Systems | Development | Analytics | API | Testing

AI-accelerated development has fundamentally changed how software is built, and across the industry, its impact on quality is already measurable. In SmartBear’s Closing the AI software quality gap study, we found nearly 70% of software professionals report application quality is declining as AI speeds up code generation, with development velocity increasingly outpacing teams’ ability to test effectively.

Imagine you’re mechanic trying to fix a car. There’s this magic piece of kit that allows you to pause the engine and see inside every moving part. You can tweak parts live, test changes instantly and measure which parts are slowing the whole thing down. This is JavaScript debugging in Chrome. Using Chrome DevTools, you can pause execution, inspect variables and scope, and follow code as it runs. So you can see what the code is actually doing at runtime, without assumptions.

Across all industries in the market, enterprises face rising pressure to modernize quickly, reduce risk, and enable data‑driven innovation — all while optimizing cost and simplifying increasingly complex hybrid environments. Hybrid cloud has become the operating model for this shift, yet organizations still struggle with fragmented tools and unpredictable expenses.

Python dominates AI development. It's where teams build their agents, orchestration layers, and the backend systems that turn LLM calls into products people actually use. Over the past year, those systems have matured rapidly. What used to live in notebooks and prototypes is now running in production, serving real users with real expectations around reliability and performance. That maturity brings infrastructure requirements. Tokens need to stream in order.

The wrong distribution platform slows down every release cycle. This guide breaks down the top mobile app distribution platforms so your team can ship, test, and iterate without the friction.

Ever felt like your POS system is doing almost everything you need… but not quite? Perhaps your POS system won't accommodate your unique billing process, has issues at busy times, or requires your employees to change their work processes instead of accommodating those practices. This is the case with most point-of-sale system software that is sold commercially. They are designed for the general public, not for your specific business operations. And businesses are starting to outgrow that limitation.

If you want your agentic AI projects to succeed, you must build a solid foundation of quality data integration, context, and governance.

CLIs, MCP, and the Real Governance Tradeoffs Shaping Enterprise AI Agents.