The world of computer vision is constantly evolving, with researchers always seeking faster, more efficient ways for AI to 'see.' A core component of modern computer vision is the Vision Transformer (ViT), a powerful architecture inspired by natural language processing. However, ViTs often rely on a computationally expensive mechanism called 'attention.' Now, researchers have proposed a new method called 'Activator,' which aims to streamline the ViT by replacing the attention mechanism with a more efficient component called a Gated Linear Unit (GLU). Think of it like swapping out a complex gear system for a sleeker, faster engine. This GLU acts as a gatekeeper, controlling the flow of information within the network. The result? Activator performs competitively with existing ViTs while potentially reducing the computational burden. The researchers tested Activator on standard image datasets like CIFAR-10 and CIFAR-100, achieving comparable or even better accuracy than traditional ViTs and other alternatives like the MLP-Mixer and Synthesizer. This suggests that Activator could be a promising new direction for building faster and more efficient vision transformers, potentially opening doors for more sophisticated computer vision applications on devices with limited resources. While more research is needed to fully understand the potential of Activator, this initial work suggests a bright future for faster, more accessible computer vision.