#scala

#scala

sbt 2.x uses Scala 3.x for build definitions and plugins, while still supporting Scala 2.x. It embraces a simpler build.sbt via common settings and introduces an incremental test feature.

While the codebase is fresh and grows fast under the umbrella of the local environment, we tend to rely on debugging tools, which were created specifically for that purpose. The app is half-baked, and the code is split open. We observe it through the lens of our IDE and with the speed of our brain. Everything is possible; we may pause execution for minutes, and the whole system is a white box - an open book for us.

If there's one thing I want you to take away from this article, it's this: testing harness is the most important thing for vibe-coding. Not prompt engineering, not fancy plugins, just constraining your AI outside AI toolchain. I'm calling it harness because it's not only tests. It's tests, types, linters, and any other automated checks you can put in place. The more you rely on AI, the more harness you need.

A real Tetris loop has time (ticks), concurrent inputs (keystrokes), state transitions (collision, locking, line clears), and non-determinism (piece generation). In many imperative designs, these concerns end up tangled in shared mutable state, which tends to produce bugs that are: hard to reproduce (timing-dependent), hard to test (logic mixed with effects), hard to debug (replay isn't deterministic).

We're pleased to announce the release of Scala 3.8 - a significant release that modernizes the Scala ecosystem and paves the way for Scala 3.9 LTS. This release introduces a standard library compiled by Scala 3 itself, stabilizes highly-anticipated features like Better Fors (SIP-62) and runtimeChecked (SIP-57), and introduces experimental features including flexible varargs and strict equality pattern matching. A runtime regression was detected after publishing Scala 3.8.0 artifacts.

High-level view of the travel search workflow, highlighting parallel searches, explicit decision points, and iterative refinement. In Scala, we define this workflow using Workflows4s, encoding both state and transitions explicitly in the type system. Instead of opaque state blobs or untyped contexts, the state of the process is represented using algebraic data types - types like Started, Found, Sent, and Booked - each corresponding to a distinct point in the workflow's lifecycle.

Over the past months, I've watched two clients move from Scala (Play, Slick, Akka, Akka Http ... ) to Kotlin (Spring, JPA/Hibernate). In my current role, an engineering decision was made to move away from Scala. The decision was driven less by Scala's shortcomings and more by long-term career risk management: leaders understandably favor stacks (Java/Kotlin) that maximize hiring flexibility in a volatile market.