thehardwarestore.ai

Reactive Resume is a full-stack, open-source resume builder and job application tracker. It provides a rich web interface for creating, editing, and managing resumes with real-time preview, multi-format export (PDF, DOCX, Markdown, JSON), and ATS compatibility analysis. The system includes an AI-powered assistant for resume tailoring and cover letter drafting, an MCP server for LLM integration, and a semantic CSS stylesheet system for fine-grained PDF styling. The architecture is a monorepo with a React frontend (TanStack Router, Zustand, Lingui i18n), a Node.js API layer (oRPC, Drizzle ORM, PostgreSQL), and dedicated packages for PDF generation, DOCX export, authentication, and UI components. Key entry points include the resume builder interface, public resume sharing pages, an AI agent chat, and a job application board with stage tracking and CSV import/export. External dependencies include PostgreSQL, Redis, AI provider APIs (OpenAI, Anthropic, Gemini), and cloud storage (Vercel Blob or S3). The codebase is well-structured with extensive unit and integration tests, a reusable UI component library, and a modular package architecture that supports self-hosting via Docker or Vercel.

Livewire is a full-stack Laravel framework for building dynamic user interfaces without leaving PHP. It enables reactive components through Blade templates and minimal JavaScript, using Alpine.js for frontend interactivity. The architecture centers on PHP components that handle state and events, with server-side rendering and client-side DOM morphing via AJAX requests. Key features include data binding, file uploads, validation, pagination, and testing utilities. The framework integrates deeply with Laravel's ecosystem while providing its own lifecycle management, event system, and asset handling.

This is a full-stack web application for performing open-source intelligence (OSINT) analysis on any website. The system runs 40+ checks encompassing DNS, SSL/TLS, HTTP security headers, mail configuration (SPF/DKIM/DMARC), social media presence, trackers, blocklists, threat feeds, carbon footprint, tech stack, and more. The frontend is built with Astro and React, featuring a reactive results view with a masonry grid, advisory panel, and progress bar. The backend is a Node.js Express server (also serverless-friendly) with individual API handlers per check, wrapped in a common middleware layer for error handling, rate limiting, and skip logic. The application is designed for self-hosting and supports deployment to Vercel, Netlify, Docker, Render, and Fly.io. It relies on many external sources (DNS, WHOIS, SSL Labs, HIBP, Google Safe Browsing, Shodan, etc.) and uses Puppeteer/Chromium for headless browser tasks. The codebase demonstrates a clean modular architecture, reusable UI components, and a novel analysis rule system that transforms raw data into structured findings with severity levels.

Loco is a batteries-included, Rails-inspired web framework for Rust, built on Axum, Sea-ORM, and Tera. It provides a full-stack MVC architecture with built-in authentication (JWT, API tokens, magic links), background job processing (Redis, Postgres, SQLite queues), mailer (SMTP/stub), caching (in-memory, Redis), storage (local, S3, Azure, GCS), cron scheduling, and code generation via CLI. The framework emphasizes convention over configuration and includes an interactive project generator (`loco new`) that scaffolds complete SaaS or API apps with optional React frontend. It also ships an agent skill system (`.claude/skills/loco/`) for AI-assisted development, alongside a dedicated evaluation harness for measuring code-generation quality. The repository contains two example applications (`demo`, `reference_spa`), a code generation crate (`loco-gen`), a project scaffolding crate (`loco-new`), an Astro documentation website, and an integrated eval framework. The codebase is well-structured with clean trait abstractions, extensive snapshot testing, and a clear separation between generated and hand-written model code. It is actively maintained with recent commits, responsive issue triage, and regular releases tracked in a detailed CHANGELOG.

This repository is the official FastAPI full-stack template, providing a production-ready starter for full-stack web applications. It consists of a FastAPI backend (Python 3.14, SQLModel ORM, PostgreSQL, Alembic migrations) and a React frontend (TypeScript, Vite, TanStack Router/Query, Tailwind CSS, shadcn/ui). The template ships with complete authentication (JWT, password hashing with Argon2/Bcrypt, password recovery via email), user and item CRUD, role-based access control, and a responsive dashboard UI. Deployment is handled via Docker Compose with Traefik, and the project includes comprehensive CI/CD pipelines for testing (pytest, Playwright) and deployment (FastAPI Cloud or self-hosted). While it is a template, the code is structured for immediate extension and includes many reusable patterns such as generic CRUD utilities, email templates written in React Email, and auto-generated OpenAPI client code for the frontend. The repository is not a data research project; it is a pure application template with no data pipelines, notebooks, or ML components. It is actively maintained, with frequent commits and a strong community following.

Reflex is a full-stack Python web framework that compiles Python code into a React frontend, enabling developers to build interactive web applications entirely in Python. In production, it serves as a stateful, event-driven framework with a component system, reactive state management, and a compiler that generates JavaScript/React from Python. The architecture is a multi-package monorepo: core types and compiler (reflex-base), UI component libraries (reflex-components-* wrapping Radix UI, Recharts, Plotly, etc.), a build SDK for cloud deployment (reflex-build-sdk), a hosting CLI (reflex-hosting-cli), a durable workflow engine (reflex-workflow), observability via OpenTelemetry (reflex-otel), documentation generation (reflex-docgen), and benchmarking infrastructure (reflex-bench). Key entry points include the CLI (reflex run, reflex deploy), the Python API (rx.App, rx.State, rx.Component), and the compiler that transforms Python component trees into React code. Data flows involve user-defined state classes with computed vars and event handlers, which the compiler serializes into JavaScript state management; the server uses Starlette with Socket.IO for real-time event propagation, and state can be persisted in memory, disk, or Redis. External dependencies include Node.js, Bun, React, Radix UI, Recharts, Plotly, SQLAlchemy, Redis, Postgres, and numerous npm packages. The framework is actively maintained with extensive unit, integration, and performance tests, and it dogfoods its own documentation site.

The Dext Framework is a comprehensive, modular full-stack framework for Delphi (v11+) designed to bring modern web development patterns to the native Delphi ecosystem. It is Apache 2.0 licensed and provides a coherent set of libraries covering web API construction (minimal APIs, MVC controllers, middleware pipeline, SignalR-compatible real-time hubs), an ORM with code-first migrations, dependency injection, background jobs, event bus, resilience pipeline, testing/mocking utilities, desktop UI patterns (MVU/MVVM with VCL binding), AI integration (MCP server, agent graph), CLI tooling, and telemetry/dashboard. The architecture is heavily inspired by ASP.NET Core/EF Core, with fluent builders (`TDextServices`, `TApplicationBuilder`), specification pattern, attribute-based configuration, and a focus on zero-allocation performance (custom SIMD-optimized collections, ring-buffer logging, direct codecs). It includes extensive documentation (The Dext Book in English and Portuguese), benchmark harnesses, and 40+ example applications covering real-world use cases from simple CRUD to multi-tenant e-commerce. The codebase is actively maintained, with multi-version Delphi support (XE2 through 13), community database drivers (UniDAC), and a growing ecosystem of IDE integration and design-time tooling.

Nuxt is a production-grade, open-source meta-framework for building full-stack web applications with Vue.js. It provides a comprehensive developer experience with file-based routing, server-side rendering (SSR), static site generation (SSG), incremental static regeneration (ISR), and a powerful module ecosystem. The repository is a monorepo containing the core framework (`@nuxt/nuxt`), a toolkit for module authors (`@nuxt/kit`), configuration schema (`@nuxt/schema`), and multiple builder integrations (Vite, Webpack, Rspack) plus a Nitro-based server engine (`@nuxt/nitro-server`). Architecturally, Nuxt operates in two phases: build-time and runtime. At build time, it loads configuration, installs modules, scans components and pages, generates optimized code (auto-imports, route definitions, plugin arrays), and bundles the application using the configured builder. At runtime, it handles SSR/CSR rendering, data fetching via `useFetch`/`useAsyncData`, state management with `useState`, routing with vue-router, and server-side logic via Nitro. The framework is highly extensible through hooks, modules, and layers, allowing customization at every level. Key entry points include `createNuxt`, `loadNuxt`, and `build` from `@nuxt/kit`, the CLI (`nuxi`), and runtime composables like `useFetch`, `useAsyncData`, `useHead`, and `useRouter`. External dependencies include Vue 3, Vite/Webpack/Rspack, Nitro, unhead, ufo, devalue, and many others. The codebase is actively maintained with a large test suite (unit, integration, e2e) and a sophisticated diagnostics system for developer feedback.

DeepTutor is a full-stack AI-powered learning companion that provides chat, problem-solving, research, visualization, and structured learning capabilities. The system features a modular architecture with a Python/FastAPI backend, Next.js/React frontend, and a CLI interface. It implements an agent-based system where capabilities like deep solve, deep question, and deep research are implemented as loop capabilities with access to tools such as web search, code execution, and knowledge base retrieval. The platform includes a three-layer memory system for contextual awareness, supports multiple RAG backends (LlamaIndex, PageIndex, GraphRAG, LightRAG, IMA), and enables integration with external knowledge bases (Obsidian, MarginNote 4) and partner agents (Claude Code, Codex). The application employs a plugin architecture for extensibility, allowing new capabilities, tools, and subagents to be added via entry points. Configuration is managed through JSON/YAML files with runtime overrides, and services are decoupled via provider abstractions for LLMs, embeddings, search, and media generation. Real-time communication is facilitated by WebSockets and an internal event bus (StreamBus). The system supports multi-user deployments with role-based access control, isolated workspaces, and grant-based resource sharing for skills, knowledge bases, and partners. Deployment options include local installation, Docker containers, and CLI-only mode. The codebase demonstrates strong engineering practices with extensive unit testing, clear separation of concerns, and reusable patterns such as the capability protocol, tool composition, and atomic file operations. While the system's feature richness introduces complexity, its modular design allows for selective adoption of components.

highlight.io is an open-source, full-stack observability platform that provides session replay, error monitoring, logging, distributed tracing, and metrics dashboards. The system is architected as a monorepo with a Go backend (serving GraphQL APIs via gqlgen), a React/TypeScript frontend (using Vite, Apollo Client, and a custom UI component library), and SDKs for over a dozen languages and frameworks (JavaScript/TypeScript, Go, Python, Ruby, Java, Rust, Elixir, .NET, etc.). Data is ingested via OpenTelemetry (OTLP) and custom SDKs, processed through Kafka queues, stored in ClickHouse for analytics and PostgreSQL for relational data, with Redis caching and S3 for session replay assets. The platform includes alerting (Slack, Discord, Microsoft Teams, webhooks), integrations with GitHub, Jira, Linear, Vercel, and more, as well as AI-powered features like session insights and error grouping. The primary entry points are the frontend SPA (routed via React Router) and the backend GraphQL endpoints (private-graph for authenticated users, public-graph for data ingestion). Key data flows include: browser SDK capturing session events and errors → public-graph → Kafka → ClickHouse/Postgres; server SDKs sending logs/traces/metrics via OTLP → OpenTelemetry collector → ClickHouse; and background workers (Lambda functions) for digests, session exports, and alert evaluations. The system is designed for both cloud-hosted and self-hosted deployments (Docker Compose, AWS ECS). While primarily an application, the repository contains significant data engineering components: ClickHouse materialized views for aggregations, Kafka for stream processing, and custom query parsing (ANTLR grammar) for search across logs, traces, and sessions. The codebase demonstrates high craftsmanship with a well-structured monorepo, extensive testing (Go unit tests, e2e tests across frameworks), and a reusable UI component library. However, the large number of SDKs and integrations introduces maintenance complexity, and some legacy patterns (e.g., coexistence of old and new dev tools) indicate ongoing refactoring.

Cabloy is a full-stack modular application framework built on two integrated platforms: Vona (backend) and Zova (frontend). It provides a decorator-driven, metadata-rich architecture for building enterprise applications with features like ORM, caching, authentication, RBAC, file/image handling, payments, SSR, and CLI scaffolding. The system is organized into suites and modules, with auto-generated metadata files that enable type-safe dependency injection and code generation. It includes demo applications like e-commerce and training management, demonstrating real-world usage patterns. The architecture is based on an onion/middleware pattern for interceptors, guards, and pipes, and uses a bean container for dependency injection. The backend uses Koa, Knex, Redis, BullMQ, and Zod, while the frontend uses Vue 3, Vite, Quasar, and TanStack Query. The framework emphasizes code-first API documentation via Zod-to-OpenAPI conversion, and provides a CLI for scaffolding modules, components, and CRUD resources. External dependencies include PostgreSQL, MySQL, SQLite, Redis, and various cloud services for file/image storage and payment processing.

TSRPC is a TypeScript RPC framework designed for building type-safe, high-performance client-server applications. It provides both HTTP and WebSocket transports with optional JSON compatibility. The framework is built on a custom binary serialization algorithm (TSBuffer) that supports advanced TypeScript type features like union types, intersection types, and mapped types, eliminating the need for external IDL languages. TSRPC includes a rich middleware system called Flows, allowing interception and transformation of data at various stages of request/response processing. The core architecture is abstract and extensible: BaseServer, BaseConnection, and BaseCall form the foundation, with concrete implementations for HTTP (HttpServer, HttpConnection, ApiCallHttp) and WebSocket (WsServer, WsConnection, ApiCallWs). The client side mirrors this with HttpClient and WsClient that handle protocol encoding/decoding, timeouts, and automatic request aborting. The framework also supports server-to-client messages, broadcasting, heartbeat management, and graceful shutdown. With over 1900 GitHub stars and an active maintenance history (latest version 3.4.21), the codebase demonstrates solid engineering practices: comprehensive test coverage across multiple transport modes (HTTP, HTTPS, WS, WSS, both binary and JSON), clean separation of concerns, and robust error handling. The repository includes benchmarking tools and integration tests that verify correctness under various conditions.

This repository contains a full-stack application for AI-powered short drama generation. The system enables users to create drama projects, extract characters and scenes from text using AI models, generate consistent character and scene images, create storyboards, and produce final videos. It manages the entire lifecycle from text input to multimedia output, with real-time task tracking and asset management. The backend is built with Spring Boot (Java 17) and follows a layered architecture with controllers, services, decorators (for Redis caching), facades (for asynchronous persistence), and mappers (using MyBatis-Plus). The frontend is a Vue 3 + Vite application using the Composition API for state management and communicating with the backend via REST APIs and WebSocket for real-time updates. The system integrates multiple technologies: MySQL for primary data storage, Redis for caching, RabbitMQ for asynchronous task processing, MinIO for object storage, Elasticsearch for full-text search, and Qdrant for vector-based context retrieval in storyboard generation. Key architectural patterns include the Strategy pattern for swapping AI model providers, the Decorator pattern for adding cross-cutting concerns like caching, and the Facade pattern for decoupling asynchronous operations. Reusable components include utility classes for Redis, MinIO, and Qdrant operations, as well as Vue 3 composables for global loading state and model configuration. The application demonstrates strong engineering practices in handling complex AI workflows while maintaining modularity and extensibility.

CloudSaver is a full-stack web application (Vue 3 + Express) for searching and saving cloud storage resources from multiple sources. It scrapes Telegram channels for shared files, parses links to popular cloud drives (115, Quark), and allows users to transfer them directly to their own cloud storage via cookie-based API. The system includes user authentication (JWT), role-based access, a responsive UI for desktop and mobile, and Docker support for easy deployment. The backend is structured with dependency injection (Inversify) and follows a controller-service-model pattern, while the frontend uses Pinia for state management and separate routing for PC and mobile views. The codebase is well-organized and demonstrates solid engineering practices, though it remains a typical CRUD application without exceptional innovation.

Xibo CMS is a full-stack digital signage content management system that enables users to create, schedule, and manage content displayed on screens. The backend is built with PHP (Slim framework, Symfony EventDispatcher, Phinx migrations) and exposes RESTful APIs consumed by a modern React/TypeScript SPA frontend (Vite, TanStack Query, Tailwind CSS). It supports player communication via XMR (message relay) and legacy SOAP-based XMDS. Key features include layout and playlist management, media library, scheduling with dayparting and recurrence, reporting (proof of play, bandwidth, session history), OAuth2 authentication, multi-tenancy via folders and permissions, and a widget/module system with connectors. The codebase is large and actively maintained, with extensive frontend tests (Vitest) and Docker-based development/deployment. However, it carries some technical debt from a legacy jQuery/Bootstrap UI that coexists with the modern React frontend, and a high volume of database migrations indicates long evolution.

The repository is a full-stack application template providing role-based access control (RBAC) with a modern frontend and multiple backend options. It features a React/Vite frontend built with Shadcn UI and Tailwind CSS, offering comprehensive UI components, dashboards, and charting capabilities. Two backend implementations are provided: Spring Boot (Java) and NestJS (TypeScript), both featuring JWT authentication, PostgreSQL integration, and CRUD operations for users, roles, permissions, and groups. The frontend showcases a well-crafted component library including reusable UI primitives, custom extensions like tree selectors and context menus, and themed chart containers. State management is handled via Zustand, with internationalization via react-intl and API mocking using MSW. The backends follow standard layered architectures with service-repository patterns, DTO validation, and security configurations. While the Spring Boot backend contains a non-standard Java 25 version specification (likely a typo) posing build risks, the NestJS backend uses current LTS Node.js versions. The project demonstrates solid full-stack engineering with modular design, clear separation of concerns, and reusable UI patterns. Despite the Java version concern in one backend, the template remains a valuable foundation for enterprise admin panels with modern tooling.

LogoAI is a Next.js-based web application that enables users to generate professional logos using AI models. The system provides authentication via Clerk, rate limiting through Upstash Redis, and stores generation history in a PostgreSQL database using Drizzle ORM. Users can create logos with customizable styles, colors, and AI models, then view their generation history or browse a public gallery of all generated logos. The application follows modern Next.js patterns with the App Router, server actions for data mutations, and a component-based UI using Shadcn UI primitives.

This is a full-stack web application starter kit combining Angular (frontend) and Spring Boot (backend) with JWT-based authentication. The system provides user registration, login, role-based access control (USER/ADMIN), and protected API endpoints. The frontend communicates with the backend via RESTful APIs, with authentication handled through JWT tokens stored in cookies. The architecture follows a standard client-server pattern with clear separation of concerns: Angular handles UI presentation and user interactions, while Spring Boot manages business logic, data persistence, and security. Key features include route guards for client-side protection, custom JWT filters for server-side authentication, and centralized service layers for API communication.

Stb (图床) is a full-stack image hosting service built as a pnpm monorepo. The backend is a Node.js/Express application with MongoDB (Mongoose) providing REST APIs for user authentication, image upload, album management, admin operations, and system configuration. The frontend is a Vue 3 single-page application using Pinia for state management, Vue Router with lazy-loaded views, and a mix of Ant Design Vue and Element Plus UI libraries. The system supports multiple storage backends (local filesystem, S3-compatible, Alibaba OSS, Tencent COS, Qiniu, Upyun, SFTP, FTP, WebDAV, Telegram, GitHub) via a pluggable adapter pattern, and integrates AI-based content moderation (Tencent, Aliyun, NSFWJS). Deployment is Docker-based with a multi-stage build and GitHub Actions CI/CD for multi-architecture images. The application includes a comprehensive admin panel for user, image, log, album, role group, invite code, and configuration management, as well as OAuth login (GitHub, Google, Linux DO), email verification, and a slider captcha component. While the codebase is functional and well-structured with clear separation of concerns, it exhibits some technical debt: mixed UI libraries increase bundle size, the configuration view is monolithic and exposes sensitive credentials to the client, there are no automated tests, and some patterns (e.g., IP detection, Docker entrypoint sed) are fragile. Overall, it is a solid, production-ready image hosting solution with reusable utility modules.

AgentKit is a full-stack framework for building constrained, reliable LLM agents, developed by BCG X and now deprecated. The codebase consists of a FastAPI backend (Python 3.10) that orchestrates LangChain-based agents through a meta-agent and a simple router, executing tools like SQL queries, PDF question-answering, visualizations, image generation, and summarization. The Next.js 14 frontend provides a conversational chat interface with streaming responses, tool action rendering, and configurable UI components. The application is Dockerized with PostgreSQL, Redis, Minio, and Caddy, and supports optional features such as user authentication (NextAuth), feedback collection (LangSmith), and Celery-based background tasks. The architecture is monolithic in code but service-oriented in deployment, with configuration-driven agent behavior via YAML files. While the codebase demonstrates solid engineering patterns (e.g., streaming callbacks, base tool classes, state management), it is explicitly deprecated, relies on outdated LangChain imports and API versions, and requires significant maintenance to be viable for new projects. The repository serves as a reference for building constrained agents but is not recommended for production use without substantial updates.