Song Search with CLAP
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;
Looking for hackers with the skills:
This project is part of:
Hack Week 25
Activity
Comments
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13 days ago by gcolangiuli | Reply
Project finished! (for what an MVP can be finished) have a look at the result on the github repo. You can also look the presentation slide.
Similar Projects
Testing and adding GNU/Linux distributions on Uyuni by juliogonzalezgil
Join the Gitter channel! https://gitter.im/uyuni-project/hackweek
Uyuni is a configuration and infrastructure management tool that saves you time and headaches when you have to manage and update tens, hundreds or even thousands of machines. It also manages configuration, can run audits, build image containers, monitor and much more!
Currently there are a few distributions that are completely untested on Uyuni or SUSE Manager (AFAIK) or just not tested since a long time, and could be interesting knowing how hard would be working with them and, if possible, fix whatever is broken.
For newcomers, the easiest distributions are those based on DEB or RPM packages. Distributions with other package formats are doable, but will require adapting the Python and Java code to be able to sync and analyze such packages (and if salt does not support those packages, it will need changes as well). So if you want a distribution with other packages, make sure you are comfortable handling such changes.
No developer experience? No worries! We had non-developers contributors in the past, and we are ready to help as long as you are willing to learn. If you don't want to code at all, you can also help us preparing the documentation after someone else has the initial code ready, or you could also help with testing :-)
The idea is testing Salt (including bootstrapping with bootstrap script) and Salt-ssh clients
To consider that a distribution has basic support, we should cover at least (points 3-6 are to be tested for both salt minions and salt ssh minions):
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- Patching
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- Bonus point: Java part for product identification, and monitoring enablement
- Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
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If something is breaking: we can try to fix it, but the main idea is research how supported it is right now. Beyond that it's up to each project member how much to hack :-)
- If you don't have knowledge about some of the steps: ask the team
- If you still don't know what to do: switch to another distribution and keep testing.
This card is for EVERYONE, not just developers. Seriously! We had people from other teams helping that were not developers, and added support for Debian and new SUSE Linux Enterprise and openSUSE Leap versions :-)
In progress/done for Hack Week 25
Guide
We started writin a Guide: Adding a new client GNU Linux distribution to Uyuni at https://github.com/uyuni-project/uyuni/wiki/Guide:-Adding-a-new-client-GNU-Linux-distribution-to-Uyuni, to make things easier for everyone, specially those not too familiar wht Uyuni or not technical.
openSUSE Leap 16.0
The distribution will all love!
https://en.opensuse.org/openSUSE:Roadmap#DRAFTScheduleforLeap16.0
Curent Status We started last year, it's complete now for Hack Week 25! :-D
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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
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Resources
tbd (Gitlab repo)
Help Create A Chat Control Resistant Turnkey Chatmail/Deltachat Relay Stack - Rootless Podman Compose, OpenSUSE BCI, Hardened, & SELinux by 3nd5h1771fy
Description
The Mission: Decentralized & Sovereign Messaging
FYI: If you have never heard of "Chatmail", you can visit their site here, but simply put it can be thought of as the underlying protocol/platform decentralized messengers like DeltaChat use for their communications. Do not confuse it with the honeypot looking non-opensource paid for prodect with better seo that directs you to chatmailsecure(dot)com
In an era of increasing centralized surveillance by unaccountable bad actors (aka BigTech), "Chat Control," and the erosion of digital privacy, the need for sovereign communication infrastructure is critical. Chatmail is a pioneering initiative that bridges the gap between classic email and modern instant messaging, offering metadata-minimized, end-to-end encrypted (E2EE) communication that is interoperable and open.
However, unless you are a seasoned sysadmin, the current recommended deployment method of a Chatmail relay is rigid, fragile, difficult to properly secure, and effectively takes over the entire host the "relay" is deployed on.
Why This Matters
A simple, host agnostic, reproducible deployment lowers the entry cost for anyone wanting to run a privacy‑preserving, decentralized messaging relay. In an era of perpetually resurrected chat‑control legislation threats, EU digital‑sovereignty drives, and many dangers of using big‑tech messaging platforms (Apple iMessage, WhatsApp, FB Messenger, Instagram, SMS, Google Messages, etc...) for any type of communication, providing an easy‑to‑use alternative empowers:
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- Surveillance mitigation - End‑to‑end OpenPGP encryption ensures relay operators never see plaintext.
- Digital sovereignty - Communities can host their own infrastructure under local jurisdiction, aligning with national data‑policy goals.
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Goals
As I indicated earlier, this project aims to drastically simplify the deployment of Chatmail relay. By converting this architecture into a portable, containerized stack using Podman and OpenSUSE base container images, we can allow anyone to deploy their own censorship-resistant, privacy-preserving communications node in minutes.
Our goal for Hack Week: package every component into containers built on openSUSE/MicroOS base images, initially orchestrated with a single container-compose.yml (podman-compose compatible). The stack will:
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- Utilize Enhanced Security with SELinux by using purpose built utilities such as udica we can quickly generate custom SELinux policies for the container stack, ensuring strict confinement superior to standard/typical Docker deployments.
- Allow the use of bind or remote mounted volumes for shared data (
/var/vmail, DKIM keys, TLS certs, etc.). - Replace the local DNS server requirement with a remote DNS‑provider API for DKIM/TXT record publishing.
By delivering a turnkey, host agnostic, reproducible deployment, we lower the barrier for individuals and small communities to launch their own chatmail relays, fostering a decentralized, censorship‑resistant messaging ecosystem that can serve DeltaChat users and/or future services adopting this protocol
Resources
- The links included above
- https://chatmail.at/doc/relay/
- https://delta.chat/en/help
- Project repo -> https://codeberg.org/EndShittification/containerized-chatmail-relay
Enhance git-sha-verify: A tool to checkout validated git hashes by gpathak
Description
git-sha-verify is a simple shell utility to verify and checkout trusted git commits signed using GPG key. This tool helps ensure that only authorized or validated commit hashes are checked out from a git repository, supporting better code integrity and security within the workflow.
Supports:
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- Checking out trusted commits
Ideal for teams and projects where the integrity of git history is crucial.
Goals
A minimal python code of the shell script exists as a pull request.
The goal of this hackweek is to:
- DONE: Add more unit tests
- New and more tests can be added later
- New and more tests can be added later
- Partially DONE: Make the python code modular
- DONE: Add code coverage if possible
Resources
- Link to GitHub Repository: https://github.com/openSUSE/git-sha-verify
Improvements to osc (especially with regards to the Git workflow) by mcepl
Description
There is plenty of hacking on osc, where we could spent some fun time. I would like to see a solution for https://github.com/openSUSE/osc/issues/2006 (which is sufficiently non-serious, that it could be part of HackWeek project).
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
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- Write prompt that will guide LLM through the backporting process. Fine tune it based on experimental results.
- Explore success rate of LLMs when backporting various patches.
Resources
- Docker
- Gemini CLI
Repository
Current version of the container with some instructions for use are at: https://gitlab.suse.de/jankara/gemini-cli-backporter
AI-Powered Unit Test Automation for Agama by joseivanlopez
The Agama project is a multi-language Linux installer that leverages the distinct strengths of several key technologies:
- Rust: Used for the back-end services and the core HTTP API, providing performance and safety.
- TypeScript (React/PatternFly): Powers the modern web user interface (UI), ensuring a consistent and responsive user experience.
- Ruby: Integrates existing, robust YaST libraries (e.g.,
yast-storage-ng) to reuse established functionality.
The Problem: Testing Overhead
Developing and maintaining code across these three languages requires a significant, tedious effort in writing, reviewing, and updating unit tests for each component. This high cost of testing is a drain on developer resources and can slow down the project's evolution.
The Solution: AI-Driven Automation
This project aims to eliminate the manual overhead of unit testing by exploring and integrating AI-driven code generation tools. We will investigate how AI can:
- Automatically generate new unit tests as code is developed.
- Intelligently correct and update existing unit tests when the application code changes.
By automating this crucial but monotonous task, we can free developers to focus on feature implementation and significantly improve the speed and maintainability of the Agama codebase.
Goals
- Proof of Concept: Successfully integrate and demonstrate an authorized AI tool (e.g.,
gemini-cli) to automatically generate unit tests. - Workflow Integration: Define and document a new unit test automation workflow that seamlessly integrates the selected AI tool into the existing Agama development pipeline.
- Knowledge Sharing: Establish a set of best practices for using AI in code generation, sharing the learned expertise with the broader team.
Contribution & Resources
We are seeking contributors interested in AI-powered development and improving developer efficiency. Whether you have previous experience with code generation tools or are eager to learn, your participation is highly valuable.
If you want to dive deep into AI for software quality, please reach out and join the effort!
- Authorized AI Tools: Tools supported by SUSE (e.g.,
gemini-cli) - Focus Areas: Rust, TypeScript, and Ruby components within the Agama project.
Interesting Links
Try out Neovim Plugins supporting AI Providers by enavarro_suse
Description
Experiment with several Neovim plugins that integrate AI model providers such as Gemini and Ollama.
Goals
Evaluate how these plugins enhance the development workflow, how they differ in capabilities, and how smoothly they integrate into Neovim for day-to-day coding tasks.
Resources
- Neovim 0.11.5
- AI-enabled Neovim plugins:
- avante.nvim: https://github.com/yetone/avante.nvim
- Gp.nvim: https://github.com/Robitx/gp.nvim
- parrot.nvim: https://github.com/frankroeder/parrot.nvim
- gemini.nvim: https://dotfyle.com/plugins/kiddos/gemini.nvim
- ...
- Accounts or API keys for AI model providers.
- Local model serving setup (e.g., Ollama)
- Test projects or codebases for practical evaluation:
- OBS: https://build.opensuse.org/
- OBS blog and landing page: https://openbuildservice.org/
- ...
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
``` {
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
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.
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
"what is it" file and directory analysis via MCP and local LLM, for console and KDE by rsimai
Description
Users sometimes wonder what files or directories they find on their local PC are good for. If they can't determine from the filename or metadata, there should an easy way to quickly analyze the content and at least guess the meaning. An LLM could help with that, through the use of a filesystem MCP and to-text-converters for typical file types. Ideally this is integrated into the desktop environment but works as well from a console. All data is processed locally or "on premise", no artifacts remain or leave the system.
Goals
- The user can run a command from the console, to check on a file or directory
- The filemanager contains the "analyze" feature within the context menu
- The local LLM could serve for other use cases where privacy matters
TBD
- Find or write capable one-shot and interactive MCP client
- Find or write simple+secure file access MCP server
- Create local LLM service with appropriate footprint, containerized
- Shell command with options
- KDE integration (Dolphin)
- Package
- Document
Resources
Extended private brain - RAG my own scripts and data into offline LLM AI by tjyrinki_suse
Description
For purely studying purposes, I'd like to find out if I could teach an LLM some of my own accumulated knowledge, to use it as a sort of extended brain.
I might use qwen3-coder or something similar as a starting point.
Everything would be done 100% offline without network available to the container, since I prefer to see when network is needed, and make it so it's never needed (other than initial downloads).
Goals
- Learn something about RAG, LLM, AI.
- Find out if everything works offline as intended.
- As an end result have a new way to access my own existing know-how, but so that I can query the wisdom in them.
- Be flexible to pivot in any direction, as long as there are new things learned.
Resources
To be found on the fly.
Timeline
Day 1 (of 4)
- Tried out a RAG demo, expanded on feeding it my own data
- Experimented with qwen3-coder to add a persistent chat functionality, and keeping vectors in a pickle file
- Optimizations to keep everything within context window
- Learn and add a bit of PyTest
Day 2
- More experimenting and more data
- Study ChromaDB
- Add a Web UI that works from another computer even though the container sees network is down
Day 3
- The above RAG is working well enough for demonstration purposes.
- Pivot to trying out OpenCode, configuring local Ollama qwen3-coder there, to analyze the RAG demo.
- Figured out how to configure Ollama template to be usable under OpenCode. OpenCode locally is super slow to just running qwen3-coder alone.
Day 4 (final day)
- Battle with OpenCode that was both slow and kept on piling up broken things.
- Call it success as after all the agentic AI was working locally.
- Clean up the mess left behind a bit.
Blog Post
Summarized the findings at blog post.
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.
Enable more features in mcp-server-uyuni by j_renner
Description
I would like to contribute to mcp-server-uyuni, the MCP server for Uyuni / Multi-Linux Manager) exposing additional features as tools. There is lots of relevant features to be found throughout the API, for example:
- System operations and infos
- System groups
- Maintenance windows
- Ansible
- Reporting
- ...
At the end of the week I managed to enable basic system group operations:
- List all system groups visible to the user
- Create new system groups
- List systems assigned to a group
- Add and remove systems from groups
Goals
- Set up test environment locally with the MCP server and client + a recent MLM server [DONE]
- Identify features and use cases offering a benefit with limited effort required for enablement [DONE]
- Create a PR to the repo [DONE]
Resources
MCP Perl SDK by kraih
Description
We've been using the MCP Perl SDK to connect openQA with AI. And while the basics are working pretty well, the SDK is not fully spec compliant yet. So let's change that!
Goals
- Support for Resources
- All response types (Audio, Resource Links, Embedded Resources...)
- Tool/Prompt/Resource update notifications
- Dynamic Tool/Prompt/Resource lists
- New authentication mechanisms
Resources
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
Result
https://github.com/e-minguez/eib-mcp
I've extensively used antigravity and its agent mode to code this. This heavily uses https://hackweek.opensuse.org/25/projects/suse-edge-image-builder-json-schema for the MCP to be built.
I've ended up learning a lot of things about "prompting", json schemas in general, some golang, MCPs and AI in general :)
Example:
Generate an Edge Image Builder configuration for an ISO image based on slmicro-6.2.iso, targeting x86_64 architecture. The output name should be 'my-edge-image' and it should install to /dev/sda. It should deploy
a 3 nodes kubernetes cluster with nodes names "node1", "node2" and "node3" as:
* hostname: node1, IP: 1.1.1.1, role: initializer
* hostname: node2, IP: 1.1.1.2, role: agent
* hostname: node3, IP: 1.1.1.3, role: agent
The kubernetes version should be k3s 1.33.4-k3s1 and it should deploy a cert-manager helm chart (the latest one available according to https://cert-manager.io/docs/installation/helm/). It should create a user
called "suse" with password "suse" and set ntp to "foo.ntp.org". The VIP address for the API should be 1.2.3.4
Generates:
``` apiVersion: "1.0" image: arch: x86_64 baseImage: slmicro-6.2.iso imageType: iso outputImageName: my-edge-image kubernetes: helm: charts: - name: cert-manager repositoryName: jetstack
Try to use AI and MCP for ACPI table analysis by joeyli
Description
Try to use AI and MCP if they can help with ACPI table analysis.
Goals
It's not easy for looking at ACPI tables even it be disassemble to ASL. I want to learn AI and MCP in Hackweek 25 to see if they can help ACPI table analysis.
Resources
Any resources about AI and MCP.
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