breadislove

5 ms·

1.
by breadislove 27d ago
I am curious if there is a way to speed it up. Would be very interesting to know how long a human expert takes to finish the task.
2.
by breadislove 1mo 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 impr
3.
by breadislove 1mo ago
you can try it here: https://dwarkesh-search-demo.vercel.app/ 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 informati
4.
by breadislove 1mo 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 t
5.
by breadislove 1mo ago
you can plugin your existing stack and use it via an openai compatible client. https://www.mixedbread.com/docs/agent/chat-completions
6.
by breadislove 1mo 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.
7.
by breadislove 1mo 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. [1]: https://www.mixedbread.c
8.
by breadislove 1mo ago
there is full lore around the naming. i can guarantee you that we are pretty dedicated around our research and product.
9.
by breadislove 1mo 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 inde
10.
Toast 1(mixedbread.com)
4 points by breadislove 1mo ago|0 comments
11.
by breadislove 1mo ago
we have not converged at all, if you look at how different the chinese models in terms of architecture you can guess that the labs are experimenting a lot as well. we are seeing all different types of hybrid architectures, different atten
12.
by breadislove 1mo ago
On what do you guys test the model. Its very dubious that there is no common retrieval benchmark such as browsecomp plus or similar tested. And what metric do you report?
13.
Mxbai-Rerank-v3.1-Listwise(mixedbread.com)
3 points by breadislove 2mo ago|0 comments
14.
by breadislove 2mo ago
adam, i'd like to get in touch and would love to run the benachmark with mixedbread as a search backend. we are doing this right now with a lot of compliance companies. would be very curious how it improves quality/cost e2e
15.
by breadislove 3mo ago
yes, your are right. what heading would you have taken here?
16.
by breadislove 3mo ago
everything worth writing, you should write yourself
17.
by breadislove 3mo ago
ah whoops, I'll fix it. ty!
18.
by breadislove 3mo ago
The ndcg loss is minimal 90.26 -> 89.65. This means it maintains most of the quality.
19.
by breadislove 3mo ago
to which email did you send it? can u send it to support please?
20.
by breadislove 3mo ago
this is the reason why we report ndcg and not recall. ndcg respects fine grained details so you get the an overview of how much details you are trading off since it would hurt the ranking.
21.
by breadislove 3mo ago
yes exactly.
22.
Asymmetric Quantization: Near-Lossless Retrieval with 97% Storage Reduction(mixedbread.com)
110 points by breadislove 3mo ago|44 comments
23.
by breadislove 3mo ago
slop complaining about other slop
24.
Dense Retrievers Know More Than They Can Express(mixedbread.com)
2 points by breadislove 4mo ago|0 comments
25.
by breadislove 4mo ago
very bad take. with most modern multomodal models you get way better performance then going to text first
26.
by breadislove 9mo ago
this might be interesting: https://www.theinformation.com/articles/chatgpt-doctors-star... > $150M RR on just ads, +3x from August. On <1M users. source: https://x.com/ArfurRock/status/1
27.
by breadislove 9mo ago
a good system (like openevidence) indexes every paper released and semantic search can incredible helpful since the the search api of all those providers are extremely limited in terms of quality. now you get why those system are not cheap.
28.
The whole point of OpenAI's Responses API is to help them hide reasoning traces(seangoedecke.com)
2 points by breadislove 9mo ago|0 comments
29.
Boosting Claude: Faster, Clearer Code Analysis with MGrep(elite-ai-assisted-coding.dev)
1 points by breadislove 10mo ago|0 comments
30.
Show HN: Mgrep – A Semantic, Multimodal Grep(github.com)
6 points by breadislove 10mo ago|1 comments

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