points by dcrazy 4 days ago | 0 comments
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.
[–] Spivak 4 days ago
[–] dcrazy 4 days ago
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.
[–] lnkdinsuxs 4 days ago
> 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"?
[–] dcrazy 4 days ago
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.
[–] lnkdinsuxs 4 days ago
> 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?
[–] dcrazy 4 days ago
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.
I think over time we'll realize this is the way forward, you want the smartest model with the least amount of embedded knowledge and you provide the knowledge externally or merge it into the weights for the domain you're interested in. Then you don't have to train a ginormous model every 6 months to pull in new information.