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Edobor Osamudiamen's avatar
Edobor Osamudiamen
Benin City, Nigeria
Digital Illustrator & Designer Creating impactful Visuals
2x
Hired
5.0
Rating
26
Followers
Digital Illustrator & Designer Creating impactful Visuals
Cover image for Chop App Onboarding Illustration
Chop App Onboarding Illustration
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Cover image for 🍴Olili Foods | Brand Illustrations
🍴Olili Foods | Brand Illustrations
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151
Cover image for 📄404 Error Page Illustrations and Patterns
📄404 Error Page Illustrations and Patterns
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Cover image for 📊StockHut Trade | Product Illustrations | Icon Design
📊StockHut Trade | Product Illustrations | Icon Design
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109
Cipher UI's avatar
Cipher UI
Benin City, Nigeria
UI/UX designer specialising in motion and interaction.
65
Followers
UI/UX designer specialising in motion and interaction.
Cover image for Just wrapped up a new
Just wrapped up a new UI animation concept in Jitter ✨ Designed in Figma. Animated in Jitter. Available for UI animation, landing pages, and Web3 product design projects.
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Cover image for Just designed a prediction market
Just designed a prediction market dashboard concept for Prediq a platform inspired by the fast-paced world of forecasting, crypto, sports, and global events. I focused heavily on: dark Web3 aesthetics card-based market layouts prediction interactions financial dashboard inspiration visual hierarchy and spacing One of my favorite parts is how the green aurora background contrasts with the dark UI to create a cinematic trading atmosphere. Designed in Figma.
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Cover image for Recently designed and animated this
Recently designed and animated this UI concept using Figma + Jitter The goal was to create a clean interface that doesn’t just look good statically, but also feels interactive and alive through motion. Tools used: • Figma for the UI design • Jitter for the animation and transitions Currently improving my workflow around motion, timing, and smoother interactions. More experiments coming soon 🚀 #Figma #Jitter #UIDesign #MotionDesign #WebDesign #Web3
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Cover image for Just wrapped up this futuristic
Just wrapped up this futuristic Web3 wallet mobile UI concept. The goal was to create a wallet experience that feels premium, intuitive, and visually immersive without overwhelming the user. Highlights: • Clean portfolio overview • Minimal send transaction flow • Dark UI with neon aurora atmosphere • Strong hierarchy and spacing • Smooth fintech/Web3 inspired visuals Designed entirely in Figma. Always exploring ways to make Web3 products feel more polished and user-friendly. Available for Web3, SaaS, and mobile UI projects.
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karina 's avatar
karina
Benin City, Nigeria
Design and Engineering
FRAMER_EXPERT

expert

Design and Engineering
Cover image for Why juggle 5 AI apps
Why juggle 5 AI apps when you can have them all in one? Interface exploration with multi-models and folders. A direction that might be worth exploring if you're building AI apps
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Cover image for Hey friends! I built the
Hey friends! I built the thing. It's live. nari.design (http://nari.design) - beautiful, ready-to-ship Framer websites. Made for people who wants beautiful websites without starting from scratch 4 templates today, 2 more tomorrow. The goal? 100+ templates One lifetime deal: $99 gets you all 4 now + every template I ever build and request up to 5 custom ones, free. www.nari.design (http://www.nari.design)
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Cover image for Avin - A dark, conversion-ready
Avin - A dark, conversion-ready Framer template for crypto, Web3, and dev tool startups. Launch a polished product website in hours — no code required. Price $79. Dm if Interested https://useavin.framer.website/
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Cover image for Veriad AI (YC) redesign exploration.
Veriad AI (YC) redesign exploration.
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Ugochukwu Ofoefule's avatar
Ugochukwu Ofoefule
Benin City, Nigeria
Motion Designer for Web3 & SaaS | Brand & Product Animation
9
Followers
Motion Designer for Web3 & SaaS | Brand & Product Animation
Cover image for Motion Design Product Launch video for BirdLayer
Motion Design Product Launch video for BirdLayer
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Cover image for Hey everyone! Check out this Promo ad video I created for Bl...
Hey everyone! Check out this Promo ad video I created for Blockchain-Ads. Blockchain-Ads is an AdTech platform built to modernize digital advertising by using blockchain technology, data intelligence, and AI to improve how advertisers target and measure campaigns. I created a captivating and dynamic promo video, showcasing the benefits of working with Blockchain-Ads.  The goal of this Motion Design project was to captivate the brand's audience and communicate the brand's message clearly while keeping the audience engaged and reinforcing the brand's identity through consistent, well-crafted designs/style-frames.
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Cover image for Kinetic Typography Animation for Index
Kinetic Typography Animation for Index
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Cover image for Engaging Motion Design Product Video for Index
Engaging Motion Design Product Video for Index
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Ahmad Yakubu Ahmad's avatar
Ahmad Yakubu Ahmad
Damaturu 620101, Nigeria
AI-Powered Web Apps Engineer | React, Next.js, Node.js & AI
5.0
Rating
4
Followers
AI-Powered Web Apps Engineer | React, Next.js, Node.js & AI
Cover image for SlideForge AI 1. Short Explanation SlideForge (also
SlideForge AI 1. Short Explanation SlideForge (also branded as IntelliSlide-AI) is a full-stack AI-powered presentation generator that transforms any topic into a professionally structured, downloadable PowerPoint presentation in seconds. Users simply enter a topic, choose slide count, language, theme, and layout preference, and the platform automatically generates a fully editable .pptx presentation using Google Gemini AI and a Python-powered document generation pipeline. The platform is built for students, educators, professionals, startups, and teams who need polished presentations quickly — without spending hours designing slides manually. 2. Tech Stack Category Technologies Frontend Next.js 15 (App Router, Turbopack), React 19, TypeScript 5 Backend FastAPI (Python), python-pptx AI / LLMs Google Gemini API Styling / UI Tailwind CSS 4, shadcn/ui, Radix UI, CVA, Lucide React Forms & Validation React Hook Form v7, Zod v3 HTTP Client Axios Notifications Sonner v2 Theming next-themes Fonts Plus Jakarta Sans Tooling ESLint, PostCSS, Turbopack, TypeScript strict mode Deployment Vercel (frontend, inferred), Render (backend API) 3. Problem Solved Creating professional presentations is slow, repetitive, and often requires both design and content-writing skills. Most users spend hours: Structuring slides Writing presentation content Designing layouts Formatting visuals Exporting and polishing decks manually Even AI writing tools still require users to manually move content into presentation software. SlideForge eliminates this entire workflow by automating both: AI-powered slide content generation Real PowerPoint file creation The result is a fully editable .pptx presentation generated in under 30 seconds. 4. Results / Key Features Sub-30-second presentation generation via async job processing Multi-language support across 9 languages Flexible customization: 3 visual themes 3 layout modes 1–20 slide generation Download-ready .pptx output compatible with: Microsoft PowerPoint Google Slides Apple Keynote Fully anonymous workflow with zero signup friction Production-grade async architecture with resilient polling system Hardened frontend security with CSP, Permissions-Policy, MIME validation, and X-Frame-Options 5. Problem Professionals, students, founders, and educators frequently need presentation decks for: Meetings Reports Investor pitches Lectures Product demos Internal documentation Traditional presentation creation is: Time-consuming Design-heavy Expensive when using premium tools Repetitive across projects Even with AI writing assistants, users still have to: Copy/paste content manually Structure slides themselves Apply layouts and formatting Export files manually There was no streamlined, end-to-end system that could transform a raw topic into a polished, editable PowerPoint presentation instantly. 6. Solution SlideForge introduces a fully automated AI presentation pipeline. Workflow User enters: Topic Slide count Language Theme Layout mode Frontend validates input using Zod schemas A POST request is sent to the FastAPI backend Backend generates structured slide content using Google Gemini AI python-pptx assembles the presentation into a formatted .pptx file Frontend enters a polling loop, checking job status every 2 seconds Once complete, the browser downloads the binary PowerPoint file directly No cloud storage. No email delivery. No account required. The frontend state flow is managed through a discriminated-union state machine using a custom React hook, ensuring reliable async handling without race conditions or memory leaks. 7. Features AI Presentation Generation Google Gemini AI generates: Slide titles Bullet points Structured presentation flow Topic-aware content hierarchy Async Job Processing Background presentation generation Frontend polling system with AbortController support Resilient retry and timeout handling Multi-Language Support Supports native generation in: English Spanish French German Arabic Chinese Hindi Portuguese Japanese Theme System Users can choose between: Professional Minimal Vibrant Themes are applied consistently across the generated presentation. Layout Modes Varied Text-Heavy Image-Focused Each mode changes slide composition and density. Configurable Slide Counts Generate between 1–20 slides dynamically. Direct PowerPoint Export Real .pptx generation using python-pptx Binary streaming from backend MIME-type validation before download Automatic object URL cleanup for memory safety Dark / Light Mode System-aware theme switching Full support via next-themes Error Handling Route-level error.tsx boundaries loading.tsx fallbacks Graceful async failure recovery Toast Notifications Real-time status updates using Sonner: Generation started Success states Failure states Download completion Security Hardening Configured production-grade headers: CSP X-Frame-Options Referrer-Policy Permissions-Policy DNS prefetch control Form Validation React Hook Form + Zod enforce: Character limits Required fields Enum constraints Value ranges Memory-Safe Polling All: intervals timeouts AbortControllers object URLs are cleaned up correctly during component unmounts. No Authentication Required Fully anonymous usage with zero onboarding friction. 8. Stack (Architecture Overview) Frontend Next.js 15 App Router React 19 TypeScript 5 Tailwind CSS 4 shadcn/ui Radix UI primitives Backend FastAPI (Python) python-pptx Authentication None — public access, no accounts required. AI / LLMs Google Gemini API AI-generated slide structuring and content flow Styling / UI Tailwind CSS 4 shadcn/ui Radix UI CVA Lucide React Plus Jakarta Sans Forms & Validation React Hook Form v7 Zod v3 Networking Axios Request interceptors Timeout management MIME validation Deployment Frontend: Vercel (inferred) Backend: Render (onrender.com (http://onrender.com)) Tooling ESLint TypeScript strict mode PostCSS Turbopack 9. My Role As the sole Full-Stack Engineer and Architect on SlideForge, responsibilities included: Designed the complete async presentation generation architecture (submit → poll → download) Implemented a discriminated-union state machine to eliminate impossible UI states Built the full Next.js 15 frontend using TypeScript strict mode Developed custom async polling infrastructure with cleanup-safe lifecycle management Engineered the API integration layer with Axios interceptors, timeout management, and MIME validation Built the presentation configuration form using React Hook Form + Zod Developed responsive UI components using shadcn/ui, Radix UI, and Tailwind CSS Configured production-grade security headers and frontend hardening policies Optimized performance using Turbopack and memory-safe download handling Coordinated frontend/backend contracts for Gemini-powered slide generation and FastAPI job processing 10. Live URL https://slide-forge123.vercel.app/ 11. GitHub https://github.com/engraya/SlideForge
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Cover image for CoverFlow AI 1. Short Explanation CoverFlow
CoverFlow AI 1. Short Explanation CoverFlow (GenLetter AI) is a production-grade AI-powered cover letter generator built with Next.js 15 that helps users instantly create personalized, professional cover letters from structured inputs. It is designed for job seekers, professionals, and freelancers who want to generate well-written, tailored cover letters in seconds instead of starting from scratch. The system combines form-driven input, AI prompt engineering, and document export into a seamless writing-to-download workflow. 2. Tech Stack Next.js 15.5 (App Router) React 19 TypeScript 5.7 Google Gemini 1.5 Flash (@google/generative-ai) Zod react-hook-form shadcn/ui (New York style) Radix UI (40+ primitives) Tailwind CSS Framer Motion next-themes docx file-saver ReactMarkdown 3. Problem Solved Writing a strong cover letter is time-consuming, repetitive, and often difficult for users who struggle to translate their experience into professional language. Most users either: Copy generic templates that feel impersonal Spend hours writing and rewriting from scratch Or fail to structure their experience effectively for recruiters GenLetter AI removes this friction by turning structured personal input into a fully written, polished cover letter instantly. 4. Results / Key Features Instant AI-generated cover letters tailored to job role and company Structured 9-field input system for precise personalization High-quality formatting output suitable for real job applications Clean split-screen live preview for immediate feedback Export-ready DOCX generation for direct submission Professional prompt engineering enforcing tone, structure, and length constraints Responsive, modern UI with smooth animations Dark/light mode support for accessibility and UX comfort 5. Problem Job applicants often struggle to create cover letters that are: Personalized enough to stand out Professionally structured Fast to generate across multiple applications Consistent in tone and formatting Existing solutions are either overly generic templates or manual writing tools with no intelligence layer. 6. Solution GenLetter AI introduces a structured AI writing pipeline: User fills a 9-field form (name, role, company, experience, skills, etc.) Data is validated using Zod and react-hook-form A structured prompt template is sent server-side to Gemini 1.5 Flash The AI generates a formatted, multi-paragraph cover letter (strict output rules enforced) The result is rendered in a live split preview interface User exports the final document as a professionally formatted DOCX file This creates a fast, deterministic, and consistent AI writing system instead of unpredictable freeform generation. 7. Features 9-field structured input form (fully validated with Zod) AI cover letter generation using Google Gemini 1.5 Flash Strict prompt engineering (tone, structure, paragraph limits, formatting rules) Live split-panel preview (input + rendered output) Markdown rendering with ReactMarkdown DOCX export with A4 formatting, margins, and professional typography File download handling via file-saver Responsive UI built with shadcn/ui + Tailwind Smooth page transitions and Framer Motion animations Dark/light theme support via next-themes Landing page with Hero, How It Works, Features, and CTA sections 8. Stack Frontend Next.js 15 (App Router) React 19 TypeScript 5.7 Tailwind CSS shadcn/ui (Radix-based components) Framer Motion ReactMarkdown Backend / Server Layer Next.js Server Actions Google Gemini API integration (@google/generative-ai) Structured prompt engine (template-driven AI output control) AI / LLM Google Gemini 1.5 Flash Prompt engineering enforcing: 3–4 paragraph structure <400 word limit Professional tone Plain text output formatting Forms & Validation react-hook-form Zod schema validation (9-field structured input model) Export System docx (DOCX generation with formatting rules) file-saver (client-side download handling) Auth (Scaffolded / Not Active) next-auth v4 installed useUser hook present Not enforced in current app flow Deployment Vercel deployment configured (vercel.json present) Ready for serverless deployment Other Tools ESLint PostCSS Vite-style modern build optimizations (via Next.js tooling) 9. My Role As the developer of GenLetter AI, responsibilities included: Designed full application architecture using Next.js App Router Built structured AI prompt system for deterministic cover letter generation Integrated Google Gemini 1.5 Flash via secure server-side execution Developed 9-field validated form system using Zod + react-hook-form Built responsive UI using shadcn/ui, Radix primitives, and Tailwind CSS Implemented live preview system with Markdown rendering Developed DOCX export engine with professional formatting rules Designed landing page and user onboarding flow (Hero → Features → CTA) Implemented theme system with dark/light mode support Structured application for future scalability (auth + database scaffolding) 10. Live URL https://cover-flow.vercel.app/ 11. GitHub https://github.com/engraya/CoverFlow
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Cover image for Delectable AI 1. Short Overview Delectable is
Delectable AI 1. Short Overview Delectable is a full-stack, AI-powered recipe discovery web application that helps users find, explore, and interact with recipes using both traditional search and natural language queries. It enables users to browse trending, vegetarian, and cuisine-specific recipes, get AI-assisted cooking guidance directly on recipe pages, and export recipes to PDF — all within a clean, responsive interface with full dark mode support. 2. Tech Stack Category Technology Frontend React 18, TypeScript, Vite, TanStack Query v5, React Router v6, Tailwind CSS, Zod, DOMPurify, jsPDF, react-icons, Vitest Backend Node.js, Hono.js, TypeScript, Zod External APIs Spoonacular Recipe API, Google Gemini AI API (@google/generative-ai) Database None — stateless proxy architecture Tooling npm workspaces (monorepo), concurrently, ESLint, tsx, GitHub Actions CI Deployment Static frontend (Vite) + Node.js API server; CI/CD via GitHub Actions 3. Problem Solved Recipe discovery is fragmented and inefficient. Users typically have to: Search across multiple food blogs and platforms Filter manually through inconsistent UI systems Deal with irrelevant results that don’t match intent Leave recipe pages to ask basic cooking questions Even when filters exist, they rarely support nuanced intent like: “high-protein meals under 30 minutes with no dairy” Delectable solves this by unifying recipe search, filtering, and AI cooking assistance into one intelligent interface. 4. Results / Key Features AI-powered smart search: Natural language queries (e.g., “high-protein meals under 30 minutes”) are converted by Gemini into structured Spoonacular filters In-page recipe copilot: AI assistant on every recipe page for substitutions, scaling, allergies, and cooking guidance Cuisine-first discovery: Trending, vegetarian, and cuisine-specific browsing powered by real-time Spoonacular data PDF export: Download any recipe as a formatted PDF for offline use Secure API proxy: All external API keys are server-side only (no client exposure) Rate limiting: In-memory token bucket system (30 req/60s & 20 req/60s per IP) for AI endpoints Production-grade type safety: Full TypeScript + Zod validation at all API boundaries Dark mode + responsive UI: Fully custom Tailwind-based design system with mobile-first layout 5. Problem (Expanded Context) Traditional recipe platforms are rigid and keyword-dependent. They: Require exact search terms Fail to understand user intent Lack contextual cooking assistance Force users to leave the page for clarifications (substitutions, allergies, scaling, etc.) There is no unified layer between intent → recipe data → cooking guidance. 6. Solution Delectable introduces an AI layer at two key touchpoints: 1. AI Search Layer Natural language queries are processed server-side by Google Gemini, which extracts structured filters such as: diet cuisine intolerances max cooking time These are then passed to Spoonacular’s API as precise parameters. 2. AI Recipe Copilot Each recipe page includes a Gemini-powered assistant that receives full recipe context (title, ingredients, instructions) and enables grounded, context-aware conversations about: substitutions dietary adjustments scaling recipes simplified explanations The backend is a lightweight Hono.js proxy API that: Validates all requests with Zod Enforces CORS policies Applies IP-based rate limiting Separates data fetching (Spoonacular) from intelligence (Gemini) 7. Features Landing page with featured content and category entry points Trending recipes feed (Spoonacular random endpoint) Vegetarian recipe category page Cuisine-based browsing via dynamic routes Keyword-based search (Spoonacular API) AI natural language search (Gemini-powered intent parsing) Advanced filters: cuisine, diet, intolerances, max ready time Full recipe detail pages (ingredients, instructions, metadata) HTML sanitization using DOMPurify for all external recipe content PDF export via jsPDF AI Recipe Copilot chat interface (substitutions, scaling, allergy queries) Dark mode toggle with CSS variable theming Loading skeletons and empty states across all async views Error boundaries with retry support Legacy route redirection for /searched/:term 404 Not Found page API health check endpoint (GET /health) Corporate HTTPS proxy support (HTTPS_PROXY, NO_PROXY) CI pipeline (lint → typecheck → test → build on every PR/push) 8. Stack (Full Architecture) Frontend React 18.3.1 TypeScript (strict mode) Vite (build tool + dev server) React Router v6 (lazy-loaded routing) TanStack React Query v5 (server state management) Zod (runtime validation) DOMPurify (HTML sanitization) jsPDF (PDF generation) react-icons (HeroIcons 2) Backend Node.js 20+ Hono.js (routing, middleware, logging) TypeScript (strict, NodeNext modules) tsx (dev runtime) Zod (request/response validation) undici (HTTP client with proxy support) Custom in-memory rate limiter (token bucket per IP) Database None — fully stateless architecture with real-time API fetching from Spoonacular. Authentication None — public-facing application with no user accounts. AI / LLMs Google Gemini API (@google/generative-ai) Model: gemini-3-flash-preview Structured JSON mode for deterministic filtering Conversational mode for recipe copilot Styling / UI Tailwind CSS v3 Custom CSS variables for theming Fonts: DM Sans, Fraunces, JetBrains Mono Fully custom component system (no UI library) Deployment Frontend: Static Vite build (deployable to any CDN/static host) Backend: Node.js server (apps/api/dist/index.js) CI/CD: GitHub Actions (lint → typecheck → test → build) Other Tools npm workspaces (monorepo) concurrently (parallel dev servers) Vitest + Testing Library + jsdom ESLint + typescript-eslint PostCSS + Autoprefixer vite-plugin-svgr 9. My Role As the developer of Delectable, responsibilities included: Architecture design: Built a monorepo using npm workspaces separating web and api packages with clear deployment boundaries Backend engineering: Developed a Hono.js API server with typed routes, middleware, Zod-based validation, and in-memory rate limiting API integration: Integrated Spoonacular API with secure proxy logic and structured response handling AI integration: Implemented Gemini-powered natural language search + recipe copilot with structured JSON outputs and prompt engineering Frontend development: Built full React 18 application with feature-based architecture, lazy routes, and TanStack Query for server state Type safety: Enforced strict TypeScript + Zod validation across frontend and backend Security: Implemented server-side API proxying, DOMPurify sanitization, and rate limiting for AI endpoints UI/UX: Designed all UI components from scratch including skeleton loaders, empty states, error handling, and PDF export UX DevOps: Configured CI pipeline using GitHub Actions for linting, typechecking, testing, and builds Developer experience: Set up Vite proxying, HMR, and concurrent dev workflows for seamless local development 10. Live URL https://delectable.vercel.app/ 11. GitHub https://github.com/engraya/Delectable
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Cover image for Transform Your Document Creation with AI-Driven DocuPilot
DocuPilot AI 1. Short Overview DocuPilot AI is a full-stack SaaS document generation platform powered by Google Gemini AI that instantly converts structured form inputs into professional-grade business and legal documents. It is built for freelancers, consultants, and small business owners who regularly need documents like invoices, contracts, NDAs, proposals, resumes, and more — without starting from scratch, paying for expensive tools, or hiring legal help. 2. Tech Stack Category Technology Framework Next.js 16.2.6 (App Router, RSC) Language TypeScript 5 (strict mode) AI Google Gemini (gemini-3-flash-preview) via @google/genai v2 Database Supabase (PostgreSQL) Authentication Supabase Auth (Email/Password + OAuth) Payments Stripe (Checkout, Webhooks, Billing Portal) Styling Tailwind CSS v4 + shadcn/ui + Lucide React PDF Export @react-pdf/renderer v4 DOCX Export docx v9 Forms & Validation react-hook-form v7 + Zod v4 Notifications Sonner v2 Runtime React 19.2.4 Deployment Vercel (SSR-ready architecture) 3. Problem Solved Creating professional documents is still slow, repetitive, and expensive. Freelancers and small businesses typically: Use generic templates that still require heavy editing Pay for enterprise legal/document tools Or spend hours formatting documents manually DocuPilot AI removes this friction by transforming structured input into fully written, professionally formatted documents in seconds using AI. 4. Key Results & Highlights 9 AI-powered document types generated from structured inputs Dual export system: PDF (free) and DOCX (premium) In-browser export with zero backend rendering dependency AI-powered section-level editing (rewrite, simplify, translate, improve tone) Shareable document links with secure expiring tokens Template marketplace (save, fork, reuse workflows) Stripe-powered freemium model with full billing lifecycle Server-side usage enforcement with monthly quota resets Production-grade authentication with Supabase SSR + OAuth support Fully responsive UI with dark/light mode support 5. Problem (Context) Professionals constantly need documents like: Contracts before starting work Invoices after project completion NDAs before discussions Proposals to win clients But existing solutions are either: Too generic (template-based tools) Too expensive (enterprise legal platforms) Or too manual (word processors + formatting effort) There is no fast, AI-native, affordable system built specifically for independent professionals. 6. Solution DocuPilot AI solves this with a structured AI pipeline: User selects a document type Fills a guided structured form Data is sent to a domain-specific Gemini prompt engine AI returns structured JSON sections The app renders it into an editable document interface Users can then: Edit sections with AI (tone, clarity, summarization, translation) Export as PDF or DOCX Save as reusable templates Share via secure public links All within a single, unified SaaS experience. 7. Core Features AI Document Generation Form-to-document pipeline powered by Gemini with custom prompt builders and strict Zod validation per document type. Supported Document Types Invoice Contract NDA Business Proposal Quotation Scope of Work Resume Cover Letter Employment Letter AI Section Editor Inline AI tools per section: Rewrite Simplify Make Professional Summarize Translate Export Engine PDF export via @react-pdf/renderer DOCX export via docx (premium tier) Document Sharing Secure shareable links Expiring tokens stored in database Public read-only route for clients Template System Save reusable templates Fork public templates Customize workflows per use case Freemium Billing Free: 3 documents/month Premium: $19/month unlimited access Stripe Checkout + Billing Portal + Webhooks Usage Enforcement Server-side quota tracking Monthly reset via Supabase RPC Authentication & Security Supabase Auth (Email + OAuth) SSR session handling Middleware route protection UX Enhancements Dark/light mode Responsive dashboard Usage analytics Toast notifications 8. Stack (Full Architecture Overview) Frontend Next.js 16 App Router, React 19, TypeScript strict mode, Tailwind CSS v4, shadcn/ui Backend Next.js API Route Handlers (13 endpoints) + Supabase service role client Database Supabase PostgreSQL: profiles documents templates document_shares subscriptions AI Layer Google Gemini (gemini-3-flash-preview) with: 9 domain-specific prompt builders structured JSON outputs configurable temperature control Payments Stripe v22: Checkout sessions Subscription lifecycle Billing portal Webhook handling Export System PDF: @react-pdf/renderer DOCX: docx v9 Client-side download handling 9. My Role As the sole developer, I handled the entire product lifecycle: Designed full system architecture (App Router + RSC + API layer separation) Built AI pipeline with structured prompt registry (9 document-specific engines) Developed all backend API routes (13 endpoints) Designed Supabase schema and server-side usage enforcement logic Implemented authentication with SSR session handling and OAuth flows Integrated full Stripe billing lifecycle (checkout, webhooks, subscriptions) Built export engine for both PDF and DOCX formats Developed full UI using RSC + shadcn/ui + Tailwind Implemented secure sharing system with expiring tokens Built dashboard analytics and usage tracking system 10. Live URL https://docupilot-ai-seven.vercel.app/ 11. GitHub Repository https://github.com/engraya/DocupilotAI
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