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Keyword vs Semantic Search With AI Oct 30th 2025, 15:00 by Alejandro Duarte When building a search for an application, you typically face two broad approaches: Traditional keyword-based search — match words exactly or with simple variants. Semantic (or vector) search — match meaning or context using AI embeddings. There's also a hybrid approach, but I will leave that for a future article. Instead, in this post, I'll walk you through how the two broad approaches work in Python using MariaDB and an AI embedding model, highlight where they differ, and show code that you can adapt. Building Reactive Microservices With Spring WebFlux on Kubernetes Oct 30th 2025, 14:00 by Mikhail Povolotskii Migrating from a monolithic Java 8 system to a reactive microservice architecture on Kubernetes allowed us to dramatically improve performance and maintainability. In this article, I'll share our journey, key Spring Cloud Kubernetes features we adopted, the challenges we faced during development, and the lessons we learned along the way. Business Logic We have a data processing logic that streams information into S3 storage using Kafka, Spark Streaming, and Iceberg. Initially, we encountered multiple challenges, such as file optimization issues and Spark's unpredictable memory behavior. After addressing these issues, we achieved significant cost savings. Once the insert service was completed, we needed to select an appropriate search engine service. We chose Trino as it fulfilled the needs of our data science department. We also serve customers who perform operations on our S3 data, which can result in high system load. Before this modernization, our platform ran on an old monolithic architecture built with Java 8, which created several performance and maintenance challenges. Improving Developer Productivity With End-to-End GenAI Enablement Oct 30th 2025, 13:00 by Nabin Debnath This is a very common scenario that every developer can relate to — I am focused on a feature, and suddenly my project buddy requests a PR review or asks for help when a test case is failing. Now, I need to context-switch to help my buddy, or the code review will be delayed.  Every engineering team faces the same bottlenecks — context switching, boilerplate work, delayed code reviews, and slow onboarding. The goal is to improve developer enablement and boost productivity through automation. Generative AI amplifies that goal. From writing user stories to generating test cases, GenAI can automate repetitive tasks and provide real-time guidance. But the challenge is to connect all those capabilities cohesively rather than treat them as isolated tools. How to Get a Frequency Table of a Categorical Variable as a Data Frame Oct 30th 2025, 12:00 by Chaitanya Sagar Categorical data is data with a predefined set of values. Using "Child," "Adult," or "Senior" instead of a person's age as a number is one example of age categorization. However, before using categorical data, one must know about various forms of categorical data First of all, categorical data may or may not be defined in an order. To say that the size of a box is small, medium, or large means that there is an order described as small < medium < large. The same does not hold for, say, sports equipment, which could also be categorial data, but differentiated by names like dumbbell, grippers, or gloves; that is, you can order the items on any basis. Those that can be ordered are known as "ordinal" while those where there is no such ordering are "nominal" in nature. Building a New Testing Mindset for AI-Powered Web Apps Oct 30th 2025, 11:00 by Kailash Pathak The technology landscape is undergoing a profound transformation. For decades, businesses have relied on traditional web-based software to enhance user experiences and streamline operations. Today, a new wave of innovation is redefining how applications are built, powered by the rise of AI-driven development. However, as leaders adopt AI, a key challenge has emerged: ensuring its quality, trust, and reliability. Unlike traditional systems with clear requirements and predictable outputs, AI introduces complexity and unpredictability, making quality assurance (QA) both more challenging and more critical. Business decision-makers must now rethink their QA strategy and investments to safeguard reputation, reduce risk, and unlock the full potential of intelligent solutions.

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