Vector search, a technology that captures the semantic meaning of data using embedding models trained on vast amounts of information, can help determine the quality of blog post comments before scrolling down the page. By converting comments into vector embeddings and scoring their contextual similarity, it's possible to identify high-quality comments that are relevant to the topic at hand. This technique has practical applications beyond just blog posts, such as fraud detection in financial services, where Revolut uses vector search to detect suspicious transactions every day. The technology is also being explored for other use cases, with developers encouraged to build innovative applications using vector search. A Chrome extension has been built to demonstrate the usefulness of vector search in analyzing blog comments, and its source code is available for others to use and modify.