I cannot find a single aspect of this file that even remotely hints at 'neurosymbolic' intelligence. And the post by Gary Marcus truly exhibits the type of person he is.
I am not sure it is inherent to LLM code generation as much as the training data and the tuning of the model. Emphasizing verbose code with lots of explicit explanation. Possibly the stuff you see in CS textbooks. And probably lots of vibe code style edits where the LLM fixes a bug, always adding further complexity to the code.
Funny thing is you could create measurable criterias explaining what is wrong with the code. Ie. function line count or cyclomatic complexity and then letting those guide the code generation.
Very true, with the right feedback loop AI would do a wonderful job of refactoring.
But if AI is the primary author and consumer of this code, that would be an unnecessary step. No need to clean it up for our feeble little human minds.
I was just interested in what this file actually does - and am finding it hard to grok, scrolling through on a mobile device!
https://github.com/yasasbanukaofficial/claude-code/blob/main...
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How is that “neurosymbolic”?
It just looks like poorly structured overly verbose ai generated code.