Hi everyone, I’m trying to wrap my head around how machine learning models actually improve over time, and I’d love a simple explanation . From what little I understand about machine learning, a model like Claude Code needs data to train on. In this case, it’s code-essentially millions of repositories and files available on the internet. Then, complex mathematical equations and training algorithms are run over this data to teach the machine how to code. Here is what I don’t get: How do they keep making it better? At some point, isn’t the data mostly the same? (I mean, it’s already trained on almost all the public code on the internet). If the core data is the same, and the task is the same ( “Write a script that does X” ), how does a newer version of Claude write drastically better, cleaner, and smarter code than the older version? What are engineers actually changing or improving behind the scenes when they release a stronger model? Is it the way they filter the data? The training process itself? Or something else entirely? Would love to hear your insights! submitted by /u/Previous-Growth-9919
Originally posted by u/Previous-Growth-9919 on r/ClaudeCode