mhenders

Regarding different languages and frameworks, I found this informative: https://www.youtube.com/watch?v=FQPlEnKav48

(minor contributor) I’ve been following the project for a while and pleasantly surprised by the ability to manually convert Python programs to Seq without needing to make too many changes. Note, most of my experimentation has been with smallish programs I’ve written. I like that I can still think “Pythonically” and compose mostly correct Seq code using familiar idioms, e.g. list/set/dict comprehensions. The standard library is very readable and a source for “from import” type functionality. Some of the other features I’ve come to appreciate: pipeline operator |>, JIT compile or create an executable (seqc run, seqc build), match statements, and strong typing.

I enjoyed reading through https://ziglearn.org/

http://www.dalkescientific.com/company.html

Boy, what a body of work. Would love to hear your take on Seq if you get a chance to dig in more. I come from more of a Python background and really like what I see:

  - Python syntax for the most part
  - Mypy style type annotations
  - |> for pipelining and ||> to parallelize
  - match constructs (ML-ish with guards)
  - s'ACGT' and k'ACGT' for sequences and k-mers

It's relatively early in the project but I think the choices they have made give them a chance to continue to build momentum.

They are also doing some interesting work to allow pipelines to run in parallel.

https://github.com/seq-lang/Tapir-LLVM https://seq-lang.org/tutorial.html?highlight=tapir#paralleli...

The Github site has some benchmarking info: https://github.com/seq-lang

I believe Jupyter support is in the works and a REPL would seem possible/likely.

Traction is incredibly difficult, but there seem to be some good signs. I'm not a working bio data scientist, but I think the project so far has shown good taste on what to recreate vs. what to make easy to interface with. e.g. https://seq-lang.org/tutorial.html?highlight=bwa#calling-bwa...

Love your bio, and those are some cool links.

To your first question, seeing that in context of their paper abstract, I think the emphasis should be on the last part of the sentence, "...which marries the ease and productivity of Python with C-like performance."

I'm not sure this is what you are looking for but there is an example in their docs of dna-to-protein.

https://seq-lang.org/cookbook.html?highlight=protein#dna-to-...

I'm admittedly a huge fan, but it's hard to find any downsides, even for such a young language. A couple of things you might notice if working in the REPL:

- There's no quick way to del/clear the current environment. Namespaces are planned but not yet implemented.

- If a function is redefined, there is no automatic recompilation of dependent functions. It's easy though to list or recall existing functions.

Give it a go. It's a lot of fun. Some of the core team have been invited to present at Lang.NEXT.