The problem, IMO, with open-weight models is that you accustom to the capabilities of frontier models too quickly; and downgrading to an open-weight "frontier minus 2" or "frontier minus 3" model is often painful, since they feel way less useful than their newer closed-weights counterpart. To be honest, I don't know any companies using OW models at a large scale for their operations (agents or chat assistants).
I don't know about that. The frontier models have all sorts of strange regressions and quirks. They're more "intelligent" but, like hyper-intelligent humans who speak up without any skin in the game, I've found that they have a strong pull to add complexity to anything they touch. Truth is, I don't need frontier-level intelligence to write a CRUD app or debug my config file. The vast majority of software work is basic pattern matching.
Perhaps because they are dumber, they produce better results? IMO an excellent well-tuned harness combined with a "frontier minus x" model produces the highest quality result. DS4.1 and Qwen3.8, far from being a compromise, legit give me better results. For my personal definition of "better".
jcmontx · · focus · HN ↗
perrygeo · · focus · HN ↗
Perhaps because they are dumber, they produce better results? IMO an excellent well-tuned harness combined with a "frontier minus x" model produces the highest quality result. DS4.1 and Qwen3.8, far from being a compromise, legit give me better results. For my personal definition of "better".