Systems | Development | Analytics | API | Testing

Cursor is fantastic at cranking out code changes. I recently used it to splice a brand-new downstream API call into one of our Go microservices, and the diff looked great. The unit tests finished before I lifted my coffee mug, yet I still had zero certainty the change would survive contact with real traffic. That gap is all about integration tests, so I paired Cursor with proxymock and the outerspace-go demo service to prove the behavior end to end.

At Koyeb, we provide high-performance serverless infrastructure for intensive applications across CPUs, GPUs, and accelerators. We take code, build it, and run it in fully isolated, secure microVMs on bare-metal servers around the world. We scale automatically when needed, down to zero when idle, with cold starts as low as 250ms. Over the past few months, we’ve been working with an increasing number of teams using Koyeb to orchestrate and run AI-generated code at massive scale.

AI testing careers are shifting in ways that most people in QA are not fully prepared for, and the changes are creating opportunities that did not exist even a few years ago.

The SmartBear community is made up of curious, collaborative builders. They’re developers, testers, creators, and innovators who every day are experimenting with new technologies, customizing workflows, and solving complex problems.

AI systems change faster than traditional QA models can react, which means quality risks now emerge in real time rather than at release.

‍ Note: This article is the second in this series. Check out the first one here: Why we ditched frontier AI agents and built our own.

After attending Oracle AI World, one thing is clear: Oracle is all in on AI. From embedded agents across Fusion Applications to the AI-native Oracle Database and OCI’s massive model-training capabilities, Oracle is positioning itself as the platform where enterprise AI comes to life. The energy was infectious. Every session, demo, and conversation carried a sense of acceleration: faster decisions, faster workflows, faster innovation.

Organizations waste an average of $12.9 million annually due to poor data quality. Traditional ETL processes built on hard-coded rules break with schema changes, batch processing misses real-time insights, and manual mapping creates bottlenecks that delay critical business decisions by days or weeks.