DEV Community: Anacarmem Araújo Rêgo

Java + AI: Beyond APIs: into runtime, performance, and system design

Anacarmem Araújo Rêgo — Tue, 07 Apr 2026 01:47:33 +0000

The Java ecosystem is quietly becoming a powerful foundation for building production-grade AI systems not just consuming models, but optimizing how they run, scale, and integrate.

💡 Let’s go deeper into the technical layer:

🔹 JVM as an AI runtime enabler

Modern JVM optimizations (JIT, escape analysis, vectorization) allow Java to handle CPU-bound workloads efficiently especially relevant for preprocessing pipelines, feature engineering, and real-time inference orchestration.

🔹 Project Panama (Foreign Function & Memory API)

Direct interop with native AI libraries (like TensorFlow, ONNX Runtime, or custom C++ inference engines) without JNI overhead.

👉 Lower latency + safer memory access = better performance in inference layers.

🔹 Project Loom (Virtual Threads) + AI workloads

AI systems are I/O-heavy (model calls, embeddings, vector DB queries).

Virtual Threads enable massive concurrency with minimal footprint:

Parallel prompt processing
Async model orchestration without reactive complexity
Scalable API gateways for LLM-based services

🔹 Vector Search & Embeddings in Java

Java is increasingly used to integrate with vector databases (FAISS, Pinecone, Weaviate).

Efficient handling of embeddings pipelines using:

Off-heap memory (ByteBuffer / Panama MemorySegment)
SIMD-friendly operations (via JVM intrinsics)

🔹 Garbage Collection & Latency-sensitive AI systems

Low-latency collectors like ZGC and Shenandoah are critical when:

Running real-time inference
Serving embeddings at scale
Avoiding GC pauses in high-throughput pipelines

🔹 Framework ecosystem (rising quietly)

LangChain4j → LLM orchestration in Java
Deep Java Library (DJL) → unified API for AI engines
Spring AI → integration layer for enterprise AI applications

🔹 Structured Concurrency for AI orchestration

Parallelizing:

Multiple model calls
Fallback strategies (multi-model inference)
Retrieval-Augmented Generation (RAG) pipelines

With deterministic cancellation and error propagation.

🔥 Architectural shift:

Java is not trying to replace Python in model training it’s positioning itself as the runtime backbone for scalable AI systems:

API layers
Orchestration
High-throughput inference
Enterprise integration

📌 Takeaway:

If Python is the “brain” of AI, Java is becoming the nervous system coordinating, scaling, and delivering intelligence reliably in production.

Java is evolving and the recent changes go far beyond syntax sugar

Anacarmem Araújo Rêgo — Tue, 07 Apr 2026 01:39:48 +0000

What’s new from a technical perspective:

🔹 Project Loom (Virtual Threads)Virtual Threads drastically reduce the cost of concurrency by decoupling threads from OS threads. This enables high-throughput, blocking-style code without the complexity of reactive paradigms.
👉 Ideal for I/O-bound systems (APIs, microservices, messaging).

🔹 Structured Concurrency (preview)Introduces a more deterministic way to manage concurrent tasks, improving cancellation, error propagation, and observability across threads.

🔹 Project Panama (Foreign Function & Memory API)Provides safer and more efficient interoperability with native code (C/C++), eliminating the need for JNI in many cases and improving performance-critical integrations.

🔹 Project Valhalla (Value Objects preview)Brings value types (inline classes), enabling better memory layout and reducing heap overhead especially relevant for high performance and data-intensive applications.

🔹 Pattern Matching (instanceof, switch)Moves Java toward algebraic data type patterns, simplifying polymorphic logic and reducing verbosity in domain modeling.

🔹 Sealed Classes + Records synergyTogether, they enable more expressive domain-driven design with controlled hierarchies and immutable data carriers.

🔹 Garbage Collectors (ZGC, Shenandoah)Low-latency collectors now support large heaps with near-pause-less behavior, making Java more suitable for real-time and high-availability systems.

🔹 JIT & JVM optimizationsContinuous improvements in HotSpot (e.g., better inlining, escape analysis, and vectorization) are pushing Java performance closer to native in many scenarios.

🔥 Architectural impact:Java is shifting from a “thread-per-request is expensive” model to a scalable concurrency model with minimal cognitive overhead potentially reducing the need for reactive frameworks in many cases.

📌 Takeaway:Modern Java is not just evolving,
it’s redefining trade-offs between performance, simplicity, and scalability at the JVM level.

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Anacarmem Araújo Rêgo — Tue, 07 Apr 2026 01:32:22 +0000

Pros and Cons of Ubuntu: Exploring Advantages and Disadvantages of the Linux Operating System

Anacarmem Araújo Rêgo — Mon, 01 Apr 2024 22:56:39 +0000

Advantages:

Free and open-source: Ubuntu is distributed for free and its source code is available for anyone. This allows you to use, modify, and distribute the system freely.
Active community and support: Ubuntu has a large community of users and developers who provide support, documentation, and resources online. This makes it easier to get help and solutions for technical issues.
Ease of use: Ubuntu is designed to be beginner-friendly, with an intuitive graphical interface and a wide range of pre-installed applications. This makes it easier for new users to start using the operating system.
Security: Ubuntu is known for its strong security, with regular security updates and a robust permissions system that protects against malware threats and hackers.
Stability and performance: Ubuntu is known for its solid stability and performance, making it a reliable choice for servers and production environments. It is also optimized to work well on a wide range of hardware.
Hardware support: Ubuntu supports a wide range of hardware, including desktop computers, laptops, servers, and embedded devices. This means you can run it on a variety of devices, from older PCs to high-end hardware.
Software ecosystem: Ubuntu has access to a wide range of software available in its official repositories, making it easy to install and update applications. Additionally, many popular applications are developed and tested on Ubuntu, ensuring a consistent user experience.

Disadvantages:

Software compatibility: Some specific software may not be compatible with Ubuntu, especially applications developed exclusively for Windows. While there are alternatives and emulation solutions available, there may be cases where the required software is not available on Ubuntu.
Limited hardware support: While Ubuntu supports a wide variety of hardware, there may be cases where support for specific hardware drivers is limited. This can result in compatibility issues or reduced functionality on certain devices.
Learning curve: For users accustomed to other operating systems such as Windows or macOS, there may be a learning curve when switching to Ubuntu. While the system is designed to be beginner-friendly, it may take some time to become familiar with its interface and features.
Software updates: While regular system updates are important for maintaining the security and performance of Ubuntu, some updates may cause compatibility issues or instability on certain systems. It is important to be aware of this and take precautions when installing critical updates.

Utilization of Apache Kafka in the Java Ecosystem: Scalability, Real-Time Processing, and System Integration

Anacarmem Araújo Rêgo — Mon, 01 Apr 2024 22:23:03 +0000

System Integration: Kafka is often used to integrate distributed systems, allowing Java applications to send and receive messages asynchronously. This is useful for connecting microservices, legacy systems, and other components of a distributed architecture.
Stream Processing: The Kafka Streams API enables Java developers to process data in real-time directly within Kafka. This is useful for implementing streaming processing pipelines, such as filtering, transformation, and data aggregation.
Asynchronous Messaging Systems: Kafka is a popular choice for implementing asynchronous messaging systems in Java, where messages are produced and consumed independently and asynchronously. This is useful for communication between distributed application components and pub/sub messaging systems.
Event Storage: Kafka can be used as a durable and fault-tolerant event storage system. Java applications can write events to Kafka for purposes such as auditing, retrospective analysis, and fault recovery.
Monitoring and Log Tracking: Kafka is often used to collect and process logs from Java applications, such as server logs, application logs, and performance metrics. This is useful for real-time monitoring, fault analysis, and problem diagnosis in distributed systems.

Exploring Why IntelliJ IDEA Reigns Supreme as the Ultimate Java IDE

Anacarmem Araújo Rêgo — Mon, 01 Apr 2024 19:46:04 +0000

Deep Integration: IntelliJ IDEA offers exceptional integration with popular Java tools and frameworks. For example, it has features that facilitate importing and configuring Maven and Gradle projects. Additionally, IntelliJ supports application servers like Tomcat, JBoss, and WildFly, allowing developers to run, debug, and manage applications directly from the IDE.

Ease of Use: IntelliJ's user interface is known for its intuitiveness and efficiency. Features such as intelligent code suggestions (via "IntelliSense" feature), automated code refactoring, and quick navigation between classes, methods, and files make writing code more agile and productive. Additionally, IntelliJ offers a wide range of customizable keyboard shortcuts that enable developers to perform common tasks quickly and easily.

Advanced Features: IntelliJ offers a variety of advanced features that help developers write, debug, and maintain high-quality Java code. For example, its debugging tool is highly powerful, allowing developers to inspect variables, track code execution, and quickly identify issues. Additionally, IntelliJ has static code analysis features that help identify errors and potential issues before code execution even begins.

Language Support: IntelliJ provides comprehensive support for the Java language, including the latest versions of the language. This means developers can take advantage of the latest Java features, such as lambda expressions, streams, and modules, while writing code in the IDE. Additionally, IntelliJ supports other programming languages besides Java, such as Kotlin, Scala, and Groovy, allowing developers to work on projects involving a variety of technologies.

Active Community and Ecosystem: IntelliJ has an active and engaged user community that contributes plugins, tutorials, and additional resources to the IDE. This means developers can customize and extend IntelliJ's functionality according to their specific needs. Additionally, the IntelliJ Marketplace offers a wide selection of plugins developed by the community, covering a variety of areas such as integration with front-end development tools, testing frameworks, and more.

Comprehensive (or Nearly Comprehensive) Guide to Setting Up Your Development Environment in Java and Kotlin

Anacarmem Araújo Rêgo — Wed, 27 Mar 2024 23:10:30 +0000

Install Java Development Kit (JDK):

Go to the official Oracle website or adopt OpenJDK and download the latest version of the JDK compatible with your operating system.
Follow the installation instructions provided for your OS.
After installation, set the JAVA_HOME environment variable to the JDK installation directory. This helps other tools and applications locate the JDK:

You will search in the search bar for "environment variables";
FFind the "Path" within the system variables and click on "Edit";
Click on "New" and paste the following path (java version may vary):

C:\Program Files\Java\jdk-19\bin

4.Create a new variable named "JAVA_HOME" and enter the path without the "" at the end in the "Variable Value" field.

And now to confirm the JDK installation you will enter in the command prompt:

java --version

Choose an Integrated Development Environment (IDE):

Decide on an IDE suitable for Java and Kotlin development. IntelliJ IDEA is highly recommended for its robust support for both languages.
Download and install your chosen IDE from its official website.
During installation, you may customize the installation options based on your preferences.
After installation, launch the IDE and complete the initial setup wizard, including theme selection and plugin installation.

Install Kotlin:

If you're using IntelliJ IDEA, Kotlin is often bundled with it. Simply create a new Kotlin project, and the necessary Kotlin libraries will be automatically downloaded.
If not using IntelliJ IDEA, download the Kotlin compiler from the official Kotlin website and follow the installation instructions provided.
Set up the Kotlin plugin in your IDE if it's not already included. This enables Kotlin support in your Java projects.

Version Control System:

Install Git from the official Git website or using a package manager suitable for your operating system.
Follow the installation instructions provided for your OS.
After installation, open a terminal/command prompt and configure Git with your name and email using the 'git config' command.

Dependency Management:

Choose between Apache Maven and Gradle as your build automation tool.
Install Maven or Gradle by downloading the binaries from their official websites and following the installation instructions provided.
Configure your IDE to use Maven or Gradle for dependency management when creating or importing projects.

Testing Framework:

Decide on a testing framework such as JUnit or TestNG for writing unit tests.
Configure your IDE to recognize and run tests written in your chosen framework.
Install any necessary plugins or extensions for seamless integration with your IDE.

Database Integration (Optional):

If your project requires database interaction, install and configure a database server such as MySQL, PostgreSQL, or H2.
Download and install the database server software following the instructions provided by the respective vendors.
Set up database drivers and connections in your project to enable communication between your application and the database.

Code Style and Quality Tools:

Configure your IDE to follow code style guidelines such as Google Java Style or Kotlin Coding Conventions.
Install and configure static code analysis tools like Checkstyle, PMD, or FindBugs to enforce coding standards and identify potential issues in your code.

Documentation:

Set up a documentation tool such as Javadoc (for Java) or Dokka (for Kotlin) to automatically generate API documentation from code comments.
Configure your build tool (Maven or Gradle) to generate documentation as part of the build process.

Project Setup:

Create a new project in your IDE by selecting the appropriate project type (Java or Kotlin) and providing a project name and location.
Configure project settings such as SDK and language level to match your JDK version and language preferences.
Alternatively, clone an existing project from version control (e.g., Git) by providing the repository URL and authentication credentials.

Learn and Explore:

Take the time to familiarize yourself with Java and Kotlin syntax, features, and best practices.
Explore tutorials, documentation, and sample projects to deepen your understanding and enhance your skills in both languages.

By following these detailed steps, you'll have a fully prepared development environment for Java and Kotlin development, enabling you to efficiently write, test, and maintain your code.

Step-by-step guide for a Java and Kotlin development environment.

Anacarmem Araújo Rêgo — Wed, 20 Mar 2024 23:17:10 +0000

Hello, I will make some posts about the step-by-step preparation of the environment in case you are a Java and/or Kotlin developer.

Installation of Docker Compose:

Anacarmem Araújo Rêgo — Wed, 20 Mar 2024 23:11:42 +0000

Create a directory for Docker Compose:
mkdir -p ~/.docker/cli-plugins/

Download Docker Compose:
curl -SL https://github.com/docker/compose/releases/download/v2.11.2/docker-compose-linux-x86_64 -o ~/.docker/cli-plugins/docker-compose

Grant execution permissions:
chmod +x ~/.docker/cli-plugins/docker-compose

Verify the installation:
docker compose version