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In this talk, I will show how you can use ScalaPy to leverage an excellent NLP library like Spacy, minimizing your discomfort of using Python, and enhancing your comfort with Scala 3 for the remaining parts of your application.
In many domains of computer science, state-of-the-art frameworks are written in Python. Natural Language Processing (NLP) is such a domain, and if you want to use cutting-edge approaches, you have to resort to Python libraries. In this talk, I will show how you can use ScalaPy to leverage an excellent NLP library like Spacy, minimizing your discomfort of using Python, and enhancing your comfort with Scala 3 for the remaining parts of your application. I will show everything from configuring your (minimal) environment to interacting with Python, defining facades, and controlling memory usage.
I propose that we can extend Mirrors to operations, and use the most natural DSL of all - plain trait definitions.
I would like to go over the most relevant tools and changes that can improve your developer experience.
In this talk, we will take a tour around the `error` function. Starting from the simple use-cases and ending up with sophisticated yet developer-friendly error messages.
In this talk, we’ll show first the different techniques we can use to apply constraints is our domains. Then, we’ll present Iron, its features, extensions, and integrations. We’ll finish by showcasing a fully integrated constraint-enforcing app.
