Java Web Development (JSP/Servlets) Services

Goose — the open-source, Rust-based AI developer agent from Block (donated to the Linux Foundation’s Agentic AI Foundation) — interacts natively with your local development environment via the Model Context Protocol (MCP). In this tutorial, you will learn how to build stateless, cloud-native Java microservices using Quarkus LangChain4j and expose them as governed MCP extensions that Goose can discover and run seamlessly.

Autonomous AI coding agents like Goose go far beyond simple code autocompletion. Built in Rust for speed and portability, Goose runs on your local machine, inspects files, runs terminal commands, and uses tools over MCP to automate complex engineering tasks.

Java Meets the Spreadsheet

Apache POI has been the standard Java library for reading and writing Excel files for over twenty years. It handles the majority of everyday spreadsheet tasks well. But a growing category of real-world Excel files now contains formulas that POI's evaluator cannot execute at all.

This is one of several situations Java developers hit when working with spreadsheets that are not obvious until you are already in production. Business users produce, share, and reason about data in spreadsheets. Finance teams model in Excel. Operations teams track inventory in Excel. Analysts hand deliverables to engineering as .xlsx files. Java applications end up interacting with all of it: back-office services accept Excel uploads, pricing engines run calculations that were originally authored in a workbook, reporting tools export data in a format the recipient can open in Excel without formatting problems.

Scaling JMS Listeners With Java Virtual Threads

Event-driven architecture is widely used in enterprise systems to decouple services, absorb traffic spikes, and move work out of request paths. Java Message Service (JMS), now standardized as Jakarta Messaging, remains common in systems built around ActiveMQ, IBM MQ, Solace, TIBCO EMS, and similar brokers.

Java 21 virtual threads give these systems another scaling option. A JMS listener often spends more time waiting on a database, HTTP service, cache, or file system than it spends using the CPU. Moving that blocking work to virtual threads can reduce platform-thread pressure without forcing the application into a reactive programming model.

Artificial intelligence is changing software engineering, impacting automation, user interaction, data analysis, and application development. Developers are evaluating how their technology stacks fit with these changes. For Java developers in enterprise settings, a main question is whether the Java enterprise ecosystem is prepared for AI.

The short answer is yes. You do not need to abandon Java or wait for a new platform to build AI-enabled applications. Java already provides a mature ecosystem of AI libraries, model providers, APIs, and integration patterns. Jakarta EE offers the capabilities required to deploy these technologies in production-grade enterprise systems today.

The Problem: Our p99 Was 3-5 Seconds

Our PyFlink pipeline was missing its latency SLO by seconds. The pipeline itself was straightforward: consume events from Kafka, transform them, serialize them as Protobuf, and write the results to downstream systems. Yet under production load, p99 end-to-end latency was consistently in the 3-5 second range.

Profiling pointed us to an unexpected bottleneck: we were deserializing Protobuf messages in Python, even though the Flink runtime processing our stream was JVM-based. Every record that entered the Python path had to cross the JVM-to-Python process boundary, get parsed by a Python UDF, and then cross back. The business logic wasn't the problem. The doorway was.