Introducing Toast 1
95 points by mplappert 3 hours ago | 26 comments
tolugenius 2 hours ago
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?
replybreadislove 2 hours ago
the issue with smaller general models (see at the charts) are way behind the frontier models when it comes to search. we've found that there is huge uplift of having a fast dedicated model. from our perspective, having a very good index is the biggest lever and then having a specialised model.
replydocheinestages 46 minutes ago
Are the benchmarks comparing just the models while keeping the harness the same (the open-source Toast harness)?
replybreadislove 42 minutes ago
yes for the retrieval benchmarks. For officeqa pro v2 we used Codex (as databricks did) and for Harvey LAB we used the vanilla harvey benchmark. For these benchmarks we added minimal tools to use mixedbread search and toast 1.
replyandai 2 hours ago
Article should probably explain what "Mixedbread Search" is.
replybreadislove 2 hours ago
Mixedbread Search is a multimodal & multilingual search product, where you can upload any kind of data and make it searchable. Its powered by Wholembed [1] v3, a late interaction retrieval model.
replymoffkalast 2 hours ago
> performs best with Mixedbread Search, but it can work with any search backend
replyBread-first search, is it?
amazingamazing 2 hours ago
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
replyAnyway, back to this - it seems to be more like the AI equivalent of algolia than google
ChrisArchitect 2 hours ago
Dunno about this branding/naming scheme - every time it comes up we have to double-check that it isn't some spoof/joke page
replybreadislove 2 hours ago
there is full lore around the naming. i can guarantee you that we are pretty dedicated around our research and product.
replyandai 2 hours ago
An inside joke that confuses your target audience and makes you sound like a joke, is probably not what you want as your brand.
replylevmiseri 2 hours ago
(FWIW I like the branding. Hugging Face doesn't seem to struggle because of its name either)
replyInsideOutSanta 25 minutes ago
If you get big, the naming no longer matters. We made jokes about the Wii until everybody had one. But if you don't get big, and most people have no idea what they're looking at when they see your product for the first time, naming definitely matters.
reply
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 think in a lot of ways Google peaked and is now on the decline into a profitable but much less relevant services company.
For example, a couple of days ago I described a problem with my refrigerator's water dispenser to Google Gemini, and it told me exactly how to fix it. I then went looking for a video and fixed the thing in under 15 minutes. The only way that Gemini could have been better is if it linked to a video itself.
Do you mean search into less-well-known topics? Or something else?
At least validation seems faster than without, but you get what you pay for when it comes to llm intelligence