Parth Pawar

Enthusiastic and motivated third-year Computer Science and Engineering student pursuing a Bachelor's of Technology. Highly detail-oriented and a strong team player, known for exceptional organizational skills and the ability to manage multiple projects accurately and efficiently. Proven track record of successfully handling diverse priorities with a positive and proactive attitude. Reliable and organized, ready to embrace additional responsibilities to support and achieve team objectives.

• Laser Transreciever, 2 Months, A Laser Transmitter and Receiver Module with Arduino is a wireless communication system that employs laser technology for data transmission between two points. Consisting of a laser transmitter module, a receiver module, and Arduino microcontrollers, this setup enables the modulation, transmission, reception, and decoding of data via modulated laser beams.
• Clipsy, 2 Months, Clipsy is a cross-platform clipboard solution that seamlessly connects the clipboard functionality between Windows and Android devices. This innovative tool enables users to access and share clipboard content effortlessly across these platforms. With Clipsy, you can copy text or files on your Windows computer and instantly access them on your Android device, and vice versa. This streamlined clipboard synchronization enhances user productivity by eliminating the need for manual transfers or intermediary steps.
• Speedify, 2 Months, The application provides a web interface for users to search for products on Amazon, sending queries to Amazon's search page, and scraping the results using BeautifulSoup to extract product information. This data is stored in a SQLite database, checking for existing entries based on the ASIN and logging new products and their prices. Users can view the price history of products via the /price_history/ endpoint, which queries the database and generates a plot using Matplotlib, embedding the plot in the web page through HTML and Jinja templating
• DocInsight, 2 Months, The application is an AI-powered tool designed to provide automated insights and answers to queries from documents, specifically CSV files. Leveraging Retrieval-Augmented Generation (RAG), the system uses embedding models to understand the content of documents and employs a Large Language Model (LLM) to generate accurate, context-aware responses to user queries.