Port OTPClient to GTK >= 4.18 by pstivanin

Project Description

OTPClient is currently using GTK3 and cannot easily be ported to GTK4. Since GTK4 came out, there have been quite some big changes. Also, there are now some new deprecation that will take effect with GTK5 (and are active starting from 4.10 as warnings), so I need to think ahead and port OTPClient without using any of those deprecated features.

Goal for this Hackweek

• fix the last 3 opened issues (https://github.com/paolostivanin/OTPClient/issues/402, https://github.com/paolostivanin/OTPClient/issues/404, https://github.com/paolostivanin/OTPClient/issues/406) and release a new version
• continue the rewrite from where we left last year
• if possible, finally close this 6 years old issue: https://github.com/paolostivanin/OTPClient/issues/123

---

pudc - A PID 1 process that barks to the internet by mssola

Description

As a fun exercise in order to dig deeper into the Linux kernel, its interfaces, the RISC-V architecture, and all the dragons in between; I'm building a blog site cooked like this:

• The backend is written in a mixture of C and RISC-V assembly.
• The backend is actually PID1 (for real, not within a container).
• We poll and parse incoming HTTP requests ourselves.
• The frontend is a mere HTML page with htmx.

The project is meant to be Linux-specific, so I'm going to use io_uring, pidfs, namespaces, and Linux-specific features in order to drive all of this.

I'm open for suggestions and so on, but this is meant to be a solo project, as this is more of a learning exercise for me than anything else.

Goals

• Have a better understanding of different Linux features from user space down to the kernel internals.
• Most importantly: have fun.

Resources

• https://github.com/mssola/pudc

---

Smart lighting with Pico 2 by jmodak

Description

I am trying to create a smart-lighting project with a Raspberry Pi Pico that reacts to a movie's visuals and audio that involves combining two distinct functions: ambient screen lighting(visual response) and sound-reactive lighting(audio response)

Goals

• Visuals: Capturing the screen's colour requires an external device to analyse screen content and send colour data to the MCU via serial communication.
• Audio: A sound sensor module connected directly to the Pico that can detect sound volume.
• Pico 2W: The MCU receives data fro, both inputs and controls an LED strip.

Resources

• Raspberry Pi Pico 2 W
• RGB LED strip
• Sound detecting sensor
• Power supply
• breadboard and wires

---

x64id: An x86/x64 instruction disassembler by m.crivellari

Description

This is an old side project. An x86/x64 machine code decoder. It is useful to get instructions' length and identify each of its fields.

Example:

C7 85 68 FF FF FF 00 00 00 00

This is the instruction:

MOV DWORD PTR SS:[LOCAL.38],0

What follows are some of the information collected by the disassembler, based on the specific instruction:

RAW bytes (hex): C7 85 68 FF FF FF 00 00 00 00
Instr. length: 10
Print instruction fields:
        Located Prefixes 0:

        OP: 0xC7
        mod_reg_rm: 0x85
        disp (4): 0xFFFFFF68
        Iimm: 0x0

Lacks the mnemonic representation: from the previous machine code is not able to produce the "MOV..." instruction, for example.

Goals

The goal is almost easy: partially implement the mnemonic representation. I have already started during the weekend, likely tomorrow I will push the branch!

Resources

• The project: https://github.com/DispatchCode/x64-Instruction-Decoder/
• This is useful to avoid gdb and objdump in local: https://defuse.ca/online-x86-assembler.htm
• Another interesting resource is https://godbolt.org/

Progress

• An initial implementation can be found at: https://github.com/DispatchCode/x64-Instruction-Decoder/tree/mnemonic-support It is described under the "Mnemonic translation" in the README file!

Let's consider this example:

[...other bytes...] 43 89 44 B5 00 01 00 [...other bytes...]

---

Add a machine-readable output to dmidecode by jdelvare

Description

There have been repeated requests for a machine-friendly dmidecode output over the last decade. During Hack Week 19, 5 years ago, I prepared the code to support alternative output formats, but didn't have the time to go further. Last year, Jiri Hnidek from Red Hat Linux posted a proof-of-concept implementation to add JSON output support. This is a fairly large pull request which needs to be carefully reviewed and tested.

Goals

Review Jiri's work and provide constructive feedback. Merge the code if acceptable. Evaluate the costs and benefits of using a library such as json-c.

---

OS self documentation, health check and troubleshooting by roseswe

Project Description

The aim of this hackweek project is to improve the utility "cfg2html" so that it is even more usable under SLES and perhaps also under Rancher.

cfg2html (see also https://github.com/cfg2html/cfg2html) itself is a very mature utility for collecting and documenting information of an operating system like Linux, AIX, HP-UX and others.

Goal for this Hackweek

The aim is to extend cfg2html

• for SLES and SLES-for-SAP apps, high availability
• Improve code for MicroOS 5.x, SUMA, Edge and k8s environments
• fix shellbeauity warnings
• possibly add more plugins
• SUMA/Salt integration to collect.

Resources

Required skills: Bash, shell script and the SUSE products mentioned.

https://github.com/cfg2html/cfg2html

https://www.cfg2html.com/

---

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;

---

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).

---

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
• Change screen time tracking logic from "global" (week-long) to daily subtraction and weekly addition of remainders (more intuitive than current "weekly time budget method)
• Add logic to fill in chore fields/lines, ideally with pictures, falling back to text.

Resources

tbd (Gitlab repo)

---

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

---