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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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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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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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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Make more sense of openQA test results using AI by livdywan

Description

AI has the potential to help with something many of us spend a lot of time doing which is making sense of openQA logs when a job fails.

User Story

Allison Average has a puzzled look on their face while staring at log files that seem to make little sense. Is this a known issue, something completely new or maybe related to infrastructure changes?

Goals

• Leverage a chat interface to help Allison
• Create a model from scratch based on data from openQA
• Proof of concept for automated analysis of openQA test results

Bonus

• Use AI to suggest solutions to merge conflicts
  • This would need a merge conflict editor that can suggest solving the conflict
• Use image recognition for needles

Resources

• TensorFlow
• open-webui
• openQA instance of openSUSE
• openQA documentation

Timeline

Day 1

• Conversing with open-webui to teach me how to create a model based on openQA test results
  • Asking for example code using TensorFlow in Python
  • Discussing log files to explore what to analyze
  • Drafting a new project called Testimony (based on Implementing a containerized Python action) - the project name was also suggested by the assistant

Day 2

• Using NotebookLLM (Gemini) to produce conversational versions of blog posts
  • Writing openQA tests in Python
  • Developing in distrobox containers
• Researching the possibility of creating a project logo with AI
  • Asking open-webui, persons with prior experience and conducting a web search for advice

Highlights

• I briefly tested compared models to see if they would make me more productive. Between llama, gemma and mistral there was no amazing difference in the results for my case.
• Convincing the chat interface to produce code specific to my use case required very explicit instructions.
• Asking for advice on how to use open-webui itself better was frustratingly unfruitful both in trivial and more advanced regards.
• Documentation on source materials used by LLM's and tools for this purpose seems virtually non-existent - specifically if a logo can be generated based on particular licenses

Outcomes

• Chat interface-supported development is providing good starting points and open-webui being open source is more flexible than Gemini. Although currently some fancy features such as grounding and generated podcasts are missing.
• Allison still has to be very experienced with openQA to use a chat interface for test review. Publicly available system prompts would make that easier, though.

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WebUI for your data by avicenzi

A single place to view every bit of data you have.

Problem

You have too much data and you are a data hoarder.

• Family photos and videos.
• Lots of eBooks, TV Shows, Movies, and else.
• Boxes full of papers (taxes, invoices, IDs, certificates, exams, and else).
• Bank account statements (multiple currencies, countries, and people).

Maybe you have some data on S3, some on your NAS, and some on your local PC.

• How do you get it all together?
• How do you link a bank transaction to a product invoice?
• How to tag any object type and create a collection out of it (mix videos, photos, PDFs, transactions)?
• How to store this? file/folder structure does not work, everything is linked together

Project Description

The idea is a place where you can throw all your data, photos, videos, documents, binaries, and else.

Create photo albums, document collections, add tags across multiple file-formats, link content, and else.

The UI should be easy to use, where the data is not important for now (could be all S3 or local drive).

Similar proposals

The closest I found so far is https://perkeep.org/, but this is not what I'm looking for.

Goal for this Hackweek

Create a web UI, in Svelte ideally, perhaps React.

It should be able to show photos and videos at least.

Resources

None so far, this is just an idea.

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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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