NetBeans on foojay.io - Friends of OpenJDK

Observing Apache NetBeans with OpenTelemetry and Dash0

Sun, 14 Jun 2026 22:03:09 +0000

What happens when you point an observability agent at your IDE rather than your application?

The question nobody thinks to ask

Observability is something we do to our applications. We instrument services, add trace context to HTTP requests, collect JVM heap metrics, ship logs to a backend. The IDE we use to write that instrumentation is — by convention — invisible. It just runs. When it is slow, we assume it is busy. When it crashes, we restart it. We have no data.

More Free Shells for your Java IDE

Tue, 31 Dec 2024 14:30:57 +0000

You may be using one of the terminal applications installed on your operating system or use the included terminal panel of the IDE.

Not all shells are included in your IDE. Here is a list of shells by default supported for the IDE’s based on my Windows computer.

|———————————–|—————|———|—————–| | Shell | IntelliJ IDEA | Eclipse | Apache NetBeans | | Command Prompt | ✅ | ✅ | | | Powershell | ✅ | | | | WSL (Windows Subsystem for Linux) | ✅ | ✅ | | | Git Bash | (1) | ✅ | | | Cygwin | (1) | | ✅ | | SSH (Secure SHell) | | ✅ | ✅ |

Dissection of Joeffice: Open Source Office Suite in Java

Tue, 11 Jul 2023 09:33:30 +0000

10 years ago, I challenged myself to write an office suite in Java in 30 days . This is how the first version of Joeffice was born. This created quite some buzz, including articles on Slashdot , Ars Technica and PC World .

Last month I released a second update. Let’s explore how I did this. By levering Java libraries, I was able to create such a complex piece of software very quickly.

Foojay Podcast #12: State and Future of the IDEs

Mon, 23 Jan 2023 10:14:45 +0000

In this podcast, we are talking to some of the key people working on different IDEs, Integrated Development Environments.

Those are applications that provide tools to computer programmers for software development.

An IDE typically consists of at least a source code editor, build automation tools, and a debugger.

Let’s learn how these tools evolved, and the challenges they face to stay up-to-date with the many evolutions in Java and all other programming languages.

Deep Learning in Java Using Deep Netts (Part 2)

Wed, 03 Aug 2022 09:48:18 +0000

Deep Netts is a deep learning development toolkit that enables Java developers to easily add modern AI to their apps. It provides a deep learning IDE and a Java-native deep learning library for embedding AI models into Java apps.

In part 1 , we looked at the Deep Netts Community Edition, which is capable of solving basic machine learning problems, and provides a standard set of features.

If you have more complex challenges, more data, and need better performance or just want friendly tools to quickly try and learn AI, you should take a look at Deep Netts Professional Edition.

Getting Started with Deep Learning in Java using Deep Netts

Tue, 05 Jul 2022 07:22:18 +0000

Deep Netts is pure Java deep learning library with a friendly, Java centric API.

It makes it easy for Java developers to quickly start using deep learning and it is easy to integrate with existing Java applications.

It supports commonly used neural network architectures (feed forward networks, convolutional networks) for classification, regression and image recogniton tasks.

Adding Deep Netts to your Project

To be able to use Deep Netts in Maven based Java project, add the following dependency into dependencies section of your pom.xml file:

Deep Learning in Java for Nuclear Physics using Deep Netts

Thu, 16 Jun 2022 09:01:44 +0000

Background

The CLAS12 detector at Jefferson Lab conducts nuclear physics experiments to study the structure of nucleons and new particle states.

At the core of detector setup are drift chambers that measure charged particles resulting from the interaction of an electron beam with a liquid hydrogen target.

The charged particles are reconstructed by combining segments of the track measured in 6 separate drift chambers along the particle trajectory.

In high-intensity experiments, detector systems produce noise segments increasing the combinatorics of track candidates to be analyzed by the conventional algorithm, which leads to decreased track reconstruction efficiency.

Deep Learning in Java for Drug Discovery

Fri, 20 Aug 2021 09:28:10 +0000

In the age of big-data, rising pharmaceutical costs, and an ever-increasing market demand, it is becoming apparent that drug design strategies need to adapt in order to meet patients’ therapeutic needs for a host of medical conditions.

Costly and time-intensive experimental work is one of the main bottlenecks in the drug discovery pipeline, which can take up to a decade, while experiencing a failure profile of up to 95% before a molecular candidate can be approved for patient use.