I'm a passionate full-stack developer who loves to explore new technologies! 🚀

• 🎓 I started as a front-end developer, but my curiosity led me to the back-end world, and now I'm a full-stack developer! 💻
• 🧠 I enjoy working with a variety of technologies including React, Node.js, Python, and Vite.
• 🤖 AI fascinates me and I love incorporating it into my projects.
• 🕹️ In my spare time, you'll find me tinkering with Nerves.
• 🌱 I'm always on the lookout for new technologies to learn and master.

AI 🤖 | Back-End 🏗️ | React ⚛️ | Node.js 🟩 | Python 🐍 | Vite ⚡ | Nerves 🕹️

In this project, I've delved into the world of web scraping and algorithm analysis to reverse engineer YouTube's trending topics algorithm.

• Node.js: I used Node.js to scrape YouTube pages and create a server. This server stores the scraped data and serves it to the front-end.
• Express: Express is being used to handle server-side operations.
• React: The front-end of the application is built with React. It receives data from the Node.js server and presents it in a user-friendly format.

• Web Scraping: I scraped YouTube pages to gather data on various factors such as the video transcript, top comments, number of comments, likes, dislikes, video length, video thumbnail, video title, number of subscribers, total views of the YouTuber, and the duration of the YouTuber's presence on the platform.
• Data Analysis: I used GPT-4 and GPT Vision to analyze the correlation between these factors. This analysis is key to understanding how YouTube's trending topics algorithm works.
• Reverse Engineering: The ultimate goal was to reverse engineer the algorithm. This means figuring out the rules and patterns the algorithm uses to determine trending topics.

This project could have a wide range of applications. For example, it could help content creators understand what type of content is more likely to trend, or it could be used for academic research into algorithms and social media.