This is exactly how I do most of my data analysis work in Julia.

But e.g. if you want to do fast math, you really need to design your pipeline around cache efficiency from the beginning – it's very hard to retrofit. Whereas reducing memory allocations in order to make parallel algorithms faster is something you can usually do after profiling.

* Braid was 3 years

* Cave Story was 5 years

* World of Goo was 2 years

* Limbo was about 3 years (but with 8-16 people)

So Braid seems pretty average.

I don't know of any rulings on the context window, but it's certainly possible judges would rule that would not qualify as transformative.

Having a specific goal seems to make a big difference vs. asking it to summarize the session.

The C++ versions were around ~250 lines long, and the developers generally only had time to try one approach. While the Julia versions were around ~80 lines long, and the developers had time to try several approaches. I'm sure the best theoretically possible C++ version is faster than the best Julia version, but given that we're always working with time constraints, the performance per developer hour in Julia tends to be really good in my experience.

I don't know Rust as well as I'd like, but when we've worked with some strong Rust programmers, their versions of the code are something like 4x as long as equivalent Julia code with minimal performance improvements. And since we primarily care about trading algorithms that don't have binary results, it's pretty helpful to be able to understand those at a glance.

Also, Rust's ecosystem for numeric computing still seems pretty underdeveloped, though it's getting better.

You can profile memory usage line by line in detail pretty easily with tools like @timeit or @btime

In practice I've found it pretty easy to get inner loops down to few or 0 allocations when needed for parallelization

I usually write the tests afterwards, except for very well-defined engineering problems, and the REPL exploration helps inform what tests to write.

A very minor nit: Julia is a compiled language, but it has an unusual model where it compiles functions the first time they're used. This is why highly-optimized Julia can have pretty extreme performance.

> I would say the performance is overstated by the community but out of the box it is good enough to avoid languages like C/C++ to build solutions.

For about a year we had a 2-hour problem in our hiring pipeline where the main goal was to write the fastest code possible to do a task, and the best 2 solutions were in Julia. C++ was a close third, and Rust after that.

We've ported some tens of thousands of lines of numpy-heavy Python, and in practice our Julia code is actually more concise while being about 10x-100x more performant.