Last.fm Bulk Edit is a browser userscript that enhances Last.fm's website functionality to enable bulk editing of scrobbles. It operates as a userscript that runs on Last.fm pages, adding a 'Bulk edit scrobbles' menu item to artist, album, and track pages. When activated, it fetches all scrobbles for the selected entity (handling pagination), groups them by album/album artist combinations, and presents an enhanced edit form that allows modifying multiple scrobbles simultaneously. The application also enhances Last.fm's automatic edits page with sorting and filtering capabilities, adds album names to scrobble overview pages, and creates timestamp links in scrobble listings. Built with TypeScript and webpack, it uses a modular architecture with separate features for different enhancements, a custom modal system for dialogs, and sophisticated retry logic for network requests.
This repository is the `lingam` Python package, a comprehensive, research-oriented library for causal discovery from observational data using LiNGAM (Linear Non-Gaussian Acyclic Model) and its numerous extensions. It implements over 20 algorithms, including DirectLiNGAM, ICALiNGAM, VAR-LiNGAM, RCD, CAM-UV, RESIT, LiM, mLiNGAM, GroupLiNGAM, MultiGroup variants, and more. The library provides a consistent API where each algorithm exposes `fit()` on a data matrix, followed by properties like `causal_order_` and `adjacency_matrix_`, and all support bootstrap-based stability analysis via a shared `BootstrapMixin`. Key contributions include handling of latent confounders, time series, missing data, mixed data types, and multiple datasets, alongside utilities for causal effect estimation, visualization, model evaluation, and synthetic data generation. The code is well-structured, uses standard scientific Python dependencies (numpy, scipy, scikit-learn, graphviz), and includes a thorough test suite and extensive Jupyter notebook tutorials.
This repository is the JetBrains Godot Support plugin, a multi-language IDE extension for IntelliJ IDEA and Rider that provides comprehensive GDScript language intelligence, scene editing, debugging, and project integration for the Godot game engine. It consists of several modules: a Kotlin-based GDScript language plugin with custom lexer/parser, PSI, annotations, inspections, refactoring, formatting, code completion, and LSP client; a C# debugger worker that allows inspecting Godot scenes during debugging with optimized bulk-fetch strategies; a C++ native addon (GDExtension) for bidirectional communication between the IDE and Godot editor; a PHP toolchain to generate SDK metadata from Godot's official XML documentation; and .NET ReSharper components for C# Godot project support. The plugin uses JetBrains' PolySymbols framework for unified symbol resolution and has an extensive test suite covering parsing, type inference, inspections, and debugger integration. While deeply integrated with JetBrains platform APIs and generated code, the debugger worker's child-chunking algorithm and SDK fingerprinting utility are well-crafted, self-contained components that could be reused in other contexts. The codebase is actively maintained with regular commits, but relies on several internal/experimental APIs and snapshot dependencies, posing moderate technical drift risk.
openlibrary-client is a reference Python client library for the Open Library API, maintained by the Internet Archive. It provides object-oriented models for books, authors, editions, works, and covers, wrapping the REST endpoints behind a simple client class. The library supports cookie-based authentication, read/fetch operations, and write operations such as creating new works and editions. It also includes utilities for parsing MARC dump lines and validating book import schemas, making it useful beyond simple API access.
MDV (Markdown Data & Visualization) is a tool that extends CommonMark Markdown with front-matter, fenced code blocks for charts/tables/stats, `:::` container directives for layout and styled regions, and auto-generated table of contents. It parses `.mdv` files and renders them to self-contained HTML with inline SVG charts (bar, line, pie) and CSS themes, requiring no JavaScript runtime in the output. The project is a TypeScript monorepo with three packages: `@mdv/core` (parser and renderer library), `@mdv/cli` (CLI with render, preview, and PDF export commands), and `mdv-vscode` (VS Code extension providing live side-by-side preview). The parser uses `markdown-it` as a base with a fence-aware preprocessor that handles `:::` directives and HTML comments correctly within code blocks. The renderer is designed for robustness: errors never crash the document, invalid front-matter renders a fatal banner, and chart data validation produces inline error messages. Security measures include escaping raw HTML, sanitizing style values, and preventing path traversal in data file references. The CLI supports live preview with file watching and auto-reload, and PDF export via headless Chromium (Puppeteer). The VS Code extension provides a debounced live preview that also refreshes on data file changes.
Nellavio is a production-grade, open-source Next.js dashboard starter designed for building modern SaaS products, internal tools, and admin panels. It provides 90+ reusable UI components, 60+ chart variations, authentication (Better Auth) with RBAC, i18n (next-intl), theme switching, and extensive data visualization pages (analytics, orders, customers, products, calendar, charts showcase). The architecture follows App Router with clear separation: server components for data fetching (Apollo Client GraphQL with mock fallback), client components for interactive views, Zustand for global state, and TanStack Table for data tables. The codebase is well-tested with Vitest, Storybook, and CI/CD (GitHub Actions). Despite some hardcoded demo data and recent migrations (Clerk→Better-Auth, Tremor→Recharts), the engineering practices are strong, making it a solid foundation for bootstrapping dashboard-centric applications.
tksheet is a pure Python Tkinter widget library that provides a full-featured, interactive spreadsheet-like table and treeview component for embedding in desktop applications. It renders only visible cells using Tkinter canvases, supporting cell editing, dropdowns, checkboxes, progress bars, notes/tooltips, find/replace, sorting, undo/redo, clipboard operations, and extensive theming. The architecture is modular, with separate classes for the main table, column headers, row index, text editor, and dropdown windows, all orchestrated by a central `Sheet` class that exposes a rich API for data manipulation and event handling. The library demonstrates strong software craftsmanship through its use of reusable patterns such as `Span` objects for addressing cell ranges with method chaining, `DotDict` for attribute-accessible configuration dictionaries, and a consistent `EventDataDict` structure for undo/redo and event propagation. It also includes self-contained utility modules for data formatting (`Formatter` class) and sorting (natural, version, and fast sort keys) that could be extracted and reused independently. Development of tksheet has officially ceased except for bug fixes and behavioral issues, as stated in the README. The codebase is mature and stable, with extensive documentation and a changelog spanning multiple major versions. While the library is tightly coupled to Tkinter and has some code duplication across similar components (e.g., column headers and row index), it remains a high-quality, production-ready widget for any Python GUI application requiring tabular data interaction.
KLDNS is a production-grade, multi-tenant subdomain distribution and DNS management SaaS platform. Users register subdomains under administrator-managed parent domains, pay with an internal points economy, and create/manage DNS records that are synchronously written to external DNS providers. The backend is a layered Go application (Gin HTTP handlers → services → SQLite repositories via GORM plus raw SQL) with an embedded migration system, and the frontend is a Vue 3 + TypeScript + Element Plus SPA that gets compiled and embedded into the Go binary at release time. This is purely an application repo — no data research, pipelines, or analytics artifacts are present. The core workflows are: subdomain registration with points deduction and a pending-review approval workflow (with refunds on rejection), DNS record CRUD that writes remote provider APIs first and the local database second (with compensation deletes and a dns_write_jobs queue for failed remote operations), session/API-token authentication with hashed secrets, Cloudflare Turnstile captcha verification, and a full admin backend for users, groups, domains, provider credential configs, settings, and audit logs. DNS provider integrations use a factory-registered interface with adapters for Cloudflare, DNSPod, Aliyun, AWS Route53, Google Cloud DNS, Huawei, Baidu, and others — several implementing non-trivial signing schemes (AWS SigV4, Google JWT/OAuth2, Huawei SDK-HMAC-SHA256, Baidu BCE). Testing is a notable strength: services, repositories, utilities, and most provider adapters have unit tests using httptest servers, fake repositories, and fixed-time fixtures. The main weaknesses are internal consistency issues rather than broken functionality: duplicated domain/subdomain join SQL across repositories, a documented repository refactoring in progress, hardcoded magic values (admin group 99, default group 100), two different pagination strategies between admin (server-side) and user (client-side) frontend views, Chinese-language UI and error messages not abstracted behind localization, and a hardcoded default app secret that must be changed in production. The multi-provider DNS abstraction, crypto/validation utilities, and the embedded migration runner are genuinely reusable in other Go projects.
BittyTax is a Python CLI application suite for computing UK cryptocurrency tax liabilities. It consists of three console tools: bittytax_conv normalizes raw CSV/XLSX exports from approximately 80 exchanges, wallets, block explorers and accounting platforms into standardized transaction records; bittytax applies HMRC rules such as same-day matching, bed-and-breakfast, Section 104 pooling, and ten-day matching for companies, then generates PDF/Excel/console reports for capital gains, income, margin trading, holdings, and audit; bittytax_price fetches historical and latest asset prices from CoinGecko, CoinPaprika, CoinStats, Frankfurter, and BittyTaxAPI with local JSON caching. The repository is fundamentally an application rather than a data-research project: it exposes CLI entry points, has a plugin-based parser architecture, config-driven behavior, test suites, and CI pipelines, while the data-heavy parser and price modules are application subsystems rather than analytical pipelines.
rspirv is a Rust workspace providing comprehensive SPIR-V module processing functionality. It includes a core library (rspirv) with a low-level data representation (DR) for raw instructions and a higher-level structured representation (SR) with type-safe indices. The project also features a binary parser, an assembler, a disassembler CLI (rspirv-dis), and a code generator (rspirv-autogen) that derives Rust types from the official SPIR-V JSON grammar. The parser supports custom consumers via the Consumer trait, enabling streaming or custom processing. The builder (dr/build) allows programmatic construction of SPIR-V modules. The lift module converts from DR to SR, bridging the two representations. The codebase is heavily auto-generated from the SPIR-V specification, ensuring spec compliance, but this also introduces risks of drift if the grammar submodule is not updated. The project is actively maintained with CI/CD pipelines, and the multi-crate workspace is designed for both library reuse and standalone tool usage.
Pandora Behaviour Engine Plus is a C# desktop application (Avalonia UI) for patching Skyrim's Havok animation behavior files (.hkx). It parses mod-provided patch instructions in Nemesis, FNIS, and Pandora XML formats, then injects changes into base behavior graphs to support custom animations. The architecture follows a modular DI pattern with services for mod detection, plugin loading, path management, and engine execution. Key components include a state machine for engine lifecycle, a plugin system for extensibility, and a set of reusable change types (IPackFileChange) for applying edits to Havok packfiles. The application provides both a GUI and a CLI mode, and includes extensive test data (hundreds of patch files) for validation. While the core patching logic is solid and well-structured, there are signs of technical drift (preview .NET target, some dead code) and the large test data set presents maintenance overhead. Overall, it is a strong, professional-grade modding tool with reusable patterns for behavior graph manipulation.
Ng-Matero Extensions is an Angular component library that extends Angular Material with 15+ additional UI components including grid, datetimepicker, colorpicker, popover, tooltip, select, split, dialog, drawer, loader, progress, alert, button loading, checkbox-group, and photoviewer. The library is built for Angular 22 and supports both Material Design 2 and 3 theming via Sass mixins and CSS custom properties. It provides date adapters for native, moment, luxon, and date-fns libraries. The repository includes a development app for interactive demos and a documentation site with live examples, API references, and i18n support (English and Chinese). The codebase demonstrates strong engineering practices with standalone components, CDK overlay usage, adapter patterns, and performance optimizations like coalesced style scheduling. However, it relies on several third-party dependencies (ng-select, color-picker, photoviewer) and uses some deprecated Angular Material APIs, indicating moderate maintenance risk.
This is the Postman Collection SDK, a JavaScript library that provides a complete object model for Postman Collections (v2.1 format). It enables developers to create, manipulate, serialize, and deserialize collections, items, requests, responses, variables, and other HTTP-related constructs. The library is used internally by Postman's own tools (e.g., Newman, Postman app) and by third-party integrations. It features a class hierarchy rooted in PropertyBase, with PropertyList for generic indexed lists, variable substitution via SuperString, dynamic variable generation (faker-based), URL matching patterns, content-type detection, and mutation tracking. The codebase is well-tested (100% unit test coverage enforced) and includes both Node.js and browser support. While the core architecture is clean and modular, some dependencies are outdated (lodash 4.18.1, file-type 3.9.0) and there are deprecated API surfaces maintained for backward compatibility. The SDK is actively maintained with frequent releases and CI/CD pipelines.
buji-pac4j is a bridge library that integrates pac4j (a comprehensive security library supporting OAuth, CAS, SAML, OpenID Connect, etc.) with Apache Shiro. It automatically pushes pac4j authentication and authorization profiles into the Shiro security context, allowing developers to use pac4j's wide range of authentication mechanisms while leveraging Shiro for authorization and session management. The bridge is implemented as a set of classes that adapt pac4j's session store, profile manager, and framework adapter to Shiro's APIs, plus a custom Realm, SubjectFactory, and Token. It includes sophisticated logic to avoid unnecessary session renewal when the same user's profiles are updated (e.g., after token refresh), demonstrating deep understanding of both frameworks' internals. The library is production-ready, actively maintained, and used in legacy applications that want to adopt pac4j without fully migrating away from Shiro.
KaHIP (Karlsruhe High Quality Partitioning) is a high-performance C++ library and command-line toolkit for graph partitioning, node ordering, and mapping. It implements multilevel partitioning with advanced refinement techniques including Fiduccia-Mattheyses local search, flow-based min-cut refinement, cycle improvements, and tabu search. The library provides a C API for integration into external applications, along with Python bindings and a Java JNI wrapper. The codebase includes both a sequential version and a distributed-memory parallel version (ParHIP) using MPI, enabling partitioning of large graphs on clusters. Core algorithms are well-structured with modular coarsening, initial partitioning, and uncoarsening/refinement phases, supporting multiple matching strategies (GPA, random, label propagation) and quality/speed trade-offs via preconfigured modes (Strong, Eco, Fast, Social). Despite research-grade complexity and some code duplication between serial and parallel variants, the library is actively maintained and widely used in scientific computing and graph analytics.
This repository is a comprehensive collection of iOS demonstration projects covering design patterns, data structures, Swift language features, iOS frameworks, and software engineering principles. Each demo is a self-contained Xcode project or Swift playground that illustrates a specific concept through working code. The collection serves as an educational resource for iOS developers learning various aspects of iOS development, from basic Swift syntax to advanced frameworks like Core Animation and Combine. While individual demos vary in complexity and polish, they collectively provide valuable reference implementations of common iOS development patterns.
FastMM4 is a production-grade drop-in replacement memory manager for Delphi and C++Builder on Windows. It replaces the default RTL allocator with a multi-threaded, low-fragmentation design organized into three tiers: small blocks (binned by size class), medium blocks (linked-list pools with bitmap-based binning), and large blocks (VirtualAlloc). The core logic lives in FastMM4.pas, a single monolithic unit, with FastMM4Options.inc providing extensive compile-time feature toggles (FullDebugMode, leak reporting, alignment, memory sharing, etc.). This is library code — there is no UI, no data pipeline, and no business logic; the only executables are demo applications (e.g., a Usage Tracker VCL form) and the BorlndMM.dll replacement for C++Builder.
Orchestra is a mature, composable WPF application framework for building line-of-business desktop applications on .NET (net8.0-windows through net10.0-windows). It provides a complete shell infrastructure including splash screens, about dialogs, crash recovery, keyboard shortcut customization, changelog viewers, status bars, busy indicators, and recently-used item management. The framework is built on top of Catel (MVVM, IoC, logging) and integrates with Orc.* libraries for theming, controls, and system information. It offers two shell variants: a Ribbon.Fluent shell using Fluent.Ribbon for rich ribbon UIs, and a TaskRunner shell for simpler task-oriented interfaces. The architecture is modular and service-oriented, with all major features exposed via interfaces (e.g., IShellService, IStatusService, IKeyboardMappingsService) and registered through Microsoft.Extensions.DependencyInjection. The core library (Orchestra.Core) contains the foundational services, while shell-specific packages (Orchestra.Shell.Ribbon.Fluent, Orchestra.Shell.TaskRunner) provide concrete implementations. The framework also includes a shared shell library with recovery services, progress bars, and configuration management. Key entry points include the ShellService for application lifecycle orchestration and the ApplicationInitializationServiceBase for custom startup logic. External dependencies include Catel (7.x), Fluent.Ribbon (11.x), ControlzEx, and several Orc.* packages (Orc.Theming, Orc.Controls, Orc.LogViewer, Orc.SystemInfo). The build system is powered by Cake scripts with support for multiple installer formats (InnoSetup, MSIX, Velopack), code signing, and CI/CD integration. The repository also includes example applications demonstrating both shell variants and a comprehensive test suite with public API verification. Data flows are primarily user-driven: application startup triggers splash screen, configuration loading, shell creation, and keyboard mapping initialization. User interactions (closing, keyboard shortcuts, status updates) are handled through service abstractions. The framework manages local user data (configuration backups, changelog snapshots, recently used items) but does not process external datasets or analytics.
COIN-OR CLP is a production-grade linear programming (LP) solver library written in C++. It serves as the LP engine within the COIN-OR optimization ecosystem, consumed by CBC (branch-and-cut MIP framework) and integrating with external applications via the OSI (Open Solver Interface) standard adapter. The code (not the README) shows three complementary solution pathways: primal simplex, dual simplex (both with pluggable pivot rules — Dantzig, steepest edge, Devex, positive edge), and an interior-point barrier method with pluggable Cholesky backends (native dense/sparse, MUMPS, Pardiso, WSMP, CHOLMOD). It also implements a sophisticated modular presolve system (CoinPresolveAction stack), crash heuristics (Idiot, Sprint), a network-simplex specialization with tree-based basis factorization, and dynamic column generation for GUB-structured problems via ClpGubDynamicMatrix. The architecture is genuinely component-based: abstract base classes for matrix representation (ClpMatrixBase), pivot selection (ClpPrimalColumnPivot/ClpDualRowPivot), and factorization (ClpFactorization, CoinAbcTypeFactorization) allow users to substitute custom implementations. Entry points include the ClpSimplex C++ API, a C interface (Clp_C_Interface.h), a standalone executable (ClpMain.cpp), and the OsiClpSolverInterface wrapper. However, the codebase carries substantial research lineage and technical debt. An accelerated 'ABC' (FasterCoin) simplex variant coexists with the standard Clp implementation as largely parallel code paths, ~dozens of compile-time macros gate experimental features, and significant historical code dates to 2002 IBM contributions with mixed vintage updates through 2026. Heavy conditional compilation (Cilk, pthreads, AVX2, external Cholesky libraries), manual memory management, dead '#if 0' blocks, and duplicated pricing/factorization logic (Clp vs Abc) make the code difficult to maintain and test comprehensively. Despite this, the core algorithms are numerically careful and battle-tested at industrial scale, with active CI (Linux/Windows), release automation, and a maintained unit test suite. This is a library you'd embed, not fork — the craftsmanship is in the algorithmic component interfaces (pivot selection, factorization, matrix abstraction) rather than in the overall code cleanliness.
svelte-stripe is a Svelte component library that wraps Stripe Elements and Stripe.js, providing drop-in Svelte components for building payment UIs. It exposes context-based components such as Elements, PaymentElement, CardNumber, CardExpiry, and CardCvc, and handles Stripe.js loading, element lifecycle, and SSR safety. The repository also includes a SvelteKit demo application with examples for credit card, SEPA, iDEAL, and express checkout, plus server-side payment intent creation and webhook signature verification. It is a library and example app, not a data pipeline.