Aaditya Sattawan

Full-Stack Developer | Competitive Programmer (Codeforces Specialist) | AI Enthusiast

AI & Data Science Intern · XYlofy AIIndian Institute of Technology (BHU) VaranasiVaranasi, Uttar PradeshAvailable from 15th Jul, 2026 · internship

Open to roles

Problem SolvingC++c languagePython

GitHub LinkedIn Codeforces· codeforces.com

EXPIRY ALERT ' 26

🌐 Live Demo: https://expiry-alert-fawn.vercel.app/ 🎥 Demo Video: https://drive.google.com/file/d/1LanSw-cQpBua3pJqWoPW7FwHHs7WREdO/view?usp=sharing 💻 GitHub Repository: https://github.com/Tiku57/ExpiryAlert Project Overview ExpiryAlert is an enterprise-grade document and compliance lifecycle management platform that helps organizations track contracts, licenses, certificates, audits, warranties, and other expiry-based records. It proactively alerts users before documents expire, preventing compliance issues and missed deadlines. Key Features Dashboard with analytics, KPIs, and expiry insights Complete CRUD operations for records Dynamic expiry status calculation (Active, Expiring Soon, Critical, Expired) Smart notification system with unread indicators Document upload and attachment management Record renewal workflow with history tracking Search, filtering, sorting, pagination, and bulk actions CSV export Interactive analytics and charts Responsive enterprise UI with dark mode Settings and profile management Technical Highlights Built with Next.js 16 (App Router) and TypeScript Prisma ORM with SQLite (development) and easily portable to PostgreSQL React Server Components and Server Actions Tailwind CSS + shadcn/ui for modern UI React Hook Form + Zod for type-safe validation TanStack Table for enterprise-grade data tables Recharts for analytics visualization Optimistic UI updates for a smooth user experience Design Decisions Expiry status is calculated dynamically instead of being stored, ensuring records always reflect the current date without scheduled jobs. Server Actions are used instead of traditional REST APIs to simplify architecture and improve type safety. The project is structured to support future migration to PostgreSQL and cloud deployment with minimal changes. Repository Contents The repository includes: Complete source code Professional README Detailed technical documentation (THOUGHT_PROCESS.md) Prisma schema Sample data Environment configuration example Notes for Reviewers The application contains seeded dummy data for demonstration. File uploads currently use local development storage and can be easily migrated to cloud storage (AWS S3, Cloudinary, etc.). The architecture is designed to be scalable for future features such as AI-powered document extraction, email/SMS reminders, OCR, role-based access control, and multi-tenant enterprise deployments. Thank you for your time and consideration!

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MedFlowAI (AI-Powered Healthcare Appointment & Consultation Management Platform)

🌐 Live Demo: https://medflow-ai-66y7.onrender.com 🎥 Demo Video: https://drive.google.com/file/d/1wtyDARIsW77EWnWNrtJQ_nOHc_vEn4jj/view?usp=sharing 💻 GitHub Repository: https://github.com/Tiku57/MedFlow-AI Data Model Diagram The application uses a relational database designed with Prisma ORM. User → One user can be a Patient or Doctor. Doctor → Has many Appointment Slots and Consultations. Patient → Has many Appointments, Prescriptions, and Health Records. Appointment Slot → Belongs to one Doctor and can be booked by only one Patient. Appointment → Links one Patient, one Doctor, and one Appointment Slot. Consultation → Created after a completed appointment and stores doctor notes. Prescription → Generated from Consultation using Gemini AI and belongs to a Patient. Relationships Doctor (1) ──────< Appointment Slot Doctor (1) ──────< Appointment >────── (1) Patient Appointment (1) ────── (1) Consultation Consultation (1) ────── (1) Prescription Patient (1) ──────< Prescription Patient (1) ──────< Health Profile Thought Process – Handling Concurrent Booking Requests A major challenge was preventing two patients from booking the same appointment slot simultaneously. To solve this, I used atomic database transactions with Prisma ORM and PostgreSQL row-level locking. Instead of checking availability in application memory, the booking operation updates the slot only if it is still available. Since PostgreSQL performs the operation atomically, the first request successfully books the slot while any simultaneous request safely fails and returns an appropriate message. This completely prevents double bookings and ensures consistent scheduling even under concurrent traffic. One Improvement with More Time I would implement real-time updates using WebSockets, allowing patients and doctors to instantly see appointment changes without refreshing the page. I would also add background job queues for AI processing, automated testing, and advanced analytics to improve scalability and production readiness. One Feature Intentionally Left Out I intentionally did not implement video consultations and real-time chat. These features require WebRTC, signaling servers, media streaming, and additional security considerations, which would have significantly increased development complexity. Instead, I prioritized building a reliable appointment management system, AI-powered prescription generation, secure authentication, and conflict-free scheduling to deliver a polished and production-ready MVP. Notes for Reviewers The project is fully responsive across desktop, tablet, and mobile devices. AI-powered prescription generation is implemented using Google Gemini AI. API keys are securely managed using environment variables and are not exposed in the repository. The repository contains a detailed README with setup instructions and deployment steps. The live demo is hosted on Render. Since it uses the free tier, the first request may take 30–60 seconds while the server wakes up.

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QueueCure (Real-Time Smart Clinic Queue Management System)

Project Links 🌐 Live Demo: https://your-live-demo.vercel.app 💻 GitHub Repository: https://github.com/Tiku57/Queue-Cure 🎥 Demo Video: https://drive.google.com/file/d/1tupuAkVZfU5osTT5ZCUvRG6mMhQFKrSM/view?usp=sharing QueueCure is a production-inspired, real-time clinic queue management platform designed to replace traditional paper-token systems used in neighbourhood clinics. It enables live queue tracking, predictive wait-time estimation, priority and emergency patient handling, QR-based patient tracking, and instant synchronisation across Receptionist, Doctor, Patient, and TV interfaces using Socket.IO. Tech Stack: Next.js, React, TypeScript, Express.js, Node.js, MongoDB Atlas, Socket.IO, Tailwind CSS, Zustand, Shadcn UI, and Zod. Key Engineering Highlights Real-time synchronization across all connected screens using Socket.IO (no page refreshes). Predictive wait-time engine based on recent consultation history instead of fixed estimates. Atomic MongoDB operations to prevent race conditions during concurrent queue updates. Priority & emergency queue handling for real-world clinical workflows. Offline recovery using automatic REST polling when WebSocket connections are unavailable. Scalable multi-clinic architecture using clinicId isolation and Socket.IO Rooms. What to Review Please evaluate: Receptionist workflow (patient registration in under 10 seconds) Live synchronization across Receptionist, Doctor, Patient, and TV screens Dynamic wait-time calculation Priority/Emergency queue management Responsive UI across all devices Real-time updates without page refreshes Due to the platform's upload limitations, I couldn't attach the separate Thought Process PDF and Socket Event Diagram. Their contents—including system architecture, engineering decisions, concurrency handling, scalability strategy, wait-time algorithm, and implementation details—have been incorporated into the GitHub repository documentation and README.

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Student Expense Tracker Pro (Python CLI with Budget Tracking & Analytics)

Thank you for reviewing my submission! Run: python3 expense_tracker.py Dependencies: None (Python 3 Standard Library only). Sample Data: Three expenses are preloaded for immediate testing. Storage: Data is automatically saved in expenses.json, so expenses and budget persist across sessions. Design Decisions Used Object-Oriented Programming with a dedicated Expense dataclass for clean, maintainable code. Chose JSON for persistent storage because it is lightweight, human-readable, and requires no external database. Added input validation for dates, amounts, and categories to prevent invalid data. Implemented graceful error recovery—if the JSON file becomes corrupted, the application restores default data instead of crashing. Used UUIDs to uniquely identify expenses, making deletion reliable. Organized search and delete operations into a separate Manage Expenses menu to keep the main interface simple. Features Beyond the Requirements In addition to all mandatory hackathon requirements, I implemented: Monthly budget tracking with overspending warnings. Expense search by date or category. Expense deletion. Advanced analytics (budget usage, average, highest, lowest expense, and category-wise breakdown). Export reports in both TXT and CSV formats. Persistent local storage across sessions. Testing The application was tested for: Valid and invalid user inputs. Empty datasets. Data persistence after restarting the application. Budget calculations. Search and delete operations. Report generation. Graceful exit using Ctrl + C without crashing. Future Improvements Given more time, I would add expense editing, recurring expenses, customisable categories, monthly trend visualisations, automated unit tests, and AI-powered spending insights to evolve the project into a more intelligent personal finance assistant.

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