I'm going to keep on repeating this on HN threads until I'm blue in the face, but:

There are two ways to solve a problem. Either solve the problem, or deem it irrelevant.

The implication here is that, you, the human operator, clearly are just confused. The LLM knows best. You're just a stupid human. The LLM knows objective truth, you do not. You have concerns, questions, the LLM didn't understand your question "properly"? Do not worry, the LLM objectively knows the optimal course of action. It thought through the implications of what you said, took into account all possible data, and came to the objectively correct design for your software, your society, your life.

In some sense, this problem would have been a societal problem within the next several decades anyways, but it's been hyper-accelerated by AI.

Waiting for the next Neuralink announcement...

Rather than prompt engineering, I think it should be called overall harness engineering. Anyway, that's how I feel these days

Calling it prompt engineer is doing it a disservice. With agents we’re well into process engineering, which is a ton more interesting.

The obvious baby’s first process is “plan -> execute” but as we learn about the strengths and weaknesses of LLMs you have to start unpacking that process into planning, prototyping, testing, validation, reviews, and tons of research. If you treat it like an extension of your brain that can automate some thought processes, it becomes a lot more powerful.

I find it strange that people sometimes think of knowledge as 'public property for everyone.' The essence may be one, but the mental model of knowledge is individual. For an LLM's knowledge to become mine, I need to digest it to some extent.

And programming, as the programmer who created Eliza once said, is the act of becoming a legislator of your own universe. So even if there are black boxes, if you want to build a program that fits your own worldview, studying is essential.

> I'm starting to feel like there's no place left for programmers like me who focus on quickly churning out MVPs.

Of course there is. The same way this was only possible as a result from the professor who prompted it with his specialized 10 page prompt and most importantly his deep knowledge of the problem space, the muscle memory and intuition you've built over the years is what will allow you to get more out of any AI than some guy who says "make a door dash clone" as the entire prompt

It is lean-verified, so it can be trusted unless the Lean statement of the hypothesis is not an accurate description of the hypothesis.

That view is incredibly reductionist. It really is an efficient encoding of how nature behaves. It might be a human construct, but given how best it allows to understand nature (through principles of physics), it is uncanny to be any different from the language of nature.

Reminds me of Wigner's Unreasonable effectiveness of mathematics in natural sciences [0].

[0]: https://en.wikipedia.org/wiki/The_Unreasonable_Effectiveness...

At a very high level mathematics is basically 100% text/symbolic rewriting. You start from some set of postulate assumed true and you do your thing to get a new different set of equivalent assertions on a form that is more useful.

I don’t know if LLMs will kill the working-mathematicians but at least seem like that it doesn’t seem absurd to imagine LLMs will be good at math…

It's still clear that LLMs lack spatial reasoning, either in the concrete or abstract, and while that sort of reasoning has been downplayed by academia for at least a century it is fundamental to technology and industry. (And many would say for science and mathematics too).

They will, however, get there as well either directly or as interfaces to models that do, and your core point stands.

Oh brother

AI hasn’t even taken the class of jobs associated with customer service lmao

If it were commonplace, there wouldn't be a post and discussion about it. Cheap? Arguable - while it didn't cost thousands, it wasn't free. Cheap is in the eye of the beholder. Efficient...How do we even measure that? The massive infrastructure and training to take a product to the point where someone could do this is massive. Ignoring everything behind the scenes and acting like one session and result is the whole picture of efficiency doesn't seem right. And no, nothing produced by AI makes skills irrelevant. That is the whole ongoing argument of whether people are losing cognitive ability by moving their thinking to AI.

Overall, this is an impressive proof of capability. But I wouldn't take that proof as anything more than what it is.

Intelligence was always relatively cheap. You can pick up a phone and get answers for free in most academic settings.

Everybody can be an armchair mathematician now. Just fling some thoughts in the direction of your AI setup and let it do breadth first search with AI based pruning heuristics.