We seem hyperfocused on finding more tasks to train neural networks to do. This of course leads to a moving goalpost effect like in the article, but they’re moving along an axis that doesn’t measure intelligence.
My other comment: https://news.ycombinator.com/item?id=46445511
The last few days I've been working on some particularly tricky problems, tricky in the domain and in backwards compatibility with our existing codebase. For both these problems GPT 5.2 has been able to come to the same ideas as my best, which took me quite a bit of brain racking to get to. Granted it's required a lot of steering and context management from me as well as judgement to discard other options. But it's really getting to the point that LLMs are a good sparring partner for (isolated technical) problems at the 99th percentile of difficulty
And I'm not bragging. I'm expressing awe, and humility that I am finding a machine can match me on things that I find quite difficult. Maybe those things aren't so difficult after all.
By steering I mean more steering to flesh out the context of the problem and to find relevant code and perform domain-specific research. Not steering toward a specific solution.
Mine is: write a nroff document that executes at least one macro, and is a quine.
Based on what LLMs have given me for answers so far, I'd look harder for the human-written source of the nroff code. I have written what I believe to be the only quine in the GPP macro processing language, LLMs only refer me to my code if I ask for a GPP quine. Google, Meta, OpenAI really have strip mined the entire web.
If I genuinely thought anything creative or new appeared, I'd probably be at a loss as well.
