KiHustle Jaccard Similarity Token

x402

KiHustle Jaccard Similarity Token is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-10).

Computes the Jaccard similarity score between two input text strings using token-level set intersection

Price / call$0.002

Seller volume →—

Success—

StatusUnknown

How this service works

Kostenlose Guides, Solo-Playbooks und Artikel zu KI, Automation und Side Hustles — für Menschen, die mit echten Systemen online Einkommen aufbauen wollen. Transparent finanziert über faire Affiliate-Links.

Output

A JSON object containing a 'result' field indicating the computed similarity outcome and a 'status' field confirming successful processing. The Jaccard similarity is a value between 0 (no shared tokens) and 1 (identical token sets), quantifying the proportion of shared unique tokens across both input texts.

Example use cases

Duplicate content detection

“Check how similar these two article snippets are: 'AI automation saves time for small businesses' and 'Automation with AI helps small business owners save time' — I want to know if they're too close to be treated as unique content.”

Keyword overlap scoring for SEO

“Compare these two blog post titles for token overlap: 'How to Build a Side Hustle with AI' and 'Building an AI-Powered Side Hustle from Scratch' — give me a Jaccard similarity score so I can decide if they're too redundant.”

Plagiarism check between submissions

“I have two student answers I want to compare for similarity: 'the Jaccard index measures overlap between sets' and 'Jaccard similarity calculates shared elements between two sets' — how similar are they?”

Technical details

Endpoint

POSThttps://kihustle.tech/bot-bazaar/api/v1/jaccard-similarity-token?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a lightweight, fast, token-based text similarity score without the overhead of embedding models or semantic NLP pipelines. It is ideal for duplicate detection, near-match filtering, or simple keyword overlap checks where exact token matching is sufficient. Prefer this over cosine similarity or semantic embeddings when interpretability and speed matter more than capturing paraphrased meaning.

Side effects

Missing text_a or text_b fields returns an error
Empty strings may return a zero or undefined similarity
Very long texts may hit processing limits
Malformed JSON body returns a 400-level error
Payment failure (x402) if USDC balance is insufficient

Input

Schema · Request body

Field	Type	Description
text_a	string
text_b	string

Output

Schema · Response

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

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