Converter VS EnvisEdge

The longer story is that st-material-ui incorporates https://github.com/ScalablyTyped/Converter/pull/487 in order to get much, much cleaner API. You'll probably have seen the fake literal types, some rewriting from type unions to inheritance, things like that.

Given that you want interoperability with js, I'd start by playing with https://scalablytyped.org/, then, play with the scalajs-react demos (https://github.com/ScalablyTyped/ScalaJsReactDemos) and the slinky demos (https://github.com/ScalablyTyped/SlinkyDemos). There are some libraries that scalablytyped doesn't support pretty well but you can leverage https://github.com/nafg/scalajs-facades for those.

ScalaJS is awesome. Really solid and mature project, can totally recommend.

The only thing that can be annoying is when you want to have a typesafe interface and have to write a lot of adapters for javascript libraries.

Fortunately there is even a project that can make use of typescript interfaces for those libraries, so that you can use them from ScalaJS more or less automatically: https://scalablytyped.org/

I'm using it mostly for full-stack web development with ScalaJS (https://www.scala-js.org) in the frontend (https://outwatch.github.io/docs/readme.html) and in the backend with AWS lambdas.

The ecosystem is currently in the process of porting all the libraries to Scala 3. So if you're new to Scala, I'd recommend to start with Scala 2, which is rock-solid and already very powerful.

I never worked with SQLAlchemy. But on the scala database side, popular libraries are Doobie (https://tpolecat.github.io/doobie) and Quill (https://getquill.io). Keep in mind that these are for Scala on the JVM. On the ScalaJS side I'm using the javascript library pg. But I'd like to try if it works well with Prisma soon.

The nice thing about ScalaJS is, that you can use Javascript libraries. And if there are typescript facades, then you can transpile these to Scala and use them in a type safe way (https://scalablytyped.org).

Hear me out on what I think a truly decentralised content curation.

Twitter, FB (Meta), Youtube everyone harvests user data and train their recommendation engines which are then monetised by them (often unfairly).

In the future, the data stays on the users' devices and anyone can train their models by asking the user for the consent. THe data never leaves the device and ML models get trained on user device itself. The users get to choose from a host of recommendation choices and can ask for payment in return for using their data. So no one party can build a monopoly over the platform.

Check out a cool project I have been working on to solve this https://github.com/NimbleEdge/RecoEdge

Edge computing for machine learning. Instead of running ML models on the cloud, I train them on user's device, ask these devices to offload computation between each other and give me the best performance out there. I have my own local cloud formed by my laptop, smartphone and ipad.

I built out the library for these myself, checkout https://github.com/NimbleEdge/RecoEdge