Kernel development and machine learning seem like vastly different areas of endeavor; there are not, yet, stories circulating about the vibe-coding of new memory-management algorithms. There may well be places where machine learning (and large language models — LLMs — in particular) prove to be helpful on the edges of the kernel project, though. At the 2025 North-American edition of the Open Source Summit, Sasha Levin presented some of the work he has done putting LLMs to work to make the kernel better