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Numora Matrix Multiply
x402
Numora Matrix Multiply is a paid API for AI agents from numormo.vercel.app, paid per call via x402, $0.1/call, status unknown (last checked 2026-09-15).
Multiplies two matrices (2D arrays) and returns the resulting matrix with a human-readable computation description.
Price / call$0.1
Success—
StatusUnknown
How this service works
100 pure math computation endpoints for AI agents. Statistics, financial math, linear algebra, equation solving, calculus, number theory, sequence generation, and unit conversions. Zero external dependencies. x402 micropayments on Base.
Output
Returns a JSON object with 'success' (boolean), 'result' (the product matrix as a 2D array or equivalent object), and 'computation' (a human-readable string describing what was computed, e.g. '2x2 matrix multiplied by 2x2 matrix').
Example use cases
Neural network weight transformation
“I need to multiply my weight matrix [[0.5,0.2,0.1],[0.4,0.3,0.6]] by the input matrix [[1],[0],[1]] to compute the layer output — can you run that matrix multiplication?”
2D rotation transform composition
“I have two rotation matrices I want to combine: A is [[0,-1],[1,0]] and B is [[0,1],[-1,0]] — multiply them together so I can get the composed transformation.”
Economics input-output matrix product
“Can you multiply my 3x3 industry coefficient matrix [[0.1,0.2,0.3],[0.4,0.1,0.2],[0.3,0.2,0.1]] by the demand vector [[100],[200],[150]] to get the total output vector?”
Technical details
Endpoint
POSThttps://numormo.vercel.app/api/matrix/multiplyWhen to prefer
Use this endpoint when you need a pure, serverless, zero-dependency matrix multiplication with guaranteed numeric precision and a human-readable computation summary. Prefer it over general-purpose code execution when you want a lightweight, metered micropayment per-call model without spinning up a math runtime. Ideal for AI agents doing linear algebra steps as part of a larger workflow (ML inference, geometric transforms, economics models) where calling a dedicated math API is cleaner than executing untrusted code.
Side effects
- Incompatible matrix dimensions (columns of A must equal rows of B) — returns error or success:false
- Non-rectangular arrays (jagged arrays) may cause parsing errors
- Non-numeric values in matrix elements will fail computation
- Missing required fields A or B returns validation error
- Very large matrices may exceed compute limits or timeout
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| Arequired | array | Matrix (2D array) |
| Brequired | array |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| resultrequired | object | Computation result varies by endpoint |
| successrequired | boolean | Always true on success |
| computationrequired | string | Human-readable description of what was computed |
Tags
x402
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