Systems | Development | Analytics | API | Testing

The pitch was simple: let AI write your code so you can focus on the hard problems. Three years into the AI coding revolution, and developers are focused on hard problems alright, just not the ones anyone expected. Instead of designing systems and solving business logic, engineers in 2026 spend a startling amount of their day managing the AI itself. Should you use Fast Mode or Deep Thinking? Haiku or Opus? Cursor or Claude Code or Windsurf? Should you write a SKILL.md file or a custom system prompt?

Your WireMock stubs are lying to you. They were accurate when someone wrote them six months ago, but the payment API added a metadata field in January, the inventory service switched from REST to gRPC in February, and nobody updated the stubs because the tests still pass. Meanwhile, production is breaking in ways your mocks will never catch. This is not a WireMock problem. It is a hand-written mock problem.

Production bugs that only reproduce in actual traffic can be some of the most frustrating bugs in software development. You can stare at your logs, add traces to your code, add instrumentation – and still not be able to see the actual requests that went over the wire. And that gets even harder when the requests are encrypted and the system is a black box. You can use tools like Wireshark or Kubeshark to capture the requests.

Enterprise Spring Boot APIs should be tested at three levels: unit tests for business logic, integration tests for external service behavior, and traffic replay for production edge cases. Most teams only do the first. This guide shows all three using a real Spring Boot application that calls external APIs (SpaceX, US Treasury) with JWT authentication. The kind of service that looks simple in development and breaks in production.

Modern software architectures have rendered traditional QA obsolete. In an era of distributed microservices and serverless functions, bugs are no longer just code errors; they are systemic interaction failures. While Agile successfully accelerated delivery, it left a critical gap in quality assurance. The industry's initial response, splitting focus between "Shift Left" and "Shift Right", created a fragmented safety net.

If you’re a mobile developer running builds on Jenkins, you already know the drill: a flaky agent goes down on a Friday afternoon, your Xcode version is three months behind, and the DevOps engineer who set the whole thing up left six months ago. The builds ship eventually - but at what cost? Jenkins is a powerful, battle-tested automation server. For teams building web backends or managing complex polyglot pipelines, it earns its place.

Jenkins has earned its place at the center of enterprise CI/CD. For organizations building backend services, orchestrating multi-stage deployments, and managing complex polyglot pipelines, Jenkins delivers the flexibility and control that engineering teams depend on. Ripping it out isn’t a conversation most organizations want to have - nor should it be. But mobile is different.

As AI becomes embedded in software delivery, the gap between mature DevOps organizations and those with “Incomplete DevOps” is becoming impossible to ignore, according to Perforce's 2026 State of DevOps report. Characterized by inconsistent workflows, manual processes, and inadequate standardization, "incomplete DevOps" has emerged as the leading obstacle to achieving ROI from AI investments. DevOps maturity is no longer an operational concern. It is an economic one.

Data virtualization can help modern organizations solve the complex challenges that come with managing data. With information scattered across multiple systems, accessing data can lead to operational bottlenecks in your organization.