great idea! Now, you are running into the same issue Google Trends had to solve: term disambiguation. For instance, "atom" is ambiguous in a comparison of editors like this: https://hackernewstrends.com/?q=sublime&q=atom&q=vscode. Given LLMs it might be possible to use an embedding vector (with context) instead of a text string for indexing, and if you do, this problem might go away.