Java Web Development (JSP/Servlets) Services

While working on the series of tutorial blogs for GET, POST, PUT, PATCH, and DELETE requests for API Automation using Playwright Java. I noticed that there is no logging method provided by the Playwright Java framework to log the requests and responses.

In the REST-assured framework, we have the log().all() method available that is used for logging the request as well as the response. However, Playwright does not provide any such method. However, Playwright offers a text() method in the APIResponse interface that could be well used to extract the response text.

Building applications for programmatically editing Open Office XML (OOXML) documents like PowerPoint, Excel, and Word has never been easier to accomplish. Depending on the scope of their projects, Java developers can leverage open-source libraries in their code — or plugin-simplified API services — to manipulate content stored and displayed in the OOXML structure.

Introduction

In this article, we’ll specifically discuss how PowerPoint Presentation XML (PPTX) files are structured, and we’ll learn the basic processes involved in navigating and manipulating PPTX content. We’ll transition into talking about a popular open-source Java library for programmatically manipulating PPTX files (specifically, replacing instances of a text string), and we’ll subsequently explore a free third-party API solution that can help simplify that process and reduce local memory consumption.

In the world of technology, personalization is the key to keeping users engaged and satisfied. One of the most visible implementations of personalization is through recommendation systems, which provide users with tailored content, products, or experiences based on their interactions and preferences. Historically, the first implementations of recommendation systems were built on legacy rule-based engines like IBM ODM (Operational Decision Manager) and Red Hat JBoss BRMS (Business Rule Management System). 

However, recent advances in machine learning have fundamentally changed how recommendations are generated. This article explores how legacy rules-based systems operate, their limitations, and how machine learning has disrupted this space.

Java has long been a trusted language for enterprise applications due to its versatility and ability to run seamlessly across various platforms, but as serverless platforms like AWS Lambda gain momentum, deploying Java applications in serverless platforms presents unique challenges, notably due to bloated packages and time to get initialized.

This led to an increase in the popularity and adoption of languages such as Go, Node.js, and Python for applications that are traditionally built on Java. When we take a deeper look to understand the inherent struggles that JVM-based applications have, the prominent ones are slow cold starts, high memory consumption, and runtime inefficiencies, making lighter runtimes more attractive in cloud-native environments.

The industry's increasing focus on secure container images is undeniable. Companies like Chainguard — specializing in delivering container images free of CVEs — have demonstrated the demand by recently raising an impressive $140 million at a $1.1 billion valuation. In the open-source ecosystem, Cloud Native Buildpacks, an incubating CNCF project, and their vibrant communities deliver a comparable value proposition by automating the creation of optimized and secure container images.

In this article, I'll explore Buildpack's core concepts, comparing them with Docker to illustrate their functionality and highlight how they provide a community-driven alternative to the value Chainguard brings to container security.