Book: TypeScript in Production Also by me: The TypeScript Library — the 5-book collection My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools Me: xgabriel.com | GitHub You have seen the shape of this incident before. A 500 lands in production. The frontend says "checkout failed". The Hono service that owns /checkout called the prici
Every observability vendor has bolted "AI" to their landing page. Half of those features are genuine improvements. The other half are autocomplete in a costume. After a few years of running these tools across enterprise estates, here is where AI-augmented SRE actually pays off, where it doesn't, and what we'd advise teams adopting it today. The single most defensible use case. A medium-sized estat
When you have 5 unrelated questions, should you pack them into one message to the LLM, or send 5 requests simultaneously? Which is faster? Splitting into multiple independent parallel requests is almost always faster. This isn't a gut feeling — it's determined by the underlying inference mechanism of LLMs. Let's walk through the reasoning from first principles. To understand this problem, you firs
Iris v0.4.0 ships today. It's the release where protocol-native eval crosses from "deterministic rules" into "semantic scoring" — without giving up any of what made the deterministic layer work. Three headline features plus a lot of infrastructure work that quietly compounds. I'll go through each, why it matters, and how it fits the thesis. Heuristic rules catch a lot: length, keyword overlap, PII