Create a console application for a crossword puzzle generator that can be fed with a custom list of word+explanation pairs. It may be used by people to quickly familiarize with a specific topic (e.g. a knowledge area, new hires to the company ...) to at least understand the terminology and the abbreviations that are used. Or to just have some distraction and fun :-)
I think it consists of three components:
source data. It needs to be populated with an as much as possible comprehensive list for the desired topic area. There could be a dummy list of pairs for the time being to not block the coding parts.
the algorithm that reads the data and distributes the words to match these into a rectangle of configurable size (x/y). The goal is "Swedish style", where explanation takes one field of the grid (not the same as the first letter!) and the word gets a consecutive list of fields, no fields should be blank or grayed out. The word may follow the explanation to the right or downwards.
the printing part that creates the crossword puzzle with explanations only as well as completely populated as reference/solution. It needs to print the grid, use different font sizes, handle line wrapping for the explanations to make them fit into one field. There should be an arrow to indicate if the word follows the explanation field to the right or downwards. It should finally create an easily printable format, e.g. PS or PDF or (scalable) graphics.
I'd like to use Python and overall keep it simple, a script that allows options and a plain text file as data source, no (new) libs or database magic.
This project is part of:
Hack Week 14
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Bring to Cockpit + System Roles capabilities from YAST by miguelpc
Bring to Cockpit + System Roles features from YAST
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Goals
The idea is to implement service configuration in System Roles and then add an UI to manage these in Cockpit. For some capabilities it will be required to have an specific Cockpit Module as they will interact with a reasource already configured.
Resources
A plan on capabilities missing and suggested implementation is available here: https://docs.google.com/spreadsheets/d/1ZhX-Ip9MKJNeKSYV3bSZG4Qc5giuY7XSV0U61Ecu9lo/edit
Linux System Roles:
- https://linux-system-roles.github.io/
- https://build.opensuse.org/package/show/openSUSE:Factory/ansible-linux-system-roles Package on sle16 ansible-linux-system-roles
First meeting Hackweek catchup
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- Time zone: Europe/Madrid
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Code and data
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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- 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)
- Package management (install, remove, update...)
- Patching
- 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)
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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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:
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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
- CLAP: The main model being researched;
- huggingface: Pre-trained models for CLAP;
- Free Music Archive: Creative Commons songs that can be used for testing;