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Mailboxes as Cattle: Ephemeral Email Infrastructure

When was the last time you deleted an email account on purpose? For most teams the answer is never, and that tells you something. We treat mailboxes the way we treated servers in 2008: hand-built, carefully named, kept alive indefinitely because recreating one is painful. They're pets. Meanwhile every other piece of our infrastructure — compute, queues, databases — became cattle: numbered, provisioned by code, destroyed without sentiment when the job ends. Email is finally catching up. With Nylas Agent Accounts (in beta), a mailbox is created with one call and destroyed with another, and that symmetry is the whole point. The full lifecycle in two commands Provisioning, from the CLI: nylas agent account create signup-agent@agents.yourdomain.com Or via POST /v3/connect/custom with "provider": "nylas" — no OAuth, no refresh token, just an address on a domain you've registered. Teardown is equally unceremonious: nylas agent account delete signup-agent@agents.yourdomain.com --yes (The API equivalent is a DELETE on the grant.) The signup automation recipe treats this as a loop: provision a fresh inbox, point a third-party signup form at it, catch the verification email through a message.created webhook, follow the confirmation link, delete the grant. No human inbox involved at any step, and nothing left behind. The middle of that loop is about twenty lines of webhook handler, and the recipe's version filters hard before acting — right grant, right sender, right URL shape: const { grant_id , id : messageId , from } = event . data . object ; if ( grant_id !== AGENT_GRANT_ID ) return ; const sender = from [ 0 ]?. email ?? "" ; if ( ! sender . endsWith ( " @saas-you-care-about.example.com " )) return ; const match = /https: \/\/ saas-you-care-about \. example \. com \/ confirm \? token= [^ " \s < ] +/ . exec ( message . body , ); if ( match ) await fetch ( match [ 0 ]); Nylas fires message.created within a second or two of mail arriving, so the whole signup round-trip typical

2026-06-17 原文 →
AI 资讯

The Tips Behind API Artisan: Building Laravel APIs Developers Actually Want to Use

I have just finished writing API Artisan: A Guide to Building APIs with Laravel , and I am giving it away for free. Before you commit to 300-odd pages, let me give you the short version: the tips, patterns, and small decisions that separate an API that technically works from one that developers are genuinely happy to depend on. None of this needs more hardware, a different framework, or a bigger team. It needs you to point your attention at the right things. These are the ones I keep coming back to. Start by measuring the right thing Ask most teams how they know their API is good and you get a single question back: does it work? Can I hit this endpoint and get a response? That question is necessary, and it is nowhere near enough. The question I want you to ask instead is whether your API is liveable with. Can a developer read your docs, understand your auth model, make a successful request, and handle an error without contacting support, trawling a forum, or guessing what a status code is trying to tell them? The gap between "works" and "liveable with" never shows up in a sprint retro, but it shows up everywhere else: in support volume, in integration timelines that overrun, and in the quiet moment a developer decides to build around your API rather than with it. Everything else in the book hangs off one mindset shift: an API is a product. It has users. Treat it as an implementation detail and it will behave like one. It will change without warning when your internals change, and it will be inconsistent because different people wrote different parts on different days with different conventions. Write the contract before the code The natural way to build an endpoint is to write the handler, return some data, and document it afterwards if there is time. It feels efficient, and in the short term it is. The problem is what it produces: a contract that was never designed, only discovered. Let me show you the trap, because I have watched it catch good developers. You have

2026-06-17 原文 →
AI 资讯

The $0 Bug That Cost Us $1,800 in API Calls

Last quarter our OpenAI bill went from $620 to $2,480 in 23 days. No new features shipped. No traffic spike. Zero error alerts. Deployment logs were clean. Just a number climbing in silence while five engineers stared at dashboards that gave us totals and nothing else. This is what we found. And why "cost monitoring" is completely the wrong mental model. The dashboard that answers the wrong question First thing I did was open the OpenAI usage dashboard. It showed me a total. A graph going up. A model breakdown. I knew we spent $2,480. I still had no idea which feature spent it, which service triggered it, or which user was responsible. The dashboard was answering "how much" while we were desperately asking "what caused it." Those are completely different questions. Almost every cost tool on the market only answers the first one. That distinction matters more than most engineering teams realise until they are staring at a bill like ours. Three features, zero visibility We had three features hitting GPT-4o: A document summariser, triggered manually by users An inline suggestion engine, triggered on keystrokes A batch report generator, triggered on export Any one of them could be the problem. Or all three. Or one specific tenant hammering one endpoint in a loop nobody noticed. Without attribution at the feature, service, and user level, we were just guessing. So I did what most engineers do: optimised the feature that felt most expensive. Added caching to the one that ran most often. Two weeks later the bill was still climbing. Guessing at cost problems without attribution data is exactly like debugging a performance issue without a profiler. You move things around and hope. 48 hours of real data A teammate dropped CostReveal in our Slack. I set it up that evening. The Node.js SDK wraps your existing provider calls. You instrument each one with a feature name, service context, and user or tenant ID. That is the entire integration for the base case: import { CostReveal

2026-06-17 原文 →
AI 资讯

What Recruiters Can't See On My GitHub

What Recruiters Can't See On My GitHub If you spend about 30 seconds looking at my GitHub profile, you might think I'm all over the place. React. Python. Healthcare. AI. Scrapers. Automation. Marketing tools. Job bots. Honestly, that's something I've worried about. I have over 100 repositories. Recruiters can see most of them, but not all of them. Some are private because they're client work. Some are private because they're unfinished. Some are private because they contain ideas I've spent years developing and I'm not quite ready to throw the blueprints onto the internet. From the outside, it can look random. But recently I realized something. All of those projects are solving the same problem. I hate repetitive work. My GitHub is here: https://github.com/ashb4 The Job Application That Broke Me I've applied to thousands of jobs over the years. Thousands. And one thing has always driven me absolutely insane. You upload your resume. Then the company immediately asks you to type your entire resume into fifteen different boxes. Your work history. Your education. Your skills. Everything. The computer already has the information. The resume is right there. Yet somehow I'm sitting on page seven of an application retyping information that already exists. It feels inefficient. It feels stupid. And most of all, it feels like a waste of time. Eventually I got annoyed enough to start building tools to help. Then I Noticed a Pattern At first I thought I was building unrelated projects. A job application helper. A content scheduler. A healthcare platform. An AI framework. A browser automation system. But when I stepped back, I noticed the same motivation behind almost all of them. Every project started with some version of: "There has to be a better way to do this." Take PostPunk. Most people see a social media scheduler. I see hours of repetitive posting that I never want to do again. I like creating content. I do not like manually posting the same content everywhere. So I buil

2026-06-17 原文 →
开发者

All the latest news on Android 17, Wear OS 7, and Android XR

Google’s Android 17 update includes highlights like new floating “Bubble” app windows for easier multitasking, a Screen Reaction recording mode, and a 50/50 split gaming mode for foldable phones. Meanwhile, Wear OS 7 brings Live Updates, better battery life for smart watches, and prepares connections for new Android XR smart glasses that will launch this […]

2026-06-17 原文 →