Project Description
The aim of the project is to run a sample microservice app in Kubernetes. A simple app will be written in Python and work as an online store comprising of frontend, orders, and products services. (could be more!!)
- a frontend (a simple web page, using flask)
- a product service (an inventory of the products with description and cost)
- an orders service (recording the orders with order numbers, items and cost)
Further questions to answer/explore:
- How this app is going to look
- Which components to setup in k8s (a deployment and service for each microservice, what more?)
- How the APIs are going to be exposed (so the services can talk to each other. Right now, I only know how to expose the frontend on 8080 for user interaction).
Goals for this Hackweek
The project will have several learning goals:
- How to breakdown a monolith to microservices.
- Understand how Kubernetes works.
- Learn how to design Kubernetes topology for containerized applications.
Looking for hackers with the skills:
This project is part of:
Hack Week 20
Activity
Comments
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almost 5 years ago by epromislow | Reply
I've been reading https://learning.oreilly.com/library/view/cloud-native-patterns/9781617294297/ but not working through it because the examples are all in java, and I don't want to just use the spring boot platform to hide all the details. Would be interested in the points you've listed, as well as implementing a quick-and-dirty chaos monkey to kill off random/selected connections and nodes and monitor what happens, as well as see what works for fast recoveries.
I'm at UTC-0700
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Song Search with CLAP by gcolangiuli
Description
Contrastive Language-Audio Pretraining (CLAP) is an open-source library that enables the training of a neural network on both Audio and Text descriptions, making it possible to search for Audio using a Text input. Several pre-trained models for song search are already available on huggingface
Goals
Evaluate how CLAP can be used for song searching and determine which types of queries yield the best results by developing a Minimum Viable Product (MVP) in Python. Based on the results of this MVP, future steps could include:
- Music Tagging;
- Free text search;
- Integration with an LLM (for example, with MCP or the OpenAI API) for music suggestions based on your own library.
The code for this project will be entirely written using AI to better explore and demonstrate AI capabilities.
Result
In this MVP we implemented:
- Async Song Analysis with Clap model
- Free Text Search of the songs
- Similar song search based on vector representation
- Containerised version with web interface
We also documented what went well and what can be improved in the use of AI.
You can have a look at the result here:
Future implementation can be related to performance improvement and stability of the analysis.
References
- CLAP: The main model being researched;
- huggingface: Pre-trained models for CLAP;
- Free Music Archive: Creative Commons songs that can be used for testing;
Collection and organisation of information about Bulgarian schools by iivanov
Description
To achieve this it will be necessary:
- Collect/download raw data from various government and non-governmental organizations
- Clean up raw data and organise it in some kind database.
- Create tool to make queries easy.
- Or perhaps dump all data into AI and ask questions in natural language.
Goals
By selecting particular school information like this will be provided:
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- School scores from the external evaluations exams.
- School town, municipality and region.
- Employment rate in a town or municipality.
- Average health of the population in the region.
Resources
Some of these are available only in bulgarian.
- https://danybon.com/klasazia
- https://nvoresults.com/index.html
- https://ri.mon.bg/active-institutions
- https://www.nsi.bg/nrnm/ekatte/archive
Results
- Information about all Bulgarian schools with their scores during recent years cleaned and organised into SQL tables
- Information about all Bulgarian villages, cities, municipalities and districts cleaned and organised into SQL tables
- Information about all Bulgarian villages and cities census since beginning of this century cleaned and organised into SQL tables.
- Information about all Bulgarian municipalities about religion, ethnicity cleaned and organised into SQL tables.
- Data successfully loaded to locally running Ollama with help to Vanna.AI
- Seems to be usable.
TODO
- Add more statistical information about municipalities and ....
Code and data
Enhance git-sha-verify: A tool to checkout validated git hashes by gpathak
Description
git-sha-verify is a simple shell utility to verify and checkout trusted git commits signed using GPG key. This tool helps ensure that only authorized or validated commit hashes are checked out from a git repository, supporting better code integrity and security within the workflow.
Supports:
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Goals
A minimal python code of the shell script exists as a pull request.
The goal of this hackweek is to:
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- New and more tests can be added later
- New and more tests can be added later
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Resources
- Link to GitHub Repository: https://github.com/openSUSE/git-sha-verify
Bring to Cockpit + System Roles capabilities from YAST by miguelpc
Bring to Cockpit + System Roles features from YAST
Cockpit and System Roles have been added to SLES 16 There are several capabilities in YAST that are not yet present in Cockpit and System Roles We will follow the principle of "automate first, UI later" being System Roles the automation component and Cockpit the UI one.
Goals
The idea is to implement service configuration in System Roles and then add an UI to manage these in Cockpit. For some capabilities it will be required to have an specific Cockpit Module as they will interact with a reasource already configured.
Resources
A plan on capabilities missing and suggested implementation is available here: https://docs.google.com/spreadsheets/d/1ZhX-Ip9MKJNeKSYV3bSZG4Qc5giuY7XSV0U61Ecu9lo/edit
Linux System Roles:
- https://linux-system-roles.github.io/
- https://build.opensuse.org/package/show/openSUSE:Factory/ansible-linux-system-roles Package on sle16 ansible-linux-system-roles
First meeting Hackweek catchup
- Monday, December 1 · 11:00 – 12:00
- Time zone: Europe/Madrid
- Google Meet link: https://meet.google.com/rrc-kqch-hca
Testing and adding GNU/Linux distributions on Uyuni by juliogonzalezgil
Join the Gitter channel! https://gitter.im/uyuni-project/hackweek
Uyuni is a configuration and infrastructure management tool that saves you time and headaches when you have to manage and update tens, hundreds or even thousands of machines. It also manages configuration, can run audits, build image containers, monitor and much more!
Currently there are a few distributions that are completely untested on Uyuni or SUSE Manager (AFAIK) or just not tested since a long time, and could be interesting knowing how hard would be working with them and, if possible, fix whatever is broken.
For newcomers, the easiest distributions are those based on DEB or RPM packages. Distributions with other package formats are doable, but will require adapting the Python and Java code to be able to sync and analyze such packages (and if salt does not support those packages, it will need changes as well). So if you want a distribution with other packages, make sure you are comfortable handling such changes.
No developer experience? No worries! We had non-developers contributors in the past, and we are ready to help as long as you are willing to learn. If you don't want to code at all, you can also help us preparing the documentation after someone else has the initial code ready, or you could also help with testing :-)
The idea is testing Salt (including bootstrapping with bootstrap script) and Salt-ssh clients
To consider that a distribution has basic support, we should cover at least (points 3-6 are to be tested for both salt minions and salt ssh minions):
- Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
- Onboarding (salt minion from UI, salt minion from bootstrap scritp, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)
- Package management (install, remove, update...)
- Patching
- Applying any basic salt state (including a formula)
- Salt remote commands
- Bonus point: Java part for product identification, and monitoring enablement
- Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
- Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
- Bonus point: testsuite enablement (https://github.com/uyuni-project/uyuni/tree/master/testsuite)
If something is breaking: we can try to fix it, but the main idea is research how supported it is right now. Beyond that it's up to each project member how much to hack :-)
- If you don't have knowledge about some of the steps: ask the team
- If you still don't know what to do: switch to another distribution and keep testing.
This card is for EVERYONE, not just developers. Seriously! We had people from other teams helping that were not developers, and added support for Debian and new SUSE Linux Enterprise and openSUSE Leap versions :-)
In progress/done for Hack Week 25
Guide
We started writin a Guide: Adding a new client GNU Linux distribution to Uyuni at https://github.com/uyuni-project/uyuni/wiki/Guide:-Adding-a-new-client-GNU-Linux-distribution-to-Uyuni, to make things easier for everyone, specially those not too familiar wht Uyuni or not technical.
openSUSE Leap 16.0
The distribution will all love!
https://en.opensuse.org/openSUSE:Roadmap#DRAFTScheduleforLeap16.0
Curent Status We started last year, it's complete now for Hack Week 25! :-D
[W]Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file) NOTE: Done, client tools for SLMicro6 are using as those for SLE16.0/openSUSE Leap 16.0 are not available yet[W]Onboarding (salt minion from UI, salt minion from bootstrap scritp, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)[W]Package management (install, remove, update...). Works, even reboot requirement detection
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!
A CLI for Harvester by mohamed.belgaied
Harvester does not officially come with a CLI tool, the user is supposed to interact with Harvester mostly through the UI. Though it is theoretically possible to use kubectl to interact with Harvester, the manipulation of Kubevirt YAML objects is absolutely not user friendly. Inspired by tools like multipass from Canonical to easily and rapidly create one of multiple VMs, I began the development of Harvester CLI. Currently, it works but Harvester CLI needs some love to be up-to-date with Harvester v1.0.2 and needs some bug fixes and improvements as well.
Project Description
Harvester CLI is a command line interface tool written in Go, designed to simplify interfacing with a Harvester cluster as a user. It is especially useful for testing purposes as you can easily and rapidly create VMs in Harvester by providing a simple command such as:
harvester vm create my-vm --count 5
to create 5 VMs named my-vm-01 to my-vm-05.
Harvester CLI is functional but needs a number of improvements: up-to-date functionality with Harvester v1.0.2 (some minor issues right now), modifying the default behaviour to create an opensuse VM instead of an ubuntu VM, solve some bugs, etc.
Github Repo for Harvester CLI: https://github.com/belgaied2/harvester-cli
Done in previous Hackweeks
- Create a Github actions pipeline to automatically integrate Harvester CLI to Homebrew repositories: DONE
- Automatically package Harvester CLI for OpenSUSE / Redhat RPMs or DEBs: DONE
Goal for this Hackweek
The goal for this Hackweek is to bring Harvester CLI up-to-speed with latest Harvester versions (v1.3.X and v1.4.X), and improve the code quality as well as implement some simple features and bug fixes.
Some nice additions might be: * Improve handling of namespaced objects * Add features, such as network management or Load Balancer creation ? * Add more unit tests and, why not, e2e tests * Improve CI * Improve the overall code quality * Test the program and create issues for it
Issue list is here: https://github.com/belgaied2/harvester-cli/issues
Resources
The project is written in Go, and using client-go the Kubernetes Go Client libraries to communicate with the Harvester API (which is Kubernetes in fact).
Welcome contributions are:
- Testing it and creating issues
- Documentation
- Go code improvement
What you might learn
Harvester CLI might be interesting to you if you want to learn more about:
- GitHub Actions
- Harvester as a SUSE Product
- Go programming language
- Kubernetes API
- Kubevirt API objects (Manipulating VMs and VM Configuration in Kubernetes using Kubevirt)
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
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
- Deliver the v1beta1 API and its corresponding controller implementation
- 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
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/
