You check its work and if it seems to do its job well enough most of the time, you just start to trust it.

Personally Qwen3.8-27b has not proven itself to me for the projects I’ve tried to use it for. It doesn’t seem to have as much encoded knowledge about the native Mac development stack.

This seems to be the inherent trade-off with smaller models. They can be just as "bright" as their larger contemporaries but they necessarily can't encode as much knowledge.

I think this is what the n-gram table in Qwen 4 (and 3.8-Flash-Next) is for. We’ll see if it comes down to the 27b variant.

A RAG solution would make time to first token awful.

> A RAG solution would make time to first token awful.

in your specific workflows, where specifically is the time to first token a dealbreaker? what's your typical target and what's a "no go"?

I tend to use LLMs conversationally, often as search-and-summarize but also as a pair designer. I find it very difficult to operate in this mode if prefill takes more than, say, a dozen seconds. And that only gets to the reasoning output; depending of effort level it can be multiple minutes to get a reply in a freshly compacted context.

> You check its work and if it seems to do its job well enough most of the time, you just start to trust it.

I don't think you meant to say this but it feels like you're saying "vibes".

It would be better to use a scoring framework like Harbor or similar?

I don’t really have time to be assigning my LLM homework and designing a grading rubric for it. I judge its output like I judge my peers’ PRs.