I burned all my tokens researching how to save tokens
A researcher shares their experience optimizing token usage for AI agent pipelines after hitting usage limits during deep research. The author details a method for using shared memory across multiple AI subscriptions to improve cost-efficiency and research reliability.
Why it matters
As AI agent usage grows, managing token costs and optimizing research workflows is becoming a critical challenge for developers and researchers.
Download PNG At Quesma we are researching the economics of AI agents: what agentic coding really costs and what you can do about it. For this research I am running my own deep research setup, a pipeline of agents that builds a knowledge base I can actually trust. The first version of this setup burned the whole limit of my Claude Max 5x plan in 30 minutes. This post is the story of how I fixed the cost and the trust, using only subscriptions I already pay for, and how you can build the same.
My goal was to understand the entire state of so-called tokenomics. I wanted to know what monitoring systems exist, how teams govern their AI spend, and which optimization tools and practices actually work, both in the papers and in the real world.
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