Hey Product Hunt! 👋 I'm Ciprian Ștefan Pleșca, an independent software developer and researcher, and today I'm introducing **TokenPropulsion 2.0**. I started this project with a simple question: **Can we reduce unnecessary tokens in LLM prompts while preserving the instructions that actually matter?** Rather than building another AI wrapper, I wanted to create a lightweight Python toolkit focused on measurable optimization, conservative fallback mechanisms, and reproducible experiments. The current release includes structural and lexical compression, validation mechanisms, a CLI, a Python SDK, and optional xAI integration. In our initial synthetic benchmark, token counts decreased from 8,871 to 6,249 — a 29.56% reduction, measured with cl100k_base. A few things I want to be transparent about: - This is a research-alpha release. - The benchmark uses synthetic data. - The 29.56% result is not verified Grok billing savings. - Equivalent response quality in live xAI requests has not yet been demonstrated. The project is open-source under the MIT License, and the benchmark methodology is available for inspection. I'd love feedback from the Product Hunt community, especially on three questions: 1. Where does prompt redundancy cause the most difficulty in your AI applications? 2. Which real-world workloads should we benchmark next? 3. What quality checks would you require before adopting prompt compression in production? Thanks for checking it out! **Ciprian Ștefan Pleșca** Independent Software Developer & Researcher GitHub: https://github.com/Ciprian-Local...

TokenPropulsion 2.0

Compress LLM prompts with measurable token savings

Compress LLM prompts with measurable token savings