⚠️ Problem I Solved in a Production App In a live app, users were submitting the same request mul...⚠️ Problem I Solved in a Production App In a live app, users were submitting the same request mul...
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In a live app, users were submitting the same request multiple times due to slow network responses.
This resulted in duplicate orders and inconsistent data.
✅ Solution I Implemented
I solved this by:
• Disabling actions after the first tap in Flutter
• Adding request validation on the Node.js backend
• Using Firebase to confirm transaction state in real time
🎯 Result
✔ No duplicate requests
✔ Clean and consistent data
✔ Better user trust
💡 Lesson: Never trust the client alone — always protect logic at the backend.
SupportFlow AI is a customer support management system built with Python, PySide6, FastAPI and PostgreSQL.
It helps teams manage customer requests, assign priorities and responsibilities, track conversations, and generate AI-assisted response suggestions while keeping a human in control of the final reply.
I built the application structure, interface, backend integration, database workflows, API layer and automated testing.
The conversation-revision idea sounds useful. I would also check that revision on the server when accepting the send, so a message arriving between review and click cannot slip through. Mark the draft stale while keeping the human edits visible. Two useful regression cases: a...
Agri Guard is a full-stack web application that helps farmers identify plant diseases instantly using AI. Users can upload a photo of an affected plant or describe symptoms in text, and the app returns a diagnosis along with a verified treatment plan.
What I built:
A clean, dark-themed landing page with a hero section, trust badges, feature highlights and clear call-to-action buttons (Get Started, Login to Dashboard)
A Node.js and TypeScript backend with REST API routes, utility modules and environment-based configuration
AI integration for image analysis and text-based diagnosis through the OpenRouter API
A disease database with treatment recommendations, plus scripts to extend it with new diseases
Login and dashboard flow for returning users
My focus: a clear, conversion-friendly landing page, readable typography, consistent spacing and a responsive layout that works on both desktop and mobile.
How do you connect the model's diagnosis to the treatment database? Does it have to match a known disease entry before showing a treatment, or can the model generate recommendations outside that list?
Built a highly responsive web application prototype focusing on smooth user interfaces and swift database connectivity. Optimized web performance to ensure rapid load times and cross-platform compatibility.