🤝 Top Collaborators
.png&w=640&q=75)
🤓 Latest Submissions
AI Educational Tablekeeping system
TableKeeper is a smart and modern restaurant table management platform designed to simplify the way restaurants handle reservations, table availability, customer information, and seating operations. In many restaurants, managing tables manually can become difficult, especially during busy hours. Staff members often have to handle phone reservations, walk-in customers, waiting lists, table availability, and customer requests at the same time. This can lead to confusion, long waiting times, double bookings, inefficient table usage, and a poor customer experience. TableKeeper addresses these challenges by bringing the complete table management process into one simple and organized digital platform. The primary goal of TableKeeper is to make restaurant operations more efficient while creating a smooth and convenient experience for customers. Restaurants can use the platform to maintain a real-time view of their tables and their current status. Tables can be identified as available, reserved, occupied, or waiting to be cleaned, allowing staff to understand the restaurant's seating situation at a glance. This reduces the need for manual tracking and helps staff make faster decisions during busy periods. One of the key features of TableKeeper is digital reservation management. Customers can check available tables and select a suitable date and time for their visit. Once a reservation is created, the restaurant can view and manage the booking through its dashboard. This helps reduce the chances of double bookings and makes it easier for staff to organize upcoming reservations. The system can also maintain important booking information such as customer name, contact details, reservation time, number of guests, and special requests. TableKeeper also focuses on improving table utilization. Restaurants often lose valuable time and revenue when tables remain unused because their availability is not properly tracked.
6 Oct 2026
DecisionLab | AI-Powered Product Comparison
Information overload makes online shopping exhausting. Consumers spend hours comparing specs, prices, and conflicting reviews across dozens of tabs, often leading to decision fatigue and buyer's remorse. Standard filters like "price" or "average rating" are rarely enough to capture nuanced, real-world needs like "I need a lightweight laptop for college that can also handle light gaming." DecisionLab solves this by acting as your personal AI shopping assistant. Our platform uses an intuitive three-step process to simplify complex buying decisions. First, users search for and select the products they want to compare, while our system fetches real-time data and pricing. Next, our conversational AI conducts an interactive interview to discover the user's hidden preferences, budget constraints, and specific use cases, dynamically determining what features matter most. Finally, the AI synthesizes this personal context with the product data to deliver a definitive recommendation, complete with a customized scoring breakdown and clear trade-offs. DecisionLab was primarily built primarily using NativelyAI, the AI coding platform that accelerates product development from idea to working app. I used the full token budget to generate core features, structure, and functionality. Once the token budget was fully used, I completed the remaining minor tasks and polish some parts manually. Built with React and powered by Supabase Edge Functions, DecisionLab takes the stress out of shopping by turning hours of tedious research into a simple conversation.
10 Aug 2026
