Systems | Development | Analytics | API | Testing

Dashboards, sampling, and data lakes were built for human debugging. Closing the bug-to-fix loop for AI agents requires rethinking how runtime data is collected and correlated. Observability as we know it is on its way out. For over a decade, we built telemetry stacks around a single consumer: a human, staring at a dashboard, trying to make sense of a system under stress.

WWDC 2026 delivered plenty to talk about. Apple's renewed AI push, Xcode 27 (that we shipped to customers in beta within 24 hours of the keynote!), and refreshing Liquid Glass. It also delivered a notable absence: no M5 Mac minis yet, which we covered separately. Now that the dust has settled, Bitrise’s Ben Boral went looking for the announcements that slipped past the highlight reel. If you're a mobile developer, these three are worth your time.

console.log() is a foundational tool for developers learning JavaScript. It sends messages to the browser’s DevTools console, so we can see what our code is doing at runtime. This allows us to: Because it prints information directly to the console, we can observe values, program flow, and potential issues. In this post we’re going to cover the basics of console.log() syntax, the nuances of formatting and the essential DevTools add-ons that turn console.log() from a window into a dashboard.

AI agents move fast. Securing them shouldn't slow you down. Kong and Noma are partnering to change that. This program delivers high-quality, reliable integrations providing real business value for customers..

Cloud testing security remains a source of confusion for many IT teams. It’s not simply about protecting your test environment, nor is it interchangeable with general cloud security. In the context of load testing and performance testing, cloud testing security means safeguarding the data, assets, and processes involved in evaluating how your website or API performs under stress, all within a cloud-based environment.

In the first two articles of this series we showed what the OctoPerf MCP Server does. This one is for the builders: how we designed it, and specifically how we kept its token cost under control. Because here is the thing nobody tells you when you start writing a Model Context Protocol server: the hard part is not exposing your API to an LLM. The hard part is not exposing too much of it.

This is the third post in our "Features Sitting Idle" series, where we shine a light on OctoPerf features that are already in your account but rarely used to their full potential. This is a blind spot many teams discover too late. Tests pass, metrics look fine, yet real users report slowness or errors after a release. The root cause is almost always the same: the load test was built against the HTTP protocol layer, but the user pain happens in the browser, above that layer.

One line from Snowflake Summit 2026 stood out above everything else. Christian Kleinerman, EVP of Product at Snowflake: "We do not want any of you spending money with Snowflake, in any use case, if you are not getting more value in return." It's a refreshing commitment, and it points directly at the cost efficiency conversation we've been having with customers around open lakehouse architectures. Here's the core argument: data movement doesn't directly generate value.