A Java Geek weekly 50

Frameworks promise to speed up one’s development pace provided one follows the mainstream path. The path may be more or less narrow. I’m a big fan of the Spring ecosystem because its design is extensible and customizable at different abstraction levels: thus, the path is as large as you need it to be.

Functional Programming is becoming more and more popular. Spring provides a couple of DSLs for the Kotlin language. For example, the Beans DSL and the Routes DSL allow for a more functional approach toward Spring configuration. On the type side, Vavr (previously Javaslang) is pretty popular in Java, while Kotlin has Arrow.

In this post, I’d like to describe how one can use Arrow’s type system with Spring Data. Ultimately, you can benefit from the explanations to craft your custom Spring Data repository.

These findings support the hypothesis that GPTs based on LLMs perform well on prompts that are more popular and have reached a general consensus yet struggle on controversial topics or topics with limited data. The variability in the applications’s responses underscores that the models depend on the quantity and quality of their training data, paralleling the system of crowdsourcing that relies on diverse and credible contributions. Thus, while GPTs can serve as useful tools for many mundane tasks, their engagement with obscure and polarized topics should be interpreted with caution. LLMs' reliance on probabilistic models to produce statements about the world ties their accuracy closely to the breadth and quality of the data they’re given.

Think of it as an unholy combination of bash and Lisp.