Accelerometer — AI Progress Latest Snapshot is a paid API for AI agents from x402-accelerometer-feed.fly.dev, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-18).
Returns the latest readings of 129 public AI-progress and technology time series in a single JSON payload, covering benchmarks, compute, prediction markets, pricing, stocks, and more.
Use this when you need the current state of AI progress in one call: the latest reading of 129 public series (frontier benchmark scores GPQA/HLE/ARC-AGI/METR, Epoch compute trends, AGI odds on Polymarket/Manifold/Kalshi, model pricing, AI stocks and crypto, energy, space, robotics, biomed, research output). One JSON row per series: latest and previous value with dates, % change, signal quality, source URL. Free 3-row sample at /preview.
A JSON object containing a snapshot_id, run timestamp, and an array of up to 129 source rows. Each row includes: a series ID, domain, metric name, unit, latest value with date, previous value with date, percent change vs previous, number of historical points, signal quality rating, confidence level, data access type, and source URL. Also includes a list of all covered domains and metadata like schema version and counts of OK sources.
GEThttps://x402-accelerometer-feed.fly.dev/ai-progress/latestChoose this endpoint when you need a broad, multi-domain snapshot of AI progress in a single call rather than querying individual data sources separately. It is ideal for dashboards, briefings, investment research, or automated monitoring workflows that need heterogeneous AI metrics (benchmarks, compute, markets, pricing) normalized into a consistent per-series format. Prefer historical sibling endpoints if you need a past date's snapshot rather than the latest.
| Field | Type | Description |
|---|---|---|
| properties | string |
{
"type": "json",
"example": {
"stub": false,
"run_at": "2026-09-16T12:25:28.530204+00:00",
"domains": [
"agents",
"autonomy",
"benchmarks",
"biomed",
"capital",
"compute",
"crypto",
"ecosystem",
"energy",
"industrial",
"inference",
"macro",
"markets",
"narrative",
"patents",
"prediction",
"pricing",
"research",
"robotics",
"science",
"space"
],
"sources": [
{
"id": "agents.x402_services_count",
"url": "https://agentic.market",
"unit": "count",
"access": "free_api",
"domain": "agents",
"latest": 2865,
"metric": "x402 pay-per-request services (agentic.market)",
"cadence": "weekly survey, accumulating",
"n_points": 9,
"previous": 1779,
"confidence": "verified",
"first_date": "2026-04-21",
"latest_date": "2026-09-16",
"previous_date": "2026-07-12",
"signal_quality": "strong",
"delta_pct_vs_previous": 61.0455
},
{
"id": "autonomy.metr_time_horizon_50",
"url": "https://github.com/METR/cross-domain-horizon (data/external/logistic_fits/headline.csv; METR 'Measuring AI Ability to Complete Long Tasks', metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/)",
"unit": "minutes",
"access": "dataset_download",
"domain": "autonomy",
"latest": 112.230447,
"metric": "METR frontier task-completion horizon @50% (minutes)",
"cadence": "updated as METR re-fits new models",
"n_points": 15,
"previous": 54.226342,
"confidence": "verified",
"first_date": "2019-02-14",
"latest_date": "2025-04-16",
"previous_date": "2025-02-24",
"signal_quality": "strong",
"delta_pct_vs_previous": 106.9667
}
],
"composite": null,
"n_sources": 129,
"emitted_at": "2026-09-16T12:28:08+00:00",
"snapshot_id": "2026-09-16",
"n_sources_ok": 124,
"composite_note": "No cross-source composite is offered. Each row is one source's latest observation as pulled at run_at, with its previous observation and the % change between them.",
"schema_version": "0.2"
}
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