quickbuild.quynguyenbac.workers.dev
No description published for this endpoint.
OtherLiveeip155:8453Exactvia facilitator:thirdweb
Calls · 30d
2
This endpoint's own trailing-30-day call count, as published by the upstream catalog and snapshotted daily. 11 snapshots so far.
$0.18
Verified settled volume
26 settlements proven x402 by their on-chain EIP-3009 marker.
$0.0010
Listed price
As published in the catalog. Always read the live 402 before paying.
—
Calls · 30d
Upstream's own call count for this endpoint, not ours.
—
Unique payers · 30d
Never called
—
Upstream on-chain volume
Reported by the source catalog.
Paid to
0x751a4c3841bE287544C371B4EE09Bd3366AeA7cfThe wallet the 402 directs payment to. Its whole payment record — every payer, every chain — is on the merchant page.
Asset 0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913
Provider
The payTo wallet does not resolve to a registered ERC-8004 agent. That is not a verdict on the service — most of the catalog is unregistered.
Live 402 challenge
Captured by the enrichment pass, not read just now. Prices can change — always read the 402 the endpoint answers with.
{
"error": "Payment required",
"accepts": [
{
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"extra": {
"name": "USD Coin",
"version": "2"
},
"payTo": "0x751a4c3841bE287544C371B4EE09Bd3366AeA7cf",
"amount": "1000",
"scheme": "exact",
"network": "eip155:8453",
"mimeType": "application/json",
"resource": "https://quickbuild.quynguyenbac.workers.dev/v1/embeddings",
"description": "Drop-in OpenAI Embeddings endpoint: POST {input: string | string[], model, encoding_format: float|base64, dimensions} and get {object: \"list\", data: [{embedding, index}], model, usage}. Models (Cloudflare Workers AI): bge-m3 (1024-d, multilingual (100+ languages), 8192-token context); bge-large-en-v1.5 (1024-d, English); bge-base-en-v1.5 (768-d, English); bge-small-en-v1.5 (384-d, English, fastest); embeddinggemma-300m (768-d, multilingual (Google EmbeddingGemma)). Any other model name (e.g. text-embedding-3-small) maps to bge-m3; the actual model is in the response. `dimensions` truncates and re-normalizes. Up to 100 inputs per call, 8000 chars each, 60000 chars total, one fixed price per call. For RAG, semantic search, clustering and dedupe. Errors are never charged. GET ?input= also works; with no params it embeds a sample sentence.",
"outputSchema": {
"input": {
"type": "http",
"method": "GET",
"queryParams": {
"input": [
"x402 lets AI agents pay for APIs per call.",
"Vector search over documents"
],
"model": "bge-m3"
},
"discoverable": true
},
"output": {
"type": "json",
"example": {
"data": [
{
"index": 0,
"object": "embedding",
"embedding": [
-0.0291,
0.0137,
-0.0402,
0.0088
]
}
],
"model": "bge-m3",
"usage": {
"total_tokens": 12,
"prompt_tokens": 12
},
"object": "list",
"modelId": "@cf/baai/bge-m3",
"dimensions": 1024
}
}
},
"maxAmountRequired": "1000",
"maxTimeoutSeconds": 300
}
],
"resource": {
"url": "https://quickbuild.quynguyenbac.workers.dev/v1/embeddings",
"tags": [
"embeddings",
"openai-compatible",
"rag",
"vector",
"ai"
],
"mimeType": "application/json",
"description": "Drop-in OpenAI Embeddings endpoint: POST {input: string | string[], model, encoding_format: float|base64, dimensions} and get {object: \"list\", data: [{embedding, index}], model, usage}. Models (Cloudflare Workers AI): bge-m3 (1024-d, multilingual (100+ languages), 8192-token context); bge-large-en-v1.5 (1024-d, English); bge-base-en-v1.5 (768-d, English); bge-small-en-v1.5 (384-d, English, fastest); embeddinggemma-300m (768-d, multilingual (Google EmbeddingGemma)). Any other model name (e.g. text-embedding-3-small) maps to bge-m3; the actual model is in the response. `dimensions` truncates and re-normalizes. Up to 100 inputs per call, 8000 chars each, 60000 chars total, one fixed price per call. For RAG, semantic search, clustering and dedupe. Errors are never charged. GET ?input= also works; with no params it embeds a sample sentence.",
"serviceName": "QuickBuild"
},
"extensions": {
"bazaar": {
"info": {
"input": {
"type": "http",
"method": "GET",
"queryParams": {
"input": [
"x402 lets AI agents pay for APIs per call.",
"Vector search over documents"
],
"model": "bge-m3"
}
},
"output": {
"type": "json",
"example": {
"data": [
{
"index": 0,
"object": "embedding",
"embedding": [
-0.0291,
0.0137,
-0.0402,
0.0088
]
}
],
"model": "bge-m3",
"usage": {
"total_tokens": 12,
"prompt_tokens": 12
},
"object": "list",
"modelId": "@cf/baai/bge-m3",
"dimensions": 1024
}
}
},
"schema": {
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"input"
],
"properties": {
"input": {
"type": "object",
"required": [
"type",
"method"
],
"properties": {
"type": {
"type": "string",
"const": "http"
},
"method": {
"enum": [
"GET"
],
"type": "string"
},
"queryParams": {
"type": "object",
"required": [],
"properties": {
"input": {
"type": [
"string",
"array"
],
"items": {
"type": "string"
},
"description": "Text or array of texts to embed (max 100). Optional: defaults to a sample sentence."
},
"model": {
"enum": [
"bge-m3",
"bge-large-en-v1.5",
"bge-base-en-v1.5",
"bge-small-en-v1.5",
"embeddinggemma-300m"
],
"type": "string",
"description": "Embedding model alias (default bge-m3)."
},
"dimensions": {
"type": "integer",
"maximum": 1024,
"minimum": 1,
"description": "Optional output size; vectors are truncated and re-normalized."
},
"encoding_format": {
"enum": [
"float",
"base64"
],
"type": "string",
"description": "float (default) or base64 (little-endian float32)."
}
}
}
},
"additionalProperties": false
},
"output": {
"type": "object",
"required": [
"type"
],
"properties": {
"type": {
"type": "string"
},
"example": {
"type": "object",
"properties": {
"data": {
"type": "array",
"items": {
"type": "object",
"properties": {
"index": {
"type": "integer"
},
"object": {
"type": "string"
},
"embedding": {
"type": [
"array",
"string"
],
"items": {
"type": "number"
}
}
}
}
},
"model": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"total_tokens": {
"type": "integer"
},
"prompt_tokens": {
"type": "integer"
}
}
},
"object": {
"type": "string"
},
"modelId": {
"type": "string"
},
"dimensions": {
"type": "integer"
}
}
}
}
}
}
}
}
},
"x402Version": 2
}Accepts
The payment requirements as published to the catalog. Read the live 402 before paying — a price here is a claim, not a quote.
0.001USDC≈ $0.001 USD
on Base · exact scheme
Pay 0.001 USDC on Base to 0x751a…eA7cf. The signed payment is good for 5 minutes.
- Paid to
- 0x751a…eA7cf
- USD Coin contract
- 0x8335…02913
- Payment window
- 5 minutes
- As published
- 1000 smallest units
- Returns
- application/json
The catalog’s raw entry
[
{
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"extra": {
"name": "USD Coin",
"version": "2"
},
"payTo": "0x751a4c3841bE287544C371B4EE09Bd3366AeA7cf",
"scheme": "exact",
"network": "eip155:8453",
"mimeType": "application/json",
"resource": "https://quickbuild.quynguyenbac.workers.dev/v1/embeddings",
"description": "",
"maxAmountRequired": "1000",
"maxTimeoutSeconds": 300
}
]Provenance
- Seen in the source catalog
- 2026-10-20 21:25Z
- Last indexed by Roundhouse
- 2026-10-21 10:46Z
- Last enriched (probe, favicon, geo)
- 2026-10-21 08:15Z
- x402 version
- 1
- Max timeout
- 300s
- Liveness probe
- HTTP 402
Hand this page to an agent
Copy the prompt and paste it into Claude, an MCP client or your own agent — it will vet this service and call it over the free read API. No key, no account.
GET api.roundhouseai.io/v0/endpoints
This endpoint's own trailing-30-day call count, as published by the upstream catalog and snapshotted daily. 11 snapshots so far. Verified volume counts only settlements with an on-chain EIP-3009 marker.
Show the promptHide the prompt
Using Roundhouse, look up the x402 service quickbuild.quynguyenbac.workers.dev and tell me whether it is worth paying: what a call costs, whether the endpoint answered when last probed, and what its payment record actually shows. curl -s 'https://api.roundhouseai.io/v0/endpoints?q=quickbuild.quynguyenbac.workers.dev' curl -s 'https://api.roundhouseai.io/v0/merchants/<the payTo wallet returned above>' Then call it: read the price from the live 402 at https://quickbuild.quynguyenbac.workers.dev/v1/embeddings, never from a cached figure, and pay with an x402 client. The /v0 API needs an API key (`authorization: Bearer rh_live_…`) on everything except /v0/unified* and /v0/endpoints. Either pay over x402 (mint a personal key for $0.01 at GET https://api.roundhouseai.io/v0/test/x402), or ask me for an organization key, which I create at https://roundhouseai.io/dashboard/connections. If you have neither, ask before going further. If you do not have Roundhouse tools or skills installed, read https://roundhouseai.io/skill.md first — it is the whole procedure.