This repository is a curated list (awesome list) of resources for SwiftUI and Combine, including articles, tutorials, videos, example projects, libraries, open-source apps, courses, and books. It does not contain any source code or executable components; it is purely a markdown-based index of external links. The list is organized by category and serves as a reference for developers building applications with SwiftUI. There are no data pipelines, models, or software artifacts to analyze. The repository is maintained with periodic updates and community contributions.
SingleFileZ is a browser extension (Chrome/Firefox/Edge) and CLI tool that saves complete web pages as self-extracting HTML/ZIP polyglot files. The core output format embeds a ZIP archive containing all page resources (HTML, CSS, images, fonts, frames) inside an HTML wrapper with a JavaScript bootstrap that extracts and displays the page without external dependencies. The extension offers extensive configuration including multiple profiles, URL-based auto-save rules, cloud uploads (Google Drive, Dropbox, GitHub, WebDAV), an annotation editor for notes/highlights/cut operations, batch saving, and file name templating. Architecturally, the project is a multi-browser extension with background scripts orchestrating page capture, content scripts handling in-page UI and resource collection, and a task queue for managing parallel saves. It relies heavily on message passing between contexts and includes custom infrastructure: a binary serialization format (YABSON) for efficient data transfer, a full ZIP read/write library with support for encryption and streaming, and a fork of Mozilla's Readability.js for article extraction. The CLI component uses Docker to run headlessly, delegating to the `single-filez-cli` npm package. The codebase is large and feature-rich but shows signs of maintainability strain: core modules are distributed as minified build artifacts in `lib/`, the README states SingleFileZ has been merged into its parent project SingleFile (suggesting this repo is superseded), and it relies on Manifest v2 which is being phased out by browsers. The custom polyfills and browser-specific workarounds add complexity. Despite this, several components (YABSON serialization, the ZIP library, cloud upload abstractions) are well-isolated and could be reused independently with minimal modification.
This repository is a PHP-based living style guide boilerplate that generates a web-based style guide from HTML markup snippets and Markdown documentation. The system scans directories of markup files, renders them with syntax highlighting (Prism.js), and provides a navigable interface with color swatches, font stacks, and documentation sections. It is a simple, monolithic PHP application with a single entry point (index.php) and a helper functions file (functions.php). While the code is clean and well-commented, it relies on outdated practices (no dependency management, no modern PHP, legacy CSS vendor prefixes) and has not been updated since 2018. The repository serves as a starting point for teams to create custom style guides, but it requires significant modernization to be used effectively in contemporary front-end workflows.
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.
Reggaeton Be Gone is a single-file Raspberry Pi application that continuously samples ambient audio and uses an Edge Impulse machine learning model to classify whether reggaeton music is playing. When the classification confidence exceeds a configurable threshold (default 0.95), the script triggers a Bluetooth test routine against a configured speaker address, using either repeated rfcomm connection attempts or l2ping packets. The system also displays status messages on a 128×32 OLED screen and logs events to a file. The code is a tightly coupled prototype with hardcoded paths, global state, and no error handling, reflecting its 'dirty and messy' design as acknowledged by the author. There are no reusable components beyond trivial display and logging helpers; the project's value lies in its novel concept and successful integration of audio ML with Bluetooth experimentation on physical hardware.
ShaderlabVS is a Visual Studio Extension (VSIX) designed to enhance the development experience for Unity Shaderlab, CG, and HLSL files within the Visual Studio IDE. It provides core editor features including syntax highlighting, code completion, outlining, quick info tooltips, and signature help. The architecture is divided into a core extension project (`ShaderlabVS`) that integrates with the Visual Studio Editor API, a data library (`ShaderlabVS.Data`) that manages keyword and function definitions via a custom `.def` file format, and build tools (`ShaderlabVS.LexTools`) that generate lexer tables using `gplex.exe`. Despite the functionality, the project is effectively stale and superseded. The README explicitly states that ShaderlabVS has been upgraded to "Omni Shader Tools for Unity," which features a completely rewritten parser. The codebase targets Visual Studio 2013, 2015, and 2017, relying on .NET Framework 4.5 and older VSSDK APIs. This makes it incompatible with modern Visual Studio versions (2019/2022) without significant refactoring. While the code demonstrates solid engineering practices for its time (MEF usage, separation of data/logic, unit tests), it is no longer the recommended solution for Unity shader development in Visual Studio. The repository contains a custom lexer generator and a robust data parsing system that could serve as a reference for building language services, but the extension itself is legacy. Users seeking similar functionality should migrate to the Omni Shader Tools project mentioned in the README.
This repository contains a single Arduino sketch for an RTL8720dn-based WiFi deauther. It hosts a captive web interface on the board's own WiFi network, scans for nearby access points, and allows a user to initiate deauthentication attacks against selected networks by sending crafted deauth frames. The architecture is a monolithic embedded firmware with a simple event loop: it serves HTML pages, parses HTTP POST/GET requests, and uses a submodule for packet injection. The code is tightly coupled to the target hardware and lacks modularity, abstraction, or tests. It is a functional but unrefined hobbyist project with no reusable components.
This repository is a curated list of links for image-related OSINT (Open Source Intelligence) tools and resources. It categorizes tools into reverse image search, stock image search, facial recognition, image investigation, and image manipulation. The repository contains no executable code, only a README.md file with the list of links. As such, it functions as a reference resource rather than a software application or data processing pipeline. The README provides organized tables and descriptions but lacks any code, data processing, or analytical components.
The Web Tooling Benchmark is a performance measurement suite designed to evaluate JavaScript-related workloads in common web developer tools. It executes benchmarks for tools like Babel, TypeScript, UglifyJS, and others, measuring their throughput in runs per second. The architecture consists of a Node.js CLI interface and a browser-compatible bundle, both driven by a benchmark suite that runs each tool's core functionality on standardized third-party code samples. Key entry points are src/cli.js (for Node.js) and src/bootstrap.js (for browser). The suite reads input files from the third_party directory, processes them via each tool's API, and aggregates results using geometric mean. External dependencies include the benchmarking framework, various JavaScript tools (acorn, babel, typescript, etc.), and utilities for file system virtualization and output formatting.
CameraBench is a benchmark and research project for evaluating camera motion understanding in videos, comparing classical Structure-from-Motion (SfM) methods and modern Vision-Language Models (VLMs). The repository serves as a landing page with download scripts for the test dataset (videos and labels from Hugging Face), a leaderboard showing model performance, and links to fine-tuned Qwen2.5-VL models. The actual evaluation and training code resides in a separate repository (t2v_metrics). The repo lacks a self-contained pipeline, dependency management, or data processing scripts; it is essentially a static site with download helpers. A notebook template for a ReLU activation function exercise is included but is unrelated to the core project. The benchmark is novel and published at NeurIPS 2025, but the codebase itself is minimal and not reusable beyond data retrieval.
This repository is a historical demo application showcasing JWT-based authentication and authorization in a Spring WebFlux reactive stack. It implements a custom security filter chain that first authenticates via HTTP Basic (returning a signed JWT) and then validates Bearer tokens on protected endpoints. The code uses Spring Boot 2.1.1, Java 8, and the Nimbus JOSE library for JWT signing and verification. The architecture is a simple monolith with in-memory user details and hardcoded secrets, intended for educational purposes rather than production use. The README explicitly states the project has been superseded by a modernized version (Spring Boot 4, Java 21, Spring Security 7) and is kept for historical reference. The code demonstrates key patterns: custom ServerAuthenticationSuccessHandler, functional bearer token converter, and a dummy ReactiveAuthenticationManager for stateless JWT authentication. However, it suffers from significant technical drift (outdated dependencies, Java 8, deprecated APIs) and lacks proper error handling, tests, and configurability.
This repository is a community archive of Capture The Flag (CTF) challenges, solutions, and security talks from the SecTalks meetup group across multiple cities (Sydney, London, Berlin, etc.). It contains hundreds of challenge files (binaries, scripts, packet captures, images) and corresponding solution writeups and scripts. The repo is not a unified software application or data pipeline; it is a heterogeneous collection of security-related artifacts spanning many years. The primary value is as a reference for CTF participants and security enthusiasts. The code quality varies widely: some solution scripts are well-crafted cryptographic attack implementations (e.g., RSA common modulus, CTR nonce reuse) that could be reused in other CTF contexts, while many are one-off scripts tightly coupled to specific challenges. The repository lacks centralized dependency management, and many challenges require specific environments or tools. It is actively maintained with recent commits and talks.
This repository implements a headless CMS architecture using Nuxt.js (v2.15) as the frontend and a full WordPress installation (v4.9.8) as the backend content source. The Nuxt application consumes WordPress REST API endpoints (extended by plugins like ACF to REST API, WP API Menus, and Contact Form 7) to render pages statically or server-side. The frontend includes custom Vue components for navigation, lazy-loaded images, a carousel slider, contact forms, and a deferred rendering mixin for performance. The WordPress backend is fully vendored within the repository, including core files, a custom theme that redirects WordPress routes to the Vue app, and numerous plugins for custom fields, user roles, SVG uploads, and CORS support. The project appears to be a starter template or production site for a headless WordPress + Nuxt stack, but the vendored WordPress core and outdated dependencies (WordPress 4.9.8, Nuxt 2.15, deprecated node-sass) indicate significant technical drift and maintenance challenges.
This is a lightweight SMTP server written in Go, designed to receive large volumes of email, parse MIME content, and store messages in MongoDB. It includes a web interface for viewing messages, managing greylisting, and real-time notifications via WebSockets (using the embedded Incus library). The server implements core SMTP commands (HELO/EHLO, MAIL, RCPT, DATA, AUTH, STARTTLS, XCLIENT) and supports TLS, greylisting, and spam filtering via regex. The architecture is monolithic: a single binary runs both the SMTP listener and an HTTP server for the web UI. The codebase is functional but shows signs of age: it uses deprecated dependencies (gopkg.in/mgo.v2, github.com/robfig/config, github.com/gosexy/redis), lacks a go.mod file, and has no automated tests. The SMTP protocol handling is reasonably robust, and the MIME parsing is non-trivial, but the overall project would benefit from modernization and testing.
This is a full-stack blog/CMS application built with ThinkJS 3.x (Node.js MVC framework) on the backend and Vue.js 2 + iView UI on the admin panel. The backend serves both a RESTful API and server-rendered public pages (ThinkJS templates). The admin SPA provides CRUD for posts, pages, categories, tags, comments, user management, and site configuration. Authentication uses JWT tokens. Image upload supports local storage or Qiniu CDN. Email notifications can be sent for new comments. The database is MySQL, with models defined using ThinkJS ORM. The codebase follows traditional MVC patterns but relies on heavily outdated dependencies (Vue 2, Webpack 3, iView 2, Node 16), uses an insecure `eval` in the admin, and lacks tests or CI. It is functional for its intended purpose but shows no recent maintenance and would require significant modernization for reuse.
This repository is a minimal static website hosted on GitHub Pages at butter.bot. The site is a humorous memorial page for a lobster named 'Butter Bot' who survived a request to prepare lobster recipes, ending with a catchphrase 'Exfoliate the Butter'. The actual code content is a single index.html file with CSS animations and a simple layout. The repository also contains a non-trivial GitHub Actions workflow (clawsweeper-dispatch.yml) that dispatches events to an external ClawSweeper app for issue/PR management, demonstrating advanced CI/CD patterns like token generation, ingress fingerprinting, and concurrency management. The workflow is the most technically sophisticated part of the repo, but it is tightly coupled to the external ClawSweeper service. The entire repository serves as a domain landing page and a demonstration of the ClawSweeper integration, rather than a standalone application.
This repository is a curated list of free software network services and web applications that can be self-hosted. It is maintained by the community and contains hundreds of entries organized by category, along with links, licenses, and deployment methods. It does not contain any executable code; it is purely a documentation and reference resource. The list is actively maintained and used as a directory for discovering self-hosted alternatives to various SaaS services.
This repository is a Chinese translation (2nd edition) of Wes McKinney's "Python for Data Analysis" book, presented as markdown chapter files. It is an educational resource, not a software application or a full data pipeline. The content covers pandas, NumPy, Matplotlib, and other data analysis libraries with embedded code examples for learning purposes. It is not a project that can be executed directly; there are no Python scripts, notebooks, or data files included. The code examples are based on older library versions (circa 2017) and use deprecated APIs such as `ix` and `read_table`. External datasets referenced (e.g., tips.csv, MovieLens) are not bundled, making reproduction impossible without manual setup. The repository has not been actively maintained, and its technical drift is high due to ecosystem changes. It is best used as a reference or study guide, not as a reusable codebase.
This repository is a curated 'awesome list' of Python data science resources. It does not contain any source code, data pipelines, models, or applications. Its sole purpose is to serve as a directory of links to libraries, tutorials, books, and talks for data science practitioners. The value is in the curation and categorization of external resources, not in any internal logic or data processing. It is a reference document, not a functional codebase. As such, it cannot be classified as a traditional 'application' or 'data_research' project in the software engineering sense, but it serves the data science community as a discovery tool. There are no reusable software components to extract. The list is well-organized into categories like Core Libraries, Statistics, Machine Learning, Deep Learning, and specialized domains like Bio Image Analysis and Epidemiology, making it a valuable starting point for practitioners looking for tools in specific areas.
This repository is a comprehensive collection of Android interview questions and answers, organized into topics such as Java basics, Android fundamentals, advanced Android, computer networks, operating systems, databases, data structures and algorithms, and non-technical interview questions. It is purely a documentation resource with no executable code, build scripts, or data pipelines. The content was last updated around 2020 and is now deprecated, with the author promoting a paid version in a separate knowledge planet. While the repository provides a broad coverage of interview topics, it lacks any software components, making it useful only as a reference for interview preparation. It is not a software application or a data research project.