Founder shares practical framework for operating Claude AI efficiently in a startup

An AI startup founder describes how early-stage distraction from AI trends cost him focus and money, prompting him to develop a more disciplined approach to using Anthropic's Claude. He identifies three operational layers — extending Claude Code via configuration, calling the API directly, and embedding Claude as an agent — and explains when to use each. Key cost-control tools highlighted include the open-source ccusage utility for tracking spend from local session logs and OpenTelemetry integration for monitoring model edit acceptance rates. The founder also explains how prompt caching can cut input costs by roughly 90%, but warns that even a single volatile token at the start of a context window can invalidate the entire cache. His core argument is that the real skill is not in prompting but in building reliable, measurable systems around the model.