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Why this list is different The "best" email API depends entirely on what you're building. A side project optimizing for the free tier needs different things than a Series B SaaS sending two million transactional emails a month. This post grades eight providers against the criteria that actually move the needle in production, and tells you which one to pick for which use case. Most roundups in th
Disclosure: I'm a senior backend tech lead and I run HostingGuru, where Telegram alerts ship as a built-in feature. This tutorial works on any platform — it's the manual version of what HostingGuru does for you. Useful even if you never become a customer. There's a hierarchy of where production alerts go, ranked by how likely you are to actually see them. Email → 14% open rate within an hour, less
What do you need for UCP? There are two levels of UCP readiness. The first is the minimum viable manifest — the bare requirements to pass validation and appear in the UCP directory. The second is the agent-ready setup — what it actually takes for an AI agent to browse, cart, and check out at your store without friction. Think of this as your UCP checklist — the minimum requirements plus the recomm
My scrapers run on PythonAnywhere. My phone runs Termux. I wanted them to talk to each other. The standard options all had the same problem: they required infrastructure I didn't want to maintain. Firebase — cloud lock-in, SDK overhead, costs money at scale Ngrok — exposes a port on my phone, dies when the tunnel resets A VPS with Redis — another server to maintain, SSH into, keep alive Webhook to
Before you train a model, you need data in the right format. This took me longer than I expected and taught me a lot about how LLMs actually learn. I used MedQA USMLE — real medical licensing exam questions used to certify doctors in the US. It's available on HuggingFace for free. from datasets import load_dataset dataset = load_dataset("GBaker/MedQA-USMLE-4-options") Each sample looks like this:
Series: AI Isn’t an Engineering Problem Anymore (Part 2) In the last post, I talked about hitting a usage limit while debugging my robot and realizing how repetitive my own AI usage had become. When we use LLMs, whether through APIs or tools, it feels like every request is new. The inefficiency isn’t from using AI too much. You don’t ask once, you iterate. These are the most interesting ones. Some
Series: How Machines Learn: A Complete Guide from Zero to AI Engineer Phase 6: Machine Learning (The Core) You've been hearing "machine learning" for years now. Your phone uses it. Netflix uses it. Your spam filter uses it. Every tech company puts it in their job posts. And yet, if someone asked you right now to explain what machine learning actually is in plain words, you might freeze up a little