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?
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.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.
⚠️ One caveat worth noting: output compression works well for chat-style Q&A, but use it carefully on long multi-step agent runs. The model’s own previous answers carry reasoning forward between steps — compress those too aggressively and it can quietly lose the thread by step 6 or 7 with no obvious error. Input compression (rtk) doesn’t have this problem since you’re removing noise the model never needed. Output compression removes scaffolding it was actually using.
- 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.
Don’t put it in the cart at all
Before compressing anything, scope what gets injected at session start — rules files, memory files, giant CLAUDE.md dumps. A well-trimmed session start usually beats any compression trick applied after the fact.
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?
My Other Blogs:
- Personal Agentic AI Assistant – Architecture
- Openclaw Personal AI Assistant Complete Series
- “Skills” in Claude Aren’t About Prompts — They’re About Context Design
- Practical Tips When Working with AI Coding Assistants
- Trying MCP for the First Time — What Stood Out
- Subagents: The Building Block of Agentic AI
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