YURIformer Touts DEQs, posit16 for LLM Training

This is a credible niche ML infra project, but it is still early and reads more like a foundation than a finished library.

• –DEQs are the most substantive part so far, and they fit the low-memory training story well
• –posit16 is a useful signal: the project is thinking about numerical stability, not just model structure
• –The missing neural architectures mean the library is not yet end-to-end for most users
• –The vibe-coded framing may attract contributors, but it also raises the bar for documentation and test coverage
• –The biggest upside is experimentation speed for researchers who want to swap learning rules and numerics without rewriting everything