Multi-pod, autoscalable Elixir application in Kubernetes using K8s resources by socon

Description

Elixir / Erlang use their own solutions to create clusters that work together. Kubernetes provide its own orchestration. Due to the nature of the BEAM, it looks a very promising technology for applications that run in Kubernetes and requite to be always on, specifically if they are created as web pages using Phoenix.

Goals

• Investigate and provide solutions that work in Phoenix LiveView using Kubernetes resources, so a multi-pod application can be used
• Provide an end to end example that creates and deploy a container from source code.

Resources

https://github.com/dwyl/phoenix-liveview-counter-tutorial https://github.com/propedeutica/elixir-k8s-counter

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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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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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Dartboard TUI by IValentin

Description

Our scalability and performance testing swiss-army knife tool Dartboard is a major WIP so why not add more scope creep? Dartboard is a cli tool which enables users to:

• Define a "Dart" config file as YAML which defines the various components to be created/setup when Dartboard runs its commands
• Spin up infrastructure utilizing opentofu/terraform providers
• Setup K3s or RKE2 clusters on the newly created infrastructure
• Deploy Rancher (with or without downstream cluster), rancher-monitoring (Grafana + Prometheus)
• Create resources in-bulk within the newly created Rancher cluster (ConfigMaps, Secrets, Users, Roles, etc.)
• Run various performance and scalability tests via k6
• Export/Import various tracked metrics (WIP)

Given all these features (and the features to come), it can be difficult to onboard and transfer knowledge of the tool. With a TUI, Dartboard's usage complexity can be greatly reduced!

Goals

• Create a TUI for Dartboard's "subcommands"
• Gain more familiarity with Dartboard and create a more user-friendly interface to enable others to use it
• Stretch Create a TUI workflow for generating a Dart file

Resources

https://github.com/charmbracelet/bubbletea

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suse-rancher-supportconfig by eminguez

Description

Update: Live at https://github.com/e-minguez/suse-rancher-supportconfig I finally didn't used golang but used gum instead

SUSE's supportconfig support tool collects data from the SUSE Operating system. Rancher's rancher2_logs_collector.sh support tool does the same for RKE2/K3s.

Wouldn't be nice to have a way to run both and collect all data for SUSE based RKE2/K3s clusters? Wouldn't be even better with a fancy TUI tool like bubbletea?

Ideally the output should be an html page where you can see the logs/data directly from the browser.

Goals

• Familiarize myself with both supportconfig and rancher2_logs_collector.sh tools
• Refresh my golang knowledge
• Have something that works at the end of the hackweek ("works" may vary )
• Be better in naming things

Resources

All links provided above as well as huh

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Implement a CLI tool for Trento - trentoctl by nkopliku

Description

Implement a trentoctl CLI for interacting with a trento installation

Goals

• learn rust
• implement an initial trentoctl tool to enhance trento automation
• have fun

Resources

trento rust. TUIs listed on this other hackweek project Hack on rich terminal user interfaces

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A CLI for Harvester by mohamed.belgaied

[comment]: # Harvester does not officially come with a CLI tool, the user is supposed to interact with Harvester mostly through the UI [comment]: # Though it is theoretically possible to use kubectl to interact with Harvester, the manipulation of Kubevirt YAML objects is absolutely not user friendly. [comment]: # Inspired by tools like multipass from Canonical to easily and rapidly create one of multiple VMs, I began the development of Harvester CLI. Currently, it works but Harvester CLI needs some love to be up-to-date with Harvester v1.0.2 and needs some bug fixes and improvements as well.

Project Description

Harvester CLI is a command line interface tool written in Go, designed to simplify interfacing with a Harvester cluster as a user. It is especially useful for testing purposes as you can easily and rapidly create VMs in Harvester by providing a simple command such as: harvester vm create my-vm --count 5 to create 5 VMs named my-vm-01 to my-vm-05.

Harvester CLI is functional but needs a number of improvements: up-to-date functionality with Harvester v1.0.2 (some minor issues right now), modifying the default behaviour to create an opensuse VM instead of an ubuntu VM, solve some bugs, etc.

Github Repo for Harvester CLI: https://github.com/belgaied2/harvester-cli

Done in previous Hackweeks

• Create a Github actions pipeline to automatically integrate Harvester CLI to Homebrew repositories: DONE
• Automatically package Harvester CLI for OpenSUSE / Redhat RPMs or DEBs: DONE

Goal for this Hackweek

The goal for this Hackweek is to bring Harvester CLI up-to-speed with latest Harvester versions (v1.3.X and v1.4.X), and improve the code quality as well as implement some simple features and bug fixes.

Some nice additions might be: * Improve handling of namespaced objects * Add features, such as network management or Load Balancer creation ? * Add more unit tests and, why not, e2e tests * Improve CI * Improve the overall code quality * Test the program and create issues for it

Issue list is here: https://github.com/belgaied2/harvester-cli/issues

Resources

The project is written in Go, and using client-go the Kubernetes Go Client libraries to communicate with the Harvester API (which is Kubernetes in fact). Welcome contributions are:

• Testing it and creating issues
• Documentation
• Go code improvement

What you might learn

Harvester CLI might be interesting to you if you want to learn more about:

• GitHub Actions
• Harvester as a SUSE Product
• Go programming language
• Kubernetes API

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file-organizer: A CLI Tool for Efficient File Management by okhatavkar

Description

Create a Go-based CLI tool that helps organize files in a specified folder by sorting them into subdirectories based on defined criteria, such as file type or creation date. Users will pass a folder path as an argument, and the tool will process and organize the files within it.

Goals

• Develop Go skills by building a practical command-line application.
• Learn to manage and manipulate files and directories in Go using standard libraries.
• Create a tool that simplifies file management, making it easier to organize and maintain directories.

Resources

• Go Standard Libraries: Utilize os, filepath, and time for file operations.
• CLI Development: Use flag for basic argument parsing or consider cobra for enhanced functionality.
• Go Learning Material: Go by Example and The Go Programming Language Documentation.

Features

• File Type Sorting: Automatically move files into subdirectories based on their extensions (e.g., documents, images, videos).
• Date-Based Organization: Add an option to organize files by creation date into year/month folders.
• User-Friendly CLI: Build intuitive commands and clear outputs for ease of use. This version maintains the core idea of organizing files efficiently while focusing on Go development and practical file management.

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