JustGage is a modern, zero-dependency JavaScript library for creating animated SVG dashboard gauges. It is a complete rewrite of the legacy RaphaelJS-based v1.x, using native SVG APIs and ES6+ class-based architecture. The library provides a highly customizable gauge with support for donut, differential, pointer, target lines, custom sectors, and multiple animation easing functions. The source is organized into modular components: core gauge logic (`src/core/JustGage.js`), configuration handling (`src/core/config.js`), a requestAnimationFrame-based animator (`src/core/GaugeAnimator.js`), a native SVG renderer with a Raphael-like wrapper (`src/rendering/svg.js`), and utility modules for colors, formatting, DOM, and general helpers. The build system uses esbuild to produce ESM, CJS, and UMD outputs, and tests are run with the Node.js native test runner and jsdom. The codebase demonstrates good engineering practices: separation of concerns, comprehensive test coverage (unit, integration, edge cases), and CI/CD pipelines. However, the main `JustGage` class is large (857 lines) and contains some duplicated geometry/path logic between gauge types, and the SVG renderer is tightly coupled to gauge geometry via a constant import. Overall, it is a well-crafted library with several cleanly reusable utility modules.
This is a production-grade Kubernetes CSI (Container Storage Interface) driver for JuiceFS, a cloud-native distributed POSIX filesystem. It enables dynamic and static provisioning of JuiceFS volumes as Kubernetes PersistentVolumes. The system consists of multiple microservices running in a Kubernetes cluster: a CSI Controller (StatefulSet) that handles volume creation, deletion, expansion, and snapshots; a CSI Node (DaemonSet) that manages mount and unmount operations; an optional webhook component for sidecar injection and validation; and a Dashboard (Deployment+React frontend) for management and troubleshooting. It supports three mount modes: default mount-pod (dedicated pod for JuiceFS client), sidecar (inject into application pod via mutating webhook), and process (deprecated). The codebase demonstrates a strong command of Kubernetes controller patterns, with sophisticated features like graceful upgrade of mount pods via FUSE file descriptor passing, time-ordered caching for dashboard APIs, and a builder pattern for mount pod creation. The architecture is well-modularized but has a large configuration surface.
Kubewall is a production-grade, open-source Kubernetes dashboard that provides real-time multi-cluster management through a single-binary web application. It combines a Go backend (Echo framework, client-go informers) with a React/TypeScript frontend (Redux Toolkit, shadcn/ui, TanStack Router) to offer live monitoring, YAML editing, pod log streaming, port forwarding, and an AI-powered troubleshooting assistant (kwAI). The architecture relies on Server-Sent Events (SSE) to push Kubernetes resource updates to the browser, with an in-memory informer cache per cluster. The backend exposes REST endpoints for every standard Kubernetes resource type as well as Custom Resource Definitions (CRDs), while the frontend uses virtualized data tables, Radix UI primitives, and lazy-loaded Monaco editor for editing. The system supports multiple kubeconfig authentication methods and includes a Helm chart for deployment.
Frappe Insights is a full-stack business intelligence application enabling users to connect to data sources, build queries visually or via SQL, create visualizations, and assemble dashboards and workbooks. The decoupled architecture features a Vue.js 3 frontend communicating with a Python/Frappe backend through REST APIs. Key capabilities include a multi-database query builder using Ibis and SQLGlot, ECharts-powered visualizations, role-based access control, and DuckDB-based data warehousing for caching imported tables. The frontend leverages Vue composition API for reusable logic, while the backend uses Frappe's DocType system for extensibility. The project demonstrates solid engineering practices with modular API design, reusable components, and abstraction layers, though complexity arises from supporting numerous database types and deep Frappe integration.
Ham Dashboard is a browser-based web application that displays configurable content in a grid layout. It supports images (with rotation), videos, iframes (web pages), and RSS feeds. The application is built with vanilla JavaScript, HTML, and CSS, requiring no backend or build process. Configuration can be loaded from localStorage, config.js, config.json, or other JS/JSON files via URL parameters or file picker. Key features include a settings UI for configuration, full-screen viewing, right-click to rotate content, double-click to zoom, automatic content refresh, and breadcrumb-aware navigation between different dashboard configurations. The application is designed to be lightweight and easily hostable on static platforms like GitHub Pages or Cloudflare Pages.
Linus Dashboard is a Home Assistant custom integration that auto-generates a plug-and-play Lovelace dashboard from the entity, device, area, and floor registries. The frontend is a TypeScript strategy that dynamically builds views, chips, cards, and popups based on device class and domain, while the Python backend creates nested group entities (area → floor → global) for each domain, aggregates numeric sensors, and provides a configuration flow for exclusions and embedded dashboards. It integrates optionally with Magic Areas and Linus Brain for entity resolution, falling back to native registry data. The system is bilingual (EN/FR) and heavily automated with release scripts, CI workflows, and a fake-house test environment. Architecturally, the project is a hybrid: a Python backend following Home Assistant component patterns and a TypeScript frontend built with Rspack, relying on external cards (Mushroom, Browser Mod, Card Mod, Swipe Card, Stack-in-Card). Core engineering patterns include abstract base classes (AbstractCard, AbstractChip, AbstractView), factory classes with dynamic imports, a central Helper class that acts as a registry cache, and memoization/performance profiling utilities. The backend uses a shared NestedGroupMixin to build hierarchical entity groups and pure aggregation logic in aggregate.py. The codebase is well-tested with Vitest and pytest, and includes extensive documentation and release automation, but also suffers from a large god object (Helper.ts >1600 lines), duplicated platform boilerplate, vendored minified third-party bundles, and partial French localization. It is actively maintained with recent commits, a responsive issue tracker, and regular beta/stable release cycles.
This repository implements a Home Assistant custom integration that adds a visual, drag-and-drop card builder for Lovelace dashboards. Users can design custom dashboard cards by composing blocks (text, icons, gauges, charts, weather, etc.) in a canvas, configuring styles, entity bindings, and actions—all without writing YAML or code. The system includes a dedicated panel within Home Assistant, a custom Lovelace card renderer (`custom:card-builder-renderer-card`) for runtime display, and a marketplace for sharing cards via cardbuilder.pro. Architecturally, the project is split into a Python backend (`custom_components/card_builder/`) and a TypeScript/Lit frontend (`frontend/`). The backend handles integration setup, WebSocket APIs for CRUD on cards, presets, media, and account management. The frontend is a single-page application with panel views (dashboard, card list, editor), a designer canvas with Moveable-based manipulation, and a block system where each block declares its configuration, editor UI, and rendering logic. Key subsystems include a style resolver with preset inheritance and entity bindings (direct, map, threshold, condition, template), a drag-and-drop manager with adapters for canvas and containers, a chart data provider for history/statistics, and a document model with versioned migration. The codebase demonstrates high craftsmanship in its block abstraction, style system, and binding engine—patterns that could be extracted for other visual editors. However, some files are large and tightly coupled, with lingering FIXME comments and a few fragile DOM workarounds. The integration is actively maintained, with a clear roadmap and pro version planned, though the external marketplace dependency introduces some risk.
JARVIS2 is a real-time dashboard framework designed for Raspberry Pi, built with Flask (Python) and Mithril (JavaScript). It features live-updating widgets via server-sent events (SSE) and a plugin-based job system for data fetching. The backend schedules periodic jobs (e.g., weather, public transport, email, RSS) that push data to connected browsers. The frontend renders widgets using Mithril components and updates them reactively as events arrive. The architecture is a monolith: a single Flask application with background scheduling via APScheduler. Key entry points are the Flask routes for dashboards, individual widgets, and the SSE event stream. External dependencies include various APIs (Yr, Entur, Avinor, Google APIs, Sonos, etc.) and libraries (Flask, APScheduler, requests, BeautifulSoup, soco). The codebase is well-structured, with clear separation between job implementations, static assets, and templates. It includes a comprehensive test suite and CI pipeline. The project is actively maintained, targeting Python 3.14 and using modern tooling (uv, ruff). However, it retains some deprecated dependencies (oauth2client) and Python 2 compatibility shims, indicating moderate technical drift.
Materio is a free admin dashboard starter template built on Next.js 14 App Router, MUI v5, and Tailwind CSS, published by ThemeSelection. It ships as two parallel, functionally identical codebases: javascript-version/ and typescript-version/. Everything in the repo is static UI - hardcoded demo data, mock ApexCharts, placeholder auth forms - there is no API layer, database, or authentication logic. Its job is to be a polished starting point for admin applications, and it executes that role well: an extensive per-component MUI theme override system, a custom collapsible vertical menu subsystem, and a cookie-persisted settings context for dark/light mode. Architecturally it follows Next.js App Router conventions: root layout (app/layout.jsx), a dashboard layout that wraps the VerticalLayout shell (navigation, navbar, footer), and a blank layout for login/register/error pages. The most interesting engineering lives in src/@menu (a self-contained vertical navigation layer with context-driven collapse/expand and breakpoint behavior), src/@core/theme (dozens of isolated MUI component overrides), and a few genuinely reusable React hooks (useObjectCookie, useImageVariant). All dashboard data is fabricated; adopters must wire real data and backend logic themselves. The template is actively maintained (last commit March 2026, one open issue, automated issue triage workflows). Drift risks are version pinning to Next.js 14 / MUI v5.15 and the maintenance cost of keeping JS and TS variants in sync.
MainWP Dashboard is a WordPress plugin that provides a centralized management console for controlling multiple WordPress child sites from a single dashboard. It enables administrators to perform bulk updates, backups, security monitoring, uptime checks, content publishing, user management, and cost tracking across all connected sites. The system is architected as a monolithic WordPress plugin with modular extensions (logs, cost tracker, API backups, system monitor) that each have their own database tables, admin pages, REST endpoints, and cron jobs. Key entry points include the main plugin bootstrap (mainwp.php), AJAX handlers via MainWP_Post_Base_Handler, REST API v2 controllers under /mainwp/v2/, and a new Abilities API (wp-abilities/v1/) for programmable site operations. Data flows involve periodic cron tasks that sync child site status, updates, and health metrics into custom database tables, which are then exposed through dashboard widgets, email notifications, and REST endpoints. External dependencies include the WordPress core, phpseclib for encryption, and various JavaScript libraries (jQuery, Semantic UI, DataTables, ApexCharts, Dropzone, GridStack). The codebase demonstrates professional engineering with extensive test coverage (PHPUnit for abilities, REST, and regression tests), a structured class hierarchy, and security-conscious patterns like nonce verification, capability checks, and credential encryption at rest. However, it also carries legacy complexity from years of development, including large raw SQL queries, singleton overuse, and some deprecated PHP practices.
This repository is the operational backbone for Bazel's continuous integration system. It orchestrates Buildkite pipelines for hundreds of Bazel projects (including Bazel itself, rules repositories, and downstream projects like TensorFlow), manages cloud infrastructure on Google Cloud Platform (GCE, GCR, Cloud SQL, BigQuery), and provides a suite of tools for release management, cherry-picking, metrics collection, and a monitoring dashboard. The architecture is polyglot: a Rust agent uploads test artifacts from Build Event Protocol files, a Go service collects and publishes pipeline performance metrics, a Spring Boot backend with a Next.js frontend serves a dashboard for tracking GitHub issues and build stats, and Python scripts (primarily `bazelci.py`) handle dynamic pipeline generation and CI orchestration. Infrastructure is managed via Terraform, with GitHub Actions for automated PR review of the Bazel Central Registry and release workflows. The codebase is actively maintained but shows signs of technical drift, including hardcoded versions, references to end-of-life platforms, and a large number of manually defined pipelines that risk configuration drift.
Glance is a self-hostable web application designed as a customizable dashboard (startpage) that aggregates information from various sources into widgets. Built in Go, it features a modular widget architecture where each widget type (e.g., RSS, weather, clock) implements a common interface, allowing users to create personalized pages with multiple columns and widgets. The application serves HTML pages with embedded static assets (CSS, JS, images) and uses Go templates for server-side rendering, enhanced by a comprehensive set of template functions. Client-side JavaScript adds interactivity for dynamic updates, masonry layouts, and popovers. The system relies on a YAML-driven configuration (`glance.yml`) that supports variable substitution, file includes, and defines pages, columns, and widget settings. Data fetching is handled by centralized HTTP utilities with caching and user-agent rotation to avoid blocking external services. A theming system generates CSS variables from HSL-based configuration, enabling light/dark modes and custom themes. Optional authentication secures the dashboard, and the application includes diagnostic and CLI tools for maintenance. While the project demonstrates strong engineering in areas like request deduplication (singleflight), flexible configuration, and reusable template functions, it faces challenges due to its extensive widget proliferation (30+ types), coupling between frontend and backend logic, and reliance on an outdated Go version (1.26.6) which is no longer supported, indicating moderate technical drift. Despite these issues, Glance remains a popular and functional solution for personal dashboards.
EvoNexus is an AI-powered business operating system with a dashboard for managing various applications and services. It has features for managing systems, knowledge base, and user authentication, and uses Docker for containerization.
This is a React admin dashboard template built with CoreUI, React 19, Vite, Redux, and Chart.js. It provides a comprehensive set of example pages demonstrating CoreUI components, form controls, charts, widgets, authentication flows, and icons. The codebase is well-structured with lazy-loaded routes, a simple Redux store for theme and sidebar state, and reusable layout components (AppSidebar, AppHeader, AppContent). It serves as a starting point for building admin panels rather than a production application. The template includes extensive documentation (ARCHITECTURE.md, DEVELOPMENT.md) and uses JSDoc comments. It is tightly coupled to the CoreUI ecosystem and contains no real backend integration or data fetching logic. The repository is primarily a showcase and scaffold for developers.
This is a monorepo containing starter templates and final code for the official Next.js Learn courses. It includes multiple independent Next.js applications demonstrating the Pages Router, App Router, data fetching, authentication, SEO optimization, and more. The code is educational, well-structured, and follows modern Next.js patterns (server actions, streaming, suspense, etc.). It is not a production application but serves as a reference implementation for learning. The dashboard example includes a full CRUD invoice management system with PostgreSQL, NextAuth authentication, and Tailwind CSS styling. The basics examples cover static generation, dynamic routes, API routes, and TypeScript. The SEO example demonstrates performance optimization techniques like dynamic imports and lazy loading.
This repository is a curated collection of over 30 independent Streamlit in Snowflake (SiS) demo applications, each self-contained in its own folder. The demos span a wide range of use cases including business intelligence dashboards, LLM-powered chatbots (Cortex, DeepSeek-R1, RAG), geospatial H3 visualizations, custom UI components (AgGrid, ECharts, Slickgrid), CI/CD pipelines with Snowflake CLI, and embedding SiS into external portals via Next.js. Each app includes a `streamlit_app.py` entry point, a `data/creation_script.sql` for Snowflake object setup, and sample data. The code is educational, heavily commented, and demonstrates patterns like caching, dynamic page navigation, and local/cloud-aware connections. While the apps are not production-grade (some hardcoded values, duplication), they serve as excellent reference implementations for building SiS applications.
This repository is a Vue 3 admin dashboard template (vue3-admin-better) built with Element Plus, Vuex, Vue Router, and Rspack as the build tool. It provides a full-featured management system UI with login, registration, error pages, and over 40 demo CRUD pages for common business modules like projects, teams, tasks, customers, knowledge base, and workflows. The architecture follows a standard SPA pattern with dynamic routing based on RBAC permissions (intelligence/all modes), multiple layout variations (vertical/horizontal), theme customization (three themes persisted to localStorage), and a mock API layer using MockJS. Key entry points include `src/main.js` (app bootstrap), `src/layouts/index.vue` (main layout container), and `src/router/index.js` (route definitions). The state management uses Vuex with modules for user, routes, settings, tabs, and error logs. External dependencies include Vue 3, Element Plus, ECharts, Axios, and vuedraggable. The codebase demonstrates typical admin template patterns but has several maintenance and security concerns: mock interceptors are forced in production (a serious risk), a local `layouts` package dependency, fragile FontAwesome-to-Element-Plus icon mapping, and hardcoded mock data with no real backend integration. These issues limit direct production readiness without significant refactoring.
Dapr Dashboard is a web-based UI for Dapr (Distributed Application Runtime) that provides visibility into Dapr applications, components, and configurations. The application consists of a Go backend serving REST APIs and WebSocket connections, and an Angular frontend. It supports three deployment platforms: Kubernetes, Standalone (self-hosted), and Docker Compose. The backend uses platform-specific adapters to interact with each environment, querying Kubernetes APIs, local filesystems, or Docker Compose configurations as needed. The frontend displays instance information, component details, configuration data, control plane status (Kubernetes-only), and real-time log streaming via WebSocket. Despite recent commits, the project is explicitly marked as archived in the README, with users directed to Diagrid Dashboard as an alternative.
NDlib is a Python library for modeling and simulating diffusion processes on complex networks, with a strong focus on epidemic compartment models and opinion dynamics. It provides a collection of built-in models (SIR, SEIR, voter model, bounded confidence, etc.), a flexible compartment-based composition system for building custom models, and a domain-specific language (NDQL) for defining models declaratively. The library also includes an interactive dashboard server for code-free experimentation, visualization modules (matplotlib and bokeh), and a sensitivity analysis runner via SALib. While the codebase demonstrates solid architectural patterns such as abstract base classes, configuration objects, and block schema registries, it suffers from significant technical drift: it retains Python 2 compatibility shims (six, future), uses outdated dependency versions, and has brittle test infrastructure. The dashboard and parser rely on dynamic code generation that is fragile. Overall, NDlib is a functional research-oriented library with reusable compartment and configuration patterns, but its maintenance has stalled, making it risky for new projects without substantial modernization.
ioBroker.vis is a visualization adapter for the ioBroker smart home platform, providing a web-based dashboard editor and runtime. It renders interactive views composed of widgets that bind to ioBroker data points (states/objects) via a real-time socket.io connection. The architecture is monolithic: a single-page application served by an ioBroker adapter, with all editor and runtime logic bundled in global JavaScript files using jQuery, CanJS, jQuery UI, and a homebrewed binding DSL. The widget system supports inline transformations (math, date formatting, JSON extraction) and extensibility via external widget sets. Development of this version has been officially stopped in favor of the rewritten vis-2 adapter, and the repository only receives critical fixes. The codebase exhibits significant technical drift due to outdated dependencies (jQuery 1.x, deprecated AppCache, IE8 polyfills), a monolithic design with tight coupling, and a license inconsistency (CC BY-NC in source files versus MIT in package.json). It is functional but should not be used for new projects.