Hi everyone,

I’d like to introduce a new Java desktop toolkit, built from the ground up.

It includes dozens of components, accessibility support across three operating systems (Windows, macOS, and Linux), advanced language support, text direction, video, music, 3D, and charts.

The appearance is fully customizable (including dark themes), animations are native, and all the features you’d expect are there: printing, desktop integration, and testability.

I hope the community enjoys it. Although it is—admittedly—the product of AI, it was crafted with great care over the course of several months, ultimately saving tokens for anyone looking to build desktop applications.

https://limn-toolkit.github.io/limn-toolkit/

Enjoy

What Java topics should every fresher prepare before attending their first technical interview?

I'm preparing for Java developer interviews and want to focus on the topics that matter most.

Hi guys, I compiled material that I’m used to conduct technical interviews into one material.
It’s covering Java Core and Spring Boot, so it’s not fully about Java.
All the code is tricky and hide a lot of different problems, this kind of brain teasers helping to start conversation about code and learn more about candidate experience. You can read only code snippets and figure out on your own or read instructions below.
I hope this material will be useful for someone.

I've been experimenting with optimizing a conventional Spring Boot application on HotSpot JDK 27, without switching to GraalVM Native Image.

The setup was a 512 MiB / 1 vCPU Ubuntu VM with no swap, running Spring Boot 4.1.1 and MariaDB on the same machine.

I compared a regular executable JAR with an extracted deployment using Spring AOT and a trained JDK AOT cache (Leyden).

Results:

• Startup: 11.91s → 3.79s (68% reduction)
• Median app RSS: 212 → 187 MiB
• Minimum host MemAvailable: 25.6 → 66 MiB
• Request latency: essentially unchanged

One interesting issue: the JVM initially rejected the trained cache because transferring JARs changed their timestamps. The application still started normally, so I had to verify cache acceptance through JVM logs.

I also tried replacing Spring JDBC with manually integrated MyBatis Core. It didn't produce meaningful memory savings and required additional transaction management.

These are single-run observations at 5 rps, with the optimizations combined rather than isolated.

Full write-up and measurements:
https://pvrlabs.xyz/articles/spring-boot-jdk27-optimizations.html?s=rjp

Has anyone experimented with JDK AOT cache in actual deployments? I'm curious how you manage cache training and validation as part of your build pipeline.