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points by koolba 1 day ago | 0 comments

> My theory is that if you make Qwen understand you, Qwen can elaborate as much as needed to get any of those models get good results, because it's filling in details.

How is this different than just telling the model you’re using to fill in the gaps itself?

If you’re not manually reviewing the intermediate hop, how does that add to the “understanding you”?

[–] xlayn 1 day ago

there is no difference... each case may be different, I have a local qwen gpu setup... and is fast... but not as fast as claude. Fable is amazing and if it cost the same as my qwen setup I'll be running fable and never had qwen in first place. But Fable is crazy expensive, Qwen is not as fast but cheaper... Haiku is fucking dumb IN MY HANDS... BUT my local qwen is quick enough to be briefed, get the idea and delegate. I work with Fable mostly delegating to opus/sonnet and it's cheaper than just fable, I can't stand working with opus or the other models.

Why Qwen directing the others? For the things claude refuse, Qwen can make very specific questions and still get an answer without triggering the thing.

[–] brabel 18 hours ago

I am using Haiku 5 and it’s amazing for the cost. I almost always use it instead of Opus5.5 for simpler tasks. In my experience it’s much better than the Qwen models!

[–] make3 1 day ago

I think that his idea is that a lot of preparation / setup work is boiler plate / brain dead stuff that can be done by a shallower model.

It's like, find the relevant scientific papers by using Google search and the scientific paper MCPs, summarizing stuff, etc. Then the big model can come in and actually think on the artifacts

[–] skeptic_ai 1 day ago

Big model can easily delegate to luna to do this task.

I ask task x -> big model thinks and delegate to luna heavy dumb work.