Here's what I did:
1. Built a proxy that intercepts Codex's calls to OpenAI and rewrites them on the fly.
2. Replayed 3,807 rounds of SWE-bench Verified traces through it: avg prompt 44k → 6k tokens (-87%).
3. Posted it to HN to get the next reduction applied to my confidence interval — starting with the inevitable "How about accuracy?"
npx -y pando-proxy · github.com/human-software-us/pando-proxy
camelliaPTM•1h ago
george_ciobanu•1h ago
Because the proxy has a little bit of overhead per turn, the break-even point depends entirely on session length.
Short sessions (e.g., 2 rounds): The proxy's overhead might actually cost you more than you save.
Long sessions (e.g., 69 to 190 rounds): The token savings on the main model are massive and completely dwarf the small model's overhead.
It's not a universal win for quick, one-off queries, but the math becomes highly favorable on long, complex debugging sessions.