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One of the most widely adopted full-stack combinations today pairs Spring Boot with React, creating a powerful and flexible architecture for modern web applications. This stack leverages Java’s robustness on the back end while taking advantage of React’s dynamic and responsive user interface capabilities. Developing a full-stack application with these technologies requires careful planning, from structuring the project to implementing essential features. This article provides a step-by-step guide to building a basic CRUD application, laying the groundwork for more advanced topics like database integration and deployment, which will be covered in future articles. Java has remained a dominant server-side technology…

OpenAI has introduced free fine-tuning for its GPT-4o Mini model, allowing developers to customize the model for their specific needs without additional costs. This move is aimed at enhancing the accessibility and adaptability of AI-powered applications, making it easier for businesses and researchers to refine the model with their own data for improved performance in specialized tasks. Initially, free fine-tuning is available to developers in OpenAI’s Tier 4 and 5 usage plans, which represent the company’s highest pricing tiers. However, OpenAI has stated that it will gradually roll out access to lower-tier users over time. This free fine-tuning opportunity will…

When it comes to building web APIs, most developers are familiar with REST (Representational State Transfer), which has been the dominant approach for years. RESTful APIs typically rely on multiple endpoints, each serving specific types of data, and return responses in formats like JSON or XML. While REST is widely adopted, it can sometimes lead to inefficiencies—especially when dealing with complex applications that require multiple API calls to fetch related data. Enter GraphQL, an API query language developed by Meta that offers a more efficient and flexible alternative to REST. Unlike REST, which relies on multiple request-specific URLs, GraphQL allows…

In today’s data-driven world, organizations generate and process vast amounts of information at an unprecedented scale. Data flows continuously across various systems, influencing real-time operations and long-term strategic decisions. To manage this complexity, many enterprises are turning to operational data stores (ODS)—a crucial intermediary layer that consolidates data from multiple sources and makes it immediately available for business needs. Unlike a data warehouse, which is designed for deep analysis and historical insights, an operational data store focuses on real-time data processing. It acts as a temporary landing zone where data from various organizational functions—such as CRM, IT ticketing, HR, marketing,…

Microsoft has rolled out .NET Aspire 8.1, an important update to its cloud-native stack for building distributed applications. This latest version brings exciting new features that enhance the ability to work with containerized applications and Python-based services. Released on July 23, .NET Aspire 8.1 now includes support for automatically building container images from Dockerfiles, making it easier for developers to manage containerization tasks. Additionally, it introduces the capability to launch Python-based services, expanding the versatility of the platform for different application needs. One of the standout features in .NET Aspire 8.1 is the addition of two new extensions for Dockerfile…

Shiny for Python 1.0 has officially launched, bringing with it a new feature designed to streamline the creation of generative AI chatbots. The newly introduced Chat() component makes it easier than ever to implement AI-powered chatbots, offering flexibility by supporting any large language model (LLM) you prefer. According to the announcement, this feature is specifically tailored to simplify the process of integrating popular LLM interfaces, including OpenAI, Anthropic, Google, LangChain, and Ollama. Developers can easily leverage this component to build powerful chatbot applications with a variety of backend LLMs, providing a highly customizable solution for a range of use cases.…

In Java, expressions are evaluated within the context of statements, which are fundamental units of code that dictate the actions a program performs. These statements are used for a variety of tasks, such as declaring variables, making decisions, and controlling the flow of execution. Java supports two types of statements: simple and compound. Each serves a unique purpose and plays an essential role in writing clear and efficient code. A simple statement is essentially a single, standalone instruction that performs a specific task. It is typically terminated with a semicolon (;), which signals the end of the instruction. For example,…

In the age of generative AI, large language models (LLMs) are drastically transforming the landscape of how information is processed and how questions are answered across a multitude of industries. Despite their vast potential, these models still face notable challenges, such as generating information that can be inaccurate, relying on outdated knowledge, and executing reasoning paths that are difficult to trace. These limitations can hinder their effectiveness, particularly in environments where precision and transparency are crucial. To address these concerns, retrieval-augmented generation (RAG) has emerged as a groundbreaking solution. By combining the generative capabilities of LLMs with external, dynamically updated…

IBM has recently enhanced its watsonx.ai enterprise tools platform by integrating the Mistral Large language model (LLM), marking a significant expansion in the range of AI models available to developers. The move strengthens IBM’s offering for AI-driven enterprise solutions, providing users with access to a sophisticated generative model designed to tackle complex tasks. This collaboration is expected to benefit developers working with advanced reasoning, multilingual communication, and various enterprise-specific requirements. Mistral Large brings several key capabilities to the table, including its ability to handle retrieval-augmented generation (RAG). This specialization allows the model to manage lengthy chat interactions and large-scale document…

In a recent article titled “10 Big DevOps Mistakes and How to Avoid Them,” I interviewed industry leaders to uncover critical DevOps “gotchas” that can derail your software development efforts. These insights revealed more challenges than could be covered in a single piece, prompting me to share an additional set of obstacles that DevOps teams and IT leaders should be mindful of to ensure smoother implementation and operation. Below, I discuss 10 more potential roadblocks to avoid when scaling your DevOps practices. Resistance to ChangeOne of the most common obstacles to successful DevOps adoption is resistance to change. Whether it’s…