saylorinnovations.com
No description published for this endpoint.
DataLiveeip155:8453Exactvia facilitator:thirdweb
Calls · 30d
2
This endpoint's own trailing-30-day call count, as published by the upstream catalog and snapshotted daily. 14 snapshots so far.
$15.76
Verified settled volume
1,657 settlements proven x402 by their on-chain EIP-3009 marker.
$0.010
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
0xA2A8dF0f7CaaBEa4e120a2568118f141569fc8aaThe 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.
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"resource": {
"url": "https://saylorinnovations.com/api/ai/hf-datasets?q=squad&sort=downloads",
"tags": [
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"find training dataset by topic",
"most downloaded datasets",
"huggingface datasets for fine",
"nlp dataset lookup"
],
"iconUrl": "https://saylorinnovations.com/og-image.jpg",
"mimeType": "application/json",
"description": "Search Hugging Face datasets by keyword - downloads, likes, gated flag, last modified, description and tags, sorted by downloads, likes or trending",
"serviceName": "Saylor Innovations Data Desk"
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"extensions": {
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"bazaar": {
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"output": {
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"example": {
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"count": 10,
"query": "squad",
"source": "Hugging Face Hub",
"datasets": [
{
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"url": "https://huggingface.co/datasets/rajpurkar/squad",
"tags": [
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"task_ids:extractive-qa",
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],
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"author": "rajpurkar",
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"description": "Dataset Card for SQuAD Dataset Summary Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, wh…",
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"last_modified": "2024-03-04T13:54:37.000Z"
},
{
"id": "rajpurkar/squad_v2",
"url": "https://huggingface.co/datasets/rajpurkar/squad_v2",
"tags": [
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"likes": 268,
"author": "rajpurkar",
"created": "2022-03-02T23:29:22.000Z",
"description": "Dataset Card for SQuAD 2.0 Dataset Summary Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles…",
"downloads_30d": 49512,
"last_modified": "2024-03-04T13:55:27.000Z"
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{
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"created": "2022-03-02T23:29:22.000Z",
"description": "Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a s…",
"downloads_30d": 16538,
"last_modified": "2022-10-25T09:50:53.000Z"
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},
"schema": {
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"input": {
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],
"properties": {
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"type": "string",
"const": "http"
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"method": {
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"queryParams": {
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"properties": {
"q": {
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"description": "Dataset name or keyword, e.g. squad, code, medical"
},
"sort": {
"type": "string",
"description": "optional downloads (default), likes, trendingScore, lastModified, createdAt"
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}
},
"additionalProperties": false
},
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"properties": {
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"example": {
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"properties": {
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"description": "Sort order applied"
},
"count": {
"type": "number",
"description": "Number of items returned"
},
"query": {
"type": "string",
"description": "The query as the server interpreted it"
},
"source": {
"type": "string",
"description": "Data source the values come from"
},
"datasets": {
"type": "array"
}
}
}
}
}
}
}
},
"method": "GET",
"example": {
"sort": "downloads",
"count": 10,
"query": "squad",
"source": "Hugging Face Hub",
"datasets": [
{
"id": "rajpurkar/squad",
"url": "https://huggingface.co/datasets/rajpurkar/squad",
"tags": [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text"
],
"gated": false,
"likes": 1246,
"author": "rajpurkar",
"created": "2022-03-02T23:29:22.000Z",
"description": "Dataset Card for SQuAD Dataset Summary Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, wh…",
"downloads_30d": 208594,
"last_modified": "2024-03-04T13:54:37.000Z"
},
{
"id": "rajpurkar/squad_v2",
"url": "https://huggingface.co/datasets/rajpurkar/squad_v2",
"tags": [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
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"format:parquet",
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"gated": false,
"likes": 268,
"author": "rajpurkar",
"created": "2022-03-02T23:29:22.000Z",
"description": "Dataset Card for SQuAD 2.0 Dataset Summary Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles…",
"downloads_30d": 49512,
"last_modified": "2024-03-04T13:55:27.000Z"
},
{
"id": "lhoestq/custom_squad",
"url": "https://huggingface.co/datasets/lhoestq/custom_squad",
"tags": [
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"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K",
"arxiv:1606.05250"
],
"gated": false,
"likes": 0,
"author": "lhoestq",
"created": "2022-03-02T23:29:22.000Z",
"description": "Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a s…",
"downloads_30d": 16538,
"last_modified": "2022-10-25T09:50:53.000Z"
},
{
"id": "hazyresearch/based-squad",
"url": "https://huggingface.co/datasets/hazyresearch/based-squad",
"tags": [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2402.18668"
],
"gated": false,
"likes": 2,
"author": "hazyresearch",
"created": "2024-03-12T20:41:28.000Z",
"description": "Please consider citing the following if you use this task in your work: @inproceedings{Rajpurkar2018SQuAD2, title={Know What You Don't Know: Unanswerable Questions for SQuAD}, author={Pranav Rajpurka…",
"downloads_30d": 6197,
"last_modified": "2024-06-01T22:57:00.000Z"
},
{
"id": "research-backup/qa_squadshifts_synthetic_random",
"url": "https://huggingface.co/datasets/research-backup/qa_squadshifts_synthetic_random",
"tags": [],
"gated": false,
"likes": 0,
"author": "research-backup",
"created": "2022-12-23T03:26:02.000Z",
"description": "TBA",
"downloads_30d": 5639,
"last_modified": "2023-01-15T18:58:41.000Z"
},
{
"id": "SQuADDS/SQuADDS_Layouts",
"url": "https://huggingface.co/datasets/SQuADDS/SQuADDS_Layouts",
"tags": [
"license:mit",
"size_categories:10K<n<100K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2312.13483",
"quantum",
"superconducting"
],
"gated": false,
"likes": 1,
"author": "SQuADDS",
"created": "2026-07-25T19:37:01.000Z",
"description": "SQuADDS Layouts - versioned GDS artifacts for superconducting quantum hardware SQuADDS Layouts is the geometry-artifact companion to SQuADDS_DB, the Superconducting Qubit And Device Design and Simula…",
"downloads_30d": 3644,
"last_modified": "2026-08-28T22:53:37.000Z"
},
{
"id": "allenai/squad-gen2mc",
"url": "https://huggingface.co/datasets/allenai/squad-gen2mc",
"tags": [
"size_categories:10K<n<100K",
"format:parquet",
"format:optimized-parquet",
"modality:text",
"library:datasets",
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"gated": false,
"likes": 0,
"author": "allenai",
"created": "2025-09-29T23:18:13.000Z",
"description": null,
"downloads_30d": 3344,
"last_modified": "2025-09-29T23:18:14.000Z"
},
{
"id": "SQuADDS/SQuADDS_DB",
"url": "https://huggingface.co/datasets/SQuADDS/SQuADDS_DB",
"tags": [
"license:mit",
"size_categories:10K<n<100K",
"format:json",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:polars",
"library:mlcroissant",
"arxiv:2312.13483",
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"quantum"
],
"gated": false,
"likes": 11,
"author": "SQuADDS",
"created": "2023-12-19T04:07:36.000Z",
"description": "SQuADDS_DB - a Superconducting Qubit And Device Design and Simulation Database The SQuADDS (Superconducting Qubit And Device Design and Simulation) Database Project is an open-source resource aimed a…",
"downloads_30d": 2179,
"last_modified": "2026-07-27T04:42:20.000Z"
},
{
"id": "ludwigschmidt/squadshifts",
"url": "https://huggingface.co/datasets/ludwigschmidt/squadshifts",
"tags": [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"size_categories:10K<n<100K"
],
"gated": false,
"likes": 3,
"author": "ludwigschmidt",
"created": "2022-03-02T23:29:22.000Z",
"description": "Dataset Card for \"squadshifts\" Dataset Summary SquadShifts consists of four new test sets for the Stanford Question Answering Dataset (SQuAD) from four different domains: Wikipedia articles, New York…",
"downloads_30d": 1953,
"last_modified": "2024-01-18T11:16:19.000Z"
},
{
"id": "squad-rnd/loki-expLLM",
"url": "https://huggingface.co/datasets/squad-rnd/loki-expLLM",
"tags": [
"language:fr",
"language:en",
"license:cc-by-nc-4.0",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:polars",
"library:mlcroissant",
"cybersecurity",
"honeypot"
],
"gated": false,
"likes": 3,
"author": "squad-rnd",
"created": "2026-01-19T15:35:56.000Z",
"description": "Dataset Card for Loki Honeypot Interaction Dataset Dataset Summary This dataset contains interaction logs from the Loki project, a research initiative focused on creating scalable, LLM-powered honeyp…",
"downloads_30d": 1799,
"last_modified": "2026-10-16T06:30:02.000Z"
}
]
},
"license": "LicenseRef-Saylor-Agent-Use-1.0",
"mimeType": "application/json",
"freshness": "fetched live (cached up to 1 hour for large reference files)",
"tokenCount": null,
"inputSchema": {
"q": "Dataset name or keyword, e.g. squad, code, medical",
"sort": "optional downloads (default), likes, trendingScore, lastModified, createdAt",
"limit": "optional 1-25, default 10"
},
"serviceName": "Saylor Innovations Data Desk",
"instructions": "STANDARD PATH (works with stock x402 clients): send payload.transaction — a base64 partially-signed Solana transaction — and we route verify/settle through an x402 facilitator that sponsors the fee and submits it. Include the chosen accepts entry as \"accepted\" so we can match terms. ALTERNATIVE PATH (no facilitator): Pick ONE entry from accepts and use the reference and session from THAT entry — each asset has its own. USDC or USDT: send an SPL transfer of the exact amount. Native SOL (asset=So11111111111111111111111111111111111111112, extra.native=true): send that many lamports with a system transfer. Either way include extra.reference as a read-only account (Solana Pay style), then retry with PAYMENT-SIGNATURE: base64({x402Version:2, payload:{session, reference}}). If your client cannot attach a reference, send payload.signature instead (USDC only). SOL amounts are quoted live and expire in 120s; stablecoins do not move. The facilitator path verifies and settles in this request. Only the alternative self-submitted Solana path needs retries while the transfer is indexed (typically 3-4 attempts over 10-20s). On that path, a 402 saying \"payment not found on-chain yet\" means keep retrying.",
"outputSchema": {
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"datasets": {
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"settlementModes": [
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"typicalLatencyMs": 700
},
"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.
0.01USDC≈ $0.01 USD
on Base · exact scheme
Pay 0.01 USDC on Base to 0xA2A8…fc8aa. The signed payment is good for 2 minutes.
- Paid to
- 0xA2A8…fc8aa
- USD Coin contract
- 0x8335…02913
- Payment window
- 2 minutes
- As published
- 10000 smallest units
- Returns
- application/json
10000units
on solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp · exact scheme
That figure is in the token’s smallest units. Roundhouse does not hold this contract’s decimals, so it is shown as published rather than converted.
- Paid to
- 7LSjfr…Xacy7
- Token contract
- EPjFWd…TDt1v
- Payment window
- 2 minutes
- Returns
- application/json
The catalog’s raw entry
[
{
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"payTo": "0xA2A8dF0f7CaaBEa4e120a2568118f141569fc8aa",
"scheme": "exact",
"network": "eip155:8453",
"mimeType": "application/json",
"resource": "https://saylorinnovations.com/api/ai/hf-datasets",
"description": "",
"maxAmountRequired": "10000",
"maxTimeoutSeconds": 120
},
{
"asset": "EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v",
"payTo": "7LSjfrJf8fNsB8VA9u7N3WEn25smQLpob3SvyUvXacy7",
"scheme": "exact",
"network": "solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp",
"mimeType": "application/json",
"resource": "https://saylorinnovations.com/api/ai/hf-datasets",
"description": "",
"maxAmountRequired": "10000",
"maxTimeoutSeconds": 120
}
]Provenance
- Seen in the source catalog
- 2026-10-17 07:04Z
- Last indexed by Roundhouse
- 2026-10-25 15:16Z
- Last enriched (probe, favicon, geo)
- 2026-10-18 23:15Z
- x402 version
- 1
- Max timeout
- 120s
- 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. 14 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 saylorinnovations.com 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=saylorinnovations.com' 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://saylorinnovations.com/api/ai/hf-datasets, 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.