Evernote is a popular cloud-based note-taking application. It has the advantage of being available on many platforms, thus notes taken on an Android phone are readily available on the desktop, and vice versa.
Unfortunately there's no official Evernote client for Linux. It can be used through its Web UI, but a more lightweight client would be desirable as well.
There are various inofficial open source clients, notably nixnote2, everpad, and geeknote. None of them is available out-of-the-box on openSUSE.
nixnote2
nixnote2 is a C++/Qt application. It can be installed from OBS and seems to work quite well. Personally I don't like the UI so much, it's a big window that easily gets in the way of other work.
geeknote
geeknote is a command line application written in python that allows editing notes in markdown format. I like this idea a lot. geeknote is currently not available on OBS. It doesn't seem to be actively maintained at the moment. Its biggest disadvantage is the lack of a local cache, every single geeknote invocation requires a time-consuming server access. The command line syntax is also a little odd.
everpad
everpad, a GUI application written in python and Qt (using PySide), is best-in-class wrt desktop integration. It has been written with Ubuntu/Unity integration in mind but works well on other desktops as well. It even has a d-bus API. Unfortunately it has a lot of python dependencies. There is an OBS project with an everpad package but I failed to make it run on my TW system because of missing python libraries; moreover the package seems to be quite outdated.
Project idea
Package everpad so that it builds and runs cleanly on TW and Leap, and consider pushing it to factory. Package geeknote, too. Evaluate the possiblity to use the everpad cache with geeknote through the d-bus API, combining the strengths of everpad and geeknote.
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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!
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):
./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
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
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
- 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.
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/
Saline (state deployment control and monitoring tool for SUSE Manager/Uyuni) by vizhestkov
Project Description
Saline is an addition for salt used in SUSE Manager/Uyuni aimed to provide better control and visibility for states deploymend in the large scale environments.
In current state the published version can be used only as a Prometheus exporter and missing some of the key features implemented in PoC (not published). Now it can provide metrics related to salt events and state apply process on the minions. But there is no control on this process implemented yet.
Continue with implementation of the missing features and improve the existing implementation:
authentication (need to decide how it should be/or not related to salt auth)
web service providing the control of states deployment
Goal for this Hackweek
Implement missing key features
Implement the tool for state deployment control with CLI
Resources
https://github.com/openSUSE/saline
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.
Framework laptop integration by nkrapp
Project Description
Although openSUSE does run on the Framework laptops out-of-the-box, there is still room to improve the experience. The ultimate goal is to get openSUSE on the list of community supported distros
Goal for this Hackweek
The goal this year is to at least package all of the soft- and firmware for accessories like the embedded controller, Framework 16 inputmodule and other tools. I already made some progress by packaging the inputmodule control software, but the firmware is still missing
Resources
As I only have a Framework laptop 16 and not a 13 I'm looking for people with hardware that can help me test
Progress:
Update 1:
The project lives under my home for now until I can get an independent project on OBS: Framework Laptop project
Also, the first package is already done, it's the cli for the led-matrix spacer module on the Framework Laptop 16. I am also testing this myself, but any feedback or questions are welcome.
You can test the package on the Framework 16 by adding this repo and installing the package inputmodule-control
Update 2:
I finished packaging the python cli/gui for the inputmodule. It is using a bit of a hack because one of the dependencies (PySimpleGUI) recently switched to a noncommercial license so I cannot ship it. But now you can actually play the games on the led-matrix (the rust package doesn't include controls for the games). I'm also working on the Framework system tools now, which should be more interesting for Framework 13 users.
You can test the package on the Framework 16 by installing python311-framework16_inputmodule and then running "ledmatrixctl" from the command line.
Update 3:
I packaged the framework_tool, a general application for interacting with the system. You can find it some detailed information what it can do here. On my system everything related to the embedded controller functionality doesn't work though, so some help testing and debugging would be appreciated.
Update 4:
Today I finished the qmk interface, which gives you a cli (and gui) to configure your Framework 16 keyboard. Sadly the Python gui is broken upstream, but I added the qmk_hid package with the cli and from my testing it works well.
Final Update:
All the interesting programs are now done, I decided to exclude the firmware for now since upstream also recommends using fwupd to update it. I will hack on more things related to the Framework Laptops in the future so if there are any ideas to improve the experience (or any bugs to report) feel free to message me about it.
As a final summary/help for everyone using a Framework Laptop who wants to use this software:
The source code for all packages can be found in repositories in the Framework organization on Github
All software can be installed from this repo (Tumbleweed)
The available packages are:
framework-inputmodule-control (FW16) - play with the inputmodules on your Framework 16 (b1-display, led-matrix, c1-minimal)
python-framework16_inputmodule (FW16) - same as inputmodule-control but is needed if you want to play and crontrol the built-in games in the led-matrix (call with ledmatrixctl or ledmatrixgui)
framework_tool (FW13 and FW 16) - use to see and configure general things on your framework system. Commands using the embedded controller might not work, it looks like there are some problems with the kernel module used by the EC. Fixing this is out of scope for this hackweek but I am working on it
qmk_hid (FW16) - a cli to configure the FW16 qmk keyboard. Sadly the gui for this is broken upstream so only the cli is usable for now
Packaging Mu on OBS by joeyli
Description
Packaging Microsoft Mu project
Goals
Packaging Mu RPM on OBS.
Resources
https://microsoft.github.io/mu/
https://github.com/microsoft/mu
https://github.com/microsoft/mu_basecore
https://github.com/microsoft/mutianoplatforms
https://github.com/microsoft/mutianoplus
https://github.com/microsoft/mu_plus
Hackweek 22: Look at Microsoft Mu project
https://hackweek.opensuse.org/22/projects/look-at-microsoft-mu-project
https://drive.google.com/file/d/1BT31i7z3qh13adj9pdRz3lTUkqIsXvjY/view?usp=drive_link
Update Haskell ecosystem in Tumbleweed to GHC-9.10.x by psimons
Description
We are currently at GHC-9.8.x, which a bit old. So I'd like to take a shot at the latest version of the compiler, GHC-9.10.x. This is gonna be interesting because the new version requires major updates to all kinds of libraries and base packages, which typically means patching lots of packages to make them build again.
Goals
Have working builds of GHC-9.10.x and the required Haskell packages in 'devel:languages:haskell` so that we can compile:
git-annexpandocxmonadcabal-install
Resources
- https://build.opensuse.org/project/show/devel:languages:haskell/
- https://github.com/opensuse-haskell/configuration/
- #discuss-haskell
- https://www.twitch.tv/peti343