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Token maximizing vs cascade yield
| Feature | SigRank | tokenmaxxer |
|---|---|---|
| What it measures | Cascade yield (Υ = cache_read × output / input²) | Token maximization (burning more) |
| What it tells you | How efficiently you compound signal | How much you burned |
| Leaderboard | Yes — ranked by Yield, class tiers | No |
| Platform coverage | Claude Code, Cursor, Copilot, Gemini, 15+ | Limited |
| MCP server | Yes | No |
| Privacy | Token counts only — never prompt content | Token counts only |
tokenmaxxer celebrates burning more tokens. SigRank celebrates compounding them. Maximizing tokens is the opposite of efficiency.
SigRank's Yield (Υ) metric measures the architecture of your token cascade — is signal compounding, or are tokens burned? That's the question tokenmaxxer can't answer.
tokenmaxxer celebrates burning more tokens. SigRank celebrates compounding them. Maximizing tokens is the opposite of efficiency. SigRank's Yield (Υ) = (cache_read × output) / input² measures cascade efficiency.
Yes. They're complementary. Use tokenmaxxer for its function, and run sigrank submit to publish your cascade score to the leaderboard.
Yield (Υ) = (cache_read × output) / input² — token-cascade efficiency from real sessions. Works across Claude, GPT, Gemini, Cursor, Copilot, and any AI coding tool.
Visit signalaf.com/score to enroll and submit your token telemetry. SigRank will compute your Yield, your rank, and your operator class. Token counts only — never prompt content, never code.
Stop maxxing. Start compounding.
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