Robustors is a professional platform dedicated to building branded YouTube channels that are designed for long-term success. The website features a clean and modern design, starting with a bold and engaging homepage that captures the essence of the services offered. Visitors can explore the services page, which provides a detailed breakdown of offerings, including channel branding, strategic growth tools, and expert guidance, all visually represented with appealing icons. The contact section ensures seamless communication, featuring an easy-to-use inquiry form and links to social media platforms for broader engagement. Robustors is committed to empowering creators to thrive in the competitive digital landscape with tailored solutions and a focus on sustainable growth.

Invis is a cutting-edge chat app designed for lightning-fast communication powered by the Phoenix framework, ensuring instant message delivery and a seamless experience. With robust encryption, your chats stay private and secure. Invis also features a versatile chatbot with multiple personalities, ready to assist, entertain, or keep the conversation flowing. Stay connected faster, smarter, and safer with Invis!

This project is a real-time human detection system that utilizes computer vision to identify individuals in video frames using the YOLO object detection model. It processes each frame to detect humans, ensuring that only unique detections are saved by calculating the distance between bounding boxes and filtering out overlapping detections. The system runs in a background thread, allowing continuous monitoring without interrupting the main processing. The detected images are stored and can be compiled into a Word report, making it suitable for applications like security, attendance tracking, and activity monitoring.

This project is a real-time human detection system that utilizes computer vision to identify individuals in video frames using the YOLO object detection model. It processes each frame to detect humans, ensuring that only unique detections are saved by calculating the distance between bounding boxes and filtering out overlapping detections. The system runs in a background thread, allowing continuous monitoring without interrupting the main processing. The detected images are stored and can be compiled into a Word report, making it suitable for applications like security, attendance tracking, and activity monitoring.