I found this talk to be great. It goes through the history of OOP and how some of the ideas for the more modern ECS were embedded in the culture at the formation of OOP in the 1960s to 1980s but somehow weren't adopted.
It was pretty clear, even 20 years ago, that OOP had major problems in terms of what Casey Muratori now calls "hierarchical encapsulation" of problems.
One thing that really jumped out at me was his quote [0]:
> I think when you're designing new things, you should focus on the hardest stuff. ... we can always then take that and scale it down ... but it's almost impossible to take something that solves simple problems and scale it up into something that solves hard [problems]
I understand the context but this, in general, is abysmally bad advice. I'm not sure about language design or system architecture but this is almost universally not true for any mathematical or algorithmic pursuit.
> I'm not sure about language design or system architecture but this is almost universally not true for any mathematical or algorithmic pursuit.
I don't agree. While starting with the simplest case and expanding out is a valid problem-solving technique, it is also often the case in mathematics that we approach a problem by solving a more general problem and getting our solution as a special case. It's a bit paradoxical, but a problem that be completely intractable if attacked directly can be trivial if approached with a sufficiently powerful abstraction. And our problem-solving abilities grow with our toolbox of ever more powerful and general abstractions.
Also, it's a general principle in engineering that the initial design decisions, the underlying assumptions underlying everything, is in itself the least expensive part of the process but have an outsized influence on the entire rest of the project. The civil engineer who halfway through the construction of his bridge discovers there is a flaw in his design is having a very bad day (and likely year). With software things are more flexible, so we can build our solution incrementally from a simpler case and swap bits out as our understanding of the problem changes; but even there, if we discover there is something wrong with our fundamental architectural decisions, with how we model the problem domain, we can't fix it just by rewriting some modules. That's something that can only be fixed by a complete rewrite, possibly even in a different language.
So while I don't agree with your absolute statement in general, I think it is especially wrong given the context of language design and system architecture. Those are precisely the kind of areas where it's really important that you consider all the possible things you might want to do, and make sure you're not making some false assumption that will massively screw you over at some later date.
> ... it is also often the case in mathematics that we approach a problem by solving a more general problem and getting our solution as a special case.
This is a really good point. LLL and "Feynman's" integral trick come to mind. There are many others.
I got it in my head that this doesn't apply to NP-complete problems so should be discounted. When trying to "solve" NP-complete problems, the usual tactic is to restrict the problem domain into something tractable and then try to branch out other regions of applicability.
> Those are precisely the kind of areas where it's really important that you consider all the possible things you might want to do, and make sure you're not making some false assumption that will massively screw you over at some later date.
I will say that abstraction is its own type of optimization and generalization like this shouldn't be done without some understanding of the problem domain. My guess is that we're in agreement about this point and the talk essentially makes this argument explicitly.
So, this is pretty difficult to test in a real-world environment, but I did a little LLM experiment. Two prompts, (A) "Implement a consensus algorithm for 3 nodes with 1 failure allowed." vs. (B) "Write a provably optimal distributed algorithm for Byzantine agreement in asynchronous networks with at least 1/3 malicious nodes". Prompt A generates a simple majority-vote approach and says "This code does not handle 'Byzantine' failures where nodes can act maliciously or send contradictory information." Prompt B generates "This is the simplified core consensus logic of the Practical Byzantine Fault Tolerance (PBFT) algorithm".
I would say, if you have to design a good consensus algorithm, PBFT is a much better starting point, and can indeed be scaled down. If you have to run something tomorrow, the majority-vote code probably runs as-is, but doesn't help you with the literature at all. It's essentially the iron triangle - good vs. cheap. In the talk the speaker was clearly aiming for quality above all else.
So to be clear, I love all of those languages and wish the design space of prototype-based inheritance was explored more.
Having said that: why would he though? In this particular talk he's trying to argue to people who program in C++ why the historical C++ architectures are limiting it, he's not trying to convince anyone to switch languages. So those languages aren't his audience.
> I understand the context but this, in general, is abysmally bad advice.
The context, for the record, is inventing good general software architectures (and by extension generalized programming paradigms) for everyone to use. I agree with you that this is bad advice for generally fixing things, but for this context it absolutely makes sense to me. The hard problems are more likely to cover all the walls you'd bump into if you start from the oversimplified ones, so they are much better use-cases to battle-test ideas of what good architectures or programming paradigms are.
It was pretty clear, even 20 years ago, that OOP had major problems in terms of what Casey Muratori now calls "hierarchical encapsulation" of problems.
One thing that really jumped out at me was his quote [0]:
> I think when you're designing new things, you should focus on the hardest stuff. ... we can always then take that and scale it down ... but it's almost impossible to take something that solves simple problems and scale it up into something that solves hard [problems]
I understand the context but this, in general, is abysmally bad advice. I'm not sure about language design or system architecture but this is almost universally not true for any mathematical or algorithmic pursuit.
[0] https://www.youtube.com/watch?v=wo84LFzx5nI&t=8284s