Distilling proprietary LLMs into smaller, runnable models

Researchers at babelfish published a method for extracting knowledge from closed-source large language models and compressing it into smaller, open models that can run locally. For indie developers, this means potential ways to build on powerful LLMs without API costs or vendor lock-in—though the practical legality and licensing implications remain unclear.