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Show HN: Codex context bloat? 87% avg reduction on SWE-bench Verified traces
If you had to build a context window manager in 24h, would you stick to the existing model or come up with something better?<p>Here's what I did:<p>1. Built a proxy that intercepts Codex's calls to OpenAI and rewrites them on the fly.<p>2. Replayed 3,807 rounds of SWE-bench Verified traces through it: avg prompt 44k → 6k tokens (-87%).<p>3. Posted it to HN to get the next reduction applied to my confidence interval — starting with the inevitable "How about accuracy?"<p>npx -y pando-proxy · github.com/human-software-us/pando-proxy
The project addresses a critical bottleneck in LLM-based software engineering (token cost and context limits) with impressive initial compression results on SWE-bench. However, it currently lacks market validation, a clear moat against model providers who are expanding context windows natively, and detailed team information.