One of the software industry’s most important pain points in testing is “Coverage”. It is manually impossible to generate test cases while covering edge cases and different scenarios with the scale of how much code gets written each day. AI Agents here save time and improve test coverage and reliability. Developers can now focus on core logic while AI handles repetitive and error-prone tasks like writing unit tests.

Object-oriented programming is a programming model that designs software around objects and data, as opposed to procedures and logic. Generally, object-oriented programming delivers more reusable, maintainable, and scalable code. This is in no small part due to its core concepts like inheritance, abstraction, and encapsulation. As we explained before, encapsulation involves combining data and the methods that operate on it into one unit, usually a class.

Command-line interfaces (CLIs) have been an essential part of computing for decades. However, creating a CLI from scratch can be tedious. Developers need to handle inputs, validate arguments, and provide user-friendly error messages, which can quickly become complex. This is where the Python argparse module comes in.

In today’s data-driven world, organizations are increasingly turning to data products to gain actionable insights, improve decision making, and fuel innovation. Data products — productized datasets, metadata, and domain logic — are designed to democratize data access and make it easier for teams to maximize their data’s value without requiring deep technical expertise.

Last year, we introduced Freight clusters, a new, cost-effective Confluent Cloud cluster type—purpose-built for high-throughput, latency-insensitive workloads—such as observability data, batch pipelines, and AI/ML data ingestion. Since then we've been working with early access customers to take their workloads to production, and in doing so, have helped them achieve 90% lower infrastructure costs, while maintaining the reliability we all know and expect from Confluent Cloud.

Imagine a scenario – you have done an excellent job developing an incredibly sleek and feature-packed mobile application. The pain of working countless hours and putting your personal life on hold is about to pay off. But just before the launch, you uncover a wide array of bugs that don’t scatter evenly across the application and appear in specific areas or parts of the code like user authentication or payment gateway.

As of 2025, Android powers approximately 71.85% of the global mobile operating system market. For developers and QA teams, this variety is both exciting and overwhelming.

Did you know the global digital health market is on track to surpass $660 billion by the end of 2025? That’s no surprise, considering how healthcare apps have become indispensable in our daily lives.