History never repeats itself but it often rhymes
Hint: Transformer rhymes with Diffusion Transformer
ComfyUI's X post says history never repeats itself but often rhymes, with the hint that Transformer rhymes with Diffusion Transformer. The post links to Brian Armstrong's account of Coinbase fine-tuning the open-weight Qwen3.5-9B on internal data, where it outperformed frontier models on fraud prevention at lower cost and speed.
History never repeats itself but it often rhymes
Hint: Transformer rhymes with Diffusion Transformer
We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case). I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves. We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them: "/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started" I imagine we'll have some backend services that could be replaced entirely by this new architecture.
Source: ComfyUI · x.comPublished