Project Description
This project will create a simple chat-bot for tutoring children for school. Lessons will be pre-configured by feeding in a document and requesting the material be taught to a child in consideration of the child's age, etc.
Goal for this Hackweek
Create an interface to have student/teacher logins, where a teacher can configure a lesson for the day. A configured lesson is simply providing initial prompts to the chat-bot.
Resources
https://github.com/dmulder/TinyTutor
This project is part of:
Hack Week 23
Activity
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The Agentic Rancher Experiment: Do Androids Dream of Electric Cattle? by moio
Rancher is a beast of a codebase. Let's investigate if the new 2025 generation of GitHub Autonomous Coding Agents and Copilot Workspaces can actually tame it. 
The Plan
Create a sandbox GitHub Organization, clone in key Rancher repositories, and let the AI loose to see if it can handle real-world enterprise OSS maintenance - or if it just hallucinates new breeds of Kubernetes resources!
Specifically, throw "Agentic Coders" some typical tasks in a complex, long-lived open-source project, such as:
❥ The Grunt Work: generate missing GoDocs, unit tests, and refactorings. Rebase PRs.
❥ The Complex Stuff: fix actual (historical) bugs and feature requests to see if they can traverse the complexity without (too much) human hand-holding.
❥ Hunting Down Gaps: find areas lacking in docs, areas of improvement in code, dependency bumps, and so on.
If time allows, also experiment with Model Context Protocol (MCP) to give agents context on our specific build pipelines and CI/CD logs.
Why?
We know AI can write "Hello World." and also moderately complex programs from a green field. But can it rebase a 3-month-old PR with conflicts in rancher/rancher? I want to find the breaking point of current AI agents to determine if and how they can help us to reduce our technical debt, work faster and better. At the same time, find out about pitfalls and shortcomings.
The CONCLUSION!!!
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State of the Union document was compiled to summarize lessons learned this week. For more gory details, just read on the diary below! 
Exploring Modern AI Trends and Kubernetes-Based AI Infrastructure by jluo
Description
Build a solid understanding of the current landscape of Artificial Intelligence and how modern cloud-native technologies—especially Kubernetes—support AI workloads.
Goals
Use Gemini Learning Mode to guide the exploration, surface relevant concepts, and structure the learning journey:
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- Understand how Kubernetes and related cloud-native technologies are used in the AI ecosystem (model training, deployment, orchestration, MLOps).
Resources
Red Hat AI Topic Articles
- https://www.redhat.com/en/topics/ai
Kubeflow Documentation
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Q4 2025 CNCF Technology Landscape Radar report:
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- https://www.cncf.io/wp-content/uploads/2025/11/cncfreporttechradar_111025a.pdf
Agent-to-Agent (A2A) Protocol
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Explore LLM evaluation metrics by thbertoldi
Description
Learn the best practices for evaluating LLM performance with an open-source framework such as DeepEval.
Goals
Curate the knowledge learned during practice and present it to colleagues.
-> Maybe publish a blog post on SUSE's blog?
Resources
https://deepeval.com
https://docs.pactflow.io/docs/bi-directional-contract-testing
Uyuni Health-check Grafana AI Troubleshooter by ygutierrez
Description
This project explores the feasibility of using the open-source Grafana LLM plugin to enhance the Uyuni Health-check tool with LLM capabilities. The idea is to integrate a chat-based "AI Troubleshooter" directly into existing dashboards, allowing users to ask natural-language questions about errors, anomalies, or performance issues.
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Resources
Backporting patches using LLM by jankara
Description
Backporting Linux kernel fixes (either for CVE issues or as part of general git-fixes workflow) is boring and mostly mechanical work (dealing with changes in context, renamed variables, new helper functions etc.). The idea of this project is to explore usage of LLM for backporting Linux kernel commits to SUSE kernels using LLM.
Goals
- Create safe environment allowing LLM to run and backport patches without exposing the whole filesystem to it (for privacy and security reasons).
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Resources
- Docker
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Repository
Current version of the container with some instructions for use are at: https://gitlab.suse.de/jankara/gemini-cli-backporter
Improve/rework household chore tracker `chorazon` by gniebler
Description
I wrote a household chore tracker named chorazon, which is meant to be deployed as a web application in the household's local network.
It features the ability to set up different (so far only weekly) schedules per task and per person, where tasks may span several days.
There are "tokens", which can be collected by users. Tasks can (and usually will) have rewards configured where they yield a certain amount of tokens. The idea is that they can later be redeemed for (surprise) gifts, but this is not implemented yet. (So right now one needs to edit the DB manually to subtract tokens when they're redeemed.)
Days are not rolled over automatically, to allow for task completion control.
We used it in my household for several months, with mixed success. There are many limitations in the system that would warrant a revisit.
It's written using the Pyramid Python framework with URL traversal, ZODB as the data store and Web Components for the frontend.
Goals
- Add admin screens for users, tasks and schedules
- Add models, pages etc. to allow redeeming tokens for gifts/surprises
- …?
Resources
tbd (Gitlab repo)
mgr-ansible-ssh - Intelligent, Lightweight CLI for Distributed Remote Execution by deve5h
Description
By the end of Hack Week, the target will be to deliver a minimal functional version 1 (MVP) of a custom command-line tool named mgr-ansible-ssh (a unified wrapper for BOTH ad-hoc shell & playbooks) that allows operators to:
- Execute arbitrary shell commands on thousand of remote machines simultaneously using Ansible Runner with artifacts saved locally.
- Pass runtime options such as inventory file, remote command string/ playbook execution, parallel forks, limits, dry-run mode, or no-std-ansible-output.
- Leverage existing SSH trust relationships without additional setup.
- Provide a clean, intuitive CLI interface with --help for ease of use. It should provide consistent UX & CI-friendly interface.
- Establish a foundation that can later be extended with advanced features such as logging, grouping, interactive shell mode, safe-command checks, and parallel execution tuning.
The MVP should enable day-to-day operations to efficiently target thousands of machines with a single, consistent interface.
Goals
Primary Goals (MVP):
Build a functional CLI tool (mgr-ansible-ssh) capable of executing shell commands on multiple remote hosts using Ansible Runner. Test the tool across a large distributed environment (1000+ machines) to validate its performance and reliability.
Looking forward to significantly reducing the zypper deployment time across all 351 RMT VM servers in our MLM cluster by eliminating the dependency on the taskomatic service, bringing execution down to a fraction of the current duration. The tool should also support multiple runtime flags, such as:
mgr-ansible-ssh: Remote command execution wrapper using Ansible Runner
Usage: mgr-ansible-ssh [--help] [--version] [--inventory INVENTORY]
[--run RUN] [--playbook PLAYBOOK] [--limit LIMIT]
[--forks FORKS] [--dry-run] [--no-ansible-output]
Required Arguments
--inventory, -i Path to Ansible inventory file to use
Any One of the Arguments Is Required
--run, -r Execute the specified shell command on target hosts
--playbook, -p Execute the specified Ansible playbook on target hosts
Optional Arguments
--help, -h Show the help message and exit
--version, -v Show the version and exit
--limit, -l Limit execution to specific hosts or groups
--forks, -f Number of parallel Ansible forks
--dry-run Run in Ansible check mode (requires -p or --playbook)
--no-ansible-output Suppress Ansible stdout output
Secondary/Stretched Goals (if time permits):
- Add pretty output formatting (success/failure summary per host).
- Implement basic logging of executed commands and results.
- Introduce safety checks for risky commands (shutdown, rm -rf, etc.).
- Package the tool so it can be installed with pip or stored internally.
Resources
Collaboration is welcome from anyone interested in CLI tooling, automation, or distributed systems. Skills that would be particularly valuable include:
- Python especially around CLI dev (argparse, click, rich)
Improve chore and screen time doc generator script `wochenplaner` by gniebler
Description
I wrote a little Python script to generate PDF docs, which can be used to track daily chore completion and screen time usage for several people, with one page per person/week.
I named this script wochenplaner and have been using it for a few months now.
It needs some improvements and adjustments in how the screen time should be tracked and how chores are displayed.
Goals
- Fix chore field separation lines
- Change screen time tracking logic from "global" (week-long) to daily subtraction and weekly addition of remainders (more intuitive than current "weekly time budget method)
- Add logic to fill in chore fields/lines, ideally with pictures, falling back to text.
Resources
tbd (Gitlab repo)
openQA log viewer by mpagot
Description
*** Warning: Are You at Risk for VOMIT? ***
Do you find yourself staring at a screen, your eyes glossing over as thousands of lines of text scroll by? Do you feel a wave of text-based nausea when someone asks you to "just check the logs"?
You may be suffering from VOMIT (Verbose Output Mental Irritation Toxicity).
This dangerous, work-induced ailment is triggered by exposure to an overwhelming quantity of log data, especially from parallel systems. The human brain, not designed to mentally process 12 simultaneous autoinst-log.txt files, enters a state of toxic shock. It rejects the "Verbose Output," making it impossible to find the one critical error line buried in a 50,000-line sea of "INFO: doing a thing."
Before you're forced to rm -rf /var/log in a fit of desperation, we present the digital antacid.
No panic: we have The openQA Log Visualizer
This is the UI antidote for handling toxic log environments. It bravely dives into the chaotic, multi-machine mess of your openQA test runs, finds all the related, verbose logs, and force-feeds them into a parser.
Goals
Work on the existing POC openqa-log-visualizer about few specific tasks:
- add support for more type of logs
- extend the configuration file syntax beyond the actual one
- work on log parsing performance
Find some beta-tester and collect feedback and ideas about features
If time allow for it evaluate other UI frameworks and solutions (something more simple to distribute and run, maybe more low level to gain in performance).
Resources