Laminar VS mypy

My quite niche open source project broke this threshold last year, via Github sponsorships. Of course, I put a lot of time into it, so it's not "passive income" or even "market rate income", but still, without these sponsorships I wouldn't be able to work on it so much.

The project is Laminar, a UI library for Scala.js https://laminar.dev

I've noticed that Laminar and the newly released Kyo point that they don't use a lot of macros as a feature. Laminar says "Easy to understand: no macros", while Kyo emphasizes "Note: defer is currently the only macro in Kyo. All other features use regular language constructs." It seems that using less macros is something library users will like.

https://laminar.dev/ might be what you need. Though I wish there was a more beginner friendly (I'm not from front-end world) tutorial for me to follow along.

Have you looked at Laminar and Tyrian? Especially Tyrian seems to be close to what you're looking for.

For Scala there is Laminar, which has an even flashier website with nice docs. I haven't tested it out though, as I have never used Scala.

I found this scala library called Laminar which looks super similar to solid. They use signals and has no virtual dom. State changes are represented by signals and events by event streams. Thus they seems to have feature parity with RXJS as they can model all sorts of async stuff. Best part is they get to keep writing their markup in C-style syntax than XML based JSX. It looks super elegant,minimalist and has type safety.

This is very true. I really hate svelte single file components. But then I tried JSX for breaking things down. I love solid but I don't feel really good about angle brackets within C style syntax. I saw this Scala library that stick with simple statically typed function syntax than html tags. I don't understand why people still wants to stick with xml like tags. In laminar markup is written like this scala div( h1("Hello world", color := "red"), inputCaption, input(inputMods, name := "fullName"), div( ">>", button("Submit"), "<<" ) ) I wish solid team makes their HyperScript syntax as performant as JSX.

Laminar (Scala framework) hasn't been mentioned yet so dropping it here as an awesome framework that support HTML-over-the-wire. It can be used together with React, HTMX, and many other frontend frameworks -- but doesn't have to be.

https://laminar.dev/

Scala.js core itself, which I maintain, does not need much innovation. We support all of Scala, and interact with any JavaScript library. That's what the core promises.

If you want to compare to Scala 3, it's worth pointing out that you can use Scala.js with any Scala version >= 2.12.2. In particular, you can use it with Scala 3 and benefit from all its innovations. ;)

Innovation comes mainly from libraries, notably UI libraries. Laminar (https://laminar.dev/) is a great example.

In terms of roadmap, we are mostly working on "boring" stuff: improving performance (of the generated code, and of the linker), fixing bugs when they get reported, etc.

Perhaps, when Wasm gets more features for deeper interoperability with JavaScript (manipulating objects notably), we will take another look at targeting Wasm. People usually expect all languages to target Wasm now, "because it's fast". Truth is, it's fast for languages with linear memory. There is no evidence yet that it will be fast for memory-managed languages with objects and virtual dispatch.

It's got type annotations and mypy has a discussion about it here as well: https://github.com/python/mypy/issues/1282

There is already an AOT compiler for Python: Nuitka[0]. But I don't think it's much faster.

And then there is mypyc[1] which uses mypy's static type annotations but is only slightly faster.

And various other compilers like Numba and Cython that work with specialized dialects of Python to achieve better results, but then it's not quite Python anymore.

[0] https://nuitka.net/

[1] https://github.com/python/mypy/tree/master/mypyc

In Python there is MyPy that can help with this. https://www.mypy-lang.org/

It's funny you should say this.

Reading this article prompted me to future-proof a program I maintain for fun that deals with time; it had one use of utcnow, which I fixed.

And then I tripped over a runtime type problem in an unrelated area of the code, despite the code being green under "mypy --strict". (and "100% coverage" from tests, except this particular exception only occured in a "# pragma: no-cover" codepath so it wasn't actually covered)

It turns out that because of some core decisions about how datetime objects work, `datetime.date.today() < datetime.datetime.now()` type-checks but gives a TypeError at runtime. Oops. (cause discussed at length in https://github.com/python/mypy/issues/9015 but without action for 3 years)

One solution is apparently to use `datetype` for type annotations (while continuing to use `datetime` objects at runtime): https://github.com/glyph/DateType

PEP 695 is great. I've been using mypy every day at work in last couple years or so with very strict parameters (no any type etc) and I have experience writing real life programs with Rust, Agda, and some Haskell before, so I'm familiar with strict type systems. I'm sure many will disagree with me but these are my very honest opinions as a professional who uses Python types every day:

* Some types are better than no types. I love Python types, and I consider them required. Even if they're not type-checked they're better than no types. If they're type-checked it's even better. If things are typed properly (no any etc) and type-checked that's even better. And so on...

* Having said this, Python's type system as checked by mypy feels like a toy type system. It's very easy to fool it, and you need to be careful so that type-checking actually fails badly formed programs.

* The biggest issue I face are exceptions. Community discussed this many times [1] [2] and the overall consensus is to not check exceptions. I personally disagree as if you have a Python program that's meticulously typed and type-checked exceptions still cause bad states and since Python code uses exceptions liberally, it's pretty easy to accidentally go to a bad state. E.g. in the linked github issue JukkaL (developer) claims checking things like "KeyError" will create too many false positives, I strongly disagree. If a function can realistically raise a "KeyError" the program should be properly written to accept this at some level otherwise something that returns type T but 0.01% of the time raises "KeyError" should actually be typed "Raises[T, KeyError]".

* PEP 695 will help because typing things particularly is very helpful. Often you want to pass bunch of Ts around but since this is impractical some devs resort to passing "dict[str, Any]"s around and thus things type-check but you still get "KeyError" left and right. It's better to have "SomeStructure[T]" types with "T" as your custom data type (whether dataclass, or pydantic, or traditional class) so that type system has more opportunities to reject bad programs.

* Overall, I'm personally very optimistic about the future of types in Python!

[1] https://github.com/python/mypy/issues/1773

# is fixed: https://github.com/python/mypy/issues/12987.

You probably already know but you can add type hints and then check for consistency with https://github.com/python/mypy in python.

Modern Python with things like https://learnpython.com/blog/python-match-case-statement/ + mypy + Ruff for linting https://github.com/astral-sh/ruff can get pretty good results.

I found typed dataclasses (https://docs.python.org/3/library/dataclasses.html) in python using mypy to give me really high confidence when building data representations.