Search returns things you have to read. An answer engine reads them for you.
I built a search engine on top of 50k saved tweets. Ask it something and it returns the five most relevant chunks — found through hybrid retrieval (BM25 keyword search plus vector search) and reranked by a cross-encoder. A Gemma 4 MoE layer already runs in the background too, writing its own reflections on how saved documents connect to each other. You get the chunks back, ranked. Then you read them and synthesise.
That last step bothered me. The model already synthesises when generating reflections. The retrieval already works. The only missing piece was wiring them together at query time.
So I added POST /search?mode=answer.
What it does
