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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).

  1. 0xcacaf132130cb32963b15435c833ff8c831c501bed3facac9b0e78671af2dca2
  2. 0x150092e0c3d020e401934ab802eefe49197e09e5af7725dbed4cdab8780166b9
  3. 0xe50fdf2248863d8766526cc9796415ab621b5013700c7a019f7fe867129cf8ee
  4. 0xf3ddccaf03d094ae916fe99eedc08b6f3939ef2d8a6aedae795448bc56310671
  5. 0xc7a56189762dfc1814ca2a106adecada2f26a3a42fe39a8ac0e0dcf8ff8c9dd8
  6. 0x17555f1094b0594745d8a3e17aa84c18abce39a679829b6bf98577153b3986dc
  7. 0xe5b53eeb1ed45191e547ac8c2ba0700d8d2de16606435f60547d190c37aa790e
  8. 0x5834296e26b45b367c486ab47daa093ebd1cceb139c08d5a05565e7f58d60ad8
  9. 0x60edff6daefbb90d469fe971d673eefe00ca18bf80b94c9d61cd862d8b4161fa
  10. 0xf29e8f51356c1b4788c06270d1e5deddc4d369add09932704ccd63f7e6f91c95
  11. 0xd854065b5246baf332a9ace158b6f99219183612c97d95e881ab7a7e7fb3bd6b
  12. 0x02a11c0460d1a42b6246e1c715f0c3098d82c39154a56e918e2c22b56ca2c978
  13. 0x985acd37cc044d5aa6ad959194da8e91c81045df83517891ed92f1763a9ab825
  14. 0x5b20d8aab05225a3497eadcce89d2e2a586a510fb31334793d3f46f88f0157c5
  15. 0xe94ca5b798e9899ad611e57835af42266e20f53f44977e487d81365e30972beb
  16. 0x703f95866af09c5e5d6ead7411e9a10f964a41a2aff64d4a55ff0053c023a023
  17. 0xd0651cb9bc8155eb52b5247d506e74b8a935057dc190ba52091702ac25ed3a40
  18. 0x9a9024edb5069d88a324facb310a5ce8e495214bbaa968f077bede437390194f
  19. 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a
Day root
0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8
Chained
0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a
Published
On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae

How to check it · This row as JSON