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Currently working as a Full-Stack Developer building web applications using Java and Node.js on the backend and HTML, CSS, JavaScript, and Tailwind CSS on the frontend.
I work with MySQL and MongoDB for databases and use AWS for deployment and scalability.
Open to freelance or contract opportunities with startups that need reliable full-stack support.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Resolve: an AI support agent that answers from a company’s own policies, looks up orders, and escalates weak matches to a human instead of guessing. A human approves replies before they go out.
Live demo: support-resolver.vercel.app
For the human approval step, I'd show the exact policy passage and its effective date beside the draft. Does Resolve flag a reply for re-review if the policy changes while it waits for approval?
Allergen Info UK: from “ask your server” to a 2-second scan
Every restaurant and hotel has to tell guests which of the 14 major allergens are in each dish. Paper folders and outdated PDFs make that slow and risky.
I built a UK-based SaaS that fixes it. The restaurant builds its menu and marks allergens once. The platform generates a unique QR code. Guests scan it and instantly see their menu filtered to safe dishes. There is no app to download and no tech setup for the restaurant.
Design choices I’m proud of:
1. Guests filter by allergen first, and the menu updates instantly.
2. Restaurant setup is three steps: build the menu, mark allergens, print the QR code.
3. Allergens use clear, color-coded labels, so a guest can read a dish at a glance instead of decoding small print.
Result: Restaurants swap paper folders and out-of-date PDFs for one always-current menu and guests get their answer in seconds instead of waiting for staff.
Question for you: How did the last restaurant you visited handle allergen info? Paper menu, QR code or just “ask the server”?
Allergen filters right up front. Love that.
Built an automated financial data pipeline covering approximately 2,400 NSE-listed stocks, transforming daily market data into structured analytics and client-ready reports.
Developed Python-based data collection and processing workflows using Yahoo Finance data, Pandas, and SQLite. Automated market snapshots, financial metric calculations, and formatted Excel/CSV report generation.
Implemented scheduled GitHub Actions workflows to orchestrate data collection, report generation, Google Drive uploads, and automated email delivery. Added retry mechanisms, delivery monitoring, and a Next.js dashboard to make market information and pipeline status accessible.
Key capabilities:
Automated data collection across approximately 2,400 NSE stocks.
Daily market snapshots and SQLite database management.
Financial data processing and reporting with Pandas.
Automated Excel and CSV report generation.
Google Drive integration and email-based report delivery.
Scheduled workflows, retries, and delivery verification.
Web-based financial data dashboard.
