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Here at Speedscale, our monorepo has 19 Go projects in CI. Fifteen produce container images for both amd64 and arm64; a few of those need CGO. We also publish cross-compiled CLI binaries for macOS, Linux, and Windows.

We’ve updated the StatusGator API v3 to help you see how many requests your integration can make and handle rate limits more smoothly. Responses that count toward your organization’s per-minute limit now include three headers: If a request exceeds your limit, the API returns 429 Too Many Requests and a Retry-After header telling your integration how many seconds to wait before trying again.

If you’re running FastAPI, you’re running Starlette underneath it, whether you think about it or not. At Scout, we spend a lot of time thinking about what happens at that ASGI layer, since that’s exactly where our Python agent hooks in to give you request-level visibility. Starlette 1.7.0 just landed, and it’s a release worth reading closely if you care about tracing, routing, or just keeping your app running on a supported dependency chain.

Before you start, paste this into Claude Code, Cursor, Codex, Gemini CLI, Kiro, or any assistant that can read a URL and run commands: The install-speedscale skill installs proxymock for your operating system and walks you through proxymock init. It stops when you need to complete browser sign-in, keeps your recordings on your machine, and connects the proxymock MCP server so your assistant can run the prompts later in this article.

The first number from my local PostgreSQL 16 test was roughly 1,600 statements per second. It looked impressive. It was also the least useful result in the run. The useful part was the workload. It came from queries the demo app had actually sent: the same prepared statements, parameters, reads and writes. A synthetic benchmark tells you how PostgreSQL handles a synthetic workload. It does not tell you whether your migration just broke the UPDATE your app depends on.