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ASIC Efficiency Improvement — Sequential Input-Change Attribution
x402
ASIC Efficiency Improvement — Sequential Input-Change Attribution is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Computes a sequential (waterfall) attribution report showing how changes in ASIC joules-per-terahash (J/TH) efficiency affect mining economics, comparing before and after scenarios.
Price / call$0.01
Success—
StatusUnknown
How this service works
ASIC efficiency improvement — Sequential input-change attribution: Sequential input-change attribution for ASIC efficiency improvement. Supplied mining assumptions only; formula and units disclosed. No live prices, mining or guaranteed return. Specialised preset in a shared 50-profile, 20-operation engine. 0.01 USDC includes one bounded sequential input-change attribution report.
Output
A bounded sequential input-change attribution report that breaks down how each change in J/TH (joules per terahash) across the before and after scenarios contributes to overall ASIC efficiency improvement, presented as a waterfall/step-by-step attribution using the supplied mining assumptions and disclosed formula and units. No live prices or guaranteed return projections are included.
Example use cases
Evaluating next-gen ASIC hardware upgrade
“I'm considering upgrading my miners — before the upgrade they go from 30 J/TH to 25 J/TH, and after the upgrade from 22 J/TH to 18 J/TH. Can you run a sequential attribution waterfall to show me exactly how much of the efficiency improvement comes from each input change?”
Auditing efficiency gains for mining fleet report
“I need to produce an attribution report for our mining fleet efficiency review. The baseline scenario has old J/TH of 27.5 and new J/TH of 22, and the improved scenario has old J/TH of 25 and new J/TH of 20. Break down the sequential input-change contributions for me.”
Comparing two firmware tuning stages
“We tuned our ASICs in two stages — first stage went from 35 J/TH to 28 J/TH, second stage from 28 J/TH to 21 J/TH. Can you do a waterfall attribution to show how each stage contributed to our overall efficiency improvement?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-bx-mining-efficiency-saving-waterfall?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a structured, sequential (waterfall-style) attribution of efficiency changes between two ASIC J/TH scenarios — specifically when you want to understand which input changes drive the improvement step by step, rather than a simple before/after delta. It is purpose-built for mining hardware efficiency analysis using user-supplied J/TH assumptions, and at $0.01 USDC per call it is cost-effective for one-off or automated reporting workflows.
Side effects
- Zero or negative J/TH values rejected (exclusiveMinimum: 0)
- Missing required old_jth or new_jth fields in either before or after object returns validation error
- Extremely large J/TH values (approaching max 10^16) may produce unreliable output
- Malformed JSON body returns 400 error
- Payment failure or insufficient USDC balance blocks the call (x402 payment required)
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| after | object | |
| before | object |
Tags
x402
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