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.

over 2 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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[comment]: # Harvester does not officially come with a CLI tool, the user is supposed to interact with Harvester mostly through the UI [comment]: # Though it is theoretically possible to use kubectl to interact with Harvester, the manipulation of Kubevirt YAML objects is absolutely not user friendly. [comment]: # 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.

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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.

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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.

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Small healthcheck tool for Longhorn by mbrookhuis

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As addition (read enough time), we could add changing reservation, adding new disks, etc. --> This didn't made it. But the scripts can easily be adopted.

This tool would decrease troubleshooting time, giving admins rights to the rancher GUI and could be used in automation.

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Overview

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node check: This will check all nodes, and display the status and the k3s version. If the status of the nodes is not "Ready" (this should be only reported), the cluster will be reported as having problems
deployment check: This check will list all deployments, and display the number of expected replicas and the used replica. If there are unused replicas this will be displayed. The cluster will be reported as having problems.
pvc check: This check will list of all pvc's, and display the status and the robustness. If the robustness is not "Healthy", the cluster will be reported as having problems.

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The code is writen for Python 3.11, but will also work on 3.6 (the default with SLES15.x). There is a venv present that will contain all needed packages. Also, the script can be run on the cluster itself or any other linux server.

Installation

To install this project, perform the following steps:

Create the directory /opt/k8s-check

mkdir /opt/k8s-check

Copy all the file to this directory and make the following changes:

chmod +x k8s-check.py

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Goals

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Reuse existing technology: leverage existing products whenever possible, e.g. build on top of Kubewarden as admission controller.
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Resources

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ConfigMaps: Bottles could be defined and configured using ConfigMaps.
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Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
Extensibility: Easily extend functionality with custom plugins and configurations.
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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.

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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).
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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.

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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

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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.

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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
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implement a GitHub Workspace solution
re-write documentation

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

Port the classic browser game HackTheNet to PHP 8 by dgedon

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Create a container where the game server can simply be started/stopped

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https://github.com/nodeg/hackthenet

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

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