Build a minetest server inside SUSE network
An introduction from minetest website: " Minetest is a near-infinite-world block sandbox game and a game engine, inspired by InfiniMiner, Minecraft, and the like. Minetest is available natively for Windows, OS X, GNU/Linux, Android, and FreeBSD. It is Free/Libre and Open Source Software, released under the LGPL 2.1 or later. "
In short, MineTest is a Free and Open Source re-implementation of MineCraft, but it provide many flexible features compare MineCraft. It's not only a game but also a framework for developers to extend so to make their own worlds.
Build a minetest is not difficult but the maintenance is a long-term work. The goal of this project is to create a virtual environment which could provide a chance for SUSE engineers from different site to communicate "face to face" and build the "world" together. The key point is: to make a lot of fun!
Looking for hackers with the skills:
This project is part of:
Hack Week 16
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SUSE Prague claw machine by anstalker
Project Description
The idea is to build a claw machine similar to e.g. this one:
Why? Well, it could be a lot of fun!
But also it's a great way to dispense SUSE and openSUSE merch like little Geekos at events like conferences, career fairs and open house events.
Goal for this Hackweek
Build an arcade claw machine.
Resources
In French, an article about why you always lose in claw machine games:
We're looking for handy/crafty people in the Prague office:
- woodworking XP or equipment
- arduino/raspi embedded programming knowledge
- Anthony can find a budget for going to GM and buying servos and such ;)
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!
AI + Board Games
Board games have long been fertile ground for AI innovation, pushing the boundaries of capabilities such as strategy, adaptability, and real-time decision-making - from Deep Blue's chess mastery to AlphaZero’s domination of Go. Games aren’t just fun: they’re complex, dynamic problems that often mirror real-world challenges, making them interesting from an engineering perspective.
As avid board gamers, aspiring board game designers, and engineers with careers in open source infrastructure, we’re excited to dive into the latest AI techniques first-hand.
Our goal is to develop an all-open-source, all-green AWS-based stack powered by some serious hardware to drive our board game experiments forward!
Project Goals
Set Up the Stack:
- Install and configure the TAG and PyTAG frameworks on SUSE Linux Enterprise Base Container Images.
- Integrate with the SUSE AI stack for GPU-accelerated training on AWS.
- Validate a sample GPU-accelerated PyTAG workload on SUSE AI.
- Ensure the setup is entirely repeatable with Terraform and configuration scripts, documenting results along the way.
Design and Implement AI Agents:
- Develop AI agents for the two board games, incorporating Statistical Forward Planning and Deep Reinforcement Learning techniques.
- Fine-tune model parameters to optimize game-playing performance.
- Document the advantages and limitations of each technique.
Test, Analyze, and Refine:
- Conduct AI vs. AI and AI vs. human matches to evaluate agent strategies and performance.
- Record insights, document learning outcomes, and refine models based on real-world gameplay.
Technical Stack
- Frameworks: TAG and PyTAG for AI agent development
- Platform: SUSE AI
- Tools: AWS for high-performance GPU acceleration
Why This Project Matters
This project not only deepens our understanding of AI techniques by doing but also showcases the power and flexibility of SUSE’s open-source infrastructure for supporting high-level AI projects. By building on an all-open-source stack, we aim to create a pathway for other developers and AI enthusiasts to explore, experiment, and deploy their own innovative projects within the open-source space.
Our Motivation
We believe hands-on experimentation is the best teacher.
Combining our engineering backgrounds with our passion for board games, we’ll explore AI in a way that’s both challenging and creatively rewarding. Our ultimate goal? To hack an AI agent that’s as strategic and adaptable as a real human opponent (if not better!) — and to leverage it to design even better games... for humans to play!
Port some classic game to Linux by MDoucha
Let's pick some old classic game, reverse engineer the data formats and game rules and write an open source engine for it from scratch. Some games from 1990s are simple enough that we could have a playable prototype by the end of the week.
Write which games you'd like to hack on in the comments. Don't forget to check e.g. on Open Source Game Clones, Github and SourceForge whether the game is ported already.
Hack Week 24 - Master of Orion II: Battle at Antares & Chaos Overlords
Work on Master of Orion II continues but we can hack more than one game. Chaos Overlords is a dystopian, lighthearted, cyberpunk turn-based strategy game originally released in 1996 for Windows 95 and Mac OS. The player takes on the role of a Chaos Overlord, attempting to control a city. Gameplay involves hiring mercenary gangs and deploying them on an 8-by-8 grid of city sectors to generate income, occupy sectors and take over the city.
How to ~~install & play~~ observe the decompilation progress:
- Clone the Git repository
- A playable reimplementation does not exist yet, but when it does, it will be linked in the repository mentioned above.
Further work needed:
- Analyze the remaining unknown data structures, most of which are related to the AI.
- Decompile the AI completely. The strong AI is part of the appeal of the game. It cannot be left out.
- Reimplement the game.
Hack Week 20, 21, 22 & 23 - Master of Orion II: Battle at Antares
Master of Orion II is one of the greatest turn-based 4X games of the 1990s. Explore the galaxy, colonize planets, research new technologies, fight space monsters and alien empires and in the end, become the ruler of the galaxy one way or another.
How to install & play:
- Clone the Git repository
- Run
./bootstrap; ./configure; make && make install - Copy all *.LBX files from the original Master of Orion II to the installation data directory (
/usr/local/share/openorion2by default) - Run
openorion2
Further work needed:
- Analyze the rest of the original savegame format and a few remaining data files.
- Implement most of the game. The open source engine currently supports only loading saved games from the original version and viewing the galaxy map, fleet management and list of known planets.
Hack Week 19 - Signus: The Artifact Wars
Signus is a Czech turn-based strategy game similar to Panzer General or Battle Isle series. Originally published in 1998 and open-sourced by the original developers in 2003.
How to install & play:
- Clone the Git repository
- Run
./bootstrap; ./configure; make && make installin bothsignusandsignus-datadirectories. - Run
signus
Further work needed:
- Create openSUSE package
- Implement full support for original game data (the open source version uses slightly different data file contents but original game data can be converted using a script).
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!
AI + Board Games
Board games have long been fertile ground for AI innovation, pushing the boundaries of capabilities such as strategy, adaptability, and real-time decision-making - from Deep Blue's chess mastery to AlphaZero’s domination of Go. Games aren’t just fun: they’re complex, dynamic problems that often mirror real-world challenges, making them interesting from an engineering perspective.
As avid board gamers, aspiring board game designers, and engineers with careers in open source infrastructure, we’re excited to dive into the latest AI techniques first-hand.
Our goal is to develop an all-open-source, all-green AWS-based stack powered by some serious hardware to drive our board game experiments forward!
Project Goals
Set Up the Stack:
- Install and configure the TAG and PyTAG frameworks on SUSE Linux Enterprise Base Container Images.
- Integrate with the SUSE AI stack for GPU-accelerated training on AWS.
- Validate a sample GPU-accelerated PyTAG workload on SUSE AI.
- Ensure the setup is entirely repeatable with Terraform and configuration scripts, documenting results along the way.
Design and Implement AI Agents:
- Develop AI agents for the two board games, incorporating Statistical Forward Planning and Deep Reinforcement Learning techniques.
- Fine-tune model parameters to optimize game-playing performance.
- Document the advantages and limitations of each technique.
Test, Analyze, and Refine:
- Conduct AI vs. AI and AI vs. human matches to evaluate agent strategies and performance.
- Record insights, document learning outcomes, and refine models based on real-world gameplay.
Technical Stack
- Frameworks: TAG and PyTAG for AI agent development
- Platform: SUSE AI
- Tools: AWS for high-performance GPU acceleration
Why This Project Matters
This project not only deepens our understanding of AI techniques by doing but also showcases the power and flexibility of SUSE’s open-source infrastructure for supporting high-level AI projects. By building on an all-open-source stack, we aim to create a pathway for other developers and AI enthusiasts to explore, experiment, and deploy their own innovative projects within the open-source space.
Our Motivation
We believe hands-on experimentation is the best teacher.
Combining our engineering backgrounds with our passion for board games, we’ll explore AI in a way that’s both challenging and creatively rewarding. Our ultimate goal? To hack an AI agent that’s as strategic and adaptable as a real human opponent (if not better!) — and to leverage it to design even better games... for humans to play!