LLMs just take this to the extreme. You can no longer rely on human code reviews (well you can but you give away all the LLM advantages) so then if you take out "human judgement" *from validation*[1], you have to resort to very sophisticated automated validation. This is it - it's not about "inventing a new language", it's about being much more thorough (and innovative, and efficient) in the validation process.

[1] never from design, or specification - you shouldn't outsource that to AI, I don't think we're close to an AI that can do that even moderately effective without human help.

The idea of "guardrails outside the model" is definitely appealing but I wonder if you can make it generalize well.

You iterate, yes - sometimes because the AI gets it wrong; and sometimes because you got it wrong (or didn't say exactly what you wanted, and AI assumed you wanted something else). But the less specific and clear you are in your requirements, the less likely it is you'll actually get what you want. With you not being specific in the requirements, it only really works if you want something that lots of people are building/have built before, because that will allow the AI to make correct assumptions about what to build.

Specification is worth writing (and spending a lot more time on than implementation) because it's the part that you can still control, fully read, understand etc. Once it gets into the code, reviewing it will be a lot harder, and if you insist on reviewing everything it'll slow things down to your speed.

> If the cost of writing code is approaching zero, there's no point investing resources to perfect a system in one shot.

THe AI won't get the perfect system in one shot, far from it! And especially not from sloppy initial requirements that leave a lot of edge (or not-so-edge) cases unadressed. But if you have a good requirement to start with, you have a chance to correct the AI, keep it on track; you have something to go back to and ask other AI, "is this implementation conforming to the spec or did it miss things?"

> five different versions of the thing you're building and simply pick the best one.

Problem is, what if the best one is still not good enough? Then what? You do 50? They might all be bad. You need a way to iterate to convergence

Does that mean that if I exit claude code and then later resume the session, the database is already lost? When exactly does the session end?

That's another 20 years mate.

That was my point, really - that you probably don't need to know "materials science" to declare yourself competent enough in cooking so that you can make your own food. Even if you only cooked eggs in teflon pans, you will likely be able to improvise if need arises. But once you become so ignorant that you don't even know what food is unless you see it on a plate in a restaurant, already prepared - then you're in a lot poorer position to survive, should your access to restaurants be suddenly restricted. But perhaps more importantly - you lose the ability to evaluate food by anything other than aspect & taste, and have to completely rely on others to understand what food might be good or bad for you(*).

(*) even now, you can't really "do your own research", that's not how the world works. We stand on shoulders of giants - the reason we have so much is because we trust/take for granted a lot of knowledge that ancestors built up for us. But it's one thing to know /prove everything in detail up until the basic axioms/atoms/etc; nobody does that. And it's a completely different different thing to have your "thoughts" and "conclusions" already delivered to you in final form by something (be it Fox News, ChatGPT, New York Times or anything really) and just take them for granted, without having a framework that allows to do some minimal "understanding" and "critical thinking" of your own.

I think the concern is not that "people don't know how everything works" - people never needed to know how to "make their own food" by understanding all the cellular mechanisms and all the intricacies of the chemistry & physics involved in cooking. BUT, when you stop understanding the basics - when you no longer know how to fry an egg because you just get it already prepared from the shop/ from delivery - that's a whole different level of ignorance, that's much more dangerous.

Yes, it may be fine & completely non-concerning if agricultural corporations produce your wheat and your meat; but if the corporation starts producing standardized cooked food for everyone, is it really the same - is it a good evolution, or not? That's the debate here.

With that in mind - I think one very unexplored area is "how to make the mixed AI-human teams successful". Like, I'm fairly convinced AI changes things, but to get to the industrialization of our craft (which is what management seems to want - and, TBH, something that makes sense from an economic pov), I feel that some big changes need to happen, and nobody is talking about that too much. What are the changes that need to happen? How do we change things, if we are to attempt such industrialization?

https://today.yougov.com/ratings/entertainment/fame/people/a...

Zuckerberg is 49, Sheeran is 169 (Taylor Swift is on 4; Bieber, Lady Gaga and Beyoncé are also more famous than Zuckerberg; the rest in the list are less famous)

Or: just have a convention/an algorithm to decide how quickly Claude should refresh the access token. If the server knows token should be refreshed after 1000 requests and notices refresh after 2000 requests, well, probably half of the requests were not made by Claude Code.