Inspiration: Solving the data overload problem for both businesses and consumers
What it does: Collecting raw data from the Internet, process with AI models and techniques, and present the results in a way that is much easier than a simple summary for information digestion and utilization.
How we built it: Using Python and JavaScript and machine-learning models for backend and frontend.
Challenges we ran into: Large Language Models are slow in response to data processing requests, and sometimes produces unexpected results that require writing additional algorithms to reduce errors to the minimum.
Accomplishments that we're proud of: It's actually a working application given the short timeframe and our extremely busy schedule.
What we learned: Docker is a pretty good tool for application development and distribution.
What's next for AI/ML based data processing application with Docker: Improvements for scaling up.
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