- A Spring Boot App With Half the Startup Time
The MovieManager project has been updated to use JDK 25 and the AOT cache from project Leyden. Project Leyden is part of the OpenJDK project and provides cached linking and cached performance statistics. That means the time spent linking at startup is moved to build time, and the statistics are created during a test run at build time as well.
Because of that, the JVM loads the needed classes already linked and starts compiling the hot code paths immediately. The MovieManager application starts in less than half the time with these optimizations without any code changes.
- Implementing the Planning Pattern With Java Enterprise and LangChain4j
Artificial intelligence is evolving beyond basic chat interfaces to play an active role in enterprise applications. While initial AI integrations often focus on text generation, summarization, or retrieval-augmented generation (RAG), many business challenges demand more advanced solutions. These require breaking down complex objectives into sequenced tasks and coordinating their execution. The Planning Pattern addresses this need by enabling AI to function as both a content generator and a strategist that creates execution plans.
For software engineers and architects, the Planning Pattern marks a significant advancement in intelligent systems. It separates reasoning from execution, allowing applications to use large language models while ensuring governance, observability, and reliability in enterprise settings.
- Native SQL in Java Without JDBC Boilerplate — Meet Ujorm3
If you've ever written raw JDBC, you know what's coming. Open a connection, create a PreparedStatement, set parameters by index (hope you counted right), iterate a ResultSet, close everything in a finally block, declare SQLException on every method signature… It's a lot of ceremony for "give me some rows."
I've been experimenting with Ujorm3, a new lightweight ORM library for Java 17+. Here's a realistic example — a JOIN query that maps results including a nested relation:
- Rust-Native Alternatives to Spark SQL and DataFrame Workloads
Apache Spark is one of the most powerful tools in the data and AI engineering world. It helps process massive datasets and is widely used across industries, irrespective of cloud platforms.
But when you move from learning Spark to running it in production, you start seeing real challenges.
- Skills, Java 17, and Theme Accents
Last week was about Metal and the Skin Designer. This week, the headline items are about what a brand new project looks like when you generate it: the default JDK is Java 17, and every generated project ships with an AGENTS.md authoring skill that lets any modern AI agent work on the project intelligently.
There are also some other things worth covering: a runtime accent palette on the new native themes, three Metal follow-ups (one of which introduces a new matrix-correct translate API), the JDK 11+ String API gap closed, and iOS push permission that no longer fires at app launch.