When the API literally burned your database after a typo

<p>Most applications don't fail because of bad code.</p> <p>They fail because of bad architecture decisions made early that nobody questioned <br /> until it was too late.</p>

<h2> 1. Intro — The Problem We Actually Had </h2> <p>Building real-time systems is often presented as a distributed systems problem.</p> <p>Kafka, stream processors, event buses,

<h2> The Problem We Were Actually Solving </h2> <p>We had built the Treasure Hunt Engine to power in-game treasure hunts that reward players for exploring content. At 100 000

<h2> The Problem We Were Actually Solving </h2> <p>We werent just chasing p99 latency; we were solving a fundamental mismatch between the event model and the treasure hunt logic.

<h2> The Problem We Were Actually Solving </h2> <p>We've all been there - staring at a production server that's suddenly, inexplicably slow. As an engineer, I was tasked with

<p>In this guide, we explore system. Your transaction is pending. A timeout occurs. Now you're staring at a screen wondering if you just paid $1,000 twice or if your money

<h2> The Problem We Were Actually Solving </h2> <p>It wasnt supposed to be a language swap. We had built the Treasure Hunt Engine on Go 1.20, using channels and sync.Pool to keep

<blockquote> <p>Skip the theory rabbit holes. This is the caching knowledge that shows up in system design interviews, code reviews, and the 2 AM production incidents nobody