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The newest category in ai dev tools is the token diet. local proxies like rtk, which claims 60-90%…

Yethikrishna ROriginal on Threads

the newest category in ai dev tools is the token diet. local proxies like rtk, which claims 60-90% less usage on dev commands, and nadirclaw, which routes simple prompts to cheaper models for 40-70% savings, all launched inside the last month.

the first middlemen of the agent economy just appeared.

Context

The rtk repository describes itself as a high-performance CLI proxy that reduces LLM token consumption by 60 to 90% on common dev commands, shipped as a single Rust binary with zero dependencies. It stands for Rust Token Killer.

The NadirClaw repository describes an open-source LLM router and cost optimizer that routes simple prompts to cheap or local models and complex ones to premium models, as a drop-in OpenAI-compatible proxy for Claude Code, Codex and Cursor, and says it saves 40 to 70% on AI API costs. It offers a paid Nadir Pro tier with a trained classifier, a dashboard and team billing.

How it compares

The 60 to 90% and 40 to 70% figures match the two projects' own descriptions. Both are vendor claims from the projects, not independent measurements, and the savings depend on the workload.

The note says these tools all launched inside the last month. A DEV Community post about NadirClaw is dated February 27, 2026, and a search listing for the rtk repository carries a January 22, 2026 date, so both appear to be older than a month. That is unsupported, not refuted, since a page date is not a launch date.

Whether these are 'the first middlemen of the agent economy' is the author's opinion. Model routers and gateways existed before these two.

Related work

Watch next

  • Check each project's first release date. Look for an independent test of the claimed savings.

Sources

  1. rtk-ai/rtk: CLI proxy that reduces LLM token consumption by 60-90% on common dev commands (GitHub)github.com
  2. NadirRouter/NadirClaw: open-source LLM router and AI cost optimizer (GitHub)github.com

Provenance

The note above is reproduced unedited from the original post, first published on Threads on 9 October 2026 at 00:34 IST. Sources are the papers and datasets the note draws on.

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