# Why my token-saving SDK isn't blowing up > Source: > Published: 2026-07-25 19:00:44+00:00 # Why my token-saving SDK isn't blowing up The reality is that most "token-saving" tools are basically useless for actual long-term agent workflows. I noticed this back in July, and JetBrains actually published a report around the same time confirming that these plugins have almost zero effect on real-world, long-running tasks. Despite having a solution that actually delivers the savings, I've hit a wall with promotion for two main reasons: ## The "Vibe Coder" Gap There's a massive divide between people who care about the underlying AI workflow and those who just want a magic button. Most users don't actually understand what drives token consumption. In a sea of AI-generated hype posts claiming "95% savings," a systematic technical deep dive that challenges assumptions just doesn't travel. Simple, slightly inaccurate ideas spread faster than rigorous engineering. ## The Benchmark Trap I have a bias toward scientific testing and benchmarks. To me, a claim without an eval is meaningless. However, this mindset—and maybe a bit of arrogance toward the "hype" tools—has made me resist the kind of flashy marketing that actually gets stars. I'm trying to find a middle ground: how to promote a tool based on hard data without losing the rigor of a proper evaluation harness. If you've built a coding agent or worked with [Claude](/en/tags/claude/) Code, you know that getting it to behave is easy, but making it efficient at scale is the real battle. My project has around 400 stars since mid-July, but it's still flying under the radar. ``` https://github.com/Tura-AI/tura ``` [Next AI Hallucinations in Court: A Reality Check →](/en/threads/3320/)