Both you and the grandparent comment are correct. The implementation is slow because the API that it exposes is so leaky that implementation changes (for example a tracing garbage collector) are impossible to implement without changing the API, and the API cannot easily change because of the dependence or the ecosystem on it (e.g. numpy)
We have the unique ability for genocide but also the unique ability to invent sophisticated tools, one of which is culture, whose functions include ways to override our impulses in ways we deem valuable.
Anything no-code or low code has a data model, and an agent can manipulate it in ways that are compatible with the system design. Letting an agent loose on a problem, without a good pilot, just leads to poor designs.
An algorithm written in a well specified language with precise semantics might have bugs. A "logical" argument made with natural language is orders of magnitude less precise
What I've always wondered, though, is whether that lack of precision is what allows for meaning to arise in the first place. In the gap between language and - this - .
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