Tokenmaxxing: How Top Builders Use AI To Do The Work Of 400 Engineers

Tan attributes his comeback to treating Claude Code like a high-powered tool: he provides intent, constraints, and review loops while the model executes. The leverage comes from orchestration—running many parallel “agents”—more than typing speed.

Instead of minimizing token spend, he advocates spending more to broaden retrieval (e.g., 20 sources vs 1), reconcile disagreements, and produce more reality-grounded outputs—whether for investigative writing or engineering decisions.

Having the model first generate ASCII diagrams of data flows, state machines, and error paths forces explicit architecture. This loads critical context up front and yields more correct, complete implementations than jumping straight to code.

Tan says early “it works for 80%” failures pushed him toward aggressive automated testing; he now targets roughly 80–90% coverage (not necessarily 100%) plus integration/E2E checks to make AI-written changes production-safe.

The harness is the reusable execution loop (tool calls, running commands, applying diffs); the differentiator is the markdown “skills” that encode review processes, product taste, and decision heuristics. Trying to force nuanced judgment into brittle deterministic code often backfires.