Systems | Development | Analytics | API | Testing

When we think of a typical API, part of a production-ready setup generally includes a cache. This cache allows for similar requests to be served without having to do the entire roundtrip. But when it comes to AI applications powered by large language models, traditional caching falls short. This is because queries to an AI endpoint may look different in terms of how things are worded or phrased but actually mean the same thing semantically.

Getting APIs to work with AI has become one of the major themes in the API space recently. And that’s not surprising because APIs are at the core of an AI’s ability to reach out into the world, to get access to data and information, and to invoke commands and workflows to act. This was always what APIs were for, but in this article we will dive a little deeper what that evolution looks like, and what that means for API governance and management.

I built AI Agents to handle my test automation, and they immediately tried to unionise. I’ve been doing test automation for long enough that I can spot a flaky test from far away, with the sixth sense of a person who has been personally betrayed by “WaitForTimeout(5000)” more times than they’ll admit in polite company.

AI is no longer an experimental line item in the budget. For most U.S. CXOs, the real challenge in 2026 is far more practical: where should we place our bets first? With dozens of AI use cases competing for attention, capital, and executive sponsorship, prioritization has become a boardroom conversation, not a lab discussion. Are you investing in AI initiatives that can move the needle this fiscal year, or are you spreading resources thin across pilots that never scale?

In today’s fast‑moving software world, release velocity keeps climbing, and AI is accelerating it even further. To keep quality teams aligned with rapid change, we’ve brought together two powerful capabilities: Tricentis SeaLights’ deep code-level insights and Tricentis qTest’s intelligent test management and AI-generated test creation. Here’s how these technologies integrate to create a complete, AI-driven testing feedback loop.

Adopting Behavior Driven Development (BDD) starts with enthusiasm. The first fifty scenarios are easy to write. They clarify requirements and align the team. But somewhere around scenario, the reality of Gherkin software testing sets in. Feature files become bloated. Scenarios start to conflict. The "simple" English syntax that was supposed to bridge the gap between business and technical teams becomes a maintenance nightmare that requires constant refactoring.

Streaming tokens is easy. Resuming cleanly is not. A user refreshes mid-response, another client joins late, a mobile connection drops for 10 seconds, and suddenly your “one answer” is 600 tiny messages that your UI has to stitch back together. Message history turns into fragments. You start building a side store just to reconstruct “the response so far”. This is not a model problem. It’s a delivery problem That’s why we developed message appends for Ably AI Transport.

Author: Adam Wolf ClearML Enterprise v3.28 offers new features and improvements to help administrators monitor usage, enforce policies, and streamline operations across large, multi-team environments. This release introduces enhanced usage metering with a simplified interface, improved resource policy management, improved dataset controls, and UI enhancements to provide greater clarity, control, and productivity for AI teams.

Having a build cache solution is a powerful way to speed up builds, especially at scale. Bitrise Build Cache already accelerates builds across multiple ecosystems, but to get the most out of it we also need to optimize the build cache clients themselves and ensure stability across changing network environments. In this blog post, I’ll walk through the steps we took to improve stability and performance for Bitrise Build Cache customers.

If you're running Node.js in production, you've likely heard the buzz around OpenTelemetry. It's the industry standard for observability, backed by major vendors, and it promises vendor-neutral telemetry collection across your entire stack. For many teams, it's a game-changer: finally, a unified way to collect traces, metrics, and logs without getting locked into a single vendor's ecosystem.