Project Description

The wasm ecosystem is becoming more mature and feature rich. With this, I'd like to allow developers to run their code in wasm without needing to know how to set up their tooling or build the binary. Because of this, I think it would be interesting to extend cloud native buildpacks so you can build wasm-oci images in any of the platforms that support buildpacks.

Currently, there is no work being done on this other than that I've done some limited research and opened up a ticket upstream (https://github.com/buildpacks/lifecycle/issues/820)

Goal for this Hackweek

By the end of the week, I'd like to either have a POC of a builder image using the forked cloud native lifecycle or have some areas of research to take forward.

Resources

Main repo to fork and work on (then ask to merge back upstream): https://github.com/buildpacks/lifecycle Wasm image spec: https://github.com/solo-io/wasm/blob/master/spec/README.md Buildpack builder that may come in useful: https://github.com/agracey/metabuildpack

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Looking for hackers with the skills:

go golang kubernetes containers wasm webassembly

This project is part of:

Hack Week 21

Activity

over 2 years ago: paulgonin liked this project.

almost 3 years ago: ecandino liked this project.

almost 3 years ago: atgracey added keyword "go" to this project.

almost 3 years ago: atgracey added keyword "golang" to this project.

almost 3 years ago: atgracey added keyword "kubernetes" to this project.

almost 3 years ago: atgracey added keyword "containers" to this project.

almost 3 years ago: atgracey added keyword "wasm" to this project.

almost 3 years ago: atgracey added keyword "webassembly" to this project.

almost 3 years ago: atgracey originated this project.

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Can we integrate Kubewarden with Cluster API provisioning?

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golang

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Description

I'm implementing a split-horizon DNS for my home Kubernetes cluster to be able to access my internal (and external) services over the local network through public domains. I managed to make a PoC with the k8s_gateway plugin for CoreDNS. However, I soon found out it responds with IPs for all Gateways assigned to HTTPRoutes, publishing public IPs as well as the internal Loadbalancer ones.

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A single place to view every bit of data you have.

Problem

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Family photos and videos.
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Project Description

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Similar proposals

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Resources

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Technical talks at universities by agamez

Description

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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.

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.

Goal for this Hackweek

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/rancher/terraform-provider-rancher2 https://github.com/rancher/tf-rancher-up

SUSE AI Meets the Game Board by moio

Use tabletopgames.ai’s open source TAG and PyTAG frameworks to apply Statistical Forward Planning and Deep Reinforcement Learning to two board games of our own design. On an all-green, all-open source, all-AWS stack!

Results: Infrastructure Achievements

We successfully built and automated a containerized stack to support our AI experiments. This included:

a Fully-Automated, One-Command, GPU-accelerated Kubernetes setup: we created an OpenTofu based script, tofu-tag, to deploy SUSE's RKE2 Kubernetes running on CUDA-enabled nodes in AWS, powered by openSUSE with GPU drivers and gpu-operator
Containerization of the TAG and PyTAG frameworks: TAG (Tabletop AI Games) and PyTAG were patched for seamless deployment in containerized environments. We automated the container image creation process with GitHub Actions. Our forks (PRs upstream upcoming):
- https://github.com/moio/TabletopGames/tree/hackweek24
- https://github.com/moio/PyTAG/tree/hackweek24

./deploy.sh and voilà - Kubernetes running PyTAG (k9s, above) with GPU acceleration (nvtop, below)

Results: Game Design Insights

Our project focused on modeling and analyzing two card games of our own design within the TAG framework:

Game Modeling: We implemented models for Dario's "Bamboo" and Silvio's "Totoro" and "R3" games, enabling AI agents to play thousands of games ...in minutes!
AI-driven optimization: By analyzing statistical data on moves, strategies, and outcomes, we iteratively tweaked the game mechanics and rules to achieve better balance and player engagement.
Advanced analytics: Leveraging AI agents with Monte Carlo Tree Search (MCTS) and random action selection, we compared performance metrics to identify optimal strategies and uncover opportunities for game refinement .
- more about Bamboo on Dario's site
- more about R3 on Silvio's site (italian, translation coming)
- more about Totoro on Silvio's site

A family picture of our card games in progress. From the top: Bamboo, Totoro, R3

Results: Learning, Collaboration, and Innovation

Beyond technical accomplishments, the project showcased innovative approaches to coding, learning, and teamwork:

"Trio programming" with AI assistance: Our "trio programming" approach—two developers and GitHub Copilot—was a standout success, especially in handling slightly-repetitive but not-quite-exactly-copypaste tasks. Java as a language tends to be verbose and we found it to be fitting particularly well.
AI tools for reporting and documentation: We extensively used AI chatbots to streamline writing and reporting. (Including writing this report! ...but this note was added manually during edit!)
GPU compute expertise: Overcoming challenges with CUDA drivers and cloud infrastructure deepened our understanding of GPU-accelerated workloads in the open-source ecosystem.
Game design as a learning platform: By blending AI techniques with creative game design, we learned not only about AI strategies but also about making games fun, engaging, and balanced.

Last but not least we had a lot of fun! ...and this was definitely not a chatbot generated line!

The Context: AI + Board Games

containers

Improve Development Environment on Uyuni by mbussolotto

Description

Currently create a dev environment on Uyuni might be complicated. The steps are:

add the correct repo
download packages
configure your IDE (checkstyle, format rules, sonarlint....)
setup debug environment
...

The current doc can be improved: some information are hard to be find out, some others are completely missing.

Dev Container might solve this situation.

Goals

Uyuni development in no time:

using VSCode:
- setting.json should contains all settings (for all languages in Uyuni, with all checkstyle rules etc...)
- dev container should contains all dependencies
- setup debug environment
implement a GitHub Workspace solution
re-write documentation

Lots of pieces are already implemented: we need to connect them in a consistent solution.

Resources

https://github.com/uyuni-project/uyuni/wiki

ADS-B receiver with MicroOS by epaolantonio

I would like to put one of my spare Raspberry Pis to good use, and what better way to see what flies above my head at any time?

There are various ready-to-use distros already set-up to provide feeder data to platforms like Flightradar24, ADS-B Exchange, FlightAware etc... The goal here would be to do it using MicroOS as a base and containerized decoding of ADS-B data (via tools like dump1090) and web frontend (tar1090).

Goals

Create a working receiver using MicroOS as a base, and containers based on Tumbleweed
Make it easy to install
Optimize for maximum laziness (i.e. it should take care of itself with minimum intervention)

Resources

1x Small Board Computer capable of running MicroOS
1x RTL2832U DVB-T dongle
1x MicroSD card
https://github.com/antirez/dump1090
https://github.com/flightaware/dump1090 (dump1090 fork by FlightAware)
https://github.com/wiedehopf/tar1090

Project status (2024-11-22)

So I'd say that I'm pretty satisfied with how it turned out. I've packaged readsb (as a replacement for dump1090), tar1090, tar1090-db and mlat-client (not used yet).

Current status:

Able to set-up a working receiver using combustion+ignition (web app based on Fuel Ignition)
Able to feed to various feeds using the Beast protocol (Airplanes.live, ADSB.fi, ADSB.lol, ADSBExchange.com, Flyitalyadsb.com, Planespotters.net)
Able to feed to Flightradar24 (initial-setup available but NOT tested! I've only tested using a key I already had)
Local web interface (tar1090) to easily visualize the results
Cockpit pre-configured to ease maintenance

What's missing:

MLAT (Multilateration) support. I've packaged mlat-client already, but I have to wire it up
FlightAware support

Give it a go at https://g7.github.io/adsbreceiver/ !

Project links

Technical talks at universities by agamez

Description

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.

ClusterOps - Easily install and manage your personal kubernetes cluster by andreabenini

Description

ClusterOps is a Kubernetes installer and operator designed to streamline the initial configuration and ongoing maintenance of kubernetes clusters. The focus of this project is primarily on personal or local installations. However, the goal is to expand its use to encompass all installations of Kubernetes for local development purposes.
It simplifies cluster management by automating tasks and providing just one user-friendly YAML-based configuration config.yml.

Overview

Simplified Configuration: Define your desired cluster state in a simple YAML file, and ClusterOps will handle the rest.
Automated Setup: Automates initial cluster configuration, including network settings, storage provisioning, special requirements (for example GPUs) and essential components installation.
Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
Extensibility: Easily extend functionality with custom plugins and configurations.
Self-Healing: Detects and recovers from common cluster issues, ensuring stability, idempotence and reliability. Same operation can be performed multiple times without changing the result.
Discreet: It works only on what it knows, if you are manually configuring parts of your kubernetes and this configuration does not interfere with it you can happily continue to work on several parts and use this tool only for what is needed.

Features

distribution and engine independence. Install your favorite kubernetes engine with your package manager, execute one script and you'll have a complete working environment at your disposal.
Basic config approach. One single config.yml file with configuration requirements (add/remove features): human readable, plain and simple. All fancy configs managed automatically (ingress, balancers, services, proxy, ...).
Local Builtin ContainerHub. The default installation provides a fully configured ContainerHub available locally along with the kubernetes installation. This configuration allows the user to build, upload and deploy custom container images as they were provided from external sources. Internet public sources are still available but local development can be kept in this localhost server. Builtin ClusterOps operator will be fetched from this ContainerHub registry too.
Kubernetes official dashboard installed as a plugin, others planned too (k9s for example).
Kubevirt plugin installed and properly configured. Unleash the power of classic virtualization (KVM+QEMU) on top of Kubernetes and manage your entire system from there, libvirtd and virsh libs are required.
One operator to rule them all. The installation script configures your machine automatically during installation and adds one kubernetes operator to manage your local cluster. From there the operator takes care of the cluster on your behalf.
Clean installation and removal. Just test it, when you are done just use the same program to uninstall everything without leaving configs (or pods) behind.

Planned features (Wishlist / TODOs)

Containerized Data Importer (CDI). Persistent storage management add-on for Kubernetes to provide a declarative way of building and importing Virtual Machine Disks on PVCs for

SUSE AI Meets the Game Board by moio

Results: Infrastructure Achievements

We successfully built and automated a containerized stack to support our AI experiments. This included:

a Fully-Automated, One-Command, GPU-accelerated Kubernetes setup: we created an OpenTofu based script, tofu-tag, to deploy SUSE's RKE2 Kubernetes running on CUDA-enabled nodes in AWS, powered by openSUSE with GPU drivers and gpu-operator
Containerization of the TAG and PyTAG frameworks: TAG (Tabletop AI Games) and PyTAG were patched for seamless deployment in containerized environments. We automated the container image creation process with GitHub Actions. Our forks (PRs upstream upcoming):
- https://github.com/moio/TabletopGames/tree/hackweek24
- https://github.com/moio/PyTAG/tree/hackweek24

./deploy.sh and voilà - Kubernetes running PyTAG (k9s, above) with GPU acceleration (nvtop, below)

Results: Game Design Insights

Our project focused on modeling and analyzing two card games of our own design within the TAG framework:

Game Modeling: We implemented models for Dario's "Bamboo" and Silvio's "Totoro" and "R3" games, enabling AI agents to play thousands of games ...in minutes!
AI-driven optimization: By analyzing statistical data on moves, strategies, and outcomes, we iteratively tweaked the game mechanics and rules to achieve better balance and player engagement.
Advanced analytics: Leveraging AI agents with Monte Carlo Tree Search (MCTS) and random action selection, we compared performance metrics to identify optimal strategies and uncover opportunities for game refinement .
- more about Bamboo on Dario's site
- more about R3 on Silvio's site (italian, translation coming)
- more about Totoro on Silvio's site

A family picture of our card games in progress. From the top: Bamboo, Totoro, R3

Results: Learning, Collaboration, and Innovation

Beyond technical accomplishments, the project showcased innovative approaches to coding, learning, and teamwork:

"Trio programming" with AI assistance: Our "trio programming" approach—two developers and GitHub Copilot—was a standout success, especially in handling slightly-repetitive but not-quite-exactly-copypaste tasks. Java as a language tends to be verbose and we found it to be fitting particularly well.
AI tools for reporting and documentation: We extensively used AI chatbots to streamline writing and reporting. (Including writing this report! ...but this note was added manually during edit!)
GPU compute expertise: Overcoming challenges with CUDA drivers and cloud infrastructure deepened our understanding of GPU-accelerated workloads in the open-source ecosystem.
Game design as a learning platform: By blending AI techniques with creative game design, we learned not only about AI strategies but also about making games fun, engaging, and balanced.

Last but not least we had a lot of fun! ...and this was definitely not a chatbot generated line!

The Context: AI + Board Games