Systems | Development | Analytics | API | Testing

GitHub Copilot, Claude, and Cursor have become genuine superpowers for API development. They draft OpenAPI definitions, generate endpoints, propose schema changes, and write test cases — all from inside the IDE, in real time. Teams using these tools are generating API definitions faster than most thought possible even a few years ago. That velocity is real, and it’s reshaping how engineering teams think about their toolchain.

AI adoption programs tend to end in the same place. Tools are accessible, usage is up, and there's a dedicated Slack channel for wins. Six months later, nothing about how the team works has fundamentally changed. People are doing the same things – just slightly faster. And it’s easy for programs to stall when you’re measuring the wrong thing. Adoption (whether people have access and whether they're using the tools) is visible and easy to report.

Flaky tests not only slow your pipeline but also limit how fast your entire organization can move. Learn how AI is finally breaking through it.

You add a stop button to your AI chat app: a customer support agent, a coding assistant, a research tool the user can steer mid-task. A user clicks it mid-response. The frontend stops rendering. Then you check your backend logs and realize the underlying generation is still running, and you’re still paying for every token. This is not a bug.

If you have built an AI chat feature with the Vercel AI SDK, you have used its useChat hook. You give it your messages, and it streams the reply into your UI. You may have seen our post on the custom transport we built for the Vercel AI SDK. It swaps useChat's default transport for Ably AI Transport, adding resumable streams, cross-device and multi-user sync, conversation branching, history compaction, and stop-and-approve controls.

Most enterprises sized their AI infrastructure with a playbook written for training. However, training is no longer the typical workload. Inference now eats up roughly two-thirds of all AI compute, and it is changing shape fast enough that the rules of thumb from 18 months ago just do not hold. Our view at ClearML is pretty simple: when the workload shifts this much, the platform underneath it has to shift with it.

Most debugging agents fail not because the model is wrong, but because the data going in is not ready for machine consumption. Here's what data curation actually looks like in practice. When we started building Multiplayer's debugging agent, we made the same mistake almost everyone makes. We gave our coding agent access to observability data and expected it to figure out what was relevant. It didn't.

Every enterprise engineering leader knows the frustration of a stalled delivery pipeline. You push a minor user interface optimization or rename a single CSS utility class, and suddenly, a stable deployment build turns red. Hundreds of automated test scripts break instantly, not because the application logic failed, but because a static element locator changed. This is the reality of modern software delivery.