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The New Currency Is Tokens - How to save Tokens

#ai #claude #githubcopilot #devex

New Currency

Forget cloud bills — in 2026, the line item engineers actually argue about is tokens. Every move a coding agent makes (reading a file, running a test, replying "Sure, happy to help!") has a price tag attached.

Buzz around

The dev community has been buzzing about a few repos lately, all tackling the same problem: do the same work, spend fewer tokens. What's interesting is that each one attacks it from a totally different angle — like three different ways to cut a grocery bill: buy less, waste less, and stop paying for the gift wrap.

How to spend less?

  1. rtk — github.com/rtk-ai/rtk
    The "stop paying for noise" layer. It sits between your agent (Claude Code, Copilot, Cursor, etc.) and your terminal. It compresses the output of everyday dev commands — git status, test runs, docker ps, build logs — before any of it reaches the model's context. Most CLI output is boilerplate the model never needed in the first place; the project claims 60-90% savings on routine commands as a result.

  2. caveman — github.com/JuliusBrussee/caveman
    Flips the problem around: instead of compressing what goes in, it compresses what comes out. It's a skill that makes your agent answer in short, fragment-heavy sentences instead of polite paragraphs — same technical content, way fewer words. It reports roughly 65% fewer output tokens while maintaining accuracy and has a side feature that compresses memory/context files (like CLAUDE.md), so every new session starts smaller, too.

  3. superpowers — github.com/obra/superpowers
    Doesn't compress anything directly — it goes after the most expensive token sink of all: waste. Disorganized agent sessions burn tokens re-explaining context, wandering down the wrong implementation path, and redoing work nobody planned properly. Superpowers is a structured workflow (brainstorm → plan → test-first build → isolated subagent execution → review) that keeps the agent on-task and hands work off to fresh subagents so the main conversation doesn't balloon. Less backtracking, fewer tokens paid for twice.

Wrapping up

Put together: one shrinks what comes in, one shrinks what goes out, and one stops you from paying for the same work twice. Three different layers of the same economy.

Funny thing — a year ago we were all measuring AI cost in "API calls." Now we're tuning prose style for token efficiency. The grind never stops; it just changes units.

What else have you used?

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