HTTP API for nftables by crameleon

Background

The idea originated in https://progress.opensuse.org/issues/164060 and is about building RESTful API which translates authorized HTTP requests to operations in nftables, possibly utilizing libnftables-json(5).

Originally, I started developing such an interface in Go, utilizing https://github.com/google/nftables. The conversion of string networks to nftables set elements was problematic (unfortunately no record of details), and I started a second attempt in Python, which made interaction much simpler thanks to native nftables Python bindings.

Goals

1. Find and track the issue with google/nftables
2. Revisit and polish the Go or Python code (prefer Go, but possibly depends on implementing missing functionality), primarily the server component
3. Finish functionality to interact with nftables sets (retrieving and updating elements), which are of interest for the originating issue
4. Align test suite
5. Packaging

Resources

• https://git.netfilter.org/nftables/tree/py/src/nftables.py
• https://git.com.de/Georg/nftables-http-api (to be moved to GitHub)
• https://build.opensuse.org/package/show/home:crameleon:containers/pytest-nftables-container

Results

The original goals will probably not be met, but progress is made regardless. Upon starting to unify the structure and implementing more functionality, missing JSON output support was noticed for some subcommands in libnftables. I am submitting patches as needed: - https://lore.kernel.org/netfilter-devel/20251203131736.4036382-2-georg@syscid.com/T/#u

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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 and Salt-ssh clients, but NOT traditional clients, which are deprecated.

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):

1. Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
2. Onboarding (salt minion from UI, salt minion from bootstrap scritp, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)
3. Package management (install, remove, update...)
4. Patching
5. Applying any basic salt state (including a formula)
6. Salt remote commands
7. Bonus point: Java part for product identification, and monitoring enablement
8. Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
9. Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
10. Bonus point: testsuite enablement (https://github.com/uyuni-project/uyuni/tree/master/testsuite)

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 :-)

Pending

Debian 13

The new version of the beloved Debian GNU/Linux OS

• [W] Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
• [ ] Onboarding (salt minion from UI, salt minion from bootstrap script, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)
• [ ] Package management (install, remove, update...)
• [ ] Patching (if patch information is available, could require writing some code to parse it, but IIRC we have support for Ubuntu already). Probably not for Debian as IIRC we don't support patches yet.
• [ ] Applying any basic salt state (including a formula)
• [ ] Salt remote commands
• [ ] Bonus point: Java part for product identification, and monitoring enablement
• [ ] Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
• [ ] Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
• [ ] Bonus point: testsuite enablement (https://github.com/uyuni-project/uyuni/tree/master/testsuite)

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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

• Result Presentation
• hw25-song-search Github repo

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;

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Improve/rework household chore tracker `chorazon` by gniebler

Description

I wrote a household chore tracker named chorazon, which is meant to be deployed as a web application in the household's local network.

It features the ability to set up different (so far only weekly) schedules per task and per person, where tasks may span several days.

There are "tokens", which can be collected by users. Tasks can (and usually will) have rewards configured where they yield a certain amount of tokens. The idea is that they can later be redeemed for (surprise) gifts, but this is not implemented yet. (So right now one needs to edit the DB manually to subtract tokens when they're redeemed.)

Days are not rolled over automatically, to allow for task completion control.

We used it in my household for several months, with mixed success. There are many limitations in the system that would warrant a revisit.

It's written using the Pyramid Python framework with URL traversal, ZODB as the data store and Web Components for the frontend.

Goals

• Add admin screens for users, tasks and schedules
• Add models, pages etc. to allow redeeming tokens for gifts/surprises
• …?

Resources

tbd (Gitlab repo)

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Collection and organisation of information about Bulgarian schools by iivanov

Description

To achieve this it will be necessary:

• Collect/download raw data from various government and non-governmental organizations
• Clean up raw data and organise it in some kind database.
• Create tool to make queries easy.
• Or perhaps dump all data into AI and ask questions in natural language.

Goals

By selecting particular school information like this will be provided:

• School scores on national exams.
• School scores from the external evaluations exams.
• School town, municipality and region.
• Employment rate in a town or municipality.
• Average health of the population in the region.

Resources

Some of these are available only in bulgarian.

• https://danybon.com/klasazia
• https://nvoresults.com/index.html
• https://ri.mon.bg/active-institutions
• https://www.nsi.bg/nrnm/ekatte/archive

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