Company
Date Published
Author
Adam Gordon Bell
Word count
1942
Language
English
Hacker News points
None

Summary

The blog post outlines how text embeddings and cosine similarity can be used to automate the process of finding related blog posts, which is particularly useful as the number of posts grows. Text embeddings map words into multi-dimensional space to gauge their semantic relationships, while cosine similarity measures the angle between these vectors to determine similarity. The post discusses the evolution from manually created dimensions to advanced machine learning models like Word2Vec and the OpenAI embedding API, which generate vectors based on richer semantic understanding. This approach allows for the comparison of entire texts rather than just individual words, making it more effective for identifying related content. The author describes how this technique is implemented to enhance the user experience on the Earthly Blog by recommending contextually relevant articles, emphasizing the adaptability and improvement potential as embedding technologies evolve.