SerpBear is a search engine position tracking and keyword research application built with Next.js (React) and TypeScript. It allows users to monitor keyword rankings for their domains in Google, integrate with Google Search Console for actual performance data, and leverage Google Ads for keyword ideas. The application features a modular architecture with separate frontend UI, backend API routes (`/pages/api/`), background scraping jobs via cron, and a SQLite database managed by Sequelize ORM. Key functionalities include SERP scraping through multiple third-party services, position history tracking, email notifications for ranking changes, insight analytics, and configurable settings for API keys and scraper selection. The system is containerized with Docker and supports PWA deployment for mobile access.
svg2jsx is a web application that converts SVG markup into valid JSX code for React components. It provides a real-time editor with a split pane: left for SVG input (with drag-and-drop support) and right for the generated JSX output. The conversion pipeline runs server-side via a Next.js API route, applying SVGO optimization, attribute normalization (camelCase, className, style objects), and optional formatting as a React component (with memo, TypeScript, and quote configuration). The frontend is built with React 18, Next.js 15, TanStack Query, Tailwind CSS, and react-ace for the editor. The codebase is well-structured, with clean separation of concerns between UI components, state management (React Context), and pure conversion logic. Tests cover the core conversion function. The app is designed for single-user, client-side usage with a simple API endpoint.
Kurrier is a self-hosted unified communication platform designed to aggregate email, calendars, contacts, and cloud storage into a single workspace. It functions as a multi-tenant SaaS application, supporting various protocols including IMAP/SMTP for email, CalDAV/CardDAV for scheduling and contacts, and WebDAV/S3 for file storage. The system emphasizes data ownership and privacy, allowing users to manage multiple email identities and storage volumes within isolated workspaces. The architecture follows a monorepo structure with a clear separation of concerns. The frontend (`apps/web`) is built on Next.js 16 with React 19, utilizing Server Actions for business logic and a Nitro-based worker (`apps/worker`) for background jobs like IMAP sync and email delivery. Data persistence is handled by PostgreSQL with Drizzle ORM, while Redis manages job queues and caching. The platform integrates with external providers via a strategy pattern, supporting services like Google Workspace, AWS SES, and various SMTP providers. Key components include a robust provider abstraction layer for pluggable email and storage backends, a centralized encryption vault for sensitive credentials, and a standardized Server Action wrapper for consistent error handling. The codebase demonstrates high craftsmanship in its sync logic and multi-tenancy implementation, though it carries some technical debt related to type safety and complex DAV/IMAP state management.
Mintlify's @mintlify/mdx is a thin TypeScript library that wraps next-mdx-remote-client to provide a simplified MDX rendering experience for Next.js applications. It adds rich syntax highlighting via Shiki, including support for language-specific highlighting, line highlights, diffs, and twoslash-powered TypeScript type hover popups. The library exposes three main APIs: `serialize` (server-side compilation), `MDXClient` (client-side rendering), and `MDXRemote` (RSC component that combines both). It includes pre-configured remark plugins (GFM, Smartypants, Math) and rehype plugins (Katex, syntax highlighting) with the ability to extend with custom plugins. The package also provides UI components for twoslash popups and a Shiki transformer for shell placeholder highlighting. The codebase is well-structured with clear separation of concerns, though test coverage is limited to the shell placeholder transformer.
This is a pnpm monorepo whose dominant application is apps/portal, a Next.js + tRPC + Prisma community platform for tech interview preparation. It delivers three interactive products: an anonymous salary/offer comparison tool with percentile analysis and currency conversion, a resume review system with PDF uploads, comments, stars and gamified badges, and a crowdsourced interview question bank with voting, full-text search and list curation. Authentication is NextAuth via GitHub, file uploads use Supabase storage, and all application logic is exposed through a tRPC router tree backed by PostgreSQL. apps/website is a Docusaurus static site that publishes the interview handbook content, blog posts and practice lists such as Grind 75; it is content-delivery rather than dynamic. The repo has some data/research flavour because it ships Prisma seed datasets (countries, states, companies, salary XLSX) and offer-analysis logic, but those are reference data and application features, not an analytical pipeline. The engineering follows T3 conventions: Zod-validated routers, React Hook Form, a custom UI kit and shared config packages. Strong reusable pieces include an accessible typeahead, a compound radio list, a currency exchange utility and an optimistic vote hook. Weaknesses are long and repetitive tRPC routers (especially the offers-profile update mutation), duplicated voting mutations across routers, inconsistent use of protected routers, hardcoded assumptions in seeds, and no visible automated test suite. The repository is actively maintained with recent commits and CI workflows, but it has medium technical drift: the portal pins older major versions of Next.js, tRPC and React Query, and it depends on a custom vite-plus toolchain. The Prisma schema has many 2022-era migrations and substantial complexity. It is best used as a reference architecture for building community-driven interview-prep features and as a source of isolated UI/utility patterns rather than as a drop-in application.
HeadlessX is a self-hosted web scraping and browser automation platform with anti-detection capabilities. It provides a Next.js 16 dashboard, an Express 5 API with Prisma/PostgreSQL, a BullMQ queue worker, and a published CLI. The core value proposition is anti-detection scraping via the headfox-js package (Camoufox-compatible Firefox bundles), fingerprint generation, CAPTCHA solving, and LLM-friendly markdown output. The system supports multiple operators: website scraping/crawling/mapping, Google AI Search, Tavily search, Exa search, and YouTube extraction. It includes sidecar services for HTML-to-Markdown (Go) and YouTube extraction (Python FastAPI). The architecture is a monorepo with Nx orchestration, Docker Compose deployment, and an MCP endpoint for AI assistant integration. External dependencies include Redis, Postgres, Playwright, MaxMind GeoIP, and external APIs (Tavily, Exa, Google). The codebase is well-structured with clear separation of concerns, modern TypeScript, and reusable patterns in both frontend and backend.
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.
This is a full-stack todo application built with Next.js 15, React 19, and TypeScript. It features AI-powered natural language todo input, reminders via email, workspace collaboration, and a subscription model. The application uses a PostgreSQL database with Drizzle ORM for data storage, better-auth for authentication, and Resend for sending emails. The frontend is styled with Tailwind CSS and uses shadcn-ui components. Key architectural elements include API routes for todos, reminders, workspaces, and user settings, along with a cron job for processing reminders. The codebase demonstrates good engineering practices with reusable utilities for timezone handling, email sending, toast notifications, and a command palette.
Carbon is a Next.js web application for creating and sharing beautiful images of source code. It provides a CodeMirror-based editor with extensive customization: syntax themes (30+ built-in), fonts (13 options), background colors/images (including Unsplash integration), window themes, padding, shadows, and line numbers. Users can import code via paste, file drop, or GitHub Gist URLs. Export options include PNG/SVG at multiple resolutions, clipboard copy, Twitter sharing, Imgur upload, and embed codes. Saved snippets persist via Firebase authentication with automatic Twitter/Slack unfurling. The app runs as a PWA for offline use. Architecture is a Next.js 12 monolith with API routes for oEmbed and server-side image generation (Puppeteer/Chrome AWS Lambda). State is managed through URL parameters (with base64-encoded complex state), localStorage, and React state. The codebase demonstrates solid engineering with custom CodeMirror modes for 6 languages, a sophisticated theme builder, and well-encapsulated UI components.
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.
EldoraUI is a modern, open-source React/Next.js component library distributed via a shadcn/ui-style registry. It provides over 40 animated UI components, including text animations, device mockups, 3D globes, interactive cards, and full-page blocks (headers, CTAs, pricing tables, testimonials). The repository is a monorepo with a Next.js documentation site that showcases each component with live previews, code examples, and MDX-based guides. Components are designed for copy-paste installation via CLI (`npx shadcn add`) and rely on Tailwind CSS, Motion (Framer Motion), Three.js, and Radix UI primitives. The library targets 'design engineers' building modern, animated interfaces. While the codebase is actively maintained and demonstrates strong craftsmanship in several complex components (e.g., Browser, CobeGlobe, TextAnimate), it exhibits some technical drift: pervasive typos in block names ('testimonal'), fragile DOM coupling in the table of contents, outdated TODO comments, and potential registry duplication. Overall, it is a solid, production-quality component library with high reusability.
Ganymede is a media management system focused on archiving and managing Twitch content (VODs, live streams, clips) with a Go backend using Ent ORM, Echo framework, and River job queue, and a Next.js frontend with Mantine UI. It provides video archiving, playlist management, user authentication, API keys, and administrative interfaces. The system processes media through a pipeline involving yt-dlp, FFmpeg, and TwitchDownloaderCLI, storing metadata in PostgreSQL and files on disk. It includes features like chat archiving, chapter detection, muted segment detection, and rule-based playlist automation.
OpenUBA is a full-stack User and Entity Behavior Analytics (UEBA) platform for security anomaly detection, investigation, and response. It provides a complete lifecycle for ML models (install, train, infer, version, share) via a pluggable registry supporting OpenUBA Hub, GitHub, HuggingFace, and local filesystem. The platform includes a rule engine with a visual DAG-based builder (ReactFlow), case management, real-time dashboards, multi-backend visualization (matplotlib, plotly, bokeh, etc.), and an LLM-powered assistant. The backend is built on FastAPI with PostgreSQL, SQLAlchemy, JWT/RBAC authentication, and optional Spark/Elasticsearch integrations. The frontend uses Next.js 14 (App Router), Apollo Client (GraphQL), shadcn/ui, and Recharts. A Python SDK enables programmatic access to all features. The system runs on Kubernetes with a Kopf operator managing custom resources (UBATraining, UBAInference, UBAWorkspace, UBAPipeline) for containerized model execution in isolated Docker images per ML framework (sklearn, PyTorch, TensorFlow, NetworkX). While primarily an application, it includes research-oriented components like experiment tracking, feature store, and Jupyter notebook workspaces, making it a hybrid tool for both security operations and data science workflows.
ResumeLM is an AI-powered resume builder application built with Next.js 15, React 19, and TypeScript. It provides a SaaS platform for creating, editing, and optimizing resumes for tech job applications, featuring AI-assisted content generation, resume tailoring for specific job applications, ATS optimization, and cover letter generation. The application uses Supabase for authentication and data storage, Stripe for subscription management, and integrates with multiple AI providers (OpenAI, Anthropic, Google, etc.) via OpenRouter or direct API keys. The architecture follows a modular structure with separate concerns for authentication, dashboard, resume editor, admin panel, and settings, leveraging server actions for data mutations and a robust AI abstraction layer for model selection and usage tracking.
FOSSLight Hub is an open-source compliance and security platform that manages open source software (OSS) inventories, licenses, vulnerabilities, and compliance workflows. It provides a web interface for OSS identification, license management, vulnerability tracking, self-check audits, SBOM generation (SPDX, CycloneDX), and supply chain management. The system is architected as a Spring Boot backend with MyBatis persistence, JWT-based authentication, and REST APIs (v2 and lite). It features a dual frontend: a legacy server-rendered UI using jqGrid and Thymeleaf, and a modern Next.js 'lite' client built with React, Tailwind CSS, and Recoil. Key entry points include API controllers (e.g., LiteOssController, ApiOssV2Controller), service layer components, and scheduled tasks for NVD/OSV vulnerability synchronization. External dependencies include MariaDB, a mail server, external license APIs (GitHub, ClearlyDefined), NVD/OSV vulnerability feeds, and the FOSSLight Scanner. The platform is actively developed with continuous releases and a growing feature set, though it carries some technical debt from legacy frontend libraries and enterprise-specific hardcoding.
The Eleven is a full-stack prediction market platform for live football events, deployed on X Layer (chain 196) using Uniswap v4 hooks. The system is powered by 11 autonomous AI agent personas (e.g., IlRegista, IlBomber) that monitor live match data from API-Football or Football-Data.org, evaluate binary propositions (e.g., clean sheet, corners over, next goal), and create markets via a commit-reveal mechanism. Markets are implemented as PropMarketHook contracts deployed through a factory using CREATE2. Users can stake on outcomes gaslessly via EIP-3009 signatures relayed by the x402-facilitator service, and markets are resolved deterministically after match events. The monorepo (pnpm workspaces) contains five packages: `contracts` (Foundry-based Solidity), `agent` (TypeScript runtime with Prisma persistence), `web` (Next.js 15 dApp with RainbowKit, Three.js 3D pitch, and Farcaster Frame v2 integration), `x402-facilitator` (standalone npm package for gasless USDT0 transfers), and `mcp-server` (MCP bridge exposing market tools to AI agents). The architecture is event-driven: the agent's TickLoop polls matches, emits deltas, and triggers market creation/reveal/resolution skills. The web app provides a full dApp experience including a markets list, agent detail views, a status page, and documentation. Key reusable patterns include a generic proposition template system with ABI encoding/decoding, a CREATE2 salt miner for hook deployment, a commit-reveal skill with cryptographic safety, and a gasless staking flow that can be extracted for any EIP-3009-compatible token. The codebase is well-tested (172 web tests, 229 agent tests, 85 contract tests with 100% coverage on the hook) and actively maintained with CI/CD via GitHub Actions and Railway deployment.
Readme.so is a Next.js web application that provides an online editor for generating README.md files. Users can select from predefined sections, edit their content using a Monaco Editor (desktop) or textarea (mobile), reorder sections via drag-and-drop, and download the final markdown. The application state is managed in React and persisted to localStorage with debounced saving. The frontend uses TailwindCSS for styling and integrates libraries like dnd-kit for drag-and-drop and react-markdown for preview. The app is statically exported and deployed via GitHub Pages.
This is a modern Next.js 16 starter template using the App Router, designed to jumpstart full-stack web applications. It provides a production-ready monolith with authentication via NextAuth (GitHub provider), payment processing through Stripe subscriptions, and a PostgreSQL database layer using Drizzle ORM and Neon serverless. The application includes internationalization (i18n) with next-intl, dark mode theming, custom fonts, and standard SEO features like sitemap and robots.txt. The codebase demonstrates disciplined engineering with TypeScript, Zod environment validation, Biome linting, Husky git hooks, and both unit (Jest) and end-to-end (Playwright) testing. While functionally complete and well-structured, it is fundamentally a starter kit composed of framework boilerplate and standard integrations, without innovative or deeply reusable custom abstractions beyond what is typical for such templates.
Sequelize UI is a browser-based code generation tool that allows users to visually design database schemas and generate a complete Node.js/TypeScript project using the Sequelize ORM. The application is built as a Next.js static export (client-side only) with React and Tailwind CSS, storing schemas and user preferences in localStorage. It provides a rich UI for defining models, fields, and associations, and then generates a full project scaffold including model classes, migrations, configuration files, and a minimal server. The codebase is well-structured with a clear separation between core domain logic (schema types, file tree, code generation utilities), a pluggable framework interface for code generation, and a comprehensive set of UI components. It includes extensive unit tests and end-to-end migration tests against real databases. While primarily an application, it also contains example schemas and snapshot-based test fixtures that serve as data for verifying code generation correctness.
Exosphere is a reliability runtime for AI agents, providing a state-based workflow orchestration platform. The system consists of three main components: a Python SDK (`exospherehost`) for defining nodes and execution graphs, a FastAPI state-manager backend that handles state transitions, retries, cron triggers, and secret encryption, and a Next.js dashboard for monitoring and managing workflows. The architecture follows a microservices-like pattern within a monorepo, with the SDK acting as a client library and the state-manager as the central orchestration engine. Key capabilities include dynamic graph execution, infinite parallel fanout/unite patterns, configurable retry policies (exponential, linear, fixed with jitter), signal-based state pruning and re-queuing, and a graph-level key-value store. The system relies on MongoDB for persistence and uses Beanie ODM. The codebase demonstrates solid engineering practices with well-structured controllers, middleware for request ID propagation and exception handling, and a comprehensive test suite, though some tests are incomplete or skipped. The project is in early beta (v0.0.3b1) with active development and monthly releases.