Systems | Development | Analytics | API | Testing

Take this scenario: Your team ships a release with 91% code coverage. Every test in the suite passes. The pipeline is green, and leadership signs off. But two days later, a critical defect surfaces in production. Upon investigation, you find that the changed code was never actually tested, and the tests that were run covered different paths entirely. That 91% was real, but it was just measuring the wrong thing. And as AI tools generate more of the code inside those pipelines, the gap widens.

80% of enterprise apps still use decades-old systems, but accessing their data for AI is tough. The challenge? Security risks, outdated interfaces, and slow performance. Here's the solution: API abstraction. This method creates a secure, no-code layer between AI and legacy systems. It keeps your old code intact while enabling AI to access data safely and efficiently.

50+ platform-specific questions drawn from the Databox Prompt Library, plus the framework that separates answers you can act on from answers that sound right.

Large Language Models (LLMs) are impressive, but they are not without significant flaws. Their biggest hurdles are "knowledge cut-offs" where they cannot access information created after their training, and a tendency to "hallucinate" or confidently state false information. These models often struggle with the specific or real-time data that modern businesses rely on daily.

The terminal is a developer’s most trusted tool. It sees your source code, your secrets, the commands you run in production. When a terminal adds AI and cloud features, it’s worth asking what it’s doing on the network.