I Built The Token Saver Skill To Cut My Token Use By 90%. Here Is What It Can And Cannot Do For You.

For deeper playbooks and analysis: https://natesnewsletter.substack.com/

What's really happening when your tenth message to an AI can cost much more than your first? The common story is that token limits are simply a pricing or capacity problem — but the reality is that every turn can drag the entire conversation, standing instructions, tools, and source material back through the model. In this video, I share the inside scoop on how to keep that AI desk clean and put more of your tokens toward useful work.

Why reused input compounds across a long conversation How to select evidence and send the lightest useful source What the Token Saver skill handles automatically Where prompt caching helps and where it does not How a local gateway can constrain a request before the model call

Operators, builders, and everyday knowledge workers should care because better models do not eliminate the need to manage context. The practical shift is to carry accepted results forward, keep source packets light, and stop paying repeatedly for work the model has already seen.

Token Saver guide: https://unlock-ai.natebjones.com/guides/cut-token-waste Ringer guide: https://unlock-ai.natebjones.com/guides/ringer Related reading: https://natesnewsletter.substack.com/p/context-windows-are-a-lie-the-myth

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