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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Update M2Crypto by mcepl

There are couple of projects I work on, which need my attention and putting them to shape:

• M2Crypto

Goal for this Hackweek

• Put M2Crypto into better shape (most issues closed, all pull requests processed)
• More fun to learn jujutsu
• Play more with Gemini, how much it help (or not).
• Perhaps, also (just slightly related), help to fix vis to work with LuaJIT, particularly to make vis-lspc working.

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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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Bring to Cockpit + System Roles capabilities from YAST by miguelpc

Bring to Cockpit + System Roles features from YAST

Cockpit and System Roles have been added to SLES 16 There are several capabilities in YAST that are not yet present in Cockpit and System Roles We will follow the principle of "automate first, UI later" being System Roles the automation component and Cockpit the UI one.

Goals

The idea is to implement service configuration in System Roles and then add an UI to manage these in Cockpit. For some capabilities it will be required to have an specific Cockpit Module as they will interact with a reasource already configured.

Resources

A plan on capabilities missing and suggested implementation is available here: https://docs.google.com/spreadsheets/d/1ZhX-Ip9MKJNeKSYV3bSZG4Qc5giuY7XSV0U61Ecu9lo/edit

Linux System Roles:

• https://linux-system-roles.github.io/
• https://build.opensuse.org/package/show/openSUSE:Factory/ansible-linux-system-roles Package on sle16 ansible-linux-system-roles

First meeting Hackweek catchup

• Monday, December 1 · 11:00 – 12:00
• Time zone: Europe/Madrid
• Google Meet link: https://meet.google.com/rrc-kqch-hca

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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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Gods & Steel: Tactical Prototype by pherranz

Description

A turn-based tactical combat prototype built in Godot, featuring two techno-sorcery factions in strategic warfare. This proof-of-concept demonstrates core gameplay mechanics including alternating activations, unique faction abilities, and tactical positioning on a grid-based battlefield.

Goals

Primary Objectives: Implement a complete turn-based tactical combat loop with alternating unit activation Create two distinct factions with 3-4 units each, showcasing unique mechanical identities Develop a modular code architecture for easy expansion to additional factions Deliver a playable 3v3 battle scenario with basic AI opponents

Technical Milestones: Grid-based movement and positioning system Alternating activation turn manager Unit ability system with faction-specific mechanics Basic AI decision-making (move → attack patterns) Health/damage system with win/lose conditions

Stretch Goals: Simple cover system for tactical positioning Additional faction-specific special abilities Enhanced visual feedback for actions

Resources

Technology Stack: Engine: Godot 4.2 Art Style: Top-down 64x64 pixel art Programming: GDScript Version Control: Git Tools: Aseprite/LibreSprite for pixel art, TrenchBroom for level blocking

Development Approach: Day 1: Core architecture (scenes, grid system, unit base class) Day 2: Turn management and basic movement Day 3: Combat system and faction abilities Day 4: AI implementation and balancing Day 5: Polish, bug fixing, and demo preparation

Technical Architecture: Scene Manager (handles game flow) Grid System (pathfinding, positioning) Unit Manager (turn order, activation) Faction System (modular ability definitions) AI Controller (state-based decision making)

Asset Pipeline: Placeholder art → Greybox prototyping → Final pixel art Modular unit definition using Godot's resource system Data-driven ability definitions for easy balancing

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