Cost Is the Input. Yield Is the Output.

The short version: codeburn

codeburn is an AI coding cost tracker. It reads your token logs across tools and shows how much you're spending — dollars burned, cost per session, spend over time. It does its job well: it accounts for the cost. But cost is the input, not the output. Two operators can spend the same $100 and get wildly different results. codeburn can't tell them apart.

SigRank reads the same token telemetry and asks a different question: is the cascade compounding or burning? The headline metric, Υ Yield = cache_read × output / input², rewards the operator who reuses cached context efficiently and penalizes the one who burns fresh input without leverage. codeburn counts the cost; SigRank measures the yield. Both matter. Only one tells you whether you're winning.

Feature comparison

Why cost tracking isn't efficiency scoring

codeburn answers "how much did I spend?" That's accounting, not evaluation. Two operators can spend the same $100 across tools and get wildly different outcomes. One reuses cached context efficiently and produces 60K output tokens; the other re-sends the same context every turn and produces 6K. Same cost, ten-fold difference in signal. On a cost tracker, they look identical. On a yield leaderboard, the gap is obvious.

SigRank's headline metric, Υ = cache_read × output / input², measures exactly that gap. It rewards the operator who compounds cached context into output and penalizes the one who burns fresh input without leverage. codeburn gives you the bill; SigRank tells you whether the cascade it funded is compounding or burning.

From cost to yield

If you already run codeburn, you have the token counts. SigRank reads the same telemetry and adds the scoring layer cost tracking never had:

npm install -g sigrank
sigrank enroll      # create your operator identity
sigrank submit      # reads logs, scores, signs, publishes

Prefer to inspect before you submit? Run sigrank me --dry-run to see your scored payload locally, or paste your token counts into the /score calculator to compute your Υ Yield, class tier, and compression ratio instantly, no account, no submission, just the numbers.

Frequently asked questions

Is SigRank a codeburn alternative?

They overlap on reading AI coding telemetry but diverge on what they do with it. codeburn tracks cost — how many dollars you've burned across your AI coding tools. SigRank scores yield — how efficiently you're converting those dollars into signal. If you want a cross-tool cost dashboard, codeburn is that. If you want to know whether your spend is producing efficient output, SigRank answers that. You can run both — they read the same logs.

Why is cost tracking not enough?

Cost tracking tells you what you paid, not what you got. Two operators can spend the same $100 across tools and get wildly different outcomes. One reuses cached context efficiently and produces 60K output tokens; the other re-sends the same context every turn and produces 6K. Same cost, ten-fold difference in signal. On a cost tracker, they look identical. On a yield leaderboard, the gap is obvious. Cost is the input; yield is the output.

What does codeburn not measure that SigRank does?

codeburn reports dollars spent across AI coding tools. SigRank reads the same token telemetry and derives the cascade architecture: Υ Yield (is signal compounding or burning?), compression ratio (output per input), SNR (signal density), Leverage (how much cached context amplifies your input), and Velocity (tokens per unit time). codeburn tells you what you paid; SigRank tells you whether the cascade it funded is compounding or burning.

Can I use both codeburn and SigRank?

Yes. codeburn gives you the cross-tool cost dashboard for budget tracking. SigRank gives you the efficiency layer that cost tracking cannot provide. Run `sigrank submit` to publish your cascade score to the SigRank leaderboard, and keep codeburn for the budget view. The two are complementary, not mutually exclusive. The same local logs feed both.

Which is better for improving my AI coding efficiency?

SigRank. Cost tracking can tell you when you're burning too much, but it can't tell you why. Yield tracks the root cause: an operator whose Υ is low is burning fresh input without compounding cached context. Fix the cascade and the cost drops automatically. codeburn shows the symptom; SigRank shows the disease.