TL;DR: I ran K-Nearest Neighbors (KNN) over a Google search corpus to find cross-query connections no single search can ever surface. Human learning is all about building connections in your head. Like last week, I read an ArXiv paper on quantization , which prompted me to do some Google-fu for a FP16 vs INT8 comparison on NVIDIA’s forums, and then make a site:github.com search for a Llama.cpp fork with optimized kernels to try it myself. This takes time. Google — or an LLM — can’t make these mental hops for you. So I wanted to see if I could speed this up by programmatically finding and shortlisting these connections for me to review later , using a classic algorithm from 1951 . To collect the raw material, I used my SERP API to run 100 varied Google searches on a specific topic — then merged the ~800 results into one corpus, embedded every row, and ran cosine k-NN over the whole thing.…