Hello everyone,
I’m an independent AI researcher and concept developer exploring how programming languages might evolve to become more interpretable and adaptive.
Over the past few months, I’ve been studying the possibility of a pattern-aware abstraction layer for Python — a system where recurring structural elements in code (syntax trees, logic motifs, or expression templates) could be recognized, stored, and reused automatically by the interpreter or tooling layer.
The motivation is similar to how AI models learn compact structural patterns from data. Instead of treating every piece of source code as independent text, Python could—at least theoretically—identify and represent code as a hierarchy of reusable patterns.
Possible long-term research directions might include:
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Automated detection of common syntax or logic motifs at the AST level
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Pattern dictionaries for code compression or auto-refactoring
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Semantic debugging that explains why a pattern failed rather than only where
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Tooling or plug-ins that evolve new abstractions from frequent developer behavior
I’m not proposing a language change or implementation yet, only exploring whether this aligns with Python’s design philosophy and whether similar research or tooling efforts already exist (perhaps in the static analysis, compiler, or education spaces).
If there’s community interest, I’d be glad to experiment with an external proof-of-concept tool that logs and visualizes recurring AST patterns in Python programs, to start validating feasibility.
I’d really appreciate any guidance, prior-art references, or opinions on:
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Whether this concept might fit Python’s long-term direction; and
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The best way to experiment (e.g., separate library, AST hook, or code-analysis plug-in).
Thank you for your time and insights!
Anurag Dongare
Independent Researcher