Original Summary

Hey everyone, Like many founders here running production LLM apps, my biggest bottleneck has consistently been the scaling costs of heavy text processing layers. Over the last few months, I’ve been developing a proprietary routing and context-trimming system designed to maximize token efficiency without sacrificing semantic accuracy or context memory. I recently stress-tested it against a massive workload that traditionally benchmarks at a high tier of token usage. The baseline efficiency metrics: • Total Token Overhead Reduction: 99.7% • Semantic Retention Rate: 98.4% (measured via embedding similarity tests) • Latency Impact: Sometimes slow depending on prompt i havent tracked specific latency. I am currently keeping the exact compression and algorithmic pipeline proprietary, but I am looking to onboard 2-3 early-stage B2B projects or SaaS founders who are getting crushed by their monthly API bills to run live optimization pilots. If you are spending significant capital on production tokens and want to look at the benchmark data or run a sample test on your data architecture, feel free to DM me your email address or reach out directly.   submitted by   /u/SS-SOVEREIGNTECH [link]   [comments]

  • 情报分类：开源项目与落地
  • 分类依据：内容涉及项目实践、创业、副业或变现
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/9/24 11:53:22