Thread

1. mrinterweb · 1785956587 · focus · HN ↗

   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.

   1. benjiro29 · 1785962627 · focus · HN ↗

      > Claude code does some of this by handing off the "explore" agent work to haiku.

      That is not handing off to a specialized model, its just handing off to a lighter and interior model (compared to the parent model). That by itself can create issues like the lighter model not capturing all the data that the parent needs.

      The idea is that we get specialized models that are better then general purpose models. But its rare for a specialized model to beat a strong general model.

      There is a reason why we hear less about this idea of smaller expert models, because large strong models to the tasks just as good.

      And if the tasks is repetitive to the point that specialization is useful, you can get into a situation that your better off having a program written for that reputative nature, then delegating to other models. And then have the main strong model, deal with the (semi)cleaned up data.

      1. tyre · 1785970155 · focus · HN ↗

         > There is a reason why we hear less about this idea of smaller expert models, because large strong models to the tasks just as good.

         Smaller models are cheaper, sometimes faster. I agree that the “we’re an LLM fine-tuned for X” hasn’t worked out because you can just train Claude to do X (and Anthropic will), but not burning Opus/Fable tokens on dumb-but-token-heavy tasks is good sense.

         As we move from “integrate AI into Y” to “optimize the ROI on Y”, we’ll see more of this.

         1. kumama · 1785982579 · focus · HN ↗

            castform founder here. the roi optimization makes sense. i think there are lots of usecases for which even a 2% gain in accuracy can be quite useful. off the top of my head

            - high volume customer support. higher accuracy means fewer escalation, reducing labor costs - fraud detection. catching even one extra fraud attempt could mean a lot in savings - and ofc the classic ads use-case where at scale bps in improvement could mean millions in revenue :)

            1. simianwords · 1786001448 · focus · HN ↗

               Would a bigger model be able to beat yours if some effort were put into prompt?

               1. dkersten · 1786010596 · focus · HN ↗

                  At what cost though? “Beating” isn’t enough if it costs 100x or even 10x the amount.
               2. kumama · 1786025579 · focus · HN ↗

                  the bigger model would still cost more :) at the same time, i see prompting as being orthogonal to post-training. i'd imagine post-training a smaller model with a better prompt would make it perform even better

                  1. simianwords · 1786040990 · focus · HN ↗

                     yes, can you show me tasks where this is true?

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