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Use Docker and a disposable mailbox to verify AWS SES delivery in CI before release without touching shared inboxes or leaking test mail. Shipping an app that "sent the email" is not the same as shipping an app that delivered a usable message. In AWS stacks, the miss usually shows up late: a container points at the wrong SES region, a preview environment uses stale credentials, or the message lands with broken links after templating. A disposable email address check is a simple final guardrail, and its easy to automate when your mail sender already runs in Docker. This is the pattern I recommend when teams are searching for temp mail com style testing, but need something cleaner for production-like CI. The goal is not to replace unit tests or the Amazon SES mailbox simulator . The goal is to prove that your real app container, with real environment wiring, can send one controlled message and that the inbox content is worth shipping. Why this check belongs in the release path AWS SES is strict in useful ways. If your account is still in the sandbox, you can only send to verified identities or the mailbox simulator . Even after production access, delivery problems still happen becuase the application layer and the cloud layer drift separately. A practical smoke test catches things that mocks often miss: wrong AWS_REGION or SES endpoint selection missing secrets in the container runtime template variables that render empty in preview builds broken confirmation links caused by bad environment URLs unexpected sender identity changes between staging and release That list looks basic, but it doesnt stay basic when three services, one worker, and a release job all touch outbound email. A small Docker pattern that keeps the test deterministic Keep the sender in the same Docker image you deploy, but trigger a narrow email scenario from CI. For example, seed a temporary user, call the notification path once, then poll a disposable inbox API for the matching subject line. servi

Book: The Complete Guide to Go Programming Also by me: Hexagonal Architecture in Go — the companion book in the Thinking in Go series My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools Me: xgabriel.com | GitHub You reach for unsafe.Pointer to skip a copy. Maybe a []byte you want as a string without the allocation, maybe a struct you want to reinterpret as another. The code compiles. go vet stays quiet. Tests pass. Then, weeks later, under GC pressure, a rare crash shows up in a place that has nothing to do with your change. The Go unsafe package documentation is one long doc comment. It lists the conversions that are valid and warns that everything else is not portable and not guaranteed to keep working. The trouble is that "everything else" includes a lot of code that looks obviously correct. The rules are narrow on purpose. There are effectively four patterns you are allowed to write, and the standard library stays inside all four. Here they are, in the shape you will actually use them in Go 1.23+. The one fact under every rule unsafe.Pointer is a pointer the garbage collector understands. It keeps the object it points at alive and it moves with the object if the runtime relocates a stack. uintptr is a plain integer. The GC does not treat it as a reference. Convert a pointer to a uintptr , store that integer in a variable, and as far as the runtime is concerned nothing points at that memory anymore. That single fact is behind three of the four patterns. Every legal conversion either avoids uintptr entirely or keeps it inside one expression where the compiler can prove the pointer stays live. Pattern 1: reinterpret *T1 as *T2 with the same layout The first pattern converts a *T1 to *T2 when the two types have the same memory shape. You take the address, run it through unsafe.Pointer , and cast to the target pointer type. The standard library does exactly this in math.Float64bits : func Float64bits ( f float64

By Vilius Vystartas This is the follow-up to What Actually Changed in Two Weeks . That one was about setting up a project for AI-speed delivery. This one is about something I keep re-learning on every fast delivery. You start shipping faster with AI. The code works, the feature lands, it feels good. Then a few weeks later the feedback comes back, and some of it catches you off guard. Not because anything is broken — but because a few things that seemed obvious to you weren't obvious to the other side. No drama. It happens. Here are five things I'm learning to check earlier. 1. What does "done" look like from their side? To me, done means working software. To someone else it might mean pixel-match with a design. Both are valid. What helps: A quick "what does good enough look like to you?" before the work starts. One sentence can save a lot of back and forth. 2. When will they actually look at it? Sending something doesn't mean it gets reviewed immediately. It lands in a queue like everything else. What helps: Naming a review date alongside the delivery date. "I'll share this Tuesday — could you take a look by Friday?" Turns silence from a mystery into a signal. 3. What needs to be perfect vs what can be improved later? Not everything in the feedback is the same weight. A label change and a broken flow are different things. Without saying so upfront, everything looks like an emergency. What helps: Two buckets agreed early. "Here's what I'll get right before it ships. Here's what I'd revisit in a follow-up." Makes the first feedback session more productive. 4. Could they see something before the full delivery? The first time someone sees your work often sets the tone. Showing one page or one flow halfway through can catch mismatches before they multiply. What helps: A mid-point check-in. "First page is ready — want to see if this matches what you had in mind?" Five minutes that can save a round of revisions. 5. Do they have the full picture? You've been living in this

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It's a big year for America. It's the semiquincentennial, otherwise known as America250, and the United States is cohosting the World Cup. But spectators at these events - and the millions of people who live in the cities hosting them - may not realize that they, too, are being watched. From Kansas City to New […]

Claude for investing in perpetuals Discussion | Link

Despite the fact that smartphones have become impressively capable shooters, standalone point-and-shoot cameras are enjoying a renaissance. The tiny Kodak Charmera is still wildly popular, while influencers are scrambling to find aging Canon cameras on eBay. Godox, a company best known for its photography lighting products, is the latest to join the simple camera craze, […]