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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Team Hedgehogs' Data Observability Dashboard by gsamardzhiev

Description

This project aims to develop a comprehensive Data Observability Dashboard that provides r insights into key aspects of data quality and reliability. The dashboard will track:

Data Freshness: Monitor when data was last updated and flag potential delays.

Data Volume: Track table row counts to detect unexpected surges or drops in data.

Data Distribution: Analyze data for null values, outliers, and anomalies to ensure accuracy.

Data Schema: Track schema changes over time to prevent breaking changes.

The dashboard's aim is to support historical tracking to support proactive data management and enhance data trust across the data function.

Goals

Although the final goal is to create a power bi dashboard that we are able to monitor, our goals is to 1. Create the necessary tables that track the relevant metadata about our current data 2. Automate the process so it runs in a timely manner

Resources

AWS Redshift; AWS Glue, Airflow, Python, SQL

Why Hedgehogs?

Because we like them.

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Symbol Relations by hli

Description

There are tools to build function call graphs based on parsing source code, for example, cscope.

This project aims to achieve a similar goal by directly parsing the disasembly (i.e. objdump) of a compiled binary. The assembly code is what the CPU sees, therefore more "direct". This may be useful in certain scenarios, such as gdb/crash debugging.

Detailed description and Demos can be found in the README file:

Supports x86 for now (because my customers only use x86 machines), but support for other architectures can be added easily.

Tested with python3.6

Goals

Any comments are welcome.

Resources

https://github.com/lhb-cafe/SymbolRelations

symrellib.py: mplements the symbol relation graph and the disassembly parser

symrel_tracer*.py: implements tracing (-t option)

symrel.py: "cli parser"

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Saline (state deployment control and monitoring tool for SUSE Manager/Uyuni) by vizhestkov

Project Description

Saline is an addition for salt used in SUSE Manager/Uyuni aimed to provide better control and visibility for states deploymend in the large scale environments.

In current state the published version can be used only as a Prometheus exporter and missing some of the key features implemented in PoC (not published). Now it can provide metrics related to salt events and state apply process on the minions. But there is no control on this process implemented yet.

Continue with implementation of the missing features and improve the existing implementation:

• authentication (need to decide how it should be/or not related to salt auth)

• web service providing the control of states deployment

Goal for this Hackweek

• Implement missing key features

• Implement the tool for state deployment control with CLI

Resources

https://github.com/openSUSE/saline

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Ansible for add-on management by lmanfredi

Description

Machines can contains various combinations of add-ons and are often modified during the time.

The list of repos can change so I would like to create an automation able to reset the status to a given state, based on metadata available for these machines

Goals

Create an Ansible automation able to take care of add-on (repo list) configuration using metadata as reference

Resources

• Machines
• Repositories
• Developing modules
• Basic VM Guest management
• Module zypper_repository_list
• ansible-collections community.general
  • plugins/modules/zypper.py
  • plugins/modules/zypper_repository.py
  • plugins/modules/zypperrepositoryinfo.py

Results

Created WIP project Ansible-add-on-openSUSE

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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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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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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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