← The evidence ledger
Evidence row 68022
Observed , by baseline/1.
- Kind
- listing
- About
- source_record x402_bazaar:https://api.particle.pro/v1/podcasts/search
- Recorded
- Previous row
- None: the first of its kind about this subject
- Body SHA-256
- a4edee28181efa62d787625480bba3b1c2e99337905915ee53d162cc96b4f104
- Leaf
- 0xcacbc0b96dcf1b5c8248e910652c0a63fb25daa0f5318202c387044425055444
What was observed
{
"endpoints": [
[
"x402",
"https://api.particle.pro/v1/podcasts/search"
]
],
"name": "Particle Pro",
"owner_address": "0x59f3a6dcb6f482c53872dd8f8f9e1f9161a1c5f7",
"raw": {
"accepts": [
{
"amount": "10000",
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"extra": {
"name": "USD Coin",
"version": "2"
},
"maxTimeoutSeconds": 300,
"network": "eip155:8453",
"payTo": "0x59f3a6dcb6f482c53872dd8f8f9e1f9161a1c5f7",
"scheme": "exact"
}
],
"description": "Search podcasts. Searches the podcast catalog by show name — the canonical podcast search.",
"extensions": {
"bazaar": {
"info": {
"input": {
"method": "GET",
"queryParams": {
"format_signal": [
"interview"
],
"guest_frequency": [
"regular",
"always"
],
"language": "fr"
},
"type": "http"
}
},
"schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {
"input": {
"additionalProperties": false,
"properties": {
"method": {
"enum": [
"GET"
],
"type": "string"
},
"queryParams": {
"properties": {
"cursor": {
"description": "Opaque pagination cursor from previous response",
"type": "string"
},
"format_signal": {
"description": "Filter by detected production format. Pass a comma-separated list to match any of several formats — e.g. panel,call_in. Values mirror the format.signals field on podcast responses; a show can carry several signals.",
"items": {
"type": "string"
},
"type": "array"
},
"guest_field": {
"description": "Only shows whose analyzed guests include someone listed under this ANZSRC 2020 field of research (code, slug, or title).",
"type": "string"
},
"guest_frequency": {
"description": "Filter by how often episodes feature a guest. Pass a comma-separated list to match any of several buckets — e.g. regular,always for interview-driven shows, or never for shows that don't host guests. Only matches podcasts with enough analyzed episodes to judge; values mirror the format.guest_frequency field on podcast responses.",
"items": {
"type": "string"
},
"type": "array"
},
"guest_occupation": {
"description": "Only shows whose analyzed guests include someone listed under this SOC 2018 or ISCO-08 occupation (code, slug, or title), including a SOC group or everyday name such as physicians or doctors. A word with no occupation that names a field of research, such as oncologists, filters by the field; one that names a capacity, such as founders, is rejected.",
"type": "string"
},
"has_ads": {
"description": "Filter by advertiser presence. Pass false to find ad-free shows — an assertion that requires the show to have enough analyzed episodes, so sparsely-analyzed podcasts are excluded rather than presumed ad-free.",
"enum": [
"true",
"false"
],
"type": "string"
},
"has_video": {
"description": "Filter by whether episodes have discovered video versions (e.g. YouTube releases).",
"enum": [
"true",
"false"
],
"type": "string"
},
"language": {
"description": "Filter by the podcast's language (ISO 639-1 code, e.g. 'en', 'fr'). Matches the primary language subtag, so 'fr' covers 'fr-FR'.",
"type": "string"
},
"limit": {
"description": "Results per page",
"format": "int64",
"type": "integer"
},
"max_avg_episode_minutes": {
"description": "Only podcasts whose average episode runs at most this many minutes.",
"format": "int64",
"type": "integer"
},
"max_episodes_per_week": {
"description": "Only podcasts publishing at most this many episodes per week on average (e.g. 0.5 for fortnightly-or-slower shows).",
"format": "double",
"type": "number"
},
"max_promotional_guest_share": {
"description": "Only shows where at most this share of analyzed guest appearances were mainly promotional (0-1); 0 keeps only shows with none.",
"type": "number"
},
"min_analyzed_guest_appearances": {
"description": "Only shows with at least this many guest appearances analyzed for expertise. Raise it to steady the share filters below on shows with few analyzed guests.",
"format": "int64",
"type": "integer"
},
"min_avg_episode_minutes": {
"description": "Only podcasts whose average episode runs at least this many minutes. Combine with max_avg_episode_minutes to target a length band (e.g. 20–40 minute commute shows).",
"format": "int64",
"type": "integer"
},
"min_episodes_per_week": {
"description": "Only podcasts publishing at least this many episodes per week on average (e.g. 5 for weekday dailies). Cadence is measured over the trailing 90 days; podcasts tracked too briefly to measure are excluded.",
"format": "double",
"type": "number"
},
"min_established_guest_share": {
"description": "Only shows where at least this share of analyzed guest appearances are by guests whose standing others conferred (0-1; 0 sets no bound).",
"format": "double",
"type": "number"
},
"min_in_field_guest_share": {
"description": "Only shows where at least this share of analyzed guest appearances stayed within the guest's field (0-1; 0 sets no bound).",
"format": "double",
"type": "number"
},
"popularity_threshold": {
"description": "Restrict results to podcasts whose global popularity percentile is at least this value (accepted range [0,1]). Omitting the parameter or passing 0 disables the filter; only a value greater than 0 activates it. Popularity is always global — a cume_dist ranking over all currently-charting podcasts (Apple Podcasts) — never scoped to topic_id or any other filter. When the filter is active (threshold > 0), podcasts not currently charting have no percentile and are excluded, so a high threshold combined with a narrow topic_id may return zero results; lower or remove the threshold to widen the set.",
"format": "double",
"type": "number"
},
"publishing_status": {
"description": "Filter by publishing activity: active shows have released an episode within the last 90 days; dormant shows have not.",
"enum": [
"active",
"dormant"
],
"type": "string"
},
"q": {
"description": "Search the catalog by podcast name. GET /v1/podcasts/search is the canonical search endpoint and accepts the same parameters. The search is forgiving: it handles typos, missing or extra words ('offline jon favreau' finds Offline with Jon Favreau), a name plus qualifiers ('equity techcrunch'), and pasted episode titles ('Ferrari | Acquired' finds Acquired). The best matches come first, and each result's match_quality tells you how confident the match is — including when a show matched only through its description (match_quality=description) rather than its name.",
"type": "string"
},
"slug": {
"description": "Filter by exact podcast slug (e.g., 'all-in'). Slugs are stable, human-readable identifiers returned on every podcast object.",
"type": "string"
},
"sort": {
"description": "Result ordering. relevance — the default whenever q is present — ranks by how well each podcast matches the query, breaking ties by popularity. popularity orders purely by the global popularity percentile (most popular first; podcasts not currently charting sort last), and also overrides topic_id's concentration ordering. Without q or topic_id, results are popularity-ordered either way.",
"enum": [
"relevance",
"popularity"
],
"type": "string"
},
"suitability_tier": {
"description": "Filter by brand-suitability tier (IAB Tech Lab Brand Safety & Suitability Framework, formerly GARM). Only returns podcasts whose most recent suitability analysis assigned this tier. Podcasts without a suitability analysis are excluded when this filter is set.",
"enum": [
"SAFE",
"LIMITED",
"SENSITIVE",
"UNSAFE"
],
"type": "string"
},
"topic_id": {
"description": "Filter by topic ID, ancestry slug (e.g., technology/artificial-intelligence), or ancestry path hash. Matches podcasts where the topic accounts for at least 20% of the show's episodes, ranked by topic concentration — not merely podcasts that mention it once.",
"type": "string"
}
},
"type": "object"
},
"type": {
"const": "http",
"type": "string"
}
},
"required": [
"type",
"method"
],
"type": "object"
}
},
"required": [
"input"
],
"type": "object"
}
}
},
"lastUpdated": "2026-10-19T13:06:00.961Z",
"quality": {
"l30DaysTotalCalls": 3,
"l30DaysUniquePayers": 2,
"lastCalledAt": "2026-10-19T13:06:00.665Z"
},
"resource": "https://api.particle.pro/v1/podcasts/search",
"serviceName": "Particle Pro",
"tags": [
"podcasts",
"media intelligence",
"particle pro"
],
"type": "http",
"x402Version": 2
},
"source": "x402_bazaar",
"summary": "Search podcasts. Searches the podcast catalog by show name — the canonical podcast search."
}What the leaf hashes
{"body":"a4edee28181efa62d787625480bba3b1c2e99337905915ee53d162cc96b4f104","id":68022,"kind":"listing","method":"baseline/1","observed_us":1793025466078143,"prev":null,"subject":"x402_bazaar:https://api.particle.pro/v1/podcasts/search","subject_kind":"source_record"}Proof
Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of (296695 rows).
- 0xcacaf132130cb32963b15435c833ff8c831c501bed3facac9b0e78671af2dca2
- 0x150092e0c3d020e401934ab802eefe49197e09e5af7725dbed4cdab8780166b9
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- 0xf3ddccaf03d094ae916fe99eedc08b6f3939ef2d8a6aedae795448bc56310671
- 0xc7a56189762dfc1814ca2a106adecada2f26a3a42fe39a8ac0e0dcf8ff8c9dd8
- 0x17555f1094b0594745d8a3e17aa84c18abce39a679829b6bf98577153b3986dc
- 0xe5b53eeb1ed45191e547ac8c2ba0700d8d2de16606435f60547d190c37aa790e
- 0x5834296e26b45b367c486ab47daa093ebd1cceb139c08d5a05565e7f58d60ad8
- 0x60edff6daefbb90d469fe971d673eefe00ca18bf80b94c9d61cd862d8b4161fa
- 0xf29e8f51356c1b4788c06270d1e5deddc4d369add09932704ccd63f7e6f91c95
- 0xd854065b5246baf332a9ace158b6f99219183612c97d95e881ab7a7e7fb3bd6b
- 0x02a11c0460d1a42b6246e1c715f0c3098d82c39154a56e918e2c22b56ca2c978
- 0x985acd37cc044d5aa6ad959194da8e91c81045df83517891ed92f1763a9ab825
- 0x5b20d8aab05225a3497eadcce89d2e2a586a510fb31334793d3f46f88f0157c5
- 0xe94ca5b798e9899ad611e57835af42266e20f53f44977e487d81365e30972beb
- 0x703f95866af09c5e5d6ead7411e9a10f964a41a2aff64d4a55ff0053c023a023
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- 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a
- Day root
- 0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8
- Chained
- 0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a
- Published
- On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae