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Scala Fibers, Java Virtual Threads, and Kotlin Coroutines - this talk shows how this elegant solution manifests at three different abstraction levels.
Operating system thread context switches are a fundamental bottleneck in high-concurrency JVM applications. Each switch requires kernel transitions, CPU cache invalidation, and scheduling overhead. The JVM ecosystem has developed three prominent solutions: Fibers as implemented by many Scala monadic effect systems, Java Virtual Threads (Project Loom), and Kotlin Coroutines.
While these technologies appear vastly different on the surface, functional versus imperative, library versus runtime, they actually implement the same core pattern: replacing OS thread scheduling with user-space scheduling of continuations.
This talk shows how this elegant solution manifests at three different abstraction levels. We'll take the Cats Effect library as an example of Fibers implementation library through the IO monad. Project Loom pushes it down to the JVM runtime, making it transparent to developers. In Scala, the Ox library is built upon virtual threads. Kotlin Coroutines place it at compile time through the transformation of suspend functions. We'll see them as different expressions of the same fundamental breakthrough in concurrent programming.
When writing software, we currently seem to have to choose between an imperative style - easy to read and write, hard to reason about - and a monadic style - hard to read and write, easy to reason about.This talk is about being greedy and getting the best of both worlds, because we deserve it.
In this talk, we will walk through a concrete example of a boilerplate-heavy domain. By replacing common Scala 2 workarounds with Opaque Types, Extension Methods, Enums, and Union Types, we will demonstrate how to achieve a strictly typed, decoupled architecture without the noise.
We'll explore type classes in Scala 3, using its new rules for givens, extension methods, and mechanisms for automatic derivation via mirrors or macros.
Drawing from multiple Scala LLM workshops we conducted this past year, I will share insights to significantly enhance your AI experience.
Code generation is one of the most promising applications of large language models (LLMs), offering substantial productivity boosts for developers. However, this benefit is tempered by serious concerns surrounding the correctness and security of the generated code - especially outside the happy path.
