This is an idea that's been kicking around for a while... maybe it's finally time to "make it so."
Minimally:
Develop kiwi configurations in OBS for a single-server OpenQA appliance. Apply nested virtualization options in virtual format to allow easy use existing private cloud frameworks (OpenStack, VMWare, Hyper-V).
Bigger picture:
Develop cloud-focused backends ( https://progress.opensuse.org/issues/12160 ) allowing native use of cloud frameworks instead of a monolithic instance with nested virtualization. Provide an image ready to "fire up and use" in at least one Public Cloud provider (AWS/Azure/GCE).
Looking for hackers with the skills:
This project is part of:
Hack Week 15
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Make more sense of openQA test results using AI by livdywan
Description
AI has the potential to help with something many of us spend a lot of time doing which is making sense of openQA logs when a job fails.
User Story
Allison Average has a puzzled look on their face while staring at log files that seem to make little sense. Is this a known issue, something completely new or maybe related to infrastructure changes?
Goals
- Leverage a chat interface to help Allison
- Create a model from scratch based on data from openQA
- Proof of concept for automated analysis of openQA test results
Bonus
- Use AI to suggest solutions to merge conflicts
- This would need a merge conflict editor that can suggest solving the conflict
- Use image recognition for needles
Resources
Timeline
Day 1
- Conversing with open-webui to teach me how to create a model based on openQA test results
- Asking for example code using TensorFlow in Python
- Discussing log files to explore what to analyze
- Drafting a new project called Testimony (based on Implementing a containerized Python action) - the project name was also suggested by the assistant
Day 2
- Using NotebookLLM (Gemini) to produce conversational versions of blog posts
- Researching the possibility of creating a project logo with AI
- Asking open-webui, persons with prior experience and conducting a web search for advice
Highlights
- I briefly tested compared models to see if they would make me more productive. Between llama, gemma and mistral there was no amazing difference in the results for my case.
- Convincing the chat interface to produce code specific to my use case required very explicit instructions.
- Asking for advice on how to use open-webui itself better was frustratingly unfruitful both in trivial and more advanced regards.
- Documentation on source materials used by LLM's and tools for this purpose seems virtually non-existent - specifically if a logo can be generated based on particular licenses
Outcomes
- Chat interface-supported development is providing good starting points and open-webui being open source is more flexible than Gemini. Although currently some fancy features such as grounding and generated podcasts are missing.
- Allison still has to be very experienced with openQA to use a chat interface for test review. Publicly available system prompts would make that easier, though.
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Description
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Goals
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Resources
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Description
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Description
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Resources
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Description
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- Would potentially make it easier in the future to make UI changes without Perl.
- Improve my Golang skills
Resources
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Create object oriented API for perl's YAML::XS module, with YAML 1.2 Support by tinita
Description
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Goals
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<<
key feature as an option. We could then use it in openQA as a replacement for YAML::PP to be faster.
I already created a proof of concept with a minimal functionality some weeks before this HackWeek.
Resources
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- Diff