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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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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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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ghostwrAIter - a local AI assisted tool for helping with support cases by paolodepa

Description

This project is meant to fight the loneliness of the support team members, providing them an AI assistant (hopefully) capable of scraping supportconfigs in a RAG fashion, trying to answer specific questions.

Goals

• Setup an Ollama backend, spinning one (or more??) code-focused LLMs selected by license, performance and quality of the results between:
  • deepseek-coder-v2
  • dolphin-mistral
  • starcoder2
  • (...others??)
    
• Setup a Web UI for it, choosing an easily extensible and customizable option between:
  • openwebui
  • localAI
  • lobachat
  • (...others??)
    
• Extend the solution in order to be able to:
  • Add ZIU/Concord shared folders to its RAG context
  • Add BZ cases, splitted in comments to its RAG context
    • A plus would be to login using the IDP portal to ghostwrAIter itself and use the same credentials to query BZ
  • Add specific packages picking them from IBS repos
    • A plus would be to login using the IDP portal to ghostwrAIter itself and use the same credentials to query IBS
    • A plus would be to desume the packages of interest and the right channel and version to be picked from the added BZ cases

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Learn how to integrate Elixir and Phoenix Liveview with LLMs by ninopaparo

Description

Learn how to integrate Elixir and Phoenix Liveview with LLMs by building an application that can provide answers to user queries based on a corpus of custom-trained data.

Goals

Develop an Elixir application via the Phoenix framework that:

• Employs Retrieval Augmented Generation (RAG) techniques
• Supports the integration and utilization of various Large Language Models (LLMs).
• Is designed with extensibility and adaptability in mind to accommodate future enhancements and modifications.

Resources

• https://elixir-lang.org/
• https://www.phoenixframework.org/
• https://github.com/elixir-nx/bumblebee
• https://ollama.com/

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Create SUSE Manager users from ldap/ad groups by mbrookhuis

Description

This tool is used to create users in SUSE Manager Server based on LDAP/AD groups. For each LDAP/AD group a role within SUSE Manager Server is defined. Also, the tool will check if existing users still have the role they should have, and, if not, it will be corrected. The same for if a user is disabled, it will be enabled again. If a users is not present in the LDAP/AD groups anymore, it will be disabled or deleted, depending on the configuration.

The code is written for Python 3.6 (the default with SLES15.x), but will also work with newer versions. And works against SUSE Manger 4.3 and 5.x

Goals

Create a python and/or golang utility that will manage users in SUSE Manager based on LDAP/AD group-membership. In a configuration file is defined which roles the members of a group will get.

Table of contents

• Installation
• Usage

Installation

To install this project, perform the following steps:

• Be sure that python 3.6 is installed and also the module python3-PyYAML. Also the ldap3 module is needed:

bash zypper in python3 python3-PyYAML pip install yaml

• On the server or PC, where it should run, create a directory. On linux, e.g. /opt/sm-ldap-users

• Copy all the file to this directory.

• Edit the configsm.yaml. All parameters should be entered. Tip: for the ldap information, the best would be to use the same as for SSSD.

• Be sure that the file sm-ldap-users.py is executable. It would be good to change the owner to root:root and only root can read and execute:

bash chmod 600 * chmod 700 sm-ldap-users.py chown root:root *

Usage

This is very simple. Once the configsm.yaml contains the correct information, executing the following will do the magic:

bash /sm-ldap-users.py

repository link

https://github.com/mbrookhuis/sm-ldap-users

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Edge Image Builder and mkosi for Uyuni by oholecek

Description

One part of Uyuni system management tool is ability to build custom images. Currently Uyuni supports only Kiwi image builder.

Kiwi however is not the only image building system out there and with the goal to also become familiar with other systems, this projects aim to add support for Edge Image builder and systemd's mkosi systems.

Goals

Uyuni is able to

• provision EIB and mkosi build hosts
• build EIB and mkosi images and store them

Resources

• Uyuni - https://github.com/uyuni-project/uyuni
• Edge Image builder - https://github.com/suse-edge/edge-image-builder
• mkosi - https://github.com/systemd/mkosi

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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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Automated Test Report reviewer by oscar-barrios

Description

In SUMA/Uyuni team we spend a lot of time reviewing test reports, analyzing each of the test cases failing, checking if the test is a flaky test, checking logs, etc.

Goals

Speed up the review by automating some parts through AI, in a way that we can consume some summary of that report that could be meaningful for the reviewer.

Resources

No idea about the resources yet, but we will make use of:

• HTML/JSON Report (text + screenshots)
• The Test Suite Status GithHub board (via API)
• The environment tested (via SSH)
• The test framework code (via files)

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Run local LLMs with Ollama and explore possible integrations with Uyuni by PSuarezHernandez

Description

Using Ollama you can easily run different LLM models in your local computer. This project is about exploring Ollama, testing different LLMs and try to fine tune them. Also, explore potential ways of integration with Uyuni.

Goals

• Explore Ollama
• Test different models
• Fine tuning
• Explore possible integration in Uyuni

Resources

• https://ollama.com/
• https://huggingface.co/
• https://apeatling.com/articles/part-2-building-your-training-data-for-fine-tuning/

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