Systems | Development | Analytics | API | Testing

The query count in our snapshot drawer went blank, and the unit test for that request kept passing. When we went looking in recorded traffic, the API gateway had rejected that request 210 times in a single recording. Every one of those rejections came back as 200 OK. So we searched the rest. We record our own API gateway with Speedscale, and two recording windows from November 3 to 5 (about 38 hours between them) held 1,238 responses with gRPC status INVALID_ARGUMENT. All 1,238 returned HTTP 200.

A pilot agent asks a few hundred well-behaved questions against data somebody curated for it, on a cluster nobody else is using. It works. Someone writes “AI-ready” on a slide. Meanwhile, the architecture decision that actually determines whether any of this scales gets made by default, because nobody noticed it was being made. What breaks under agents is almost never the part of the stack anyone argued about.

console.log is the foundation of logging in React Native. It gives you immediate feedback, it’s easy to use and it integrates with several developer tools. But if you want continuous visibility throughout your app’s lifecycle, console.log only takes you part of the way there. Once your app ships, you lose the convenient development environment that makes console output easy to inspect, and you want have automatic access to the logs.

When I talk to engineering leaders about how their team decides whether to build a piece of software instead of buying something off the shelf, the decision usually starts at, “well, can we?” For most of the last decade, that was a reasonable first question, because unless the team had a quarter’s worth of runway and a specialist to spare, the answer was “no.” But now, AI has made the answer “yes,” at least on the surface.