Mail client with mailing list workflow support in Rust by acervesato

Description

To create a mail user interface using Rust programming language, supporting mailing list patches workflow. I know, aerc is already there, but I would like to create something simpler, without integrated protocols. Just a plain user interface that is using some crates to read and create emails which are fetched and sent via external tools.

I already know Rust, but not the async support, which is needed in this case in order to handle events inside the mail folder and to send notifications.

Goals

• simple user interface in the style of aerc, with some vim keybindings for motions and search
• automatic run of external tools (like mbsync) for checking emails
• automatic run commands for notifications
• apply patch set from ML
• tree-sitter support with styles

Resources

• ratatui: user interface (https://ratatui.rs/)
• notify: folder watcher (https://docs.rs/notify/latest/notify/)
• mail-parser: parser for emails (https://crates.io/crates/mail-parser)
• mail-builder: create emails in proper format (https://docs.rs/mail-builder/latest/mail_builder/)
• gitpatch: ML support (https://crates.io/crates/gitpatch)
• tree-sitter-rust: support for mail format (https://crates.io/crates/tree-sitter)

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VimGolf Station by emiler

Description

VimGolf is a challenge game where the goal is to edit a given piece of text into a desired final form using as few keystrokes as possible in Vim.

Some time ago, I built a rough portable station using a Raspberry Pi and a spare monitor. It was initially used to play VimGolf at the office and later repurposed for publicity at several events. This project aims to create a more robust version of that station and provide the necessary scripts and Ansible playbooks to make configuring your own VimGolf station easy.

Goals

• Refactor old existing scripts
• Implement challenge selecion
• Load external configuration files
• Create Ansible playbooks
• Publish on GitHub

Resources

• https://www.vimgolf.com/
• https://github.com/dstein64/vimgolf
• https://github.com/igrigorik/vimgolf

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Liz - Prompt autocomplete by ftorchia

Description

Liz is the Rancher AI assistant for cluster operations.

Goals

We want to help users when sending new messages to Liz, by adding an autocomplete feature to complete their requests based on the context.

Example:

• User prompt: "Can you show me the list of p"
• Autocomplete suggestion: "Can you show me the list of p...od in local cluster?"

Example:

• User prompt: "Show me the logs of #rancher-"
• Chat console: It shows a drop-down widget, next to the # character, with the list of available pod names starting with "rancher-".

Technical Overview

1. The AI agent should expose a new ws/autocomplete endpoint to proxy autocomplete messages to the LLM.
2. The UI extension should be able to display prompt suggestions and allow users to apply the autocomplete to the Prompt via keyboard shortcuts.

Resources

GitHub repository

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Improve chore and screen time doc generator script `wochenplaner` by gniebler

Description

I wrote a little Python script to generate PDF docs, which can be used to track daily chore completion and screen time usage for several people, with one page per person/week.

I named this script wochenplaner and have been using it for a few months now.

It needs some improvements and adjustments in how the screen time should be tracked and how chores are displayed.

Goals

• Fix chore field separation lines
• Change screen time tracking logic from "global" (week-long) to daily subtraction and weekly addition of remainders (more intuitive than current "weekly time budget method)
• Add logic to fill in chore fields/lines, ideally with pictures, falling back to text.

Resources

tbd (Gitlab repo)

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Collection and organisation of information about Bulgarian schools by iivanov

Description

To achieve this it will be necessary:

• Collect/download raw data from various government and non-governmental organizations
• Clean up raw data and organise it in some kind database.
• Create tool to make queries easy.
• Or perhaps dump all data into AI and ask questions in natural language.

Goals

By selecting particular school information like this will be provided:

• School scores on national exams.
• School scores from the external evaluations exams.
• School town, municipality and region.
• Employment rate in a town or municipality.
• Average health of the population in the region.

Resources

Some of these are available only in bulgarian.

• https://danybon.com/klasazia
• https://nvoresults.com/index.html
• https://ri.mon.bg/active-institutions
• https://www.nsi.bg/nrnm/ekatte/archive

Results

• Information about all Bulgarian schools with their scores during recent years cleaned and organised into SQL tables
• Information about all Bulgarian villages, cities, municipalities and districts cleaned and organised into SQL tables
• Information about all Bulgarian villages and cities census since beginning of this century cleaned and organised into SQL tables.
• Information about all Bulgarian municipalities about religion, ethnicity cleaned and organised into SQL tables.
• Data successfully loaded to locally running Ollama with help to Vanna.AI
• Seems to be usable.

TODO

• Add more statistical information about municipalities and ....

Code and data

• Github

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Improve/rework household chore tracker `chorazon` by gniebler

Description

I wrote a household chore tracker named chorazon, which is meant to be deployed as a web application in the household's local network.

It features the ability to set up different (so far only weekly) schedules per task and per person, where tasks may span several days.

There are "tokens", which can be collected by users. Tasks can (and usually will) have rewards configured where they yield a certain amount of tokens. The idea is that they can later be redeemed for (surprise) gifts, but this is not implemented yet. (So right now one needs to edit the DB manually to subtract tokens when they're redeemed.)

Days are not rolled over automatically, to allow for task completion control.

We used it in my household for several months, with mixed success. There are many limitations in the system that would warrant a revisit.

It's written using the Pyramid Python framework with URL traversal, ZODB as the data store and Web Components for the frontend.

Goals

• Add admin screens for users, tasks and schedules
• Add models, pages etc. to allow redeeming tokens for gifts/surprises
• …?

Resources

tbd (Gitlab repo)

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Improvements to osc (especially with regards to the Git workflow) by mcepl

Description

There is plenty of hacking on osc, where we could spent some fun time. I would like to see a solution for https://github.com/openSUSE/osc/issues/2006 (which is sufficiently non-serious, that it could be part of HackWeek project).

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Hackweek 25 from openSSL office in Brno, Czechia by lkocman

Description

Join South Moravian colleagues, Austrian friends, and local community members for Hackweek 25 at the openSSL corporation office in Brno, Czechia. This will be a relaxed and enjoyable in-person gathering where we can work on our Hackweek projects side by side, share ideas, help each other, and simply enjoy the atmosphere of hacking together for a week.

Food, snacks, coffee will be available to keep everyone energized and happy throughout the week. We'd like to throw a small party on Tuesday.

Goals

• Bring together SUSE employees and community members from the South Moravian region and nearby Austria.
• Create a friendly space for collaboration and creativity during Hackweek 25.
• Support each other’s projects, exchange knowledge, and experiment freely.
• Strengthen local connections and enjoy a refreshing break from remote work.

Resources

Report from Grand openning of the office

Photos on google photos

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Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios

Description

Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.

This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.

The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.

Goals

By the end of Hack Week, we aim to have a single, working Python script that:

1. Connects to Prometheus and executes a query to fetch detailed test failure history.
2. Processes the raw data into a format suitable for the Gemini API.
3. Successfully calls the Gemini API with the data and a clear prompt.
4. Parses the AI's response to extract a simple list of flaky tests.
5. Saves the list to a JSON file that can be displayed in Grafana.
6. New panel in our Dashboard listing the Flaky tests

Resources

• Jenkins Prometheus Exporter: https://github.com/uyuni-project/jenkins-exporter/
• Data Source: Our internal Prometheus server.
• Key Metric: jenkins_build_test_case_failure_age{jobname, buildid, suite, case, status, failedsince}.
• Existing Query for Reference: count by (suite) (max_over_time(jenkins_build_test_case_failure_age{status=~"FAILED|REGRESSION", jobname="$jobname"}[$__range])).
• AI Model: The Google Gemini API.
• Example about how to interact with Gemini API: https://github.com/srbarrios/FailTale/
• Visualization: Our internal Grafana Dashboard.
• Internal IaC: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring

Outcome

• Jenkins Flaky Test Detector: https://github.com/srbarrios/jenkins-flaky-tests-detector and its container
• IaC on MLM Team: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring/jenkinsflakytestsdetector?reftype=heads, https://gitlab.suse.de/galaxy/infrastructure/-/blob/master/srv/salt/monitoring/grafana/dashboards/flaky-tests.json?ref_type=heads, and others.
• Grafana Dashboard: https://grafana.mgr.suse.de/d/flaky-tests/flaky-tests-detection @ @ text

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Kubernetes-Based ML Lifecycle Automation by lmiranda

Description

This project aims to build a complete end-to-end Machine Learning pipeline running entirely on Kubernetes, using Go, and containerized ML components.

The pipeline will automate the lifecycle of a machine learning model, including:

• Data ingestion/collection
• Model training as a Kubernetes Job
• Model artifact storage in an S3-compatible registry (e.g. Minio)
• A Go-based deployment controller that automatically deploys new model versions to Kubernetes using Rancher
• A lightweight inference service that loads and serves the latest model
• Monitoring of model performance and service health through Prometheus/Grafana

The outcome is a working prototype of an MLOps workflow that demonstrates how AI workloads can be trained, versioned, deployed, and monitored using the Kubernetes ecosystem.

Goals

By the end of Hack Week, the project should:

1. Produce a fully functional ML pipeline running on Kubernetes with:

   • Data collection job
   • Training job container
   • Storage and versioning of trained models
   • Automated deployment of new model versions
   • Model inference API service
   • Basic monitoring dashboards

2. Showcase a Go-based deployment automation component, which scans the model registry and automatically generates & applies Kubernetes manifests for new model versions.

3. Enable continuous improvement by making the system modular and extensible (e.g., additional models, metrics, autoscaling, or drift detection can be added later).

4. Prepare a short demo explaining the end-to-end process and how new models flow through the system.

Resources

Project Repository

Updates

1. Training pipeline and datasets
2. Inference Service py

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"what is it" file and directory analysis via MCP and local LLM, for console and KDE by rsimai

Description

Users sometimes wonder what files or directories they find on their local PC are good for. If they can't determine from the filename or metadata, there should an easy way to quickly analyze the content and at least guess the meaning. An LLM could help with that, through the use of a filesystem MCP and to-text-converters for typical file types. Ideally this is integrated into the desktop environment but works as well from a console. All data is processed locally or "on premise", no artifacts remain or leave the system.

Goals

• The user can run a command from the console, to check on a file or directory
• The filemanager contains the "analyze" feature within the context menu
• The local LLM could serve for other use cases where privacy matters

TBD

• Find or write capable one-shot and interactive MCP client
• Find or write simple+secure file access MCP server
• Create local LLM service with appropriate footprint, containerized
• Shell command with options
• KDE integration (Dolphin)
• Package
• Document

Resources

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Liz - Prompt autocomplete by ftorchia

Description

Liz is the Rancher AI assistant for cluster operations.

Goals

We want to help users when sending new messages to Liz, by adding an autocomplete feature to complete their requests based on the context.

Example:

• User prompt: "Can you show me the list of p"
• Autocomplete suggestion: "Can you show me the list of p...od in local cluster?"

Example:

• User prompt: "Show me the logs of #rancher-"
• Chat console: It shows a drop-down widget, next to the # character, with the list of available pod names starting with "rancher-".

Technical Overview

1. The AI agent should expose a new ws/autocomplete endpoint to proxy autocomplete messages to the LLM.
2. The UI extension should be able to display prompt suggestions and allow users to apply the autocomplete to the Prompt via keyboard shortcuts.

Resources

GitHub repository

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Try out Neovim Plugins supporting AI Providers by enavarro_suse

Description

Experiment with several Neovim plugins that integrate AI model providers such as Gemini and Ollama.

Goals

Evaluate how these plugins enhance the development workflow, how they differ in capabilities, and how smoothly they integrate into Neovim for day-to-day coding tasks.

Resources

• Neovim 0.11.5
• AI-enabled Neovim plugins:
  • avante.nvim: https://github.com/yetone/avante.nvim
  • Gp.nvim: https://github.com/Robitx/gp.nvim
  • parrot.nvim: https://github.com/frankroeder/parrot.nvim
  • gemini.nvim: https://dotfyle.com/plugins/kiddos/gemini.nvim
  • ...
• Accounts or API keys for AI model providers.
• Local model serving setup (e.g., Ollama)
• Test projects or codebases for practical evaluation:
  • OBS: https://build.opensuse.org/
  • OBS blog and landing page: https://openbuildservice.org/
  • ...

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