Project Description
The goal is to have a language model, that is able to answer technical questions on Uyuni. Uyuni documentation is too large for in-context processing, so finetuning is the way to go.
Goal for this Hackweek
Finetune a model based on llama-2-7b.
Resources
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This project is part of:
Hack Week 23
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Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios
Description
Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.
This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.
The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.
Goals
By the end of Hack Week, we aim to have a single, working Python script that:
- Connects to Prometheus and executes a query to fetch detailed test failure history.
- Processes the raw data into a format suitable for the Gemini API.
- Successfully calls the Gemini API with the data and a clear prompt.
- Parses the AI's response to extract a simple list of flaky tests.
- Saves the list to a JSON file that can be displayed in Grafana.
- New panel in our Dashboard listing the Flaky tests
Resources
- Jenkins Prometheus Exporter: https://github.com/uyuni-project/jenkins-exporter/
- Data Source: Our internal Prometheus server.
- Key Metric:
jenkins_build_test_case_failure_age{jobname, buildid, suite, case, status, failedsince}. - Existing Query for Reference:
count by (suite) (max_over_time(jenkins_build_test_case_failure_age{status=~"FAILED|REGRESSION", jobname="$jobname"}[$__range])). - AI Model: The Google Gemini API.
- Example about how to interact with Gemini API: https://github.com/srbarrios/FailTale/
- Visualization: Our internal Grafana Dashboard.
- Internal IaC: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring
Outcome
- Jenkins Flaky Test Detector: https://github.com/srbarrios/jenkins-flaky-tests-detector and its container
- IaC on MLM Team: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring/jenkinsflakytestsdetector?reftype=heads, https://gitlab.suse.de/galaxy/infrastructure/-/blob/master/srv/salt/monitoring/grafana/dashboards/flaky-tests.json?ref_type=heads, and others.
- Grafana Dashboard: https://grafana.mgr.suse.de/d/flaky-tests/flaky-tests-detection @ @ text
Background Coding Agent by mmanno
Description
I had only bad experiences with AI one-shots. However, monitoring agent work closely and interfering often did result in productivity gains.
Now, other companies are using agents in pipelines. That makes sense to me, just like CI, we want to offload work to pipelines: Our engineering teams are consistently slowed down by "toil": low-impact, repetitive maintenance tasks. A simple linter rule change, a dependency bump, rebasing patch-sets on top of newer releases or API deprecation requires dozens of manual PRs, draining time from feature development.
So far we have been writing deterministic, script-based automation for these tasks. And it turns out to be a common trap. These scripts are brittle, complex, and become a massive maintenance burden themselves.
Can we make prompts and workflows smart enough to succeed at background coding?
Goals
We will build a platform that allows engineers to execute complex code transformations using prompts.
By automating this toil, we accelerate large-scale migrations and allow teams to focus on high-value work.
Our platform will consist of three main components:
- "Change" Definition: Engineers will define a transformation as a simple, declarative manifest:
- The target repositories.
- A wrapper to run a "coding agent", e.g., "gemini-cli".
- The task as a natural language prompt.
- The target repositories.
- "Change" Management Service: A central service that orchestrates the jobs. It will receive Change definitions and be responsible for the job lifecycle.
- Execution Runners: We could use existing sandboxed CI runners (like GitHub/GitLab runners) to execute each job or spawn a container.
MVP
- Define the Change manifest format.
- Build the core Management Service that can accept and queue a Change.
- Connect management service and runners, dynamically dispatch jobs to runners.
- Create a basic runner script that can run a hard-coded prompt against a test repo and open a PR.
Stretch Goals:
- Multi-layered approach, Workflow Agents trigger Coding Agents:
- Workflow Agent: Gather information about the task interactively from the user.
- Coding Agent: Once the interactive agent has refined the task into a clear prompt, it hands this prompt off to the "coding agent." This background agent is responsible for executing the task and producing the actual pull request.
- Workflow Agent: Gather information about the task interactively from the user.
- Use MCP:
- Workflow Agent gathers context information from Slack, Github, etc.
- Workflow Agent triggers a Coding Agent.
- Workflow Agent gathers context information from Slack, Github, etc.
- Create a "Standard Task" library with reliable prompts.
- Rebasing rancher-monitoring to a new version of kube-prom-stack
- Update charts to use new images
- Apply changes to comply with a new linter
- Bump complex Go dependencies, like k8s modules
- Backport pull requests to other branches
- Rebasing rancher-monitoring to a new version of kube-prom-stack
- Add “review agents” that review the generated PR.
See also
SUSE Edge Image Builder MCP by eminguez
Description
Based on my other hackweek project, SUSE Edge Image Builder's Json Schema I would like to build also a MCP to be able to generate EIB config files the AI way.
Realistically I don't think I'll be able to have something consumable at the end of this hackweek but at least I would like to start exploring MCPs, the difference between an API and MCP, etc.
Goals
- Familiarize myself with MCPs
- Unrealistic: Have an MCP that can generate an EIB config file
Resources
Multi-agent AI assistant for Linux troubleshooting by doreilly
Description
Explore multi-agent architecture as a way to avoid MCP context rot.
Having one agent with many tools bloats the context with low-level details about tool descriptions, parameter schemas etc which hurts LLM performance. Instead have many specialised agents, each with just the tools it needs for its role. A top level supervisor agent takes the user prompt and delegates to appropriate sub-agents.
Goals
Create an AI assistant with some sub-agents that are specialists at troubleshooting Linux subsystems, e.g. systemd, selinux, firewalld etc. The agents can get information from the system by implementing their own tools with simple function calls, or use tools from MCP servers, e.g. a systemd-agent can use tools from systemd-mcp.
Example prompts/responses:
user$ the system seems slow
assistant$ process foo with pid 12345 is using 1000% cpu ...
user$ I can't connect to the apache webserver
assistant$ the firewall is blocking http ... you can open the port with firewall-cmd --add-port ...
Resources
Language Python. The Python ADK is more mature than Golang.
https://google.github.io/adk-docs/
https://github.com/djoreilly/linux-helper
Bugzilla goes AI - Phase 1 by nwalter
Description
This project, Bugzilla goes AI, aims to boost developer productivity by creating an autonomous AI bug agent during Hackweek. The primary goal is to reduce the time employees spend triaging bugs by integrating Ollama to summarize issues, recommend next steps, and push focused daily reports to a Web Interface.
Goals
To reduce employee time spent on Bugzilla by implementing an AI tool that triages and summarizes bug reports, providing actionable recommendations to the team via Web Interface.
Project Charter
https://docs.google.com/document/d/1HbAvgrg8T3pd1FIx74nEfCObCljpO77zz5In_Jpw4as/edit?usp=sharing## Description
Project Achievements during Hackweek
In this file you can read about what we achieved during Hackweek.
https://docs.google.com/document/d/14gtG9-ZvVpBgkh33Z4AM6iLFWqZcicQPD41MM-Pg0/edit?usp=sharing
Uyuni Saltboot rework by oholecek
Description
When Uyuni switched over to the containerized proxies we had to abandon salt based saltboot infrastructure we had before. Uyuni already had integration with a Cobbler provisioning server and saltboot infra was re-implemented on top of this Cobbler integration.
What was not obvious from the start was that Cobbler, having all it's features, woefully slow when dealing with saltboot size environments. We did some improvements in performance, introduced transactions, and generally tried to make this setup usable. However the underlying slowness remained.
Goals
This project is not something trying to invent new things, it is just finally implementing saltboot infrastructure directly with the Uyuni server core.
Instead of generating grub and pxelinux configurations by Cobbler for all thousands of systems and branches, we will provide a GET access point to retrieve grub or pxelinux file during the boot:
/saltboot/group/grub/$fqdn and similar for systems /saltboot/system/grub/$mac
Next we adapt our tftpd translator to query these points when asked for default or mac based config.
Lastly similar thing needs to be done on our apache server when HTTP UEFI boot is used.
Resources
Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios
Description
Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.
This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.
The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.
Goals
By the end of Hack Week, we aim to have a single, working Python script that:
- Connects to Prometheus and executes a query to fetch detailed test failure history.
- Processes the raw data into a format suitable for the Gemini API.
- Successfully calls the Gemini API with the data and a clear prompt.
- Parses the AI's response to extract a simple list of flaky tests.
- Saves the list to a JSON file that can be displayed in Grafana.
- New panel in our Dashboard listing the Flaky tests
Resources
- Jenkins Prometheus Exporter: https://github.com/uyuni-project/jenkins-exporter/
- Data Source: Our internal Prometheus server.
- Key Metric:
jenkins_build_test_case_failure_age{jobname, buildid, suite, case, status, failedsince}. - Existing Query for Reference:
count by (suite) (max_over_time(jenkins_build_test_case_failure_age{status=~"FAILED|REGRESSION", jobname="$jobname"}[$__range])). - AI Model: The Google Gemini API.
- Example about how to interact with Gemini API: https://github.com/srbarrios/FailTale/
- Visualization: Our internal Grafana Dashboard.
- Internal IaC: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring
Outcome
- Jenkins Flaky Test Detector: https://github.com/srbarrios/jenkins-flaky-tests-detector and its container
- IaC on MLM Team: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring/jenkinsflakytestsdetector?reftype=heads, https://gitlab.suse.de/galaxy/infrastructure/-/blob/master/srv/salt/monitoring/grafana/dashboards/flaky-tests.json?ref_type=heads, and others.
- Grafana Dashboard: https://grafana.mgr.suse.de/d/flaky-tests/flaky-tests-detection @ @ text
Ansible to Salt integration by vizhestkov
Description
We already have initial integration of Ansible in Salt with the possibility to run playbooks from the salt-master on the salt-minion used as an Ansible Control node.
In this project I want to check if it possible to make Ansible working on the transport of Salt. Basically run playbooks with Ansible through existing established Salt (ZeroMQ) transport and not using ssh at all.
Goals
- [v] Prepare the testing environment with Salt and Ansible installed
- [v] Discover Ansible codebase to figure out possible ways of integration
- [v] Create Salt/Uyuni inventory module
- [v] Make basic modules to work with no using separate ssh connection, but reusing existing Salt connection
- [v] Test some most basic playbooks
Resources
Uyuni read-only replica by cbosdonnat
Description
For now, there is no possible HA setup for Uyuni. The idea is to explore setting up a read-only shadow instance of an Uyuni and make it as useful as possible.
Possible things to look at:
- live sync of the database, probably using the WAL. Some of the tables may have to be skipped or some features disabled on the RO instance (taskomatic, PXT sessions…)
- Can we use a load balancer that routes read-only queries to either instance and the other to the RW one? For example, packages or PXE data can be served by both, the API GET requests too. The rest would be RW.
Goals
- Prepare a document explaining how to do it.
- PR with the needed code changes to support it
Move Uyuni Test Framework from Selenium to Playwright + AI by oscar-barrios

Description
This project aims to migrate the existing Uyuni Test Framework from Selenium to Playwright. The move will improve the stability, speed, and maintainability of our end-to-end tests by leveraging Playwright's modern features. We'll be rewriting the current Selenium code in Ruby to Playwright code in TypeScript, which includes updating the test framework runner, step definitions, and configurations. This is also necessary because we're moving from Cucumber Ruby to CucumberJS.
If you're still curious about the AI in the title, it was just a way to grab your attention. Thanks for your understanding.
Nah, let's be honest
AI helped a lot to vibe code a good part of the Ruby methods of the Test framework, moving them to Typescript, along with the migration from Capybara to Playwright. I've been using "Cline" as plugin for WebStorm IDE, using Gemini API behind it.
Goals
- Migrate Core tests including Onboarding of clients
- Improve test reliabillity: Measure and confirm a significant reduction of flakiness.
- Implement a robust framework: Establish a well-structured and reusable Playwright test framework using the CucumberJS
Resources
- Existing Uyuni Test Framework (Cucumber Ruby + Capybara + Selenium)
- My Template for CucumberJS + Playwright in TypeScript
- Started Hackweek Project