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

Results

• Information about all Bulgarian schools with their scores during recent years cleaned and organised into SQL tables
• Information about all Bulgarian villages, cities, municipalities and districts cleaned and organised into SQL tables
• Information about all Bulgarian villages and cities census since beginning of this century cleaned and organised into SQL tables.
• Information about all Bulgarian municipalities about religion, ethnicity cleaned and organised into SQL tables.
• Data successfully loaded to locally running Ollama with help to Vanna.AI
• Seems to be usable.

TODO

• Add more statistical information about municipalities and ....

Code and data

• Github

---

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).

---

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:

• 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:

• Result Presentation
• hw25-song-search Github repo

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;

---

Liz - Prompt autocomplete by ftorchia

Description

Liz is the Rancher AI assistant for cluster operations.

Goals

We want to help users when sending new messages to Liz, by adding an autocomplete feature to complete their requests based on the context.

Example:

• User prompt: "Can you show me the list of p"
• Autocomplete suggestion: "Can you show me the list of p...od in local cluster?"

Example:

• User prompt: "Show me the logs of #rancher-"
• Chat console: It shows a drop-down widget, next to the # character, with the list of available pod names starting with "rancher-".

Technical Overview

1. The AI agent should expose a new ws/autocomplete endpoint to proxy autocomplete messages to the LLM.
2. The UI extension should be able to display prompt suggestions and allow users to apply the autocomplete to the Prompt via keyboard shortcuts.

Resources

GitHub repository

---

Bring to Cockpit + System Roles capabilities from YAST by miguelpc

Bring to Cockpit + System Roles features from YAST

Cockpit and System Roles have been added to SLES 16 There are several capabilities in YAST that are not yet present in Cockpit and System Roles We will follow the principle of "automate first, UI later" being System Roles the automation component and Cockpit the UI one.

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

• Monday, December 1 · 11:00 – 12:00
• Time zone: Europe/Madrid
• Google Meet link: https://meet.google.com/rrc-kqch-hca

---

RMT.rs: High-Performance Registration Path for RMT using Rust by gbasso

Description

The SUSE Repository Mirroring Tool (RMT) is a critical component for managing software updates and subscriptions, especially for our Public Cloud Team (PCT). In a cloud environment, hundreds or even thousands of new SUSE instances (VPS/EC2) can be provisioned simultaneously. Each new instance attempts to register against an RMT server, creating a "thundering herd" scenario.

We have observed that the current RMT server, written in Ruby, faces performance issues under this high-concurrency registration load. This can lead to request overhead, slow registration times, and outright registration failures, delaying the readiness of new cloud instances.

This Hackweek project aims to explore a solution by re-implementing the performance-critical registration path in Rust. The goal is to leverage Rust's high performance, memory safety, and first-class concurrency handling to create an alternative registration endpoint that is fast, reliable, and can gracefully manage massive, simultaneous request spikes.

The new Rust module will be integrated into the existing RMT Ruby application, allowing us to directly compare the performance of both implementations.

Goals

The primary objective is to build and benchmark a high-performance Rust-based alternative for the RMT server registration endpoint.

Key goals for the week:

1. Analyze & Identify: Dive into the SUSE/rmt Ruby codebase to identify and map out the exact critical path for server registration (e.g., controllers, services, database interactions).
2. Develop in Rust: Implement a functionally equivalent version of this registration logic in Rust.
3. Integrate: Explore and implement a method for Ruby/Rust integration to "hot-wire" the new Rust module into the RMT application. This may involve using FFI, or libraries like rb-sys or magnus.
4. Benchmark: Create a benchmarking script (e.g., using k6, ab, or a custom tool) that simulates the high-concurrency registration load from thousands of clients.
5. Compare & Present: Conduct a comparative performance analysis (requests per second, latency, success/error rates, CPU/memory usage) between the original Ruby path and the new Rust path. The deliverable will be this data and a summary of the findings.

Resources

• RMT Source Code (Ruby):
  • https://github.com/SUSE/rmt
• RMT Documentation:
  • https://documentation.suse.com/sles/15-SP7/html/SLES-all/book-rmt.html
• Tooling & Stacks:
  • RMT/Ruby development environment (for running the base RMT)
  • Rust development environment (rustup, cargo)
• Potential Integration Libraries:
  • rb-sys: https://github.com/oxidize-rb/rb-sys
  • Magnus: https://github.com/matsadler/magnus
• Benchmarking Tools:
  • k6 (https://k6.io/)
  • ab (ApacheBench)

---

dynticks-testing: analyse perf / trace-cmd output and aggregate data by m.crivellari

Description

dynticks-testing is a project started years ago by Frederic Weisbecker. One of the feature is to check the actual configuration (isolcpus, irqaffinity etc etc) and give feedback on it.

An important goal of this tool is to parse the output of trace-cmd / perf and provide more readable data, showing the duration of every events grouped by PID (showing also the CPU number, if the tasks has been migrated etc).

An example of data captured on my laptop (incomplete!!):

          -0     [005] dN.2. 20310.270699: sched_wakeup:         WaylandProxy:46380 [120] CPU:005
          -0     [005] d..2. 20310.270702: sched_switch:         swapper/5:0 [120] R ==> WaylandProxy:46380 [120]
...
    WaylandProxy-46380 [004] d..2. 20310.295397: sched_switch:         WaylandProxy:46380 [120] S ==> swapper/4:0 [120]
          -0     [006] d..2. 20310.295397: sched_switch:         swapper/6:0 [120] R ==> firefox:46373 [120]
         firefox-46373 [006] d..2. 20310.295408: sched_switch:         firefox:46373 [120] S ==> swapper/6:0 [120]
          -0     [004] dN.2. 20310.295466: sched_wakeup:         WaylandProxy:46380 [120] CPU:004

Output of noise_parse.py:

Task: WaylandProxy Pid: 46380 cpus: {4, 5} (Migrated!!!)
        Wakeup Latency                                Nr:        24     Duration:          89
        Sched switch: kworker/12:2                    Nr:         1     Duration:           6

My first contribution is around Nov. 2024!

Goals

• add more features (eg cpuset)
• test / bugfix

Resources

• Frederic's public repository: https://git.kernel.org/pub/scm/linux/kernel/git/frederic/dynticks-testing.git/
• https://docs.kernel.org/timers/no_hz.html#testing

Progresses

isolcpus and cpusets implemented and merged in master: dynticks-testing.git commit

---

Uyuni read-only replica by cbosdonnat

Description

For now, there is no possible HA setup for Uyuni. The idea is to explore setting up a read-only shadow instance of an Uyuni and make it as useful as possible.

Possible things to look at:

• live sync of the database, probably using the WAL. Some of the tables may have to be skipped or some features disabled on the RO instance (taskomatic, PXT sessions…)
• Can we use a load balancer that routes read-only queries to either instance and the other to the RW one? For example, packages or PXE data can be served by both, the API GET requests too. The rest would be RW.

Goals

• Prepare a document explaining how to do it.
• PR with the needed code changes to support it

---