ddflare: (Dynamic)DNS management via Cloudflare API in Kubernetes by fgiudici

Description

ddflare is a project started a couple of weeks ago to provide DDNS management using v4 Cloudflare APIs: Cloudflare offers management via APIs and access tokens, so it is possible to register a domain and implement a DynDNS client without any other external service but their API.

Since ddflare allows to set any IP to any domain name, one could manage multiple A and ALIAS domain records. Wouldn't be cool to allow full DNS control from the project and integrate it with your Kubernetes cluster?

Goals

Main goals are:

1. add containerized image for ddflare
2. extend ddflare to be able to add and remove DNS records (and not just update existing ones)
3. add documentation, covering also a sample pod deployment for Kubernetes
4. write a ddflare Kubernetes operator to enable domain management via Kubernetes resources (using kubebuilder)

Available tasks and improvements tracked on ddflare github.

Resources

• https://github.com/fgiudici/ddflare
• https://developers.cloudflare.com/api/
• https://book.kubebuilder.io

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Install Uyuni on Kubernetes in cloud-native way by cbosdonnat

Description

For now installing Uyuni on Kubernetes requires running mgradm on a cluster node... which is not what users would do in the Kubernetes world. The idea is to implement an installation based only on helm charts and probably an operator.

Goals

Install Uyuni from Rancher UI.

Resources

• mgradm code: https://github.com/uyuni-project/uyuni-tools
• Uyuni operator: https://github.com/cbosdo/uyuni-operator

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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!

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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

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Multi-pod, autoscalable Elixir application in Kubernetes using K8s resources by socon

Description

Elixir / Erlang use their own solutions to create clusters that work together. Kubernetes provide its own orchestration. Due to the nature of the BEAM, it looks a very promising technology for applications that run in Kubernetes and requite to be always on, specifically if they are created as web pages using Phoenix.

Goals

• Investigate and provide solutions that work in Phoenix LiveView using Kubernetes resources, so a multi-pod application can be used
• Provide an end to end example that creates and deploy a container from source code.

Resources

https://github.com/dwyl/phoenix-liveview-counter-tutorial https://github.com/propedeutica/elixir-k8s-counter

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Learn enough Golang and hack on CoreDNS by jkuzilek

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.

To remedy this issue, a simple filtering mechanism has to be implemented.

Goals

• Learn an acceptable amount of Golang
• Implement GatewayClass (and IngressClass) filtering for k8s_gateway
• Deploy on homelab cluster
• Profit?

Resources

• https://github.com/ori-edge/k8s_gateway/issues/36
• https://github.com/coredns/coredns/issues/2465#issuecomment-593910983

EDIT: Feature mostly complete. An unfinished PR lies here. Successfully tested working on homelab cluster.

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Update Haskell ecosystem in Tumbleweed to GHC-9.10.x by psimons

Description

We are currently at GHC-9.8.x, which a bit old. So I'd like to take a shot at the latest version of the compiler, GHC-9.10.x. This is gonna be interesting because the new version requires major updates to all kinds of libraries and base packages, which typically means patching lots of packages to make them build again.

Goals

Have working builds of GHC-9.10.x and the required Haskell packages in 'devel:languages:haskell` so that we can compile:

• git-annex
• pandoc
• xmonad
• cabal-install

Resources

• https://build.opensuse.org/project/show/devel:languages:haskell/
• https://github.com/opensuse-haskell/configuration/
• #discuss-haskell
• https://www.twitch.tv/peti343

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Packaging Mu on OBS by joeyli

Description

Packaging Microsoft Mu project

Goals

Packaging Mu RPM on OBS.

Resources

https://microsoft.github.io/mu/

https://github.com/microsoft/mu

https://github.com/microsoft/mu_basecore

https://github.com/microsoft/mutianoplatforms

https://github.com/microsoft/mutianoplus

https://github.com/microsoft/mu_plus

Hackweek 22: Look at Microsoft Mu project

https://hackweek.opensuse.org/22/projects/look-at-microsoft-mu-project

https://drive.google.com/file/d/1BT31i7z3qh13adj9pdRz3lTUkqIsXvjY/view?usp=drive_link

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Framework laptop integration by nkrapp

Project Description

Although openSUSE does run on the Framework laptops out-of-the-box, there is still room to improve the experience. The ultimate goal is to get openSUSE on the list of community supported distros

Goal for this Hackweek

The goal this year is to at least package all of the soft- and firmware for accessories like the embedded controller, Framework 16 inputmodule and other tools. I already made some progress by packaging the inputmodule control software, but the firmware is still missing

Resources

As I only have a Framework laptop 16 and not a 13 I'm looking for people with hardware that can help me test

Progress:

Update 1:

The project lives under my home for now until I can get an independent project on OBS: Framework Laptop project

Also, the first package is already done, it's the cli for the led-matrix spacer module on the Framework Laptop 16. I am also testing this myself, but any feedback or questions are welcome.

You can test the package on the Framework 16 by adding this repo and installing the package inputmodule-control

Update 2:

I finished packaging the python cli/gui for the inputmodule. It is using a bit of a hack because one of the dependencies (PySimpleGUI) recently switched to a noncommercial license so I cannot ship it. But now you can actually play the games on the led-matrix (the rust package doesn't include controls for the games). I'm also working on the Framework system tools now, which should be more interesting for Framework 13 users.

You can test the package on the Framework 16 by installing python311-framework16_inputmodule and then running "ledmatrixctl" from the command line.

Update 3:

I packaged the framework_tool, a general application for interacting with the system. You can find it some detailed information what it can do here. On my system everything related to the embedded controller functionality doesn't work though, so some help testing and debugging would be appreciated.

Update 4:

Today I finished the qmk interface, which gives you a cli (and gui) to configure your Framework 16 keyboard. Sadly the Python gui is broken upstream, but I added the qmk_hid package with the cli and from my testing it works well.

Final Update:

All the interesting programs are now done, I decided to exclude the firmware for now since upstream also recommends using fwupd to update it. I will hack on more things related to the Framework Laptops in the future so if there are any ideas to improve the experience (or any bugs to report) feel free to message me about it.

As a final summary/help for everyone using a Framework Laptop who wants to use this software:

The source code for all packages can be found in repositories in the Framework organization on Github

All software can be installed from this repo (Tumbleweed)

The available packages are:

• framework-inputmodule-control (FW16) - play with the inputmodules on your Framework 16 (b1-display, led-matrix, c1-minimal)

• python-framework16_inputmodule (FW16) - same as inputmodule-control but is needed if you want to play and crontrol the built-in games in the led-matrix (call with ledmatrixctl or ledmatrixgui)

• framework_tool (FW13 and FW 16) - use to see and configure general things on your framework system. Commands using the embedded controller might not work, it looks like there are some problems with the kernel module used by the EC. Fixing this is out of scope for this hackweek but I am working on it

• qmk_hid (FW16) - a cli to configure the FW16 qmk keyboard. Sadly the gui for this is broken upstream so only the cli is usable for now

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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

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Port the classic browser game HackTheNet to PHP 8 by dgedon

Description

The classic browser game HackTheNet from 2004 still runs on PHP 4/5 and MySQL 5 and needs a port to PHP 8 and e.g. MariaDB.

Goals

• Port the game to PHP 8 and MariaDB 11
• Create a container where the game server can simply be started/stopped

Resources

• https://github.com/nodeg/hackthenet

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Enable the containerized Uyuni server to run on different host OS by j_renner

Description

The Uyuni server is provided as a container, but we still require it to run on Leap Micro? This is not how people expect to use containerized applications, so it would be great if we tested other host OSs and enabled them by providing builds of necessary tools for (e.g. mgradm). Interesting candidates should be:

• openSUSE Leap
• Cent OS 7
• Ubuntu
• ???

Goals

Make it really easy for anyone to run the Uyuni containerized server on whatever OS they want (with support for containers of course).

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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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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

• https://g7.github.io/adsbreceiver/
• https://github.com/g7/adsbreceiver
• https://build.opensuse.org/project/show/home:epaolantonio:adsbreceiver

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