Since the beginning of 2026, many projects such as RTK, Caveman, and Ponytail have claimed that they can reduce token usage by 80–90%. Some of them gained tens of thousands of GitHub stars in a very short time.

My previous startup was building an e-commerce chatbot. In fact, the product was almost identical to the Universal Commerce Protocol (UCP). After UCP was released, I decided not to continue pursuing that direction.

Earlier this year, I started turning the architecture we had built for e-commerce chatbot agents into an SDK. Put simply, its core idea is to use state machines and deterministic execution sequences to reduce the number of round trips to LLM providers by around 80% for the same task, thereby significantly reducing token consumption.

Here is the problem: I understand very clearly why tools such as RTK are ineffective in genuinely long-running tasks.

I published a report on July 18, and JetBrains published a report on July 20 that reached essentially the same conclusion: token-saving plugins such as RTK and Caveman have little to no effect on real-world, long-running tasks.

- JetBrains: https://blog.jetbrains.com/ai/2026/07/rtk-claude-code-token-savings/ - My report: https://turaai.net/blog#token-saving-plugins-are-mostly-stupid-idea

I also know how to achieve a real reduction of more than 80% in token consumption during long-running tasks. However, I have encountered significant difficulties promoting my open-source project.

I would summarize the main problems as follows.

1. Most coding-agent users do not care what actually drives token consumption

There is a huge gap in understanding among coding-agent users, regardless of whether they have formal software-engineering training or are self-taught "vibe coders."

A long article that systematically explains the process and challenges people's existing assumptions has very little chance of spreading in forums already flooded with AI-written posts and vibe-coded plugins claiming to reduce token usage by 95%.

Simple ideas are easier to spread. People are not purely rational; they tend to believe the things they can understand most easily.

That is also why pseudoscientific content built on incorrect causal explanations often attracts far more attention on social media than channels that carefully explain the underlying mathematics.

2. The author's arrogance

This is really a combination of two problems.

From the perspective of someone who believes strongly in scientific testing methods, evaluations, and benchmarks, software-engineering claims without an eval or benchmark can feel meaningless.

But perhaps my own arrogance and my contempt for, or even jealousy of, tools such as RTK has made me less willing to promote my work in ways that would actually be more effective.

If you have read this far, I would genuinely appreciate your advice: how can I promote my project more effectively while continuing to respect the rigor of evaluations and benchmarks?

I know that building a coding agent with Codex or Claude is relatively easy. But I also believe that there will always be people who understand the value of benchmarks and evaluation harnesses.

The project was released on July 16 and currently has a little over 400 GitHub stars. For now, it is still a relatively unknown project:

https://github.com/Tura-AI/tura