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  • Hacker News
  • Nice, but there's no mention of how Luna or DSFlash perform on the same task? (Being 25x and 50x cheaper respectively.)

    Nor of how much faster their custom model performs?

    by andai
  • Why do we need to train the model to solve for retrieval within the org, so we have to keep training it whenever new dataset is introduced , or am I missing something here ?
    by linux_devil
  • I have done my own testing and found that smaller models can beat their larger siblings on fact retrieval from documents. I haven’t investigated it in depth with a large enough dataset but my guess is that larger models overthink it while smaller ones just do it. I would like if they compared this with 5.6 Luna instead.
    by JCharante
  • This feels like the database equivalent of "use the right data structure." We've spent two years assuming the biggest general-purpose model should do everything. It makes more sense for retrieval, reranking, reasoning, and generation to each have their own optimized model if the routing cost is negligible.
    by BedVibe_Studios
  • There is a more serious question in here that's not being answered. How effective is the retrieval in finding buried needles in larger and larger haystacks. And there's a correlary question, how effective could you be in finding paired needles in that haystack where you need to hold a needle to unlock finding another needle.
    by aliljet
  • The big lab models are academically interesting but business wise they seem toast long term. There’s no way for these model companies to compete when the models are becoming a pure commodity and others offering options that are orders of magnitude cheaper.

    It’s not that the big labs couldn’t theoretically just also put out 100x cheaper options but their business model requires them to generate huge revenues from higher priced tokens or they’ll implode.

    by cmiles8
  • There is so much opportunity for purpose built models like this. Ideally a harness should spin up a subagent to offload to targeted models for specific tasks like this. I know this is not a novel idea. Claude code does some of this by handing off the "explore" agent work to haiku. I just love seeing that specialized LLMs are being developed.
    by mrinterweb
  • People are building agentic search one of three ways:

    1. Actually good retireval. There’s been a lot of progress on serving the kinds of queries agents tend to serve, from places like Hornet, MoxedBread, LightOn. Particularly in late interaction

    2. Smarter harnesses with models/judges validating the result. This is now just seen as the generator/ evaluator pattern. Here’s where people try to just use grep or some other naive retrieval system. Let the agent figure it out. But it’ll consume a lot of tokens to get good results as it iterates and loops.

    3. A model trained for retrieval. Give it dumb retriever like in (2) but it is fine tuned on the task as in (1).

    This article is 3. But we’ve been seeing this all year with SID.ai, Gleans Waldo model etc. if this interests you I’d check those out, particularly SID.

    I wrote about these 3 approaches here https://softwaredoug.com/blog/2026/06/08/three-kinds-of-agen...

    by softwaredoug

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