This repository is a minimalist CV (resume) web application built with Next.js 16, React 19, TypeScript, and Tailwind CSS. It serves as a personal portfolio site that renders resume data from a single TypeScript file into a print-friendly layout. The architecture follows Next.js App Router conventions with server-side rendering and static generation. Key features include a command menu for quick navigation, responsive design, dark mode support, SEO-optimized structured data (JSON-LD), and a production-ready Docker setup. The codebase demonstrates strong engineering practices with clear separation of concerns, reusable UI components, and attention to accessibility and performance.
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.
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.
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.
This is a production-oriented full-stack SaaS starter kit (not a data/research project). It is a monorepo template pairing a Next.js web app (landing page, auth flow, account dashboard, marketing sections) with a Flutter cross-platform app (iOS/Android/macOS/Windows/Linux/web) on top of a shared Supabase backend. The backend is a Postgres schema (users, customers, products, prices, subscriptions, invoices) protected by row-level security, plus Deno edge functions that orchestrate Stripe Checkout sessions and process Stripe webhooks. PostHog handles analytics on both frontends, Loops.so handles transactional email, and an optional Monero cryptocurrency payment variant can be toggled in via patch scripts, giving the template two billing backends.
Magic Resume is a production-oriented, bilingual resume-builder web application. Users manage resume documents in a dashboard, edit them in a multi-panel workbench with a live A4 preview, choose among nine templates, and export to PDF, PNG, JSON, or Markdown. The editor supports rich text, structured sections, custom themes, typography controls, and automatic one-page scaling. Resume data is local-first, persisted to localStorage and optionally synced to the local file system via the File System Access API. Under the hood, it is a React/TypeScript full-stack app with a somewhat unusual dual route-tree structure: an older Next.js App Router layout coexists with a newer TanStack Router source tree. State is managed with Zustand, UI uses Tailwind CSS and shadcn-style components, and rich text editing is built on Tiptap. AI features—streaming text polish, grammar checking, and vision-based PDF import—run through server routes that abstract OpenAI, Gemini, Anthropic, DeepSeek, Qwen, and Doubao behind a common transport with retries and SSE streaming. Export paths include a server-side PDF renderer, client-side html2canvas/jsPDF, browser print, JSON, and Markdown. The codebase is feature-rich and actively maintained, with some solid server-side AI abstractions and a meaningful test suite. However, it carries structural debt: near-identical section components are duplicated across nine templates, the old Next.js route tree remains alongside the newer TanStack routes, and AI model IDs/configurations are speculative and likely to age. It is deployable to Cloudflare Workers, Node, or Docker, but the architecture is hybrid and transitional rather than cleanly unified.
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.
Kanaries/Rath is an open-source augmented analytics BI platform that automates exploratory data analysis, causal discovery, and dashboard creation. It is a full-stack application with a React/TypeScript frontend (Vite, MobX, shadcn/ui) and multiple Python microservices (causal inference, narrative insight generation, database connectors). The system ingests data from files, databases (PostgreSQL, ClickHouse, MySQL, etc.), and REST APIs, then provides automated visualization (mega-automation), semi-automated exploration, causal analysis (PC, FCI, GES, XLearner), and interactive dashboard building. It also includes custom Vega renderers (WebGL, Painter) for high-performance chart rendering and an Electron desktop wrapper. While primarily an application, it contains significant data research components such as causal discovery algorithms, statistical insight detection, and text pattern extraction, making it a hybrid tool for both data exploration and advanced analytics.
Notty is a full-stack, cross-platform notes application (an Apple Notes-style clone) built with a React 19 + Vite + Tailwind 4 frontend, a Hono API running on Cloudflare Workers, and per-user Durable Objects that each own a SQLite database. Clients include a web app, a Tauri 2 desktop app (with local SQLite, markdown export/import, cloud sync, deep-link auth, and a quick-note hot corner), and a mobile variant. Realtime collaboration is powered by Yjs with three persistence paths (Durable Object SQLite, browser IndexedDB, and Cloudflare R2), and authentication uses Better Auth with passkeys, Google/GitHub/Apple social login, and anonymous sessions. The backend also serves server-rendered public pages generated from TipTap JSON with URL sanitization and RSS 2.0 feeds, and integrates Supermemory for AI-powered note indexing. File storage and full version-history snapshots live in R2, with branch management (create/checkout/merge/delete), diff views, and restore-with-auto-backup. The architecture is notable for its platform adapter pattern (web/desktop/mobile adapters abstracting storage and sync) and per-user Durable Object isolation, but the codebase carries meaningful baggage: dead code from an earlier NextAuth-based iteration (src/lib/auth.ts, old NotesContext, DarkModeSwitch, search-results.tsx), no visible unit tests beyond a race-condition QA script, a non-standard FNV-1a-based lock token instead of a real HMAC, and a merge operation that is actually a replace. Optimistic update logic in the notes context is fragile and can diverge from server state under rapid tab switches. Despite these issues, the core architecture is sound and the adapter pattern is genuinely reusable.
LiveCanvas is a collaborative whiteboard and diagramming web application built with Next.js 14, Convex as the real-time backend, and Kinde for authentication. It allows users to create teams, manage files, and edit documents using Editor.js alongside an Excalidraw-based canvas for freeform drawing. The architecture follows a standard Next.js App Router pattern with client-side rendering for authenticated routes and server components for the landing page. Data flows through Convex mutations and queries, with file content (document JSON and whiteboard elements) persisted as strings. The application is functional but exhibits several minor issues: the dark mode CSS is incorrectly defined (identical to light mode), there are typos in mutation argument names ('archieved' instead of 'archived'), and the codebase lacks tests or CI/CD configuration. While it demonstrates integration of multiple third-party libraries, the components are tightly coupled to the app's specific data model and are not easily reusable outside this project.