Project Description
Over the years, our bugzilla database has grown up in size, becoming a very valuable source of truth for most support and development cases; still searching for specific items is quite tricky and the results do not always match the expectations.
What about feeding a Maching Learning platform with the Bugzilla Database, in order to be able to query it through AI interface? Wouldn't it be nice/convenient to ask to AI: "Gimme hints about this kernel dump!" or "What is the root cause of this stack trace?"
It is the age of choice in the end, isn't it?
Goal for this Hackweek
For this Hackweek, the focus is to trigger a discussion on the following non-exhaustive list:
- What are the boundaries to be set when considering such an approach (legal, ethical, technological, whatever)
- How much of the Bugzilla DB can be used for feeding ML? ( can we use customer's data? what about partner's data?)
- Find out an open source ML solution fitting our needs;
- Find out some hardware where the solution can be eventually run on.
Anyone interested can join the discussion on the open Slack channel #discuss-bugzilla-ai
Resources
[1] https://blog.opensource.org/towards-a-definition-of-open-artificial-intelligence-first-meeting-recap/
No Hackers yet
Looking for hackers with the skills:
This project is part of:
Hack Week 23
Activity
Comments
-
about 2 years ago by paolodepa | Reply
Preliminary findings: talking to Amartya Chakraborty, who works to the Rancher AI project (https://github.com/rancher/opni), it seems that their framework can be attached to a Bugzilla instance for machine learning and pobably this will be explorated in the future
-
-
-
Similar Projects
Liz - Prompt autocomplete by ftorchia
Description
Liz is the Rancher AI assistant for cluster operations.
Goals
We want to help users when sending new messages to Liz, by adding an autocomplete feature to complete their requests based on the context.
Example:
- User prompt: "Can you show me the list of p"
- Autocomplete suggestion: "Can you show me the list of p...od in local cluster?"
Example:
- User prompt: "Show me the logs of #rancher-"
- Chat console: It shows a drop-down widget, next to the # character, with the list of available pod names starting with "rancher-".
Technical Overview
- The AI agent should expose a new ws/autocomplete endpoint to proxy autocomplete messages to the LLM.
- The UI extension should be able to display prompt suggestions and allow users to apply the autocomplete to the Prompt via keyboard shortcuts.
Resources
Try AI training with ROCm and LoRA by bmwiedemann
Description
I want to setup a Radeon RX 9600 XT 16 GB at home with ROCm on Slowroll.
Goals
I want to test how fast AI inference can get with the GPU and if I can use LoRA to re-train an existing free model for some task.
Resources
- https://rocm.docs.amd.com/en/latest/compatibility/compatibility-matrix.html
- https://build.opensuse.org/project/show/science:GPU:ROCm
- https://src.opensuse.org/ROCm/
- https://www.suse.com/c/lora-fine-tuning-llms-for-text-classification/
Results
got inference working with llama.cpp:
export LLAMACPP_ROCM_ARCH=gfx1200
HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$LLAMACPP_ROCM_ARCH \
-DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \
-Dhipblas_DIR=/usr/lib64/cmake/hipblaslt/ \
&& cmake --build build --config Release -j8
m=models/gpt-oss-20b-mxfp4.gguf
cd $P/llama.cpp && build/bin/llama-server --model $m --threads 8 --port 8005 --host 0.0.0.0 --device ROCm0 --n-gpu-layers 999
Without the --device option it faulted. Maybe because my APU also appears there?
I updated/fixed various related packages: https://src.opensuse.org/ROCm/rocm-examples/pulls/1 https://src.opensuse.org/ROCm/hipblaslt/pulls/1 SR 1320959
benchmark
I benchmarked inference with llama.cpp + gpt-oss-20b-mxfp4.gguf and ROCm offloading to a Radeon RX 9060 XT 16GB. I varied the number of layers that went to the GPU:
- 0 layers 14.49 tokens/s (8 CPU cores)
- 9 layers 17.79 tokens/s 34% VRAM
- 15 layers 22.39 tokens/s 51% VRAM
- 20 layers 27.49 tokens/s 64% VRAM
- 24 layers 41.18 tokens/s 74% VRAM
- 25+ layers 86.63 tokens/s 75% VRAM (only 200% CPU load)
So there is a significant performance-boost if the whole model fits into the GPU's VRAM.
Local AI assistant with optional integrations and mobile companion by livdywan
Description
Setup a local AI assistant for research, brainstorming and proof reading. Look into SurfSense, Open WebUI and possibly alternatives. Explore integration with services like openQA. There should be no cloud dependencies. Mobile phone support or an additional companion app would be a bonus. The goal is not to develop everything from scratch.
User Story
- Allison Average wants a one-click local AI assistent on their openSUSE laptop.
- Ash Awesome wants AI on their phone without an expensive subscription.
Goals
- Evaluate a local SurfSense setup for day to day productivity
- Test opencode for vibe coding and tool calling
Timeline
Day 1
- Took a look at SurfSense and started setting up a local instance.
- Unfortunately the container setup did not work well. Tho this was a great opportunity to learn some new podman commands and refresh my memory on how to recover a corrupted btrfs filesystem.
Day 2
- Due to its sheer size and complexity SurfSense seems to have triggered btrfs fragmentation. Naturally this was not visible in any podman-related errors or in the journal. So this took up much of my second day.
Day 3
- Trying out opencode with Qwen3-Coder and Qwen2.5-Coder.
Day 4
- Context size is a thing, and models are not equally usable for vibe coding.
- Through arduous browsing for ollama models I did find some like
myaniu/qwen2.5-1m:7bwith 1m but even then it is not obvious if they are meant for tool calls.
Day 5
- Whilst trying to make opencode usable I discovered ramalama which worked instantly and very well.
Outcomes
surfsense
I could not easily set this up completely. Maybe in part due to my filesystem issues. Was expecting this to be less of an effort.
opencode
Installing opencode and ollama in my distrobox container along with the following configs worked for me.
When preparing a new project from scratch it is a good idea to start out with a template.
opencode.json
``` {
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!!!
A
State of the Union
document was compiled to summarize lessons learned this week. For more gory details, just read on the diary below!
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.
Goals
- Investigate if and how the
grafana-llm-appplug-in can be used within the Uyuni Health-check tool. - Investigate if this plug-in can be used to query LLMs for troubleshooting scenarios.
- Evaluate support for local LLMs and external APIs through the plugin.
- Evaluate if and how the Uyuni MCP server could be integrated as another source of information.
Resources
Song Search with CLAP by gcolangiuli
Description
Contrastive Language-Audio Pretraining (CLAP) is an open-source library that enables the training of a neural network on both Audio and Text descriptions, making it possible to search for Audio using a Text input. Several pre-trained models for song search are already available on huggingface
Goals
Evaluate how CLAP can be used for song searching and determine which types of queries yield the best results by developing a Minimum Viable Product (MVP) in Python. Based on the results of this MVP, future steps could include:
- Music Tagging;
- Free text search;
- Integration with an LLM (for example, with MCP or the OpenAI API) for music suggestions based on your own library.
The code for this project will be entirely written using AI to better explore and demonstrate AI capabilities.
Result
In this MVP we implemented:
- Async Song Analysis with Clap model
- Free Text Search of the songs
- Similar song search based on vector representation
- Containerised version with web interface
We also documented what went well and what can be improved in the use of AI.
You can have a look at the result here:
Future implementation can be related to performance improvement and stability of the analysis.
References
- CLAP: The main model being researched;
- huggingface: Pre-trained models for CLAP;
- Free Music Archive: Creative Commons songs that can be used for testing;
Song Search with CLAP by gcolangiuli
Description
Contrastive Language-Audio Pretraining (CLAP) is an open-source library that enables the training of a neural network on both Audio and Text descriptions, making it possible to search for Audio using a Text input. Several pre-trained models for song search are already available on huggingface
Goals
Evaluate how CLAP can be used for song searching and determine which types of queries yield the best results by developing a Minimum Viable Product (MVP) in Python. Based on the results of this MVP, future steps could include:
- Music Tagging;
- Free text search;
- Integration with an LLM (for example, with MCP or the OpenAI API) for music suggestions based on your own library.
The code for this project will be entirely written using AI to better explore and demonstrate AI capabilities.
Result
In this MVP we implemented:
- Async Song Analysis with Clap model
- Free Text Search of the songs
- Similar song search based on vector representation
- Containerised version with web interface
We also documented what went well and what can be improved in the use of AI.
You can have a look at the result here:
Future implementation can be related to performance improvement and stability of the analysis.
References
- CLAP: The main model being researched;
- huggingface: Pre-trained models for CLAP;
- Free Music Archive: Creative Commons songs that can be used for testing;
issuefs: FUSE filesystem representing issues (e.g. JIRA) for the use with AI agents code-assistants by llansky3
Description
Creating a FUSE filesystem (issuefs) that mounts issues from various ticketing systems (Github, Jira, Bugzilla, Redmine) as files to your local file system.
And why this is good idea?
- User can use favorite command line tools to view and search the tickets from various sources
- User can use AI agents capabilities from your favorite IDE or cli to ask question about the issues, project or functionality while providing relevant tickets as context without extra work.
- User can use it during development of the new features when you let the AI agent to jump start the solution. The issuefs will give the AI agent the context (AI agents just read few more files) about the bug or requested features. No need for copying and pasting issues to user prompt or by using extra MCP tools to access the issues. These you can still do but this approach is on purpose different.

Goals
- Add Github issue support
- Proof the concept/approach by apply the approach on itself using Github issues for tracking and development of new features
- Add support for Bugzilla and Redmine using this approach in the process of doing it. Record a video of it.
- Clean-up and test the implementation and create some documentation
- Create a blog post about this approach
Resources
There is a prototype implementation here. This currently sort of works with JIRA only.
GenAI-Powered Systemic Bug Evaluation and Management Assistant by rtsvetkov
Motivation
What is the decision critical question which one can ask on a bug? How this question affects the decision on a bug and why?
Let's make GenAI look on the bug from the systemic point and evaluate what we don't know. Which piece of information is missing to take a decision?
Description
To build a tool that takes a raw bug report (including error messages and context) and uses a large language model (LLM) to generate a series of structured, Socratic-style or Systemic questions designed to guide a the integration and development toward the root cause, rather than just providing a direct, potentially incorrect fix.
Goals
Set up a Python environment
Set the environment and get a Gemini API key. 2. Collect 5-10 realistic bug reports (from open-source projects, personal projects, or public forums like Stack Overflow—include the error message and the initial context).
Build the Dialogue Loop
- Write a basic Python script using the Gemini API.
- Implement a simple conversational loop: User Input (Bug) -> AI Output (Question) -> User Input (Answer to AI's question) -> AI Output (Next Question). Code Implementation
Socratic/Systemic Strategy Implementation
- Refine the logic to ensure the questions follow a Socratic and Systemic path (e.g., from symptom-> context -> assumptions -> -> critical parts -> ).
- Implement Function Calling (an advanced feature of the Gemini API) to suggest specific actions to the user, like "Run a ping test" or "Check the database logs."
- Implement Bugzillla call to collect the
- Implement Questioning Framework as LLVM pre-conditioning
- Define set of instructions
- Assemble the Tool
Resources
What are Systemic Questions?
Systemic questions explore the relationships, patterns, and interactions within a system rather than focusing on isolated elements.
In IT, they help uncover hidden dependencies, feedback loops, assumptions, and side-effects during debugging or architecture analysis.
Gitlab Project
gitlab.suse.de/sle-prjmgr/BugDecisionCritical_Question
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
Description
Project Achievements during Hackweek
In this file you can read about what we achieved during Hackweek.