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trjordan 7 hours ago [-]
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.
Waterluvian 7 hours ago [-]
About 15 years ago I would sometimes spend hours on Google image search discovering childhood toys and filling in vague memories of locations or things. I tried this recently and it’s basically impossible. I actually get to the end of the search results in like 3 minutes and the quality is horrible now.
I think in a lot of ways Google peaked and is now on the decline into a profitable but much less relevant services company.
sejje 5 hours ago [-]
Google search is so bad compared to peak google.
Maybe the web is really that much worse, and the SEO tactics so hostile to genuine content.
But, honestly...just bring back the old google, where I have all kinds of search modifiers to perform exactly the search I want, that just returns all the matching results that have been indexed. Let me sort out the rest. How do you remove the ability to do "exact text search"? It's the most basic of search functions. Remember having "|" modifiers? AND modifiers?
If google launched that again, even as a separate engine, I think they'd have a good product again. Maybe being good doesn't pay the bills for google, though.
conception 5 hours ago [-]
Kagi
radlad 4 hours ago [-]
Recently learned I can just add a question mark to the end of my Kagi search to get an assistant answer. Kagi was already great at surfacing the most relevant pages, but now I often don't even need to click.
UltraSane 5 hours ago [-]
Yandex image search is a lot better now.
toddmorey 4 hours ago [-]
Have always joked with colleagues about how hard it is to find content in Google Docs, the office suite built by a search company.
gwbas1c 7 hours ago [-]
What kind of search do you mean?
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?
draw_down 54 minutes ago [-]
[dead]
gottagocode 7 hours ago [-]
I know it can be a deep time sink, but I notice more and more how much deeper my understanding is of a certain problem/best-practice after developing the neuropathways involved in crawling between reddit, stack overflow, etc, to get to the proper solution. I love the instant answer from google ai, but I also notice an itch to purposefully force myself to ignore it when time allows.
__s 6 hours ago [-]
ime google ai is wrong often enough that I'm still doing that to verify its results
At least validation seems faster than without, but you get what you pay for when it comes to llm intelligence
MrBuddyCasino 5 hours ago [-]
Gemini search isn’t great by default, but Deep Research gives noticeably better results.
blitzar 2 hours ago [-]
I really wanted this to be a hardware startup - the Juicero of toast.
Sadly its another software company.
layer8 2 hours ago [-]
Selling you a subscription for slices of toast in individual cartridges for their toasting hardware?
joseda-hg 1 hours ago [-]
I'm pretty confident we could steal The Though Emporium's Single use thermite based instant hot dog [1] design and make instant packaged toast, fresh to order
Seems to me if it doesn't also apply uniform melted butter it would be a hard sell.
satvikpendem 2 hours ago [-]
Looks good as I use something similar with the SearXNG MCP, but a shame this isn't an open weight model. There are some wrappers around SearXNG which seem to reduce the token counts returned thus making it easier for the calling model to understand, but a full dedicated model for search is nice. How does it compare with Perplexity, Gemini with search, and Parallel AI? Those are the cloud providers of search based models that I've seen so far.
breadislove 1 hours ago [-]
hey its a search agent for YOUR own data but can also work over the web. Mixedbread is focusing on providing evidence for agents for your internal data. Toast can interact with any search api. You should be able to provide the SearXNG api to it and it should be good to go. Here the default harness: https://github.com/mixedbread-ai/toast-harness
andai 7 hours ago [-]
Article should probably explain what "Mixedbread Search" is.
breadislove 7 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.
so, it works like notebooklm but without relying on RAG?
monkmartinez 1 hours ago [-]
I don't think so, but could be wrong. It seems to be a specialized layer like a lora or a merged model. So a RAG-agent-model thingy. No clue if it actually works. I don't understand why I would want to use it.
the thing is most agents waste most of their tokens looking up information which can cause context rot. most small models are not as good as looking up information. this model helps to lookup information for your main agent, which helps you to save tokens and still maintain quality.
tolugenius 7 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?
breadislove 7 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.
monkmartinez 1 hours ago [-]
A good index is a software and LLM problem if using the LLM for indexing. Are you looping "agents" in an embedding and encoding cycle before retrieval? There are thousands of RAG agents at this point and RAG is still not super great. A dedicated specialized model? You want to take on Qwen3.6 or Qwen3.8 wrapped a pi.dev harness agent that has been dedicated to be the "search" agent? How would you stack up?
breadislove 40 minutes ago [-]
you can look it up in the blog. RAG is not super great because of two reasons, single embedding vector models are not that good and stopped improving and second most models are not good at looking up information. we spend great time on improving the modeling side by inventing on the indexing level [1, 2]. and now we trained our model to be very good at search. it is matching the quality of Opus 5 and GPT 5.6 Sol while being faster. it helps your main agent to do the task at greater quality, while reducing cost per task.
Long time user of your embedding models. I'm trying to understand how this works and if I can leverage it.
It sounds like this is a new layer on top of your existing storage layer? So to use this, would I need to give you all of my data first? Or is there a version that can be run on prem?
breadislove 3 hours ago [-]
you can plugin your existing stack and use it via an openai compatible client.
Are the benchmarks comparing just the models while keeping the harness the same (the open-source Toast harness)?
breadislove 6 hours 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.
2 hours ago [-]
wackget 5 hours ago [-]
Me: "Huh, I wonder what 'Toast' from 'mixedbread.com' is. Sounds interesting..."
clicks link
> "Toast 1, our search agen-"
closes page
AlexErrant 3 hours ago [-]
Is this performative posting? We're supposed to care that you don't care?
Could you imagine this forum if everyone acted like you?
satvikpendem 2 hours ago [-]
Just flag it, these reddit tier comments are useless.
algoth1 4 hours ago [-]
You should take a closer look
vrganj 2 hours ago [-]
I, too, was excited for some sort of innovation in baking.
But no, more agents, more AI. Yawn.
moffkalast 6 hours ago [-]
> performs best with Mixedbread Search, but it can work with any search backend
Bread-first search, is it?
kelvinjps10 3 hours ago [-]
What are these names? before opening I thought it was going to be about bread
msie 2 hours ago [-]
Bah, i was looking forward to a new toaster!
mbirth 1 hours ago [-]
I was looking forward to a new version of the macOS CD/DVD burning software.
cryo32 7 hours ago [-]
Some days I have no idea what the fuck I am looking at.
jrflowers 4 hours ago [-]
I love to spend a dollar for a 70% correct search result
ChrisArchitect 7 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
breadislove 7 hours ago [-]
there is full lore around the naming. i can guarantee you that we are pretty dedicated around our research and product.
saratogacx 4 hours ago [-]
Looking through your blog it is very much keeping the brand baked in. It would be a nice little nod to toss a couple sentences about the branding on the about page or somewhere so if someone wants to know how you came to it they can. Leaving it to "wink, nod, inside joke you'll never know" is a bit off-putting when you are building a brand around the theme. It also makes it more memorable for those that read the blurb.
andai 7 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.
rafram 7 hours ago [-]
The brand only needs to last until the Google acquihire.
levmiseri 6 hours ago [-]
(FWIW I like the branding. Hugging Face doesn't seem to struggle because of its name either)
InsideOutSanta 6 hours 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.
m_w_ 7 hours ago [-]
lore does not make it a good or professional choice
jrflowers 4 hours ago [-]
Me, looking at an enterprise product: “Hmm, but do they guarantee FULL lore? I don’t really need to know what the lore is, just the assurance that it is full”
jrflowers 4 hours ago [-]
All the stupid made up names are taken, startups have to take regular words and make them meaningless now
6 hours ago [-]
GPerson 6 hours ago [-]
I’m disappointed this is some AI thing and not a breadboard company.
classichasclass 6 hours ago [-]
I'm disappointed it doesn't burn CDs.
amazingamazing 6 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
Anyway, back to this - it seems to be more like the AI equivalent of algolia than google
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.
Maybe the web is really that much worse, and the SEO tactics so hostile to genuine content.
But, honestly...just bring back the old google, where I have all kinds of search modifiers to perform exactly the search I want, that just returns all the matching results that have been indexed. Let me sort out the rest. How do you remove the ability to do "exact text search"? It's the most basic of search functions. Remember having "|" modifiers? AND modifiers?
If google launched that again, even as a separate engine, I think they'd have a good product again. Maybe being good doesn't pay the bills for google, though.
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
Sadly its another software company.
[1] - https://www.youtube.com/watch?v=8X1_HEJk2Hw
Seems to me if it doesn't also apply uniform melted butter it would be a hard sell.
[1]: https://www.mixedbread.com/blog/wholembed-v3
the thing is most agents waste most of their tokens looking up information which can cause context rot. most small models are not as good as looking up information. this model helps to lookup information for your main agent, which helps you to save tokens and still maintain quality.
[1]: https://www.mixedbread.com/blog/multimodal-late-interaction-... [2]: https://www.mixedbread.com/blog/wholembed-v3
It sounds like this is a new layer on top of your existing storage layer? So to use this, would I need to give you all of my data first? Or is there a version that can be run on prem?
https://www.mixedbread.com/docs/agent/chat-completions
clicks link
> "Toast 1, our search agen-"
closes page
Could you imagine this forum if everyone acted like you?
But no, more agents, more AI. Yawn.
Bread-first search, is it?
Anyway, back to this - it seems to be more like the AI equivalent of algolia than google