Evidence row 60847

What was observed

{
  "endpoints": [
    [
      "x402",
      "https://agent-data-tools.revmesh2074.workers.dev/ai/embed"
    ]
  ],
  "name": "Embeddings, OpenAI compatible",
  "owner_address": "0x2074c40346ae2a90e0e4a391bcee6f16bf5277f5",
  "raw": {
    "accepts": [
      {
        "amount": "10000",
        "asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
        "extra": {
          "name": "USD Coin",
          "version": "2"
        },
        "maxTimeoutSeconds": 300,
        "network": "eip155:8453",
        "payTo": "0x2074C40346Ae2A90e0E4A391bceE6F16BF5277f5",
        "scheme": "exact"
      },
      {
        "amount": "10000",
        "asset": "0x3c499c542cEF5E3811e1192ce70d8cC03d5c3359",
        "extra": {
          "name": "USD Coin",
          "version": "2"
        },
        "maxTimeoutSeconds": 300,
        "network": "eip155:137",
        "payTo": "0x2074C40346Ae2A90e0E4A391bceE6F16BF5277f5",
        "scheme": "exact"
      },
      {
        "amount": "10000",
        "asset": "0xaf88d065e77c8cC2239327C5EDb3A432268e5831",
        "extra": {
          "name": "USD Coin",
          "version": "2"
        },
        "maxTimeoutSeconds": 300,
        "network": "eip155:42161",
        "payTo": "0x2074C40346Ae2A90e0E4A391bceE6F16BF5277f5",
        "scheme": "exact"
      },
      {
        "amount": "10000",
        "asset": "EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v",
        "extra": {
          "feePayer": "D6ZhtNQ5nT9ZnTHUbqXZsTx5MH2rPFiBBggX4hY1WePM"
        },
        "maxTimeoutSeconds": 300,
        "network": "solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp",
        "payTo": "82wfSCZhCmZA7zwqqt6Q3FG8nqYBwUvbThP6M97fa9Q6",
        "scheme": "exact"
      }
    ],
    "description": "Embeddings, OpenAI compatible. A 1,024-dimension embedding vector for up to 4,000 characters of text in 100+ languages, from BAAI's BGE-M3. Use for semantic search, RAG retrieval, clustering and deduplication.",
    "extensions": {
      "bazaar": {
        "info": {
          "input": {
            "method": "GET",
            "queryParams": {
              "text": "pay-per-call APIs for AI agents"
            },
            "type": "http"
          },
          "output": {
            "example": {
              "dimensions": 1024,
              "embedding": [
                0.0123,
                -0.0456,
                0.0789
              ],
              "model": "@cf/baai/bge-m3"
            },
            "type": "json"
          }
        },
        "schema": {
          "$schema": "https://json-schema.org/draft/2020-12/schema",
          "properties": {
            "input": {
              "additionalProperties": false,
              "properties": {
                "method": {
                  "enum": [
                    "GET"
                  ],
                  "type": "string"
                },
                "queryParams": {
                  "properties": {
                    "text": {
                      "description": "Text to embed, up to 4,000 characters",
                      "examples": [
                        "pay-per-call APIs for AI agents"
                      ],
                      "type": "string"
                    }
                  },
                  "required": [
                    "text"
                  ],
                  "type": "object"
                },
                "type": {
                  "const": "http",
                  "type": "string"
                }
              },
              "required": [
                "type",
                "method"
              ],
              "type": "object"
            },
            "output": {
              "properties": {
                "example": {
                  "properties": {
                    "dimensions": {
                      "type": "number"
                    },
                    "embedding": {
                      "items": {
                        "type": "number"
                      },
                      "type": "array"
                    },
                    "model": {
                      "type": "string"
                    }
                  },
                  "type": "object"
                },
                "type": {
                  "type": "string"
                }
              },
              "required": [
                "type"
              ],
              "type": "object"
            }
          },
          "required": [
            "input"
          ],
          "type": "object"
        }
      },
      "builder-code": {
        "info": {
          "a": "bc_09e30bdm"
        },
        "schema": {
          "$schema": "https://json-schema.org/draft/2020-12/schema",
          "additionalProperties": false,
          "properties": {
            "a": {
              "description": "App builder code",
              "pattern": "^[a-z0-9_]{1,32}$",
              "type": "string"
            },
            "s": {
              "description": "Service builder codes",
              "items": {
                "pattern": "^[a-z0-9_]{1,32}$",
                "type": "string"
              },
              "maxItems": 11,
              "type": "array"
            },
            "w": {
              "description": "Wallet builder code",
              "pattern": "^[a-z0-9_]{1,32}$",
              "type": "string"
            }
          },
          "type": "object"
        }
      }
    },
    "iconUrl": "https://res.cloudinary.com/bdb-prod/image/upload/c_fill,ar_1:1,w_256,h_256,f_auto,q_auto/v1792567527/bazaar-icons/a67d2da18841a325.png",
    "lastUpdated": "2026-10-23T10:13:52.262Z",
    "quality": {
      "l30DaysTotalCalls": 4,
      "l30DaysUniquePayers": 2,
      "lastCalledAt": "2026-10-23T10:13:52.142Z"
    },
    "resource": "https://agent-data-tools.revmesh2074.workers.dev/ai/embed",
    "serviceName": "Embeddings, OpenAI compatible",
    "tags": [
      "text-embeddings",
      "openai-compatible",
      "embeddings",
      "vector",
      "rag"
    ],
    "type": "http",
    "x402Version": 2
  },
  "source": "x402_bazaar",
  "summary": "Embeddings, OpenAI compatible. A 1,024-dimension embedding vector for up to 4,000 characters of text in 100+ languages, from BAAI's BGE-M3. Use for semantic search, RAG retrieval, clustering and deduplication."
}

What the leaf hashes

{"body":"b1a25038971e338735386dc92f4f89bddde52ced820db86897f6df4502912db6","id":60847,"kind":"listing","method":"baseline/1","observed_us":1793025449404819,"prev":null,"subject":"x402_bazaar:https://agent-data-tools.revmesh2074.workers.dev/ai/embed","subject_kind":"source_record"}

Proof

Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of 2026-10-26 (296695 rows).

1. 0x9f840c6339f05792528f799e6f2d01f2cb80992595eb42e7be590bdf3d40a54f
2. 0xabde77c33dbf2d20705b4b3cccfed7b618011942b42bac8f8ac560b75c7faea9
3. 0xb53607074cc67596c121b39565adbc4867114240b77b0ee92431e140eb43c120
4. 0xf71fa6a252215ec2eb561c536783d172d2ac810bb66c18b6611a0145139d62ef
5. 0x24505d2a6ce77ab3069c1b2d8a639ccfb81dacee39dc6f80c01fd6b54c2df3b3
6. 0xa7cf1b64de6743c3da588a81fcdbcc607532955f3b6b3270a8b75e51f2448def
7. 0x7a37549a10e08457b1869e79c31ff56410f8e4c27c587ef7a8b0748a71e636f3
8. 0x78336eb06095924126162493544902b401af4a7b62d29a7b477848966b7ae41c
9. 0x0ca1e1981bfe3ae8078a6e70f1eefed09e9ea57a67defe2424e8e6bcbd789cb3
10. 0xad33b6ed23e6b6f31de87919e88c08498b71327e286bf36d5ea11b6c48a97eee
11. 0xb5a5da1d8cc5364d4e1e81f176a4bcbca86e02bf02b30e06db118bb7f2c487a8
12. 0x6367dad81ab5c2e6f424ad3e06310c9b75079293d62a08dfbde98ef993c35229
13. 0xf8d3e004d9a208d2c5e82ab676956d80f8a3c78387c61dad15a3a8269efee0d8
14. 0x6713795704ddd9c528d421fadd9cc96b0200a4ddbba84f88e718d4968dde888b
15. 0x62a900133713919f4eed33d8336876b7a6d538233b8a4f3ed03b29aa44f5bad0
16. 0x1ac20d30773e1f4f2f811540e9a99b5cca9177c9e2e029acc0d80cf3f1d11f0f
17. 0xd21942ae3214eff88e08ab04884fd4b11958b51af9a0741b7668e2d07a2c56c9
18. 0x9a9024edb5069d88a324facb310a5ce8e495214bbaa968f077bede437390194f
19. 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a

Day root

0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8

Chained

0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a

Published

On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae