I deeply love this idea of specialized LLMs for search. It's also extremely confusing to me how rough Google's entrance here is.

When I, a human, need an answer to anything moderately complex, it's unlikely that I get it on the first (pre-AI) round of google searching. Simple stuff, sure, but more likely I'll need to go 2-5 rounds. Maybe click a few links. Double-check my assumptions.

An LLM that can do that quickly seems like a slam dunk. I wonder what other problems benefit from that 10x-100x increase in context + 2-5 rounds with the LLM.

I guess someone who has used a search agent (or a dedicated subagent) can speak when I'd reach for a tool like this vs either just 1) a smaller general model or 2) a non-llm approach to the problem? Like it's interesting I'm just curious how a search agent compares to say a model with dedicated rag pipelines is that much different?

Are the benchmarks comparing just the models while keeping the harness the same (the open-source Toast harness)?

> performs best with Mixedbread Search, but it can work with any search backend

Bread-first search, is it?

I’m disappointed this is some AI thing and not a breadboard company.

Article should probably explain what "Mixedbread Search" is.

Some days I have no idea what the fuck I am looking at.

I know everyone loves to hate on google but i find search overviews and asking gemini to search for things way faster than any alternative. I was curious about a development near me and asked literally that and gemini pulled court records in about 20 seconds

Anyway, back to this - it seems to be more like the AI equivalent of algolia than google

Dunno about this branding/naming scheme - every time it comes up we have to double-check that it isn't some spoof/joke page

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Damn, now I'm hungry. All this bread talk.