Comment by mark_l_watson
3 days ago
I agree 100%. Often when I use increasingly powerful local models (qwen3.5:32b I love you) I mix in web search using search APIs from Brave, Perplexity, and DuckDuckGo summaries. Of course this requires that I use local models via small Python or Lisp scripts I write. I pay for the Lumo+ private chat service and it has excellent integrated search, like Gemini or ChatGPT.
EDIT: I have also experimented with creating a local search index for the common tech web sites I get information from - this is a pain in the ass to maintain, but offers very low latency to add search context for local model use. This is most useful with very small and fast local models so the whole experience is low latency.
Interesting idea on the local search index! It occurs to me that running something that passively saves down content that I browse and things that AI turns up while it does its own searches, plus a little agent to curate/expand/enrich/update the index could be super handy. I imagine once it had docs on the stuff I use most frequently that even a small model would feel quite smart.
yeah i really like this idea too, I don't need the entire internet indexed I only need the stuff i'm interested in indexed. I can imagine like a small agent i can task with "find out as much as you can about <subject>" and what it does is search the web, download the content, and index it for later retrieval. Then I can add a skill for the main agent to search the knowledge base if needed. Kind of like a rag pipeline but using agents to build a curated data source of stuff i'm interested in.
Nice idea, caching what you are already browsing.