Systems | Development | Analytics | API | Testing

A lot of teams think their AI quality problem begins when the first test run starts but it usually begins much earlier.

Node.js debugging allows us to identify and fix errors, unexpected behavior, and performance issues in server-side JavaScript applications. Instead of using the less precise console.log, we can connect a real debugger and control execution step by step. Most Node.js developers actually use one of two distinct setups: This guide will show you how to use both technologies, with a clear step-by-step workflow for each.

Five paths give Fivetran the flexibility to connect virtually any data source, from SaaS applications and flat files to application databases, proprietary APIs, and real-time event streams.

Software teams are under constant pressure to release faster. Yet testing, the safeguard that protects quality, has not kept pace with modern delivery speeds. More code and shorter sprints overwhelm QA capacity, while fragmented tools and late-stage performance checks create bottlenecks that slow everything down. The question is not whether testing needs to evolve. The question is how to evolve without a costly rip-and-replace of your existing stack.

AI has opened the door for teams to build tools they previously had to buy. With the right prompts and internal workflows, teams can generate test cases, summarize results, analyze defects, and automate parts of the testing process faster than ever. For enterprise QA and engineering leaders, that raises a practical question: “should we build our own test management layer, or adopt an AI-powered test management platform?” It’s a fair conversation to have.

If you only follow the loudest headlines, it is easy to believe AI is about to wipe out manual testing. Katalon's State of Software Quality Report 2025 tells a more useful story, and it comes from inside the industry: over 1,500 QA professionals, from individual contributors to senior executives, across North America, Europe, and Asia-Pacific.

The state of neobanking 2026 looks very different now. A few years ago, most digital banks were chasing growth at any cost. More users. More app downloads. More market buzz. Today, the focus has shifted. Investors want profitable business models. Regulators want tighter compliance. Customers expect their neobank to feel as reliable as a traditional bank, but far more seamless to use. The industry is finally moving from hype toward operational maturity.

If your team spends more hours fixing tests than writing features, you already know the problem. You don’t need another lecture on "why testing matters." You need to reduce test maintenance without gutting your coverage, and you need it to actually stick past next sprint.