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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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:
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The code for this project will be entirely written using AI to better explore and demonstrate AI capabilities.
Result
In this MVP we implemented:
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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
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Description
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Scope
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Resources
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- https://www.datadoghq.com/blog/datadog-remote-mcp-server
- https://modelcontextprotocol.io/specification/2025-06-18/index
- https://modelcontextprotocol.io/docs/develop/build-server
Basic implementation
- https://github.com/drutigliano19/suse-observability-mcp-server
Results
Successfully developed and delivered a fully functional SUSE Observability MCP Server that bridges language models with SUSE Observability's operational data. This project demonstrates how AI agents can perform intelligent troubleshooting and root cause analysis using structured access to real-time infrastructure data.
Example execution
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Project Charter
Description
Project Achievements during Hackweek
In this file you can read about what we achieved during Hackweek.
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Description
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Resources
There is a prototype implementation here. This currently sort of works with JIRA only.
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https://github.com/uyuni-project/uyuni
https://www.uyuni-project.org/uyuni-docs/en/uyuni/index.html
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Description
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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.
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Resources
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