The first few months of running large language models in production feel deceptively smooth. Demos land. Early users are impressed. Latency seems acceptable, and costs look manageable. Then the real
If you have ever stared at a slow dashboard query and thought, “We already calculated this yesterday, why are we doing it again?”, you are already circling the idea behind
If you have ever watched AI architectures stall after an impressive demo, you already know the pattern. The model worked. The architecture did not. Teams fixate on what the system
You notice it first in the graphs. CPU spikes that look like a heart monitor. Latency is creeping up just enough to make your SRE instincts twitch. A few minutes
If you have shipped an AI-powered system to production, you have likely lived this moment. The demo worked. Offline metrics looked solid. The model passed the evaluation. Then the incidents
AI-powered hiring didn’t arrive with a bang. It crept in. Quiet. A calendar invite scheduled itself. A shortlist appeared faster than expected. Someone noticed the inbox felt lighter. That’s usually
If you have ever watched a production dashboard light up during an incident, you already understand the emotional core of time-series data. Metrics spike, logs flood in, traces branch into
If you have ever watched an analytics query grind your production database to a halt, you already understand the tension behind this debate. You stored the data “correctly,” indexes looked
You only notice it after the second on-call rotation gets weird. Latency looks fine, but customers still complain. Deploys “work,” but rollbacks do not. Your architecture review doc said the