Working on applying machine learning to source code to increase code maintainability.
It started with a semantic search engine for Javascript codebases: https://codecue.com/ and now working on the accompanying code analysis tool that would guarantee efficient searchability of the code
I'd be curious to see an example of code that looks bad but yet is easy to grasp mentally. I do think these 2 features that you describe "easy to run mentally" and "look good" are correlated.
A software's usefulness is not correlated to how well it was architected. What I like about Laravel is that its fairly complex code base that is mostly composed of tiny and expressive functions
At some point in Martins book, he mentioned a forgotten codebase from one of his friend that was solely composed of tiny functions but yet achieve a fairly complex effect. That's the kind of feeling I had working at Laravel (admittedly it's been a few years). React codebase, on the other hand, seem less human-friendly, it seems to require some prior knowledge of the code to be able to dive into.
I see the cons indeed, however I thought it would be really cool to continue training of a promising model in a more capable environment in just a couple of click.
Well you can wonder why did these people act irrationaly? Could these behaviour have been detected? Could they have been helped out of this narrowed point of view of thinking that killing innocent is the right thing to do ? I don't think anyone today hasn't in mind those questions.