Inko doesn’t rely on garbage collection to manage memory.

Hmm…

Inko allows multiple borrows (both mutable and immutable borrows), and allows moving of the borrowed values while borrows exist

???!!!

To ensure correctness, Inko maintains a reference count at runtime. This count tracks the amount of ref and mut references that exist for an owned value. If the owned value is dropped but references to it still exist, a panic is produced and the program is aborted, protecting you against use-after-free errors.

Oh. It’s a bit cheeky to do runtime ref counting under the hood and then go and say you don’t rely on garbage collection. It’s not a full tracing garbage collector - it’s worse, it lets you create dangling references and then panics at runtime.

Inko looks interesting, for sure, as a Rust-lite that makes some kinds of code easier to write at the cost of more potential panics (safer, maybe…but desirable?). I’m not sure it’s for me though.

Rust is heavily based on Ruby’s design

I would not say “heavily based”. Literally only the closure/lambda syntax, which is cosmetic. Rust is mainly inspired by ML-family languages and C++.

I think Ruby is a reasonable choice for small scripts which someone might otherwise use Python for. But Rust is very well suited to more complicated or long-lasting command-line tools, especially if performance is at all a concern. Clap alone is super nice, but there are a lot of awesome libraries for making rich CLI tools easily.

• colored or owo-colors for adding terminal colors super easily
• crossterm for cross-platform terminal feature support
• rustyline for all your fancy readline needs
• cursive for TUIs
• comfy-table for command-line tables

And like…a hundred more I could mention. Idk, for anything that’s not completely trivial, which will be used and maintained by humans and not thrown away, Rust is really nice.

I suppose I shouldn’t be surprised at the negative response here, but personally this seems like the perfect application of LLMs. Yeah, it’ll need to be verified by humans, but so would human-translated code. Using an appropriately trained LLM to do the first pass translation has the potential to eliminate a lot of toil.