Update my own python audio and video time-lapse and motion capture apps and publish by dmair

Project Description

Many years ago, in my own time, I wrote a Qt python application to periodically capture frames from a V4L2 video device (e.g. a webcam) and used it to create daily weather timelapse videos from windows at my home. I have maintained it at home in my own time and this year have added motion detection making it a functional video security tool but with no guarantees. I also wrote a linux audio monitoring app in python using Qt in my own time that captures live signal strength along with 24 hour history of audio signal level/range and audio spectrum. I recently added background noise filtering to the app. In due course I aim to include voice detection, currently I'm assuming via Google's public audio interface. Neither of these is a professional home security app but between them they permit a user to freely monitor video and audio data from a home in a manageable way. Both projects are on github but out-of-date with personal work, I would like to organize and update the github versions of these projects.

Goal for this Hackweek

It would probably help to migrate all the v4l2py module based video code to linuxpy.video based code and that looks like a re-write of large areas of the video code. It would also be good to remove a lot of python lint that is several years old to improve the projects with the main goal being to push the recent changes with better organized code to github. If there is enough time I'd like to take the in-line Qt QSettings persistent state code used per-app and write a python class that encapsulates the Qt QSettings class in a value_of(name)/name=value manner for shared use in projects so that persistent state can be accessed read or write anywhere within the apps using a simple interface.

Resources

• Video time-lapse capture app
• Audio monitoring app

I'm not specifically looking for help but welcome other input.

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SUSE AI Meets the Game Board by moio

Use tabletopgames.ai’s open source TAG and PyTAG frameworks to apply Statistical Forward Planning and Deep Reinforcement Learning to two board games of our own design. On an all-green, all-open source, all-AWS stack!

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Results: Infrastructure Achievements

We successfully built and automated a containerized stack to support our AI experiments. This included:

• a Fully-Automated, One-Command, GPU-accelerated Kubernetes setup: we created an OpenTofu based script, tofu-tag, to deploy SUSE's RKE2 Kubernetes running on CUDA-enabled nodes in AWS, powered by openSUSE with GPU drivers and gpu-operator
• Containerization of the TAG and PyTAG frameworks: TAG (Tabletop AI Games) and PyTAG were patched for seamless deployment in containerized environments. We automated the container image creation process with GitHub Actions. Our forks (PRs upstream upcoming):
  • https://github.com/moio/TabletopGames/tree/hackweek24
  • https://github.com/moio/PyTAG/tree/hackweek24

./deploy.sh and voilà - Kubernetes running PyTAG (k9s, above) with GPU acceleration (nvtop, below)

Results: Game Design Insights

Our project focused on modeling and analyzing two card games of our own design within the TAG framework:

• Game Modeling: We implemented models for Dario's "Bamboo" and Silvio's "Totoro" and "R3" games, enabling AI agents to play thousands of games ...in minutes!
• AI-driven optimization: By analyzing statistical data on moves, strategies, and outcomes, we iteratively tweaked the game mechanics and rules to achieve better balance and player engagement.
• Advanced analytics: Leveraging AI agents with Monte Carlo Tree Search (MCTS) and random action selection, we compared performance metrics to identify optimal strategies and uncover opportunities for game refinement .
  • more about Bamboo on Dario's site
  • more about R3 on Silvio's site (italian, translation coming)
  • more about Totoro on Silvio's site

A family picture of our card games in progress. From the top: Bamboo, Totoro, R3

Results: Learning, Collaboration, and Innovation

Beyond technical accomplishments, the project showcased innovative approaches to coding, learning, and teamwork:

• "Trio programming" with AI assistance: Our "trio programming" approach—two developers and GitHub Copilot—was a standout success, especially in handling slightly-repetitive but not-quite-exactly-copypaste tasks. Java as a language tends to be verbose and we found it to be fitting particularly well.
• AI tools for reporting and documentation: We extensively used AI chatbots to streamline writing and reporting. (Including writing this report! ...but this note was added manually during edit!)
• GPU compute expertise: Overcoming challenges with CUDA drivers and cloud infrastructure deepened our understanding of GPU-accelerated workloads in the open-source ecosystem.
• Game design as a learning platform: By blending AI techniques with creative game design, we learned not only about AI strategies but also about making games fun, engaging, and balanced.

Last but not least we had a lot of fun! ...and this was definitely not a chatbot generated line!

The Context: AI + Board Games

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Symbol Relations by hli

Description

There are tools to build function call graphs based on parsing source code, for example, cscope.

This project aims to achieve a similar goal by directly parsing the disasembly (i.e. objdump) of a compiled binary. The assembly code is what the CPU sees, therefore more "direct". This may be useful in certain scenarios, such as gdb/crash debugging.

Detailed description and Demos can be found in the README file:

Supports x86 for now (because my customers only use x86 machines), but support for other architectures can be added easily.

Tested with python3.6

Goals

Any comments are welcome.

Resources

https://github.com/lhb-cafe/SymbolRelations

symrellib.py: mplements the symbol relation graph and the disassembly parser

symrel_tracer*.py: implements tracing (-t option)

symrel.py: "cli parser"

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Team Hedgehogs' Data Observability Dashboard by gsamardzhiev

Description

This project aims to develop a comprehensive Data Observability Dashboard that provides r insights into key aspects of data quality and reliability. The dashboard will track:

Data Freshness: Monitor when data was last updated and flag potential delays.

Data Volume: Track table row counts to detect unexpected surges or drops in data.

Data Distribution: Analyze data for null values, outliers, and anomalies to ensure accuracy.

Data Schema: Track schema changes over time to prevent breaking changes.

The dashboard's aim is to support historical tracking to support proactive data management and enhance data trust across the data function.

Goals

Although the final goal is to create a power bi dashboard that we are able to monitor, our goals is to 1. Create the necessary tables that track the relevant metadata about our current data 2. Automate the process so it runs in a timely manner

Resources

AWS Redshift; AWS Glue, Airflow, Python, SQL

Why Hedgehogs?

Because we like them.

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Ansible for add-on management by lmanfredi

Description

Machines can contains various combinations of add-ons and are often modified during the time.

The list of repos can change so I would like to create an automation able to reset the status to a given state, based on metadata available for these machines

Goals

Create an Ansible automation able to take care of add-on (repo list) configuration using metadata as reference

Resources

• Machines
• Repositories
• Developing modules
• Basic VM Guest management
• Module zypper_repository_list
• ansible-collections community.general
  • plugins/modules/zypper.py
  • plugins/modules/zypper_repository.py
  • plugins/modules/zypperrepositoryinfo.py

Results

Created WIP project Ansible-add-on-openSUSE

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Run local LLMs with Ollama and explore possible integrations with Uyuni by PSuarezHernandez

Description

Using Ollama you can easily run different LLM models in your local computer. This project is about exploring Ollama, testing different LLMs and try to fine tune them. Also, explore potential ways of integration with Uyuni.

Goals

• Explore Ollama
• Test different models
• Fine tuning
• Explore possible integration in Uyuni

Resources

• https://ollama.com/
• https://huggingface.co/
• https://apeatling.com/articles/part-2-building-your-training-data-for-fine-tuning/

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New openSUSE-welcome by lkocman

Project Description

Let's revisit our existing openSUSE welcome app.

My goal was to show Leap 16 in a new coat. Welcome app adds to the first time use experience. We've recently added donation button to our existing welcome.

Some things that I recently wanted to address were EOL and possibly upgrade notification.

I've already done some experiments with mint welcome app, but not sure if it's better than the existing one.

There is also a PR to rework existing app https://github.com/openSUSE/openSUSE-welcome/pull/36 (this should be considered as an option too)

Goal for this Hackweek

New welcome app, possibly with EOL notification for Leap.

1) Welcome application(s) with (rebrand changes) maintained under github.com/openSUSE

2) Application is submitted to openSUSE:Factory && openSUSE:Leap:16.0

3) Updated needles in openQA (probably post hackweek)

Resources

Reddit discussion about the best welcome app out there.

Github repo for the current welcome app.

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