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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Save pytorch models in OCI registries by jguilhermevanz

Description

A prerequisite for running applications in a cloud environment is the presence of a container registry. Another common scenario is users performing machine learning workloads in such environments. However, these types of workloads require dedicated infrastructure to run properly. We can leverage these two facts to help users save resources by storing their machine learning models in OCI registries, similar to how we handle some WebAssembly modules. This approach will save users the resources typically required for a machine learning model repository for the applications they need to run.

Goals

Allow PyTorch users to save and load machine learning models in OCI registries.

Resources

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AI for product management by a_jaeger

Description

Learn about AI and how it can help myself

What are the jobs that a PM does where AI can help - and how?

Goals

• Investigate how AI can help with different tasks
• Check out different AI tools, which one is best for which job
• Summarize learning

Resources

• Reading some blog posts by PMs that looked into it
• Popular and less popular AI tools

Work is done SUSE internally at https://confluence.suse.com/display/~a_jaeger/Hackweek+25+-+AI+for+a+PM and subpages.

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Gen-AI chatbots and test-automation of generated responses by mdati

Description

Start experimenting the generative SUSE-AI chat bot, asking questions on different areas of knowledge or science and possibly analyze the quality of the LLM model response, specific and comparative, checking the answers provided by different LLM models to a same query, using proper quality metrics or tools or methodologies.

Try to define basic guidelines and requirements for quality test automation of AI-generated responses.

First approach of investigation can be based on manual testing: methodologies, findings and data can be useful then to organize valid automated testing.

Goals

• Identify criteria and measuring scales for assessment of a text content.
• Define quality of an answer/text based on defined criteria .
• Identify some knowledge sectors and a proper list of problems/questions per sector.
• Manually run query session and apply evaluation criteria to answers.
• Draft requirements for test automation of AI answers.

Resources

• Announcement of SUSE-AI for Hack Week in Slack
• Openplatform and related 3 LLM models gemma:2b, llama3.1:8b, qwen2.5-coder:3b.

Notes

• Foundation models (FMs):
  are large deep learning neural networks, trained on massive datasets, that have changed the way data scientists approach machine learning (ML). Rather than develop artificial intelligence (AI) from scratch, data scientists use a foundation model as a starting point to develop ML models that power new applications more quickly and cost-effectively.

• Large language models (LLMs):
  are a category of foundation models pre-trained on immense amounts of data acquiring abilities by learning statistical relationships from vast amounts of text during a self- and semi-supervised training process, making them capable of understanding and generating natural language and other types of content , to perform a wide range of tasks.
  LLMs can be used for generative AI (artificial intelligence) to produce content based on input prompts in human language.

Validation of a AI-generated answer is not an easy task to perform, as manually as automated.
An LLM answer text shall contain a given level of informations: correcness, completeness, reasoning description etc.
We shall rely in properly applicable and measurable criteria of validation to get an assessment in a limited amount of time and resources.

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COOTWbot by ngetahun

Project Description

At SCC, we have a rotating task of COOTW (Commanding Office of the Week). This task involves responding to customer requests from jira and slack help channels, monitoring production systems and doing small chores. Usually, we have documentation to help the COOTW answer questions and quickly find fixes. Most of these are distributed across github, trello and SUSE Support documentation. The aim of this project is to explore the magic of LLMs and create a conversational bot.

Goal for this Hackweek

• Build data ingestion Data source:
  • SUSE KB docs
  • scc github docs
  • scc trello knowledge board

• Test out new RAG architecture

• https://gitlab.suse.de/ngetahun/cootwbot

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FamilyTrip Planner: A Personalized Travel Planning Platform for Families by pherranz

Description

FamilyTrip Planner is an innovative travel planning application designed to optimize travel experiences for families with children. By integrating APIs for flights, accommodations, and local activities, the app generates complete itineraries tailored to each family’s unique interests and needs. Recommendations are based on customizable parameters such as destination, trip duration, children’s ages, and personal preferences. FamilyTrip Planner not only simplifies the travel planning process but also offers a comprehensive, personalized experience for families.

Goals

This project aims to: - Create a user-friendly platform that assists families in planning complete trips, from flight and accommodation options to recommended family-friendly activities. - Provide intelligent, personalized travel itineraries using artificial intelligence to enhance travel enjoyment and minimize time and cost. - Serve as an educational project for exploring Go programming and artificial intelligence, with the goal of building proficiency in both.

Resources

To develop FamilyTrip Planner, the project will leverage: - APIs such as Skyscanner, Google Places, and TripAdvisor to source real-time information on flights, accommodations, and activities. - Go programming language to manage data integration, API connections, and backend development. - Basic machine learning libraries to implement AI-driven itinerary suggestions tailored to family needs and preferences.

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Agama installer on-line demo by lslezak

Description

The Agama installer provides a quite complex user interface. We have some screenshots on the web page but as it is basically a web application it would be nice to have some on-line demo where users could click and check it live.

The problem is that the Agama server directly accesses the hardware (storage probing) and loads installation repositories. We cannot easily mock this in the on-line demo so the easiest way is to have just a read-only demo. You could explore the configuration options but you could not change anything, all changes would be ignored.

The read-only demo would be a bit limited but I still think it would be useful for potential users get the feeling of the new Agama installer and get familiar with it before using in a real installation.

As a proof of concept I already created this on-line demo.

The implementation basically builds Agama in two modes - recording mode where it saves all REST API responses and replay mode where it for the REST API requests returns the previously recorded responses. Recording in the browser is inconvenient and error prone, there should be some scripting instead (see below).

Goals

• Create an Agama on-line demo which can be easily tested by users
• The Agama installer is still in alpha phase and in active development, the online demo needs to be easily rebuilt with the latest Agama version
• Ideally there should be some automation so the demo page is rebuilt automatically without any developer interactions (once a day or week?)

TODO

• Use OpenAPI to get all Agama REST API endpoints, write a script which queries all the endpoints automatically and saves the collected data to a file (see this related PR).
• Write a script for starting an Agama VM (use libvirt/qemu?), the script should ensure we always use the same virtual HW so if we need to dump the latest REST API state we get the same (or very similar data). This should ensure the demo page does not change much regarding the storage proposal etc...
• Fix changing the product, currently it gets stuck after clicking the "Select" button.
• Move the mocking data (the recorded REST API responses) outside the Agama sources, it's too big and will be probably often updated. To avoid messing the history keep it in a separate GitHub repository
• Allow changing the UI language
• Display some note (watermark) in the page so it is clear it is a read-only demo (probably with some version or build date to know how old it is)
• Automation for building new demo page from the latest sources. There should be some check which ensures the recorded data still matches the OpenAPI specification.

Changing the UI language

This will be quite tricky because selecting the proper translation file is done on the server side. We would probably need to completely re-implement the logic in the browser side and adapt the server for that.

Also some REST API responses contain translated texts (storage proposal, pattern names in software). We would need to query the respective endpoints in all supported languages and return the correct response in runtime according to the currently selected language.

Resources

• Agama sources
• Experimental proof of concept demo
• The respective source code change

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Longhorn UI Extension (POC) by yiya.chen

Description

The goal is to create a Longhorn UI extension within Rancher using existing resources.
Longhorn’s UI is built using React, while Rancher’s UI extensions are built using Vue. Developers will explore different approaches to integrate and extend Longhorn’s UI within Rancher’s Vue-based ecosystem, aiming to create a seamless, functional UI extension.

Goals

• Build a Longhorn UI extension (look and feel)
• Support theme switching to align with Rancher’s UI

Results

• https://github.com/a110605/longhorn-hackday
• https://github.com/a110605/longhorn-ui/tree/darkmode
• https://github.com/houhoucoop/hackweek/tree/main/hackweek24

Resources

• Longhorn UI: https://github.com/longhorn/longhorn-ui
• Rancher UI Extension: https://extensions.rancher.io/extensions/next/home
• darkreader: https://www.npmjs.com/package/darkreader
• veaury: https://github.com/gloriasoft/veaury
• module federation: https://webpack.js.org/concepts/module-federation/

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Agama installer on-line demo by lslezak

Description

The Agama installer provides a quite complex user interface. We have some screenshots on the web page but as it is basically a web application it would be nice to have some on-line demo where users could click and check it live.

The problem is that the Agama server directly accesses the hardware (storage probing) and loads installation repositories. We cannot easily mock this in the on-line demo so the easiest way is to have just a read-only demo. You could explore the configuration options but you could not change anything, all changes would be ignored.

The read-only demo would be a bit limited but I still think it would be useful for potential users get the feeling of the new Agama installer and get familiar with it before using in a real installation.

As a proof of concept I already created this on-line demo.

The implementation basically builds Agama in two modes - recording mode where it saves all REST API responses and replay mode where it for the REST API requests returns the previously recorded responses. Recording in the browser is inconvenient and error prone, there should be some scripting instead (see below).

Goals

• Create an Agama on-line demo which can be easily tested by users
• The Agama installer is still in alpha phase and in active development, the online demo needs to be easily rebuilt with the latest Agama version
• Ideally there should be some automation so the demo page is rebuilt automatically without any developer interactions (once a day or week?)

TODO

• Use OpenAPI to get all Agama REST API endpoints, write a script which queries all the endpoints automatically and saves the collected data to a file (see this related PR).
• Write a script for starting an Agama VM (use libvirt/qemu?), the script should ensure we always use the same virtual HW so if we need to dump the latest REST API state we get the same (or very similar data). This should ensure the demo page does not change much regarding the storage proposal etc...
• Fix changing the product, currently it gets stuck after clicking the "Select" button.
• Move the mocking data (the recorded REST API responses) outside the Agama sources, it's too big and will be probably often updated. To avoid messing the history keep it in a separate GitHub repository
• Allow changing the UI language
• Display some note (watermark) in the page so it is clear it is a read-only demo (probably with some version or build date to know how old it is)
• Automation for building new demo page from the latest sources. There should be some check which ensures the recorded data still matches the OpenAPI specification.

Changing the UI language

This will be quite tricky because selecting the proper translation file is done on the server side. We would probably need to completely re-implement the logic in the browser side and adapt the server for that.

Also some REST API responses contain translated texts (storage proposal, pattern names in software). We would need to query the respective endpoints in all supported languages and return the correct response in runtime according to the currently selected language.

Resources

• Agama sources
• Experimental proof of concept demo
• The respective source code change

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