Maybe it is yet another wheel
but still worth to do. The original idea is come from https://xmpp.net/.
This is only a script. I plan to build it with python. I need to study ssl related modules first. So I'm not sure when the project can be done. Nevertheless I make it as an opportunity to learn python programming.
This project is part of:
Hack Week 12
Activity
Comments
-
about 10 years ago by vitezslav_cizek | Reply
Do you mean something like https://www.ssllabs.com? Eg. https://www.ssllabs.com/ssltest/analyze.html?d=www.suse.com&s=130.57.66.10 It has public api: https://github.com/ssllabs/ssllabs-scan/
-
about 10 years ago by vitezslav_cizek | Reply
Do you mean something like ssllabs?
Example for suse.com
It has public api.-
about 10 years ago by whdu | Reply
Yes, it's the kind of thing like that. But I want to implement locally so don't depend on third part on-line api. The advantage is to work in a isolated network (for confidential considering). The most important is that the code should be freed(opened).
But anyway, people may think it's recreating the wheel. I'm okay with recreate wheels ;)
-
about 10 years ago by vitezslav_cizek | Reply
No problem.
Btw, here's another project you might be interested.
It's an SSL fingerprinting tool written in python.
-
-
Similar Projects
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/
Resurrect NWS CLI project by seanmarlow
Project Description
Many years back I created a simple python based CLI package that wrapped the NWS API to get weather forecasts, discussions and current conditions. Meanwhile I have not had time to keep it up-to-date so many pieces are broken or using deprecated features of the REST API. The package is useful to get weather information much quicker from CLI than clicking through the NWS website.
Goal for this Hackweek
The goal for this project is update and fix the package as well as add new features. Also, the documentation can be updated to be more thorough and useful.
Project URL: https://github.com/smarlowucf/wxcast
Tasks
- [x] Use Github actions instead of travis
- [x] Move metar to nws api
- [x] Convert string formatting to use F strings
- [x] Add get WFO info
- [x] Get a list of stations for wfo
- [x] Get station info
- [x] Fix 7 day forecast
- [x] Add temp unit option to metar
- [x] Add alias to metar for decoded version with conditions
- [x] Add/update documentation
- [x] Release new major version
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.
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
Results
Created WIP project Ansible-add-on-openSUSE