Systems | Development | Analytics | API | Testing

Live polls are a staple of conferences, streams, and all-hands: a question goes up on the big screen, everyone votes from their phone, and the bars race each other in realtime. There's a lot of different ways you could implement this.

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.

When automated test script generation consumes a significant chunk of engineering capacity, your roadmap was already behind the eight ball before the sprint even began.

Rubber duck debugging allows us to discover our own coding errors by retracing our steps. Instead of relying on complex black-box tools, we simply explain our own logic until the problem reveals itself. This is one of the most straightforward debugging techniques around, and it can be easily enhanced by AI tools.

Despite years of progress in cloud testing platforms, network latency remains the most stubborn – and often ignored – variable in load testing reliability. A recent study highlights that network latency can skew load test results by as much as 30%. That’s not a rounding error; it’s the difference between a site that passes in the lab and one that buckles under real-world traffic.

Token optimization is the new tokenmaxxing. Here's why burning fewer tokens produces better software and why the economics of AI make this shift inevitable.

Passing automated tests doesn’t always mean your software is ready for users. Many issues only surface when business stakeholders interact with the product in real-world scenarios and validate it against actual requirements. That’s where UAT testing software comes in. It helps teams manage test cases, collaborate with stakeholders, track defects, and streamline the final approval process before release.

A manual walkthrough, and the Claude skill that does it for you.

Ask three people in your company to pull the number of active customers this month, and you’ll probably get three different answers, even though each person labeled the metric the same way. One counts everyone who logged in, another counts only paying users, and a third filters down to a single plan tier. Nobody is wrong here. They’re all working from real data; they just never agreed on a single definition. Do that enough times, and the data itself becomes the thing everyone argues about.