I actually think Ada has good potential as an AI adjacent language because the syntax is optimised for readability (I personally find it very readable too.)
I use clojure over common lisp because I find the ecosystem (libraries) a bit more approachable (feels like there is more web oriented stuff in clojure, plus gui support with javafx.) That said, the actual experience of working in common lisp is better (bit better repl/recompilation, conditions, native compilation.) Personally, I don't find scheme as practical for writing programs as either of these two options (too fragmented and/or niche.)
A lot of people say if you don’t use LLMs then you will fall behind. I’m starting to think that not using them will be a significant advantage in the long run.
I think LLMs improve productivity in the present at a significant cost for the future. It's like cutting an R&D department. You might be able to utilize existing approaches better, but you won't make progress, and I think people are way too overconfident in believing everything important has already been developed.
I guess the counterargument here would be that LLMs could improve research as well by optimizing menial tasks. It's kind of similar to how computing has enabled brute-force proofs in math. But I think the fact that students are still required to prove theorems on paper and that problems with brute-force solutions are still studied analytically should show that tools like computers or LLMs are not at all a replacement for the typical research process.
IMO we are going to see a large class of people who have cognitive deficits brought on by AI tool usage.
I've been wondering lately about how to distinguish between tools that enhance your cognitive ability, and tools that degrade it. Jobs called a computer a "bicycle for the mind," and it seems like LLMs are an easy-chair for the mind. I'm not sure a priori how to distinguish between the two classes of tools though. Maybe there is no other tool like an LLM.
i think theres both. the LLM is an incredible tool you should be able to use well, but its a complement to your other knowledge and tools, not a replacement. if you dont add the LLM to your toolset, youre not going to be building at the same scale as people who are, and if you dont have the backing knowledge, your LLM outputs are gonna be junk because you wont be able to point it in the right direction soon enough in the context window
In particular, it makes nocmig drastically more approachable. Be warned, though, that the false positive rate is extremely high (which is to be expected).
