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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ClusterOps - Easily install and manage your personal kubernetes cluster by andreabenini

Description

ClusterOps is a Kubernetes installer and operator designed to streamline the initial configuration and ongoing maintenance of kubernetes clusters. The focus of this project is primarily on personal or local installations. However, the goal is to expand its use to encompass all installations of Kubernetes for local development purposes.
It simplifies cluster management by automating tasks and providing just one user-friendly YAML-based configuration config.yml.

Overview

• Simplified Configuration: Define your desired cluster state in a simple YAML file, and ClusterOps will handle the rest.
• Automated Setup: Automates initial cluster configuration, including network settings, storage provisioning, special requirements (for example GPUs) and essential components installation.
• Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
• Extensibility: Easily extend functionality with custom plugins and configurations.
• Self-Healing: Detects and recovers from common cluster issues, ensuring stability, idempotence and reliability. Same operation can be performed multiple times without changing the result.
• Discreet: It works only on what it knows, if you are manually configuring parts of your kubernetes and this configuration does not interfere with it you can happily continue to work on several parts and use this tool only for what is needed.

Features

• distribution and engine independence. Install your favorite kubernetes engine with your package manager, execute one script and you'll have a complete working environment at your disposal.
  
• Basic config approach. One single config.yml file with configuration requirements (add/remove features): human readable, plain and simple. All fancy configs managed automatically (ingress, balancers, services, proxy, ...).
• Local Builtin ContainerHub. The default installation provides a fully configured ContainerHub available locally along with the kubernetes installation. This configuration allows the user to build, upload and deploy custom container images as they were provided from external sources. Internet public sources are still available but local development can be kept in this localhost server. Builtin ClusterOps operator will be fetched from this ContainerHub registry too.
• Kubernetes official dashboard installed as a plugin, others planned too (k9s for example).
• Kubevirt plugin installed and properly configured. Unleash the power of classic virtualization (KVM+QEMU) on top of Kubernetes and manage your entire system from there, libvirtd and virsh libs are required.
• One operator to rule them all. The installation script configures your machine automatically during installation and adds one kubernetes operator to manage your local cluster. From there the operator takes care of the cluster on your behalf.
• Clean installation and removal. Just test it, when you are done just use the same program to uninstall everything without leaving configs (or pods) behind.

Planned features (Wishlist / TODOs)

• Containerized Data Importer (CDI). Persistent storage management add-on for Kubernetes to provide a declarative way of building and importing Virtual Machine Disks on PVCs for

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Make more sense of openQA test results using AI by livdywan

Description

AI has the potential to help with something many of us spend a lot of time doing which is making sense of openQA logs when a job fails.

User Story

Allison Average has a puzzled look on their face while staring at log files that seem to make little sense. Is this a known issue, something completely new or maybe related to infrastructure changes?

Goals

• Leverage a chat interface to help Allison
• Create a model from scratch based on data from openQA
• Proof of concept for automated analysis of openQA test results

Bonus

• Use AI to suggest solutions to merge conflicts
  • This would need a merge conflict editor that can suggest solving the conflict
• Use image recognition for needles

Resources

• TensorFlow
• open-webui
• openQA instance of openSUSE
• openQA documentation

Timeline

Day 1

• Conversing with open-webui to teach me how to create a model based on openQA test results
  • Asking for example code using TensorFlow in Python
  • Discussing log files to explore what to analyze
  • Drafting a new project called Testimony (based on Implementing a containerized Python action) - the project name was also suggested by the assistant

Day 2

• Using NotebookLLM (Gemini) to produce conversational versions of blog posts
  • Writing openQA tests in Python
  • Developing in distrobox containers
• Researching the possibility of creating a project logo with AI
  • Asking open-webui, persons with prior experience and conducting a web search for advice

Highlights

• I briefly tested compared models to see if they would make me more productive. Between llama, gemma and mistral there was no amazing difference in the results for my case.
• Convincing the chat interface to produce code specific to my use case required very explicit instructions.
• Asking for advice on how to use open-webui itself better was frustratingly unfruitful both in trivial and more advanced regards.
• Documentation on source materials used by LLM's and tools for this purpose seems virtually non-existent - specifically if a logo can be generated based on particular licenses

Outcomes

• Chat interface-supported development is providing good starting points and open-webui being open source is more flexible than Gemini. Although currently some fancy features such as grounding and generated podcasts are missing.
• Allison still has to be very experienced with openQA to use a chat interface for test review. Publicly available system prompts would make that easier, though.

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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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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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AI + Board Games

Board games have long been fertile ground for AI innovation, pushing the boundaries of capabilities such as strategy, adaptability, and real-time decision-making - from Deep Blue's chess mastery to AlphaZero’s domination of Go. Games aren’t just fun: they’re complex, dynamic problems that often mirror real-world challenges, making them interesting from an engineering perspective.

As avid board gamers, aspiring board game designers, and engineers with careers in open source infrastructure, we’re excited to dive into the latest AI techniques first-hand.

Our goal is to develop an all-open-source, all-green AWS-based stack powered by some serious hardware to drive our board game experiments forward!

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Project Goals

1. Set Up the Stack:

   • Install and configure the TAG and PyTAG frameworks on SUSE Linux Enterprise Base Container Images.
   • Integrate with the SUSE AI stack for GPU-accelerated training on AWS.
   • Validate a sample GPU-accelerated PyTAG workload on SUSE AI.
   • Ensure the setup is entirely repeatable with Terraform and configuration scripts, documenting results along the way.

2. Design and Implement AI Agents:

   • Develop AI agents for the two board games, incorporating Statistical Forward Planning and Deep Reinforcement Learning techniques.
   • Fine-tune model parameters to optimize game-playing performance.
   • Document the advantages and limitations of each technique.

3. Test, Analyze, and Refine:

   • Conduct AI vs. AI and AI vs. human matches to evaluate agent strategies and performance.
   • Record insights, document learning outcomes, and refine models based on real-world gameplay.

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Technical Stack

• Frameworks: TAG and PyTAG for AI agent development
• Platform: SUSE AI
• Tools: AWS for high-performance GPU acceleration

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Why This Project Matters

This project not only deepens our understanding of AI techniques by doing but also showcases the power and flexibility of SUSE’s open-source infrastructure for supporting high-level AI projects. By building on an all-open-source stack, we aim to create a pathway for other developers and AI enthusiasts to explore, experiment, and deploy their own innovative projects within the open-source space.

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Our Motivation

We believe hands-on experimentation is the best teacher.

Combining our engineering backgrounds with our passion for board games, we’ll explore AI in a way that’s both challenging and creatively rewarding. Our ultimate goal? To hack an AI agent that’s as strategic and adaptable as a real human opponent (if not better!) — and to leverage it to design even better games... for humans to play!

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Explore the integration between OBS and GitHub by pdostal

Project Description

The goals:

1) When GitHub pull request is created or modified the OBS project will be forked and the build results reported back to GitHub. 2) When new version of the GitHub project will be published the OBS will redownload the source and rebuild the project.

Goal for this Hackweek

Do as much as possible, blog about it and maybe use it another existing project.

Resources

• The Blog post
• Issue: poo#123858 - build.opensuse.org: /usr/lib/obs/service//go_modules.service No such file or directory

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Port git-fixup to POSIX shell script and submit to git/git by mcepl

Description

https://github.com/keis/git-fixup is an exceedingly useful program, which I use daily, and I would love to every git user could bask in its awesomeness. Alas, it is a bash script, so it is not appropriate for the inclusion in git proper.

Goals

Port the script to plain POSIX shell and submit for consideration to git@vger.kernel.org

Resources

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