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Not disagreeing or anything, but an Advent of Code sized example will not be anywhere close in complexity to your typical CRUD application. The thing is, we possibly could not even write most of these applications in a low level language, as we should not forget that while Rust indeed has good abstractions, low level languages simply leak memory-related informations to high levels as well, making it necessary to deal with them on a high level. And in these cases, time to market, speed of refactors and maintainability is much more important than the row speed (which again, may not even be that significant at all. Eg, often most work is done on the db side)

> an Advent of Code sized example will not be anywhere close in complexity to your typical CRUD application

Advent of Code size problems is the best case scenario for Python. The difference in implementation time goes down as program size increases because you spend a larger fraction of the time figuring out what you're implementing.

The reason it's not done is mainly social and not technical. Ecosystems matter and this is particularly the case in graphical desktop environments where massive amounts of time and effort are required to re-implement features matching users' expectations (e.g. Flutter).

If we're talking a server side http app server then the library requirements are significantly lower and the libraries are generally there in almost any language. To keep the language comparison the same, it's not significantly slower to implement a CRUD backend in Rust than it is in Python. Depending on your specific setup and how much pre-prep is involved it can be faster. I'm not aware of any framework in a dynamic language that can produce production grade endpoint handlers that are as terse as a Rocket app heavily leveraging request guards. The guards handle validation, conversion, session context and db connection based on the endpoint parameter types so you just write the happy path which is 1-2 lines of code for CRUD.

> speed of refactors and maintainability

Dynamic languages are significantly worse for maintainability and refactoring. I say this as someone who generally gets paid to be a frontend developer and spent years writing both Python and Clojure professionally. Despite my arguments, I do not write server side Rust for work because team coherence is more important for doing my actual job (solving a customer's problem) than programming language and I'm the only person in my company who writes Rust. I've been doing this long enough that I accept the worse solution as the cost of doing business. My personal projects don't have this constraint so perf is orders of magnitude better in exchange for a lot more knowledge and a bit more design work.

> Advent of Code size problems is the best case scenario for Python. The difference in implementation time goes down as program size increases because you spend a larger fraction of the time figuring out what you're implementing.

The difference in code size between a good and a bad language goes up as program size increases, because a large codebase makes it harder to keep it all in your head and forces you to add more layers and duplication.

>And in these cases, time to market, speed of refactors and maintainability is much more important than the row speed (which again, may not even be that significant at all. Eg, often most work is done on the db side)

About the raw speed. Maybe you want to take a look at these nice benchmarks? https://www.techempower.com/benchmarks/

You don't have to ditch Java or C# for your app and use C or Rust instead. But you can write Java and C# in a more performant way while not sacrificing time to market, speed of refactors and maintainability.

Also, if performance, throughput, stability wouldn't be a thing, we have to wonder why Twitter switched from RoR to Java? Couldn't they just buy more servers instead?

> But you can write Java and C# in a more performant way while not sacrificing time to market, speed of refactors and maintainability.

Writing Java in a more performant way often means ditching OOP and using primitive types, primitive arrays everywhere instead, and also avoiding allocations up to the point where all objects are reused. Such code is just as slow to write and as error-prone as C, and way worse than C++ where at least you have RAII, generics, STL algorithms and zero-cost abstractions.

You only have to do that in hot loops (in most problems), that you can easily profile with the very good observability tools that java provides. The rest can be maintainable, good quality OOP code.

I would even go as far and say that OOP’s encapsulation pretty much exists just for this: the “ugly” implementation can reside inside a class and you only have to care about its public API from the outside.

> you can write Java and C# in a more performant way while not sacrificing time to market, speed of refactors and maintainability.

In practice you only have so much attention and there are better places to spend it.

> Also, if performance, throughput, stability wouldn't be a thing, we have to wonder why Twitter switched from RoR to Java?

If raw speed matters we have to wonder why Twitter started with RoR, and only switched (mostly to Scala, not Java, AIUI) once they had tens of millions of users.

In a tiny handful of outlier cases where a given piece of code is run literally billions of times a day it eventually becomes worth doing performance microoptimisations. But only after you've confirmed that there's absolutely massive demand for this particular code and already done your iterative refactors and figured out what the code should do.

> I believe the majority of code is slow because it's written without any consideration at all to performance. I like Casey Muratori's philosophy of non-pessimization where true optmization (measuring, working hot spots) is rare and rarely necessary but massive speedups compared to the general state of the art are achievable by simply not writing code using patterns that are inherently slow. This isn't deep algorithmic stuff it's just avoiding copies and/or pointer chasing.

I don't/can't watch videos; I would be interested to see a written argument, but generally my view is that copies and pointer chasing are irrelevant because code is so far away from optimal already. You're right that code is mainly written without any consideration to performance, but that manifests itself first in poor choices of algorithm and/or datastructure. I don't think there's anything "deep" about, say, using a hashmap rather than searching linearly through a list; indeed I'd consider it easier to understand than avoiding copying.

> The Advent of Code runs every year and I'm not sure about C (not something I track) but there are plenty of Rust submissions and while the Rust submissions take longer to come in, it's not THAT much longer. From memory it's like 2x on a timescale of minutes. The Python programs are not faster.

Advent of Code is still extremely tiny programs, and Rust is a much much better language than C for thinking in.

True. Most business apps I've seen seemed like people did serious research into how to write the worst code from a performance point of view. Gazillions of wrappers upon wrappers, tons of indirection, data being copied and recopied thousands of times, creating FactoryFactoryFactories just for the sake of it.

If people would pay more attention on how the data flows instead of Uncle Bob and GoF, they would not only end up with a cleaner software architecture but also with more performance.

> I believe the majority of code is slow because it's written without any consideration at all to performance.

That can both be correct. I use a std. data structures for many things even if some prefix tree might be better for the operations I perform on a set. I don't think about that in most cases since computing power is cheaper than the additional work. If performance becomes a problem, I can optimize later.

Advent of Code isn't a realistic performance metric. Working software is for most software projects.