# KiHustle Word Embedding Similarity Proxy

> KiHustle Word Embedding Similarity Proxy is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-14).

Computes word embedding vectors or similarity scores for a given text string via a proxy API

## Facts

- Endpoint: POST https://kihustle.tech/api/v1/word-embed-similarity-proxy
- Price: $0.002/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/kihustle-word-embedding-similarity-proxy-b59ad1f7
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_3-jNlzTNEwE6f7sRcKlOI

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability kihustle-word-embedding-similarity-proxy-b59ad1f7 -d '<json body>'
```

Example prompt: Can you compute the word embedding similarity for the text 'artificial intelligence is transforming industries' so I can compare it semantically to other phrases?

## When to prefer this

Choose this endpoint when you need a lightweight, pay-per-call word embedding or semantic similarity computation without managing your own embedding model infrastructure. It is particularly useful for agents that need occasional on-demand text vectorization without a subscription to a larger NLP platform.

## Known failure modes

- Empty or missing 'text' field returns an error or malformed response
- Very long text strings may be truncated or rejected
- Ambiguous or non-standard input encoding may produce unexpected results
- Network timeouts if the upstream embedding model is slow
- 402 Payment Required if USDC payment is not included or insufficient

## How this service works

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## Output

Returns a JSON object with a 'result' field containing the processed embedding or similarity output and a 'status' field indicating success. The exact embedding vector or similarity score is returned under the result key.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "text": {
   "type": "string"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "result": "processed",
  "status": "success"
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/kihustle-word-embedding-similarity-proxy-b59ad1f7/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from kihustle.tech](https://www.zero.xyz/host/kihustle.tech/llms.txt)