31–40 of 124 posts
Great post. I think Mojo's claims like the speedup over Rust are a problem, like the 65000x speedup over Python. How can we differentiate between good new tech and Silicon Valley shenanigans when they use claims like that? They do nice titles and slogans but are shady in substance
As someone who practices bioinformatics, it doesn’t seem appealing. Bioinformatics is like 0.1% dealing with FASTQ files and the rest is using the ecosystem of libraries for statistics and plotting. Many of them in R, by the way.
I'm a microbiologist though, for stuff like human RNA-Seq I understand that it's often plug and play to get a gene counts table at this point.
Crystal was never able to find traction as a Ruby clone that could compete with C speeds. Why would a Python clone have any better luck? I don’t think anyone would accuse Python of being dramatically more usable than Ruby.
* Mojo provides first-class support for AoT compilation of standalone binaries [1]. Julia provides second-class support at best.
* Mojo aims to provide first-class support for traits and a modern Rust-like memory ownership model. Julia has second-class support for traits ("Tim Holy trait trick") and uses a garbage collector.
To be clear, I really like Julia and have been gravitating back to it over time. Julia has a very talented community and a massive head start on its package ecosystem. There are plenty of other strengths I could list as well.
But I'm still keeping my eye on Mojo. There's nothing wrong with having two powerful languages learning from each other's innovations.
[1]: https://docs.modular.com/mojo/manual/get-started/hello-world...
Great post, but I think the author missed a few advantages of Mojo: * Mojo provides first-class support for AoT compilation of standalone binaries [1]. Julia provides second-class support at best. * Mojo aims to provide first-class support for traits and a modern Rust-like memory ownership model. Julia has second-class support for traits ("Tim Holy trait trick") and uses a garbage collector. To be clear, I really lik…
Great post, but I think the author missed a few advantages of Mojo: * Mojo provides first-class support for AoT compilation of standalone binaries [1]. Julia provides second-class support at best. * Mojo aims to provide first-class support for traits and a modern Rust-like memory ownership model. Julia has second-class support for traits ("Tim Holy trait trick") and uses a garbage collector. To be clear, I really lik…
> Bioinformatics is like 0.1% dealing with FASTQ files and the rest is using the ecosystem of libraries for statistics and plotting. Many of them in R
Considering that, do you need AOT, memory ownership for doing plotting and statistics? I'd argue not, and that's why R and Python are so popular in Bio.
The language is far from stable, but I have had a LOT of fun writing Mojo code. I was surprised by that! The only promising new languages for low-level numerical coding that can dislodge C/C++/Fortran somewhat, in my opinion, have been Julia/Rust. I feel like I can update that last list to be Julia/Rust/Mojo now. But, for my work, C++/Fortran reign supreme. I really wish Julia had easy AOT compilation and no GC, that…
I pretty strongly disagree with the no gc part of this. A well written GC has the same throughout (or higher) than reference counting for most applications, and the Rust approach is very cool, but a significant usability cliff for users that are domain first, CS second. A GC is a pretty good compromise for 99% of users since it is a minor performance cost for a fairly large usability gain.
Great post. I think Mojo's claims like the speedup over Rust are a problem, like the 65000x speedup over Python. How can we differentiate between good new tech and Silicon Valley shenanigans when they use claims like that? They do nice titles and slogans but are shady in substance
> If I include the time for Julia to start up and compile the script, my implementation takes 354 ms total, on the same level as Mojo's. I don’t think the article mentions it explicitly, but I suppose the timing is from Julia 1.10: as far as I can remember, this kind of execution time would have been impossible in Julia 1.8 even to run a simple script. Bravo, Julia devs. Bravo.