A pilot benchmark, a $5.60 receipt, and a 97%-savings number that was actually a silent failure.
How it started
This started from a plain problem: I kept hitting token limits at work. I was using Claude Code for real engineering work, and I was burning through budget faster than I wanted. The obvious question was: can I cut that down without hurting the quality of what the agent produces? Not "just use a cheaper model and hope." Something more deliberate — a set of tools that each attack a different part of the token bill. How much context gets read. How much gets re-read. How verbose the agent's own output is. How it finds its way around a codebase in the first place.
That question turned into a side project: token-optimization-stack, a public repo with setup docs for tools that reduce token spend. And token-stack-benchmarks, a benchmark harness to actually test whether any of it worked.
I'm writing this up because the project ended, a few weeks in, in a place I didn't expect. Not with a working stack and a savings number. Instead, with proof that the token-savings numbers I was looking at were actively misleading — and a cost problem that made the whole thing stop making sense before I could even publish a result. I think both of those are more useful to write about than a clean win would have been.
