Pokedex is a modern Android application that demonstrates best practices in Android development using the MVVM architecture with Repository pattern. The app fetches and displays Pokémon data from the PokeAPI, featuring offline-first capabilities through Room database caching, pagination for efficient data loading, and smooth transition animations between list and detail views. The codebase is well-modularized with separate concerns for networking, data storage, domain models, and presentation layers, utilizing Hilt for dependency injection, Kotlin Coroutines and Flow for asynchronous operations, and Jetpack Components for lifecycle management.
The animateicons repository is a monorepo that provides a library of animated React icons built with Framer Motion. It includes a Next.js gallery for browsing and documenting the icons, a CLI for managing icons in projects, and an MCP server for AI agent integration. The core logic handles icon cataloging, search, and resolution. The application emphasizes reusable UI primitives, custom hooks for hover handling and animation configuration, and a utility for class name merging. The icon components follow a consistent pattern of exposing animation controls via refs and supporting hover-triggered animations with reduced motion respect.
The Motion repository is a comprehensive library for creating complex animations and interactions for JavaScript, React, and Vue applications. It supports various features such as animation sequencing, spring animations, layout animations, gesture handling, and scroll-linked animations. The library is actively maintained, with a large number of commits and updates in the changelogs. The code is well-structured, and several reusable components and patterns are demonstrated in the examples.
This repository implements VPoser, a variational autoencoder-based human pose prior for SMPL/X body models. It provides tools to encode/decode human poses into a low-dimensional latent space, sample novel poses, and perform inverse kinematics fitting to 2D/3D key points using the learned prior. The codebase includes data preparation scripts for the AMASS dataset, a PyTorch Lightning trainer, a flexible IK engine with LBFGS/Adam optimizers, and utilities for rotation mathematics and linear blend skinning. The system is designed as a reusable library for pose prior applications in research and graphics. Architecture-wise, the project is organized into modules: body_model (SMPL/X loading and LBS), models (VPoser VAE and IK engine), tools (rotation conversions, 6D representation, file/path helpers), data (dataset loading and preparation), train (training scripts), and tutorials (Jupyter notebooks and example scripts). Entry points include training scripts (e.g., train/V02_05/V02_05.py), IK demonstrations (tutorials/ik_example_*.py), and usage tutorials (tutorials/vposer.ipynb). External dependencies are primarily PyTorch, Lightning, OmegaConf, and optional visualization packages. Data flows: For training, AMASS .npz files are converted to .pt tensors via prepare_data.py, loaded by VPoserDS, and fed into VPoserTrainer. For inference, pretrained models are loaded via model_loader.py, enabling pose encoding/decoding or IK fitting. The IK engine iteratively optimizes pose, shape, and translation in the VPoser latent space to match target key points.
KokonutUI is an open-source UI component library for modern React applications, specifically targeting Next.js projects with Tailwind CSS v4 and Motion (Framer Motion). The repository contains over 40 reusable, animated components distributed through a shadcn-style registry, allowing developers to copy-paste or install them via `npx shadcn@latest add @kokonutui/<name>`. The library covers backgrounds (beams, flow field, shape hero), buttons (attract, gradient, hold, particle, slide-text, social, switch, v0), cards (apple activity, bento grid, card flip, card stack, carousel, currency transfer, liquid glass, mouse effect, spotlight, tweet), inputs (avatar picker, file upload, loader, team selector), navigation (action search bar, morphic navbar, profile dropdown, smooth drawer, smooth tab, toolbar), AI-specific components (AI input search, AI loading, AI prompt, AI text loading, AI voice), and text effects (dynamic, glitch, matrix, scroll, shimmer, sliced, swoosh, type writer). The project also includes custom hooks (use-auto-resize-textarea, use-click-outside, use-copy-to-clipboard, use-debounce, use-file-input, use-intersection, use-mobile, use-tags) and a comprehensive documentation site powered by Fumadocs with MDX content. A build script generates registry JSON files and agent-facing resources (llms.txt, agent skills index). The codebase follows modern React patterns (compound components, asChild, controlled/uncontrolled state, composition over props) and includes extensive AI agent skills for component architecture, Next.js best practices, and React design patterns. While the library is functional and well-structured, it lacks tests and has a large dependency surface; it is also tightly coupled to Next.js and serves as a marketing funnel for a pro version.
Cambio is a React component library that simplifies shared-element animations for dialogs/popups. It provides a compound component API (`Cambio.Root`, `Trigger`, `Portal`, `Backdrop`, `Popup`, `Title`, `Description`, `Close`) built on top of Base UI's accessible Dialog primitives and Motion's animation engine. The library offers four built-in motion presets (snappy, smooth, bouncy, reduced) configurable globally, per-component via variants, or overridden individually. It supports dismissible popups driven by drag gestures with configurable threshold and velocity, automatic respect for `prefers-reduced-motion`, and explicit reduce-motion overrides. The architecture uses React Context for state propagation and Motion's `motion.create` to wrap Base UI components. The code is well-typed in TypeScript, organized into clear modules, and demonstrates craftsmanship in its motion configuration resolution chain and drag physics handling. The repository also includes a documentation website built with Next.js and Fumadocs.
Magic Resume is a production-oriented, bilingual resume-builder web application. Users manage resume documents in a dashboard, edit them in a multi-panel workbench with a live A4 preview, choose among nine templates, and export to PDF, PNG, JSON, or Markdown. The editor supports rich text, structured sections, custom themes, typography controls, and automatic one-page scaling. Resume data is local-first, persisted to localStorage and optionally synced to the local file system via the File System Access API. Under the hood, it is a React/TypeScript full-stack app with a somewhat unusual dual route-tree structure: an older Next.js App Router layout coexists with a newer TanStack Router source tree. State is managed with Zustand, UI uses Tailwind CSS and shadcn-style components, and rich text editing is built on Tiptap. AI features—streaming text polish, grammar checking, and vision-based PDF import—run through server routes that abstract OpenAI, Gemini, Anthropic, DeepSeek, Qwen, and Doubao behind a common transport with retries and SSE streaming. Export paths include a server-side PDF renderer, client-side html2canvas/jsPDF, browser print, JSON, and Markdown. The codebase is feature-rich and actively maintained, with some solid server-side AI abstractions and a meaningful test suite. However, it carries structural debt: near-identical section components are duplicated across nine templates, the old Next.js route tree remains alongside the newer TanStack routes, and AI model IDs/configurations are speculative and likely to age. It is deployable to Cloudflare Workers, Node, or Docker, but the architecture is hybrid and transitional rather than cleanly unified.
This repository is a curated 'awesome' list of biomechanical resources. It contains no executable code, notebooks, or data pipelines. The entire content is a single README.md file with hundreds of categorized links to learning materials, datasets, software tools, books, societies, and other resources relevant to biomechanics research and education. It functions as a community-maintained directory rather than a software project or research pipeline. While the list is well-organized and frequently updated, there are no computational components to analyze or reuse.
YapAnimator is a physics-based animation library for iOS, tvOS, and macOS, written in Swift 3.1. It provides a generic `YapAnimator<T>` class that animates any value conforming to the `Animatable` protocol by decomposing it into an array of doubles and applying spring physics integration with a fixed 1/240s timestep. The library includes a convenience layer (`YapAnimatedLayer`) attached to `CALayer` via associated objects, enabling one-liner animations of layer properties like position, bounds, opacity, transform, etc. The core engine runs on a display link (CADisplayLink on iOS/tvOS, CVDisplayLink on macOS) and manages all active animators. The repository is explicitly archived and no longer maintained, with Swift 3.0/3.1 dependencies and no updates since 2017.