Create object oriented API for perl's YAML::XS module, with YAML 1.2 Support by tinita

Description

YAML::XS is a binding to libyaml and already quite old, but the most popular YAML module for perl. There are two main issues:

• It uses global package variables to influence behaviour.
• It didn't implement the loading of types like numbers and booleans according to the YAML spec (neither 1.1 nor 1.2).

Goals

Create a new interface which works object oriented. Currently YAML::XS exports a list of functions.

• The new API will allow to create a YAML::XS object containing configuration influencing the behaviour of loading and dumping.
  • It keeps the libyaml parser and emitter structs in memory, so repeated calls can save the creation of those structs
• It will by default implement the YAML 1.2 Core Schema, so it is compatible to other YAML processors in perl and in other languages
• If I have time, I would like to add the merge << key feature as an option. We could then use it in openQA as a replacement for YAML::PP to be faster.

I already created a proof of concept with a minimal functionality some weeks before this HackWeek.

Resources

• Work is currently happening on the oop branch
• Experimental release waiting for user feedback: https://github.com/perlpunk/yaml-libyaml-pm/releases
• Diff

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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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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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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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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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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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Design the new UI for storage configuration at Agama by ancorgs

Description

We are in the process of re-designing the web user interface to configure storage at Agama. We expected to have a clear idea of what we wanted before starting Hack Week. But the idea is still not that clear. So I will use use my Hack Week time to try several prototypes since I really want this to be done.

Goals

Have a prototype using Patternfly components and addressing all the use-cases we want to cover. Easy for the easy cases. Capable for the complex ones.

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Finish gfxprim application multiplexor (window manager) by metan

Project Description

I've implemented drivers for a few e-ink displays during the last hackweek and made sure that gfxprim widgets run nicely on e-ink as well. The missing piece to have a portable e-ink computer/reader/music player/... is a application that can switch between currently running applications and that can start new applications as well. Half of the solution is ready, there is a proxy gfxprim backend where applications render into a piece of a shared memory and input events (e.g. keyboard, mouse) can be multiplexed. What is missing is an interface (possibly touchscreen friendly as well) to make it user friendly.

Goal for this Hackweek

Make nekowm usable "window manager".

Resources

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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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