Kotlin

Javable is a KSP2 processor that generates Java-friendly wrappers for Kotlin classes, turning suspend functions into CompletableFuture, Flow into Stream, and handling CoroutineScope lifecycle automatically.

Every time you rename a Kotlin function parameter, the hand-written JSON schema your LLM reads is wrong — and it fails silently. kotlinx-schema derives the schema from your Kotlin types directly, via three strategies that cover runtime and compile-time generation across all Kotlin Multiplatform targets.

Kotlin encourages grouping related code in a single file, which is useful — until files grow past 1,000 lines. This article explores why that happens, how it affects LLM-based tooling, and what teams can do about it.

Why unit tests alone won’t save your LLM application in production, and how Mokksy — a Kotlin mock server with true SSE and streaming support — fills the gap that WireMock leaves wide open.

Generate JSON Schema for Kotlin Multiplatform models and API functions, so your schema stays in sync with your code.

KSP only works with Gradle, leaving Maven users out in the cold. I built ksp-maven-plugin to fix that - minimal setup, auto-discovers processors, integrates with Maven’s lifecycle. Now you can use KSP-powered libraries like Room, Moshi, and Dagger in your Maven projects.

Discover Kotlin extensions for LangChain4j designed to transform the synchronous LangChain4j API into a modern, non-blocking experience with Kotlin Coroutines. Learn about key features including coroutine support for ChatLanguageModels, Kotlin Flow for streaming responses, external customizable prompt templates, and non-blocking document processing. Enhance your Kotlin programming skills and improve application efficiency by leveraging these powerful new tools.

Kotlin Playground is HTML component which creates Kotlin-aware editors capable of running code from HTML block elements. Here I explain how to embed runnable Kotlin code block in your Hugo-powered blog.

This blog post demonstrates how to build a data processing pipeline using Kotlin coroutines and actors, showing both single-threaded and parallel implementations. It walks through creating a simple data pipeline using Kotlin’s channel and actor abstractions for clean concurrent programming. The post includes practical code examples of handling message passing between actors, managing thread pools, implementing back-pressure, and scaling from a basic sequential pipeline to a parallel version that processes data more efficiently. The explanation is backed by detailed logging output that illustrates how messages flow through the system and how parallel processing improves overall performance.