only MCP server that can:
  • Connect to a live SUSE Observability instance and authenticate (with API token)
  • Use tools to fetch data for a specific component URN (e.g., current health state, metrics, possibly topology neighbors, ...).
  • Normalize response fields (e.g., URN to "Service Name," health state DEVIATING to "Unhealthy", raw metrics).
  • Return the data as a structured JSON payload compliant with the MCP specification.

Deliverables

• MCP Server v0.1 A running Python web server (e.g., using FastAPI) with at least one tool.
• A README.md and a test script (e.g., curl commands or a simple notebook) showing how an AI agent would call the endpoint and the resulting JSON payload.

Outcome A functional and testable API endpoint that proves the core concept: translating complex StackState data into a simple, LLM-ready format. This provides the foundation for developing AI-driven diagnostics and automated remediation.

Resources

• https://www.honeycomb.io/blog/its-the-end-of-observability-as-we-know-it-and-i-feel-fine
• https://www.datadoghq.com/blog/datadog-remote-mcp-server
• https://modelcontextprotocol.io/specification/2025-06-18/index
• https://modelcontextprotocol.io/docs/develop/build-server

 Basic implementation

• https://github.com/drutigliano19/suse-observability-mcp-server

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