Neko Master is a production-grade traffic analytics platform for Clash/Mihomo and Surge proxy gateways. It collects real-time connection data via WebSocket or HTTP polling, aggregates it in SQLite (always) and optionally ClickHouse, and serves a rich dashboard built with Next.js 16, React 19, and Tailwind CSS. The system supports both direct collector-to-gateway mode and a Go-based agent mode for remote or isolated networks. The backend is a Fastify 5 server with WebSocket push, repository-patterned database access, in-memory realtime store, and health-aware dual-write routing. The frontend provides per-domain, per-IP, per-proxy, per-rule, per-country, and per-device statistics with time-range filtering, pagination, charts, and interactive visualizations including a DAG rule-chain flow. The project is a monorepo with pnpm and Turborepo, including a shared TypeScript types package and extensive CI/CD. While primarily an application, it has strong data-engineering aspects: batch buffering, time-series aggregation, GeoIP enrichment, and ClickHouse integration for large-scale deployments.
The intl-t repository is a fully-typed, object-based internationalization (i18n) library for TypeScript, React, and Next.js applications. It provides a translation engine that allows developers to access nested translation objects via property traversal (e.g., `t.page.section.title`) with full TypeScript safety. The library supports ICU message formatting for plurals, selections, and variable injection, including the ability to embed React components within translated strings. Architecturally, the library is structured as a monorepo with separate packages for core logic, framework integrations (React and Next.js), utilities, formatting, type declaration generation, and locale definitions. The core package is framework-agnostic and uses proxies to enable lazy loading of locale-specific translation trees for code splitting. Framework-specific packages provide hooks, context providers, middleware, and routing helpers tailored to React and Next.js, including Next.js App Router support for server components and static rendering. The library emphasizes zero external dependencies, modern JavaScript features, and type safety. It includes a CLI tool for generating TypeScript definitions from JSON translation files, ensuring that translation keys are fully typed and IDE-friendly. The Next.js integration offers automatic locale detection, URL localization, and caching mechanisms optimized for server-side rendering and edge environments.
A polished documentation website for Ink, a Layer 2 blockchain (OP Stack) built by Kraken. The site is implemented with Next.js 15 and Nextra 3 using the Pages Router, with MDX-authored content covering network setup, contract deployment, bridges, faucets, and ecosystem tools. It features a highly custom UI: a WebGL interactive ink animation on the homepage, a theme toggle using View Transitions API, a full-text search with a custom modal, and a 'copy page as Markdown' feature designed for LLM consumption. The codebase is meticulously crafted with extensive comments explaining design rationales (e.g., Tailwind token mapping, font loading, shader adaptations). Build tooling includes ESLint, Prettier, CSpell, remark linting, and a GitHub Actions CI/CD pipeline with Vercel deployments. No tests are present, but the code is clean, well-organized, and demonstrates strong frontend engineering skills.
This repository is a reference architecture for building production-ready React applications, implemented as a monorepo containing three distinct application templates: Next.js App Router, Next.js Pages Router, and Vite. It serves as a comprehensive guide for scalable project structure, demonstrating best practices in state management (Zustand for client state, TanStack Query for server state), testing (Vitest, Playwright, MSW), and security (JWT, RBAC). The codebase prioritizes strict TypeScript usage, unidirectional data flow, and feature-based colocation. The system functions as a boilerplate and educational tool rather than a standalone product. It provides a complete development environment including UI component libraries (Radix UI + Tailwind), form handling (React Hook Form + Zod), and API abstraction layers. The architecture is designed to be copy-pasted into new projects, with the three app implementations serving as comparative examples for different React rendering strategies. Key operational patterns include a centralized API client with Axios interceptors for authentication and error handling, a mock backend service using MSW and @mswjs/data for local development and testing, and a robust authorization system that enforces role-based access control at the component and route levels. The codebase is heavily instrumented with tests and documentation to ensure maintainability and adherence to the defined architectural standards.
Hey is a decentralized social media application built on the Lens Protocol. It provides a full-featured frontend for users to interact with the Lens blockchain, including wallet authentication, post creation and engagement (likes, comments, reposts, collects, tips), group management, notifications, and profile customization. The application uses React 19 with Vite for bundling, TypeScript for type safety, and Tailwind CSS for styling. State management combines Apollo Client for GraphQL data persistence with Zustand stores for client-side state (account, notifications, preferences). Key architectural patterns include optimistic UI updates for blockchain transactions, portal-based modals, and custom hooks for transaction handling and media uploads. The codebase demonstrates strong modularity with reusable UI components and clear separation between UI, state management, and data layers.
A collection of reusable React UI components built on top of shadcn/ui (Radix UI + Tailwind CSS), along with a Next.js documentation site. The project provides 15+ advanced components not included in the base shadcn/ui library, such as a datetime picker with keyboard navigation, a multiple-selector with async search and grouping, an interactive stepper, infinite scroll, and others. All components are designed to be copied and pasted directly into user projects and are fully customizable. The documentation site demonstrates each component with live previews, usage examples, and prop tables. The codebase is well-structured, modern (Next.js 15, React 19, TypeScript), and actively maintained.
DeerFlow is an open-source, full-stack AI agent harness built on LangGraph/LangChain, providing a production-ready platform for building, deploying, and managing conversational AI agents. The system features a Python/FastAPI backend with a modular architecture that includes agent orchestration, long-term memory (DeerMem), sandboxed code execution, a skill system with static analysis (SkillScan), MCP (Model Context Protocol) tool integration, multi-channel IM support (Telegram, Slack, Discord, Lark, etc.), scheduled tasks, subagent batches, and a comprehensive authorization framework. The frontend is a Next.js/React workspace with real-time streaming, artifact management, and a terminal UI (TUI) for headless operation. The architecture follows a gateway pattern: the FastAPI gateway handles authentication (JWT, OAuth, PATs), routing, and SSE streaming, while the core harness package (`deerflow`) implements agent logic, middleware chains, and runtime components. Persistence uses SQLAlchemy with Alembic migrations, supporting both SQLite and PostgreSQL. The system is highly extensible via a Python extension API (`deerflow-extension-api`) and a plugin system for frontend surfaces. Deployment supports Docker Compose and Kubernetes (Helm chart). While primarily an application, DeerFlow includes research-oriented components such as memory eviction benchmarks, context snapshot evaluations, and tracing integrations (LangSmith, Langfuse, OpenTelemetry). The codebase demonstrates strong engineering practices with extensive unit tests, blocking IO regression tests, and contract tests, though its large feature surface and many configuration options introduce complexity and potential for drift between environments.
Umami is a self-hosted, open-source web analytics platform that serves as a privacy-focused alternative to Google Analytics. The system tracks pageviews, sessions, events, funnels, journeys, retention, attribution, revenue, and performance metrics (Core Web Vitals) through a dual-database architecture using PostgreSQL (via Prisma ORM) and ClickHouse for analytics queries. It provides real-time dashboards, session replay, heatmaps, and a customizable board/dashboard system with shareable views. The application is built with Next.js (App Router) and React, using TypeScript throughout. It features a comprehensive REST API with auto-generated OpenAPI contracts, a generated TypeScript API client package, and an MCP (Model Context Protocol) server for AI/LLM integration. Authentication supports API keys, JWT, two-factor authentication (TOTP with backup codes), and team-based multi-tenancy with role-based permissions. Key architectural patterns include a dual-database query dispatcher (`runQuery`) that selects between Prisma and ClickHouse implementations for every analytics query, a centralized request parsing and validation layer using Zod schemas, and a hook-based React data layer with TanStack Query for caching and invalidation. The codebase also includes comprehensive test coverage with Vitest unit tests, Playwright API tests, and E2E tests, plus a coverage oracle that enforces every API endpoint is documented in the OpenAPI spec.
Vane is a privacy-focused, self-hosted AI answering engine that combines web search with large language models (LLMs) to produce cited, real-time answers. The system is built as a Next.js 16 application with a React frontend and a set of API routes that orchestrate a multi-step research pipeline: classification of user queries, parallel execution of search actions (web, academic, social, scrape, upload-based retrieval), widget generation (weather, stock, calculation), and final answer synthesis via an LLM. It supports multiple LLM providers (OpenAI, Anthropic, Gemini, Groq, Ollama, LM Studio, etc.) through a clean provider abstraction, and stores chat history in a local SQLite database managed by Drizzle ORM. The architecture is a monolith with clear separation of concerns: a model registry for LLM/embedding providers, a search agent with an action registry pattern, a widget system for structured data display, and a configuration manager that persists user settings to a JSON file. The frontend uses client-side streaming via EventSource to display research progress and final answers in real-time. Deployment is Docker-first, bundling a SearXNG instance for privacy-respecting web search, with multi-arch images and a slim variant. Key entry points include the main chat page (`/`), individual chat pages (`/c/[chatId]`), a news discovery page (`/discover`), and a library for uploaded documents. The primary API endpoints are `POST /api/chat` (streaming chat), `POST /api/search` (non-streaming search), and various CRUD routes for providers, config, and uploads. External dependencies include SearXNG (self-hosted), Open-Meteo (weather), Yahoo Finance (stocks), and the LLM provider APIs. The codebase demonstrates strong engineering practices with well-defined abstractions, a modular agent architecture, and a focus on privacy and local-first operation. It is actively maintained with a large community, though some areas (custom migration runner, large chat hook) show room for refinement.
GPT Researcher is an autonomous research agent that produces comprehensive, cited reports from web and local sources. In production, it operates as a full-stack web application: a FastAPI backend (main.py, backend/server/app.py) exposes REST and WebSocket endpoints for initiating research, streaming progress, and retrieving reports. A Next.js frontend (frontend/nextjs/) provides the user interface, and a lightweight vanilla JS frontend (frontend/scripts.js) is also available. The core Python package (gpt_researcher/) can be used programmatically via the GPTResearcher class or as a CLI tool (cli.py). Architecture follows a planner-executor pattern: given a research task, the system generates sub-queries, executes them in parallel across 20+ search engine wrappers (Tavily, Google, Bing, Brave, DuckDuckGo, ArXiv, PubMed, etc.), scrapes the resulting URLs using multiple backends (BeautifulSoup, browser automation, PyMuPDF, FireCrawl), filters and compresses the context using embedding or keyword-based methods, and finally generates a structured report with citations. The system supports multiple report types (summary, detailed, deep research) and multi-agent orchestrations (LangGraph, AG2) for more complex workflows. Key data flows: user query to sub-query generation (LLM) to parallel search across configured retrievers to URL scraping to context compression to source curation to report generation (LLM) to output as markdown/PDF/DOCX. Real-time progress is streamed via WebSocket. External dependencies include LLM providers (OpenAI, Anthropic, Google, Ollama, etc.), search APIs (Tavily, SerpAPI, etc.), and optional services (MCP servers, vector stores, image generation APIs). The codebase demonstrates strong engineering practices: extensive test suite (200+ tests), plugin architecture for retrievers, configurable context filters, cost tracking, and evaluation frameworks for quality and hallucination. However, it carries maintenance risks from its large dependency surface, multiple parallel agent implementations, and reliance on rapidly-changing external APIs.
FastGPT is a production-grade AI Agent building platform that enables users to create, deploy, and manage AI applications through a visual workflow editor. The system provides a full-stack solution including a Next.js frontend with Chakra UI, a comprehensive REST API layer, and a modular backend architecture. Core capabilities include a workflow engine with agent loops, tool calling, dataset (knowledge base) management with vector search, sandboxed code execution (JS/Python), multi-tenant team management with fine-grained permissions, billing and usage tracking, and integration with external platforms (WeChat, Feishu, Dingtalk, MCP). The platform is designed for both open-source community and commercial deployments, with feature gating via license activation. The codebase is organized as a monorepo with clear separation of concerns: shared types and constants in `packages/global`, business logic in `packages/service`, UI components in `packages/web`, data access layer in `packages/dal`, and separate services for sandbox execution and volume management. It relies on MongoDB, Redis, BullMQ, S3-compatible storage, and multiple vector database backends (PG, Milvus, OceanBase, SeekDB, OpenGauss). The architecture is predominantly monolithic with the main application as a Next.js app, but sandbox and volume manager run as independent services, indicating a hybrid monolith/microservices pattern.
The Vercel AI SDK is a comprehensive TypeScript monorepo providing a unified, provider-agnostic interface for building AI-powered applications. It abstracts interactions with dozens of LLM providers (OpenAI, Anthropic, Google, Mistral, Cohere, xAI, etc.) behind a consistent API for text generation, streaming, structured output, image/video generation, speech, transcription, embeddings, and tool calling. The architecture is modular: a core `ai` package exposes high-level functions (`generateText`, `streamText`, `generateImage`, `embed`, `transcribe`, `ToolLoopAgent`, `HarnessAgent`) and a middleware system, while separate `@ai-sdk/provider` packages define versioned model interfaces (v2–v4) and `@ai-sdk/provider-utils` provides shared utilities for schema validation, error handling, and HTTP. Each provider lives in its own package (e.g., `@ai-sdk/openai`, `@ai-sdk/anthropic`) with model-specific options and error handlers. The SDK also includes a harness/agent framework (`@ai-sdk/harness`) for integrating external coding agents (Claude Code, Codex, Cursor, GitHub Copilot, etc.) with sandboxed execution and workflow orchestration, plus UI integrations for React, Vue, Svelte, and Angular. Key data flows involve constructing prompts, calling provider APIs via fetch, streaming responses as Server-Sent Events or ReadableStreams, and transforming messages between model and UI formats. External dependencies include provider API endpoints, Zod for schema validation, and OpenTelemetry for telemetry.
Dub is a commercial open-core link attribution, short-link, and affiliate/partner marketing platform. In production it runs as a multi-tenant Next.js application with three separate front-ends: a customer dashboard (app.dub.co), a partner portal (partners.dub.co), and an admin console (admin.dub.co). The core operational surface includes short-link creation and redirection, link and QR management, click/lead/sale tracking, webhooks, API tokens, domain management, and the full partner-program lifecycle: partner onboarding, applications, commissions, rewards, discount codes, payouts, bounties, groups, campaigns, fraud/risk rules, and identity verification. Analytics are product telemetry consumed through Tinybird, not a research/data-science pipeline, so the repository is best classified as an application despite its high data-event volume. The architecture is a pnpm/Turborepo monorepo centered on apps/web and shared packages such as @dub/ui, @dub/utils, @dub/email, @dub/embed-core, @dub/embed-react, a CLI, and HubSpot/Stripe app integrations. The backend uses Next.js App Router API routes and server actions, Prisma on MySQL/PlanetScale for transactional state, Upstash Redis/QStash for caching, rate limiting, and background job orchestration, and Tinybird for real-time analytics. Authentication combines NextAuth sessions, BoxyHQ SAML, workspace-scoped API tokens, and dedicated auth wrappers for workspace, partner, admin, and cron contexts. External systems—Stripe, PayPal, Shopify, HubSpot, Slack, Google Ads, Veriff, and many importers—are connected through webhooks, OAuth callbacks, and QStash-driven cron jobs. Primary entry points are the REST API routes under /api and /(ee)/api, attribution endpoints like /api/track/click and /api/track/sale, roughly one hundred scheduled /api/cron jobs, and a large server-action layer for dashboard mutations. Data flows from link redirects and conversion events into Tinybird, while workspace, program, partner, commission, and payout state persists in the relational database. Payout aggregation, commission generation, CSV/API exports, and fraud detection are executed as batched background jobs. The codebase is actively maintained but has grown organically. It is generally well-structured and typed, with strong use of Zod schemas, safe server actions, and reusable auth wrappers. However, it also carries legacy `(old)/projects` routes, deprecated API fields, duplicated importers and postback adapters, one-off migration scripts, and several hardcoded environment/program IDs, producing a medium level of technical drift.
Presenton is an AI-powered presentation generation platform that allows users to create presentations from text prompts, document uploads, or template selections using multiple LLM and image providers. The application features a monorepo architecture with a FastAPI backend, Next.js frontend, and Electron desktop client. Key capabilities include AI-driven outline and slide generation, visual slide editing, document processing via LiteParse, export to PPTX/PDF formats, chat-based presentation refinement, and community template sharing. The system demonstrates strong engineering practices with modular service architecture, secure handling of sensitive data, and reusable components for configuration management, document conversion, and provider abstraction.
Morphic is a self-hostable AI answer engine and chat application. In production it streams LLM research results into a rich chat UI: it runs an agentic researcher loop that calls search tools (SearXNG, Tavily, Brave, Exa, Firecrawl), fetches and extracts page content, allocates numbered citations, and emits a JSONL-based 'spec' stream that a generative UI renderer turns into structured cards, grids, headings, buttons, and inline images. Alongside the core chat it ships usage-budget metering, guest/anonymous chat, file uploads to S3-compatible storage, a notes/library feature, feedback collection, model discovery and selection, chat sharing, admin invitations, and an analytics relay that forwards scrubbed PostHog events without exposing authentication tokens.
This repository is a Next.js SEO library that provides React components for generating JSON-LD structured data (Schema.org) to enhance search engine optimization. The library offers over 50 specialized components for various Schema.org types (Article, Recipe, Product, Event, etc.) and a core JsonLdScript component for rendering JSON-LD tags. It includes utility processors for normalizing flexible input data into Schema.org-compliant formats and emphasizes security through XSS prevention in JSON-LD stringification. The architecture follows a modular component pattern with strong TypeScript typing, comprehensive testing (unit and E2E), and support for both Pages Router and App Router in Next.js. The library is actively maintained with recent updates, a changeset-based versioning system, and extensive documentation.
Answer Overflow is a full-stack platform that indexes Discord community conversations and makes them publicly searchable via the web, Google, and AI assistants. It consists of a Discord bot (built with discord.js and Effect-TS) that syncs messages, threads, and channels into a Convex backend, and a Next.js web application that renders these conversations as rich, SEO-optimized pages with search, thread cards, user profiles, and an AI-powered chat assistant. The bot handles features like solution marking, auto-threading, forum guidelines consent, and sitemap generation. The web app supports multiple tenants (custom domains) and includes a dashboard for server management, analytics, and subscription billing via Stripe. The architecture is a monorepo with shared packages for the database layer (typed Convex operations via Effect Schema), a custom React reconciler for Discord messages (Reacord), an AI agent component for streaming chat threads with vector search, and observability integrations (OpenTelemetry, Sentry, PostHog, Axiom). Effect-TS is used pervasively for dependency injection, error handling, and structured concurrency across the bot and web backend. The system is highly modular, testable (dedicated convex-test package and Effect-based test layers), and demonstrates advanced patterns in type-safe database access, real-time streaming, and Discord bot engineering.
This repository is the frontend client suite for daily.dev, a developer-focused content and community platform. It is structured as a monorepo using pnpm workspaces, containing three primary packages: `packages/webapp` (Next.js application), `packages/extension` (browser extension), and `packages/shared` (shared UI components, hooks, and GraphQL logic). The system aggregates developer content, manages user profiles and communities (Squads), handles monetization (Plus subscriptions, Cores), and provides career opportunities. Key entry points include the Next.js pages router in `packages/webapp/pages` and the extension background/content scripts in `packages/extension/src`. Data flows primarily through GraphQL queries to an external `daily-api` service, with client-side state managed via TanStack Query and Jotai. External dependencies include Paddle for payments, GrowthBook for feature flags, and Cloudinary for media. The architecture emphasizes code sharing and consistency across platforms, with `packages/shared` serving as the core library for UI primitives, business logic, and API abstractions. The webapp utilizes Next.js for server-side rendering and static generation, while the extension leverages web extension APIs for browser integration. The codebase demonstrates strong engineering practices with strict TypeScript typing, comprehensive testing (Jest, Playwright), and a robust design system. However, the heavy reliance on feature flags and the use of a non-LTS Node version (v24.18) introduce some technical debt and potential compatibility risks. The platform supports multiple user journeys including content consumption, community engagement, and monetization. It features a sophisticated feed system with infinite scrolling, personalized recommendations, and content preferences. The extension provides companion features like new tab pages and frame embedding, requiring careful synchronization with the web app's authentication and permission models. The codebase is well-structured but complex, with deep nesting of contexts and modals that could impact maintainability if not carefully managed.
The application is a subscription management platform built with Next.js, Prisma, and Better Auth. It allows users to track recurring payments, set up multi-channel notifications (email, push, webhook, ntfy, Discord, Slack), view financial reports, and manage account settings. The system supports multi-currency, timezone awareness, and can be self-hosted via Docker or used as a SaaS with Paddle for payment processing. Architecturally, it follows a client-server model with Next.js App Router, separating concerns into components, lib utilities, API routes, and server actions. The data layer uses Prisma ORM with PostgreSQL, and authentication is handled via Better Auth with multiple providers. Background notification processing is implemented through a cron job hitting the `/api/cron/` endpoint. Key reusable components include a multi-channel notification system, internationalization hooks for language management, and a timezone-aware datetime picker. These components demonstrate encapsulation, minimal internal dependencies, and patterns that could be extracted for use in other projects.
This is the monorepo for Supabase, the open-source Firebase alternative built on PostgreSQL. The repository contains the complete Supabase platform including: (1) Studio - a Next.js-based management dashboard for database, auth, storage, and edge functions, (2) Docs - a Next.js documentation site with MDX content and search, (3) WWW - the main marketing website, (4) UI packages - shared React component libraries built on Radix UI and Tailwind CSS, (5) pg-meta - a TypeScript library for PostgreSQL metadata introspection, (6) ai-commands - AI/LLM integration tools, (7) Docker configurations for self-hosted deployments, and (8) numerous example applications demonstrating Supabase integrations. The architecture is a pnpm monorepo using Turbo for build orchestration. The codebase demonstrates sophisticated engineering patterns including: a custom SQL query builder with branded types for safe SQL composition (pg-meta), a comprehensive component library with design system tokens, real-time features via WebSockets, row-level security policy management, and a multi-tenant organization/project structure. The repository is actively maintained with recent commits and has strong community engagement (105k+ stars).