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OceanEye Sentinel 2.0 — Differential Code Equivalence Verifier
x402
OceanEye Sentinel 2.0 — Differential Code Equivalence Verifier is a paid API for AI agents from sentinel.oceaneye-ai.com, paid per call via x402, $0.05/call, status unknown (last checked 2026-11-04).
Fuzz-tests two code implementations against each other to determine if they are behaviorally equivalent or diverge across randomized inputs
Price / call$0.05
Success—
StatusUnknown
How this service works
OceanEye Sentinel 2.0 — Plataforma de Inteligencia Marítima Satelital con Radar SAR Sentinel-1, Segmentación SAM 3.1 (Puerta A + Control Negativo), Arena Multimodal Muse Wonder 1.0 vs Muse Spark 1.3 (n=30) y Cadena de Custodia Ed25519.
Output
Returns a JSON object indicating whether the two code snippets are behaviorally equivalent (boolean), any divergence found (object or null with differing inputs/outputs), the solver mode used, and the number of test cases checked.
Example use cases
Refactor safety check before merge
“I rewrote our sorting function and want to make sure it's still correct — can you fuzz-test the original and my new version 500 times with seed 99 and argument names ['arr'] to see if they ever produce different results?”
Detecting optimization-introduced bugs
“I optimized a matrix multiplication routine but I'm worried I broke edge cases — compare the original and optimized versions with 256 fuzz iterations, seed 42, and argument names ['matrix_a', 'matrix_b'] and tell me if they ever diverge.”
Validating two vendor implementations
“We got two implementations of a tax calculation function from different vendors — can you verify they're equivalent by running 300 fuzz iterations with seed 7 and argument names ['income', 'rate', 'deductions']?”
Technical details
Endpoint
POSThttps://sentinel.oceaneye-ai.com/x402/verify-differential-spec?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need automated differential testing of two code implementations without setting up a local fuzzing harness. Ideal for CI pipelines validating refactors, comparing vendor-supplied functions, or catching subtle behavioral regressions. Prefer this over manual unit tests when the input space is large and edge cases are unknown.
Side effects
- Sandbox execution errors if code snippets contain syntax errors or unsupported language features
- Infinite loops or timeouts in submitted code may cause the request to fail or time out
- Argument name mismatches with actual function signatures can produce misleading equivalence results
- High fuzz iterations may hit processing time limits
- False negatives possible if the diverging edge case falls outside the fuzz coverage
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| seed | integer | |
| code_a | string | |
| code_b | string | |
| arg_names | array | |
| fuzz_iterations | integer |
Output
Schema · Response
{
"type": "json",
"example": {
"divergence": {
"type": [
"object",
"null"
]
},
"equivalent": {
"type": "boolean"
},
"solver_mode": {
"type": "string"
},
"checked_cases": {
"type": "integer"
}
}
}Tags
x402
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