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Taking a bit of a detour with self-hosting the language, now that the syntactic surface, standard library, and initial dependency strategy are on a decent footing.
With any luck, by the end of the week, I'll start prepping for a 0.0.1 release.
The memory management model is automatic reference counting, with some optimizations, such as perseus for compile time reference counting where possible, and copy-on-write at runtime.
I'm the creator of Blorp -- this made me chuckle a bit. I hadn't even considered that possibility.
Yes, it's python-inspired. Some notable differences are: - no return keyword - match/if are expressions - it's functional - =? is used for early returns or binding, depending on the variant of an Option or Result that is returned
There's a lot of other differences -- it's a smaller language surface than Python overall.
Yeah, this is the idea. This chaining is exactly the same as the pipeline operator in some some function languages, except that it hopefully reads in a more familiar way to programmers of non-functional languages.
Yes, blorp does that. And it also allows local mutation and loops inside pure functions, so performance doesn't need to be left on the table in most cases.
Hi, maintanier of blorp here. I think you mean that [1,2,3].map(func(x): x *2).filter(func(x): x > 5) would iterate twice, correct? Under the hood blorp optimizes that away for the functions we can. It constructs a loop and combines logic into a single iteration where possible.
Of course, blorp also allows local mutation in loops (even in pure functions, so long as the logic is contained to the function), so if there's a specific algorithm you'd rather express in a loop, you can.
Hey - I'm the creator of Blorp, and like a lot of the ideas you have in Roc! Did you consider having opt-in purity? Or some other means of conveying impurity other than "!"?
It's called blorp because it's the name of a stegosaurus my kids and I made up.
Hey... I'm the maintainer and had no idea this was posted. The reason pure is required is because it's clear and opt-in. There's a CLI subcommand called purify that will help identify pure functions that haven't been deemed so if that's desired.
But there can be many (small) applications where pure doesn't even matter, so I don't really want to force someone to write "impure" for the main function, for example.
Still incomplete there. But yeah performance should be decent. Not aiming for parity either rust or go entirely but in the same ball park is the expectation.
Yeah, no shared mutable memory; coordination is done via channels.
How is compiling to zig? I considered doing it but chose C instead because of how much zig is still changing. I've considered using it's C compiler for targeting multiple platforms locally.
No, blorp doesn't use affine types (one or zero uses). In blorp, ownership is not explicitly controlled by users at all, so it's opaque. Under the hood, it's perceus for compile-time ownership and borrowing and automatic reference counting with copy-on-write optimizations for the runtime. This is made reasonably easy for the compiler to reason about in blorp because semantically it doesn't _really_ have in-place mutation -- `var` really means "re-bindable" to a new value; and then under the hood we'll mutate in place where we can.
I have some similar goals. Have you considered leaning more into inference than gradual typing? One pattern I like is allowing the compiler to develop a more complex mental model, but keeping it straightforward for users -- you can do that with inference, ownership, purity, effect types, etc. What I actually think is really tantalizing is using tooling to fill in some of those gaps -- for instance, the editor could know types, required capabilities etc, without the user ever needing to type anything, but when the user needs it, they can find it, query it, test against it.
Yeah, concurrency in blorp doesn't allow shared mutable references, so deadlocks aren't really a concern. Otherwise it's meant to be simple-ish -- virtual threads, channels, no async/await. Pure functions allow safe parallelism naturally, so that's fairly straightforward, though the API is still incomplete, for example the "Parallel" section here: https://blorp-lang.org/docs/lists/. It's still under heavy development (working on it right now).
What are the over-arching goals of your language?
Working on something kinda similar. No GC, Python feel, managed memory, performance approaching C. It's here: https://blorp-lang.org if you want to compare approaches.
It's not the core of koda-validate, and yeah lots of libraries have a similar capacity. Feedback I'd be interested in is if there are gaps.
In general the value prop of koda-validate is that it turns validation into typesafe building blocks, which makes validators very re-usable -- and flexible. Some other notable differences from pydantic are that it doesn't `raise` on validation errors, you don't need a typing plugin, and it's fully asyncio-compatible.
Updating my validation library for python, koda-validate (https://github.com/keithasaurus/koda-validate).
Focusing on ergonomics improvements. Just released an improvement to the __repr__ for Invalid types.
Potentially working on expanding the ability to generate validators from arbitrary typehints, ie `get_typehint_validator(list[str | int])`. It has good coverage, but I suspect I'm blind to some obvious holes. Would love feedback!
could java please also learn that stand-alone functions are cleaner than static methods? even if it's just syntax sugar...
For me the variation is one of the places where dynamic typing gets really dangerous, because as variations increase, the requirement for code archaeology does as well. At some point there is enough variation that nobody could reasonably be expected to do enough code exploration to understand the full breadth of that variation.
With types, the scope of the variation is clearly stated, and if you want to expand the variation, it should be clear how to do so, depending on the flavor (e.g. union types, sum types, generics, sub types).
static types: here's what this piece of data is
dynamic types: go look through code, tests, comments and project history to try figure out what this data is supposed to be
dynamic types are exhausting
There's a bunch of these kinds of html renderers. Here's mine: https://pypi.org/project/simple-html/
But there are many others. Not sure I understand the point of async rendering, unless you want to mix IO with rendering? Which feels a bit too close to old PHP+html for my blood.
I learned about Leonard Cohen by watching the movie McCabe and Mrs Miller. Recommended.
Don't remember that -- not even sure at the time I would have realized it was a business? I can imagine some version of that taking off in the mid 90s. Would've beat Descent on my Pentium.
Wild to see a Jackson Crossing reference on Hacker News. Haven't been there since around the time you would've had your business, but I don't think I remember an arcade like that. What was it called?
mypy is essentially the reference implementation. pyright probably is better as a type checker, but, for the referential aspect alone, I suspect many libraries will continue targeting mypy.
One potential benefit of mypy is that it comes with mypyc, a compiler that leverages mypy's evaluation of types. Since pyright and mypyc are not exactly equal, it makes sense to use mypy if you want to use mypyc.
A few quick suggestions about the landing page: - less text - more spacing in your text - try not to let your text span the entire width of the window
Isn't this what we have sports for? I think what your describing would basically be a robot team sport.
Definitely true. I've just soured on the POV that native code is the first thing one should reach for. I was surprised that it only took a few days of optimizations to convert my validation library to being significantly faster than pydantic, when pydantic as already largely compiled via cython.
If you're interested in both efficiency and maintainability, I think you need to start by optimizing the language of origin. It seems to me that with pydantic, the choice has consistently been to jump to compilation (cython, now rust) without much attempt at optimizing within Python.
I'm not super-familiar with how things are being done on an issue-to-issue / line-to-line basis, but I see this rust effort taking something like a year+, when my intuition is some simpler speedups in python could have been in a matter of days or weeks (which is not to say they would be of the same magnitude of performance gains).