Smile VS gleam

> I don't think it's right to recommend that new users move away from the package because of licensing issues

I was going to chime in to agree but then I saw how this was done - a completely innocuous looking commit:

https://github.com/haifengl/smile/commit/6f22097b233a3436519...

And literally no mention in the release notes:

https://github.com/haifengl/smile/releases/tag/v3.0.0

I think if you are going to change license especially in a way that makes it less permissive you need to be super open and clear about both the fact you are doing it and your reasons for that. This is done so silently as to look like it is intentionally trying to mislead and trick people.

So maybe I wouldn't say to move away because of the specific license, but it's legitimate to avoid something when it's so clearly driven by a single entity and that entity acts in a way that isn't trustworthy.

Check out Smile too.

Although it may be a bit more work, you can do both machine learning and AI in Java. If you are doing deep learning, you can use DeepJavaLibrary (I do work on this one at Amazon). If you are looking for other ML algorithms, I have seen Smile, Tribuo, or some around Spark.

For deploying a trained model there are a bunch of options that use Java on top of some native runtime like TF-Java (which I co-lead), ONNX Runtime, pytorch has inference for TorchScript models. Training deep learning models is harder, though you can do it for some of them in DJL. Training more standard ML models is much simpler, either via Tribuo, or using things like LibSVM & XGBoost directly, or other libraries like SMILE or WEKA.

I use smile https://github.com/haifengl/smile with ammonite and it feels pretty easy/good to work with. Of course for pure looking at data, and exploration, you're not going to beat python.

Actually, it does. Scala has Spark for data science and some ML libs like Smile.

I guess I must have a mistake somewhere. It's not much code. it's written in Kotlin with smile. My dataset is only about 32MB. I load the dataset into memory. I then use 80% of the data for training, and the other for later testing. I get just the columns I need and store them in the variable dataset.

I've heard good things about Smile, probably beats libs like Weka by far. I'm not sure if you can load a scikit-learn model though, so you might need to retrain the model in Kotlin.

I was using Smile for some period - https://haifengl.github.io/ - it's quite small and lightweight Java lib with some very basic algorithms - I was using in particularly cauterization. Along with this it provides Scala API.

Want a friendly language for building safe systems at scale? Gleam is here for you. It features modern and familiar syntax, that's reliable and scalable. Gleam runs on an Erlang virtual machine, and can run plenty of concurrent tasks. It comes with a compiler, build tool, formatter, editor integrations, and package manager all built in so you can get started right away. Congrats to the team on shipping your first major version 🙌.

While I love Clojure, I have to agree about tooling. I recently started using Gleam* and was impressed at how easy it was to get up and running with the CLI tool. I think this is an important part of getting people to adopt a language.

* https://gleam.run/

If you use languages that compile to WASM (such as Gleam https://gleam.run), and can also run Postgres via WASM, then it opens very interesting offline scenarios with codebases which are similar on both the client and the server, for instance.

Recently, Gleam has gained more popularity, and a lot of developers (including me) are learning it. At the time of this writing, it has exceeded 14k stars on GitHub; it grew really fast for the last month.

Oh, strange that github had a date of 2016 on this one: https://github.com/gleam-lang/gleam/issues/2

I was just going by that, though I do remember checking out gleam 5 years ago or so.

Re: macros, I really do think they’re a big deal and all the other newer languages I’ve used, such as Rust have some kind of macros or powerful meta programming features.

For older languages, a few, like Ruby have enough meta programmability to make nice DSLs, but many others don’t. Given the choice, I’d much rather have Elixir/Clojure style macros than other meta-programming facilities I’ve seen so far.

I had been only following this language with some interest, I guess this was born in gitlab not sure if the creator(s) still work there. This is what I'd have wanted golang to be (albeit with GC when you do not have clear lifetimes).

But how would you differentiate yourself from https://gleam.run which can leverage the OTP, I'd be more interested if we can adapt Gleam to graalvm isolates so we can leverage the JVM ecosystem.

I don't think the implementation itself is at fault, but yes, I do think that the design of dialyzer makes it an (at times) faulty type checker. The unfortunate reality of a type checker that fails sometimes is that it makes it mostly useless because you can never trust that it'll do the job.

To be clear, I've had it fail in a function where I've literally specced that very function to return a `binary` but I'm returning an `integer` in one of the cases. This is a very shallow context but it can still fail. Now add more functions, maybe one more `case`.

I think an entire rethink of type checking on the BEAM had to be done and that's why eqWalizer[0] was created and why Elixir is looking to add an actual sound, well-developed type checker. Gleam[1] I would assume is just a Hindley-Milner system so that's completely solid. `purerl`[2] is just PureScript for the BEAM so that's also Hindley-Milner, meaning it's solid. `purerl` has some performance issues caused by it compiling down to closures everywhere but if you can pay that cost it's actually pretty fantastic. With that said my bet for the best statically typed experience right now on the BEAM would be `gleam`.

0 - https://github.com/WhatsApp/eqwalizer

1 - https://gleam.run

2 - https://github.com/purerl/purerl