Evidence row 122954

What was observed

{
  "endpoints": [
    [
      "x402",
      "https://turboshop.ai/api/v1/products/prod-hnsw-vector-indexer/buy"
    ]
  ],
  "name": "turboshop.ai/api/v1/products/prod-hnsw-vector-indexer/buy",
  "owner_address": "0x0151db12e758e12825cbd71ac4f82b6e08e8cc49",
  "raw": {
    "accepts": [
      {
        "amount": "40000",
        "asset": "0x833589fcd6edb6e08f4c7c32d4f71b54bda02913",
        "extra": {
          "name": "USD Coin",
          "version": "2"
        },
        "maxTimeoutSeconds": 300,
        "network": "eip155:8453",
        "payTo": "0x0151DB12E758E12825CbD71Ac4F82B6E08E8cc49",
        "scheme": "exact"
      }
    ],
    "description": "High-speed vector computation microservice to compute dot products, cosine similarities, and ranking for 1536-d float32 embeddings.",
    "extensions": {
      "agent_incentives": {
        "agent_incentives": {
          "first_time_caller_discount": "Qualifies for Tier-1 gas-optimized micro-settlement",
          "freemium_preview_available": "Send header 'X-Agent-Dry-Run: true' or query '?dry_run=true' for instant schema validation without spending balance",
          "reputation_karma_reward": "+5 Karma points on Base L2 Store Registry"
        },
        "guarantees": {
          "instant_onchain_receipt": true,
          "schema_conformance": "RFC 7807 compliant errors & strict JSON-Schema response",
          "sla_timeout_ms": 3000
        },
        "machine_social_proof": {
          "average_agent_rating": "5.0 / 5.0",
          "sample_verified_review": "Verified machine execution on Base L2.",
          "verified_reviews_count": 0
        },
        "roi_metrics": {
          "benchmarks": {
            "execution_sla_ms": 20,
            "metric": "Normalized cosine similarity",
            "throughput": "10,000 queries/sec"
          },
          "estimated_token_savings": "65% reduction in prompt context tokens",
          "execution_latency_ms": 8
        },
        "value_proposition": "Fast cosine similarity ranking across 1536-d float32 embeddings for autonomous retrieval."
      },
      "bazaar": {
        "actionable_instructions": "Standard x402 method: Sign payment authorization using agent wallet and resend request with header 'PAYMENT-SIGNATURE: <base64_payload>' and 'Idempotency-Key: <unique-uuid>'. Direct on-chain fallback: Send 0.04 USDC on Base network to 0x0151DB12E758E12825CbD71Ac4F82B6E08E8cc49 and resend request with header 'X-Payment-Proof: tx:<evm_tx_hash>'. Preview: Pass header 'X-Agent-Dry-Run: true' or query '?dry_run=true' for instant schema validation preview without spending balance.",
        "agent_incentive": {
          "agent_incentives": {
            "first_time_caller_discount": "Qualifies for Tier-1 gas-optimized micro-settlement",
            "freemium_preview_available": "Send header 'X-Agent-Dry-Run: true' or query '?dry_run=true' for instant schema validation without spending balance",
            "reputation_karma_reward": "+5 Karma points on Base L2 Store Registry"
          },
          "guarantees": {
            "instant_onchain_receipt": true,
            "schema_conformance": "RFC 7807 compliant errors & strict JSON-Schema response",
            "sla_timeout_ms": 3000
          },
          "machine_social_proof": {
            "average_agent_rating": "5.0 / 5.0",
            "sample_verified_review": "Verified machine execution on Base L2.",
            "verified_reviews_count": 0
          },
          "roi_metrics": {
            "benchmarks": {
              "execution_sla_ms": 20,
              "metric": "Normalized cosine similarity",
              "throughput": "10,000 queries/sec"
            },
            "estimated_token_savings": "65% reduction in prompt context tokens",
            "execution_latency_ms": 8
          },
          "value_proposition": "Fast cosine similarity ranking across 1536-d float32 embeddings for autonomous retrieval."
        },
        "category": "agent_tools",
        "description": "High-speed vector computation microservice to compute dot products, cosine similarities, and ranking for 1536-d float32 embeddings.",
        "discovery_version": "1.0",
        "info": {
          "input": {
            "body": {
              "client_id": "agent-client-001"
            },
            "bodyType": "json",
            "method": "POST",
            "type": "http"
          },
          "output": {
            "example": {
              "asset_delivery": {
                "delivered_at": "2026-10-18T08:59:42.872Z",
                "license": "Commercial MIT",
                "payload": {}
              },
              "name": "HNSW Vector Indexing & Cosine Similarity Search Microservice",
              "product_id": "prod-hnsw-vector-indexer",
              "status": "success",
              "transaction_receipt": {
                "block_number": 12345678,
                "tx_hash": "0x1234567890abcdef1234567890abcdef1234567890abcdef1234567890abcdef"
              }
            },
            "type": "json"
          }
        },
        "input_schema": {
          "headers": {
            "Idempotency-Key": {
              "description": "Unique client UUID for idempotency",
              "type": "string"
            },
            "PAYMENT-SIGNATURE": {
              "description": "Standard x402 v2 signed payment authorization (base64 encoded JSON)",
              "type": "string"
            },
            "X-Agent-Dry-Run": {
              "description": "Pass 'true' to validate schema compatibility preview without spending USDC",
              "type": "string"
            },
            "X-PAYMENT": {
              "description": "Standard x402 v1 signed payment payload (base64 encoded JSON)",
              "type": "string"
            },
            "X-Payment-Proof": {
              "description": "Proof of payment: Base on-chain tx hash (tx:0x... or 0x...)",
              "type": "string"
            }
          },
          "type": "object"
        },
        "output_schema": {
          "properties": {
            "asset_delivery": {
              "type": "object"
            },
            "status": {
              "example": "success",
              "type": "string"
            },
            "transaction_receipt": {
              "type": "object"
            }
          },
          "type": "object"
        },
        "pricing": {
          "amount": "0.04",
          "asset": "USDC",
          "contract": "0x833589fcd6edb6e08f4c7c32d4f71b54bda02913",
          "network": "base"
        },
        "product_id": "prod-hnsw-vector-indexer",
        "product_name": "HNSW Vector Indexing & Cosine Similarity Search Microservice",
        "schema": {
          "$schema": "https://json-schema.org/draft/2020-12/schema",
          "properties": {
            "input": {
              "additionalProperties": false,
              "properties": {
                "body": {
                  "properties": {
                    "client_id": {
                      "description": "Optional calling agent or client ID",
                      "type": "string"
                    }
                  },
                  "type": "object"
                },
                "bodyType": {
                  "enum": [
                    "json",
                    "form-data",
                    "text"
                  ],
                  "type": "string"
                },
                "method": {
                  "enum": [
                    "GET",
                    "POST",
                    "PUT",
                    "PATCH"
                  ],
                  "type": "string"
                },
                "type": {
                  "const": "http",
                  "type": "string"
                }
              },
              "required": [
                "type",
                "method",
                "bodyType",
                "body"
              ],
              "type": "object"
            },
            "output": {
              "properties": {
                "example": {
                  "properties": {
                    "asset_delivery": {
                      "type": "object"
                    },
                    "name": {
                      "type": "string"
                    },
                    "product_id": {
                      "type": "string"
                    },
                    "status": {
                      "type": "string"
                    },
                    "transaction_receipt": {
                      "type": "object"
                    }
                  },
                  "required": [
                    "status"
                  ],
                  "type": "object"
                },
                "type": {
                  "type": "string"
                }
              },
              "required": [
                "type"
              ],
              "type": "object"
            }
          },
          "required": [
            "input"
          ],
          "type": "object"
        },
        "seller": {
          "endpoint": "https://turboshop.ai/api/v1/products/prod-hnsw-vector-indexer/buy",
          "name": "TurboShop.ai",
          "wallet": "0x0151DB12E758E12825CbD71Ac4F82B6E08E8cc49"
        },
        "service_type": "digital_good"
      }
    },
    "lastUpdated": "2026-10-18T08:59:42.931Z",
    "quality": {
      "l30DaysTotalCalls": 1,
      "l30DaysUniquePayers": 1,
      "lastCalledAt": "2026-10-18T08:59:42.669Z"
    },
    "resource": "https://turboshop.ai/api/v1/products/prod-hnsw-vector-indexer/buy",
    "type": "http",
    "x402Version": 2
  },
  "source": "x402_bazaar",
  "summary": "High-speed vector computation microservice to compute dot products, cosine similarities, and ranking for 1536-d float32 embeddings."
}

What the leaf hashes

{"body":"bd2b8ba0d64a3b99791034127c76b5d025bc3bd3db956db45af37497b1bcece7","id":122954,"kind":"listing","method":"baseline/1","observed_us":1793025622467084,"prev":null,"subject":"x402_bazaar:https://turboshop.ai/api/v1/products/prod-hnsw-vector-indexer/buy","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. 0x9cc76bfc80e78e25e3c054e46e9ef6ef1d62eebf26ec74e511be2afd9671d3da
2. 0x7f4899e5c3492538d11ca778f19e85067d2d0777c947818ce78de0bf2842e094
3. 0xd2b8bc32a57e33f1d3a6d9ad38538d799eb800c5fc667ef73c1511b6c6a4ab09
4. 0xb92166becb03bcab5fbf50c8710ddcbcb009edb8a16102229eee9866c313f63e
5. 0xc23be22513895a4aa75ae1c15d94b83cf1002ef6b7594861635f3b6a53cdd829
6. 0x53fd8253fd98aa10d1bff0270b647d565647d5494045d1b0a439a4f50501a246
7. 0x8066aeb16605a70260112fb41eae211031ab7791c9e473f5b04b266106ced4ca
8. 0xfbcae499c5b9bf8ce4011fa1326dac2724c2a7202e2e3bea20a2a97a66c05207
9. 0x536d0ce85bffba8b25263274719046b3b95426e114d55ba1e2c0e93076a0d1bd
10. 0x1a90ba89f036e98e4e1b5af7f96b79f0586042bc85461ebbe79c82b27bdc57dd
11. 0x4a4b7460b27353d096fad8c7820603e2d212a25fa0e24fdd0b1ce11cd6ca098c
12. 0xd253212b3d5f7245423cc951067880bc2069b90eeee521363fcee13ebbd606ae
13. 0x99a4e0f8b23989f58b564c1b6e419578b4388f6e0dbf6e50177c1bd476302d0e
14. 0x6713795704ddd9c528d421fadd9cc96b0200a4ddbba84f88e718d4968dde888b
15. 0x62a900133713919f4eed33d8336876b7a6d538233b8a4f3ed03b29aa44f5bad0
16. 0x1ac20d30773e1f4f2f811540e9a99b5cca9177c9e2e029acc0d80cf3f1d11f0f
17. 0xd21942ae3214eff88e08ab04884fd4b11958b51af9a0741b7668e2d07a2c56c9
18. 0x9a9024edb5069d88a324facb310a5ce8e495214bbaa968f077bede437390194f
19. 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a

Day root

0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8

Chained

0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a

Published

On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae