Hofstadter on Lisp (1983)

I find this article to be quaint -- remember reading it decades ago and feeling more receptive to its perspective. Ironically, I prefer using Clojure (though some here challenge its status as a Lisp lol) to interface with Large Language Models rather than Python. Clojure in particular is much better suited, for some reasons that Hofstadter details, and if you can interact with an LLM over a wire, you are not beholden to Python. But what we use to interface to these massive digital minds we are building, including the Bayesian sampling mathematics we use to plumb them, may have their elegance, but they are orthogonal to the nearly ineffable chaos of these deeply interconnected neural networks -- and it is in this chaotic interconnectedness where artificial intelligence is actually engendered.

> Clojure in particular is much better suited

Clojure in general is far better suited for manipulating data than anything else (in my personal experience). It is so lovely to send a request, get some data, and then interactively go through that data - sorting, grouping, dicing, slicing, partitioning, tranforming, etc.

The other way around is also true - for when you need to generate a massive amount of randomized data.

> when you need to generate a massive amount of randomized data.

Even faster than Clojure: Open VIM for a VS Code user and ask them to exit.