thehardwarestore.ai

Graphite is a free, open-source vector and raster graphics editor with a fully nondestructive editing workflow. It combines layer-based compositing with node-based generative design, enabling procedural content creation. The system is architected as a multi-crate Rust workspace with a desktop application (using Chromium Embedded Framework for UI and wgpu for GPU rendering) and a web frontend (Svelte + Vite compiled to WebAssembly). The core document model is a node graph executed by an interpreted runtime with GPU shader support via rust-gpu. Storage uses a CRDT-based format (`.gdd`) with content-addressed deltas, history, and session state, supporting collaborative editing and undo/redo. The editor features a comprehensive tool system (select, pen, brush, shape, text, etc.), a node graph editor, color management, raw image decoding, and vector path operations. The frontend communicates with the Rust backend through a message-passing architecture with hierarchical message types and a dispatcher pattern. The project also includes a CLI tool for export, a build system (cargo-run), and extensive developer tooling (bisect tool, crate hierarchy visualizer, node documentation generator).

This is a full-stack cultivation world simulator game (xianxia RPG) with a Python backend (FastAPI + asyncio) and a Vue 3 + TypeScript + PixiJS frontend, supported by an optional Electron desktop shell for Epic Games Store distribution. The simulation engine runs a month-by-month tick system orchestrating ~29 phases (lifecycle, actions, social, war, world events, etc.) producing emergent narratives driven by LLM calls (OpenAI/Anthropic compatible) for character decisions, story generation, world lore rewriting, and roleplay interactions. The codebase is heavily data-driven with static game configurations (sects, techniques, items, regions) loaded from CSV files and multi-language localization via gettext PO files. It exposes a public REST API (query/command) and WebSocket for real-time state updates, supports save/load with JSON snapshots and SQLite event storage, and includes comprehensive test coverage (pytest, vitest, Playwright) and CI/CD workflows. While the architecture shows strong engineering patterns (effect system, action registry, single-choice decision framework, phased simulation pipeline), it carries risks from tight coupling of global singleton registries, LLM dependency, and use of `eval` in effect conditions.

This repository implements the Wave Function Collapse (WFC) algorithm, a constraint-based procedural content generation technique. The system takes an input bitmap (or a tileset with adjacency rules) and generates a larger output bitmap that is locally similar to the input, meaning every N×N pattern in the output appears somewhere in the input. It supports two primary models: the OverlappingModel, which extracts patterns from a sample image, and the SimpleTiledModel, which uses predefined tiles and adjacency constraints defined in XML. The algorithm operates by maintaining a 'wave' of possible states for each output cell, iteratively collapsing the cell with the lowest Shannon entropy and propagating constraints until a fully observed state is reached or a contradiction occurs. The architecture is a straightforward console application written in C# targeting .NET 10. The entry point is Program.cs, which reads a configuration file (samples.xml) specifying model parameters and input samples. The core algorithm is encapsulated in the abstract Model class, with concrete implementations in OverlappingModel.cs and SimpleTiledModel.cs. Image loading and saving are handled by the BitmapHelper class using the SixLabors.ImageSharp library. The system outputs PNG images to an 'output' directory. External dependencies are minimal, relying only on ImageSharp for bitmap I/O. The codebase is well-structured and demonstrates clean separation of concerns. The algorithm implementation is faithful to the original paper and includes optimizations such as the AC-4 constraint propagation algorithm and multiple heuristic strategies (entropy, MRV, scanline). The tile symmetry system in SimpleTiledModel reduces the number of explicit adjacency rules needed. While the repository is primarily a reference implementation, its components—particularly the helper utilities and the core propagation logic—are reusable in other procedural generation projects.

This repository provides a collection of Blender Geometry Node groups for procedurally generating assets of a northern European French town. The main file `GeometryNodesFrenchHous.blend` contains a library of node groups for creating houses, towers, bridges, town walls, gates, cliffs, vegetation, and architectural elements like churches and windmills. Users can append or link these node groups into their own Blender projects to generate customizable 3D models via adjustable parameters. The project includes demonstration files (e.g., `Demo-MagicSchoolV2.blend`) showcasing usage examples. The architecture is based on Blender's Geometry Nodes system, functioning as a modular asset library rather than a standalone application. Each node group is self-contained, allowing extraction and reuse in new Blender projects with minimal modification. The system relies externally on Blender 5.0 (as specified in the README) for execution, with no additional software dependencies. Data flow involves parameters driving geometric generation within the node groups, outputting 3D meshes directly usable in Blender scenes. The repository shows active maintenance with a recent commit (2026-07-05) and indicates low technical drift, as the Geometry Nodes setup appears compatible with the targeted Blender version. While the internal node structures cannot be inspected without opening .blend files, the README emphasizes documentation, viewer nodes for clarity, and a variety of asset types, suggesting thoughtful organization. The project fulfills its stated purpose of providing a reusable toolkit for procedural town generation in Blender.

Craftium is a voxel-based game engine (Luanti/Minetest fork) extended with reinforcement learning capabilities. The core system provides a moddable 3D world with block-based gameplay, Lua scripting API, client-server architecture, and procedural map generation. The craftium add-on supplies Python bindings, Gymnasium environment interfaces, and RL training scripts to enable machine learning research within the game world. The system supports both traditional gameplay and AI experimentation through environment-specific mods that define reward functions, observations, and termination conditions for agents.