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Y CombinatorTokenmaxxing: How Top Builders Use AI To Do The Work Of 400 Engineers
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At a glance
WHAT IT’S REALLY ABOUT
Tokenmaxxing workflows: thin harness, fat skills, 400x output gains
- Garry Tan describes returning to coding after 13 years and rebuilding a full-featured blog and agentic research system in days by directing AI agents rather than writing most code himself.
- He introduces “tokenmaxxing” as intentionally spending more tokens to “boil the ocean,” gather broader context, cross-check sources, and increase software quality and completeness.
- He shares the practical workflow patterns behind his output—structured plans, ASCII diagrams, iterative reviews, and high test coverage—plus the need for humans to stay in the loop to provide goals, taste, and judgment.
- He explains the “thin harness, fat skills” philosophy: reuse standardized agent harnesses while investing creativity in markdown prompt “skills” that encode process and decision-making.
- He argues the near-future will be “personal AI,” where individuals control their own data and prompts, versus corporate-controlled systems that shape outputs via opaque incentives.
IDEAS WORTH REMEMBERING
5 ideasAI makes “builder mode” accessible again—if you learn to direct it.
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.
Tokenmaxxing is a strategy: pay for completeness, not just 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.
Structured prompting (plans + diagrams) reduces “vibe coding slop.”
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.
High test coverage is the antidote to brittle AI 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.
Build with a “thin harness, fat skills” mental model.
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.
WORDS WORTH SAVING
5 quotesI think that's like the defining question. Like, will you have control over your own tools or will your tools have control over you?
— Garry Tan
And then in this case, it took about $200, which was my Claude Code Max account, and probably five days. Full-featured blog platform, does everything you want...
— Garry Tan
You pay more money and you might be token maxing, but you should token max.
— Garry Tan
I feel like using OpenClaw these days is like driving a Ferrari, and it's, like, exhilarating. It's insane... but then it's also like a Ferrari in that you better be a mechanic.
— Garry Tan
Like, you could buy millions of years of- consciousness- of machine consciousness... Now I can be a time billionaire. It's not, you know, my own time. It's the time of a machine- like, doing work for me.
— Garry Tan
SPEAKERS
Garry Tan
guestCEO of Y Combinator and cofounder of Posterous.
Jared Friedman
hostY Combinator partner and Lightcone co-host.
High quality AI-generated summary created from speaker-labeled transcript.
