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.

That's totally not my experience. The AI component (as opposed to the knowledge component) is really what makes these models useful, and you could add search as a tool. Of course for that you'll be dependent on a search provider, that's true.

This is actually so ironic. Corporations spent fortunes to design cool websites, but what people really want is structured, easy to read information in the context they want.

So flow is you type search query to Gemini, Gemini uses Google search, scans few results, go to selected websites, see if there is anything relevant and then compose it into something structured, readable and easy to ingest.

It's almost like going back to 90s browsing through forums, but this time Gemini is generating equivalent of forum posts "on the fly".

Unless you can provide a (community) curated list of sources to search through (e.g. using MCP). Then I think local models may become really competitive.