Thanks for your response. I read and responded last on my phone, so let me continue from a real keyboard.

I appreciate your insight. The last time I worked as part of a large product team was in the early 90's writing real-time C, C++, and Fortran. My question is whether there is an emerging role for names that includes a richer standardized context that enables more automation. I agree that burdening reviewers with enforcing contextual names make no sense, which is why I'm developing an AI linter.

The AI linter can infer behaviors from names and other code context that autonomously generate new code that implements and enforces those behaviors. I admit that this design pattern supported by new AI tools may only be intuitive to me. Still, I've managed to build a 3 million SLOC code base that has been refactored many times by building this methodology, so I'm now curious whether I can market this capability.

An aspect of this methodology is the use of runtime type checking via a universal decorator that can be invoked dynamically or statically to lint code via annotations as smart contracts into a normalized, perhaps sub-optimized form. This decorator pattern enables autonomous white-box testing of every callable in a code base, including builtins and 3rd party code.

Developing contextual-based naming patterns using what I refer to as name decorators (both prefix and postfix that can stack) is the underpinning for the AI to infer when, where, and how to augment or generate new code. New generated code is not burdened with any of the dependencies of a dynamic version, but both approaches add independent value by supporting iterative learning by both developers and the AI.

Without expanding any further, do you foresee Python evolving to embrace more AI tools throughout the development sustainment cycle, and can you foresee where contextual names driven by a smart linter would have a meaningful role in the language?