Evidence row 731768

What was observed

{
  "count": 5,
  "tools": [
    {
      "description": "How many AI training jobs are open right now, how many were listed in the last 7 days, and the open roles by kind of work and by platform (with each platform's slug for search_ai_training_jobs). Optionally scoped to the roles open to applicants in one country.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "country": {
            "description": "ISO 3166-1 alpha-2 code (e.g. 'US', 'KE'): count only roles open to applicants in that country.",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "get_ai_training_market"
    },
    {
      "description": "What AI training work pays per hour today, by kind of work: the median and the typical range (25th to 75th percentile) of the rates open roles advertise, with how many rates and platforms each figure comes from. A rate is shown only when at least three platforms advertise it.",
      "inputSchema": {
        "additionalProperties": false,
        "type": "object"
      },
      "name": "get_ai_training_pay"
    },
    {
      "description": "For teams building AI who need expert human data: AITraining.jobs runs expert evaluation, rubrics, preference (RLHF) data, fine-tuning data, RL environments, red-teaming and benchmarks with US professionals from its own network (physicians, nurses, attorneys, accountants, engineers, scientists, translators). Returns what it delivers, the network's live counts by field, what each kind of expert is paid for AI work today, how projects are priced and run, and the page to brief it. Optionally one field.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "field": {
            "description": "One field, e.g. 'medical', 'legal', 'finance', 'coding', 'red-teaming'. Omit for every field.",
            "enum": [
              "medical",
              "legal",
              "finance",
              "coding",
              "science",
              "red-teaming",
              "languages"
            ],
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "get_expert_data_services"
    },
    {
      "description": "Hiring radar, AITraining.jobs's dataset for investors: every AI-training role on the platforms' own job boards (Mercor, micro1, Appen, TELUS, Invisible and more) since its first read, one row per role with advertised pay, field, countries and open and close dates, plus each platform by day and a change log. Returns what it covers today, its files, who uses it, the price and how to get a sample.",
      "inputSchema": {
        "additionalProperties": false,
        "type": "object"
      },
      "name": "get_hiring_radar"
    },
    {
      "description": "Search the open AI training jobs (paid work training and evaluating AI models) across 57 platforms such as Mercor, micro1, Outlier, DataAnnotation, Alignerr and Invisible. Returns each role's title, platform, kind of work, advertised pay, where it is open and a link to apply on AITraining.jobs. All filters are optional.",
      "inputSchema": {
        "additionalProperties": false,
        "properties": {
          "country": {
            "description": "ISO 3166-1 alpha-2 code (e.g. 'US', 'IN', 'BR'): only roles open to applicants in that country.",
            "type": "string"
          },
          "kind": {
            "description": "Kind of work. One of the listed slugs, e.g. 'coding', 'rlhf-eval', 'multilingual'.",
            "enum": [
              "rlhf-eval",
              "coding",
              "stem-math",
              "domain-expert",
              "agentic-eval",
              "data-labeling",
              "robotics-data",
              "multilingual",
              "audio-speech",
              "writing",
              "red-teaming",
              "research-studies"
            ],
            "type": "string"
          },
          "limit": {
            "default": 10,
            "description": "How many roles to return (1 to 25).",
            "maximum": 25,
            "minimum": 1,
            "type": "integer"
          },
          "min_hourly_usd": {
            "description": "Only roles whose advertised hourly ceiling reaches this many US dollars.",
            "maximum": 1000,
            "minimum": 1,
            "type": "integer"
          },
          "platform": {
            "description": "Platform slug, e.g. 'mercor', 'micro1', 'outlier-scale-ai'. get_ai_training_market lists them.",
            "type": "string"
          },
          "query": {
            "description": "Keywords matched against role titles, e.g. 'physician', 'Python', 'Spanish', 'lawyer'.",
            "type": "string"
          }
        },
        "type": "object"
      },
      "name": "search_ai_training_jobs"
    }
  ]
}

What the leaf hashes

{"body":"31dd68119e27c084189a04c84fa9df2dab9322d6d38c4c32fff65cc54ee30ee1","id":731768,"kind":"mcp.tools","method":"probe.mcp/1","observed_us":1793154438921718,"prev":301959,"subject":"ef4b952a-5d4b-48cf-9a26-6218c712a8ba","subject_kind":"endpoint"}

Proof

Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of 2026-10-28 (231336 rows).

1. 0x94c839e5cee5383c7a970c053cc67c40b2f2cb5adcdaf5b87c7cf193a1c54784
2. 0xe5b3fa7c95f6bc2246b13c4dd14fd3ecf9b192bdbbdd3a4cb6843945d01b2581
3. 0xda63c0ac84e02a2b39fd35207df5167b393b4c34bf2c8e0878ba1ccef0a67c6b
4. 0xaf6c0ac69c3370c96efa075eb649b839c529e18e39ce0b44f011fcaf3b8485d2
5. 0xa361a71a12cef29af94952ed0e0c83d5670273a4b1729246faefa9c98c94a5c1
6. 0x1c17567ff429f8c3d63562330a446d01f3123fe39ec9b8913f15760c554ae513
7. 0x2d62af5f23910353747698abc0a8bae4a5c830c9c890105fd3a4d1d4967c4d0d
8. 0xbb96d3a7648f1de466b54c993a4cc678041af62b5d24fcbf5a3a44cb5a5f4c48
9. 0x2b5066aca93d3066e0101c2070a65762f3e26c515dc1eca76cc4a67849907e0b
10. 0x1bf808c45e70950743d39d5f5985a8c5c87500904f3811fc0b6504504e83e256
11. 0x6bf8b6dda22d019dffcfe283b13831fe8749dcd1eb46c24828fed66fd7a03ba1
12. 0x017df795865df97dffb9e17332307b84d61758ae3aea237ff5c0be6081a1b098
13. 0x80b6c9d16d3a8a2da308ee4d36f99dee58b79195b26a42263888bf815ddead58
14. 0x088061c71633d244b99c7cc0814bbe03af458b18487f352611ae649c73589766
15. 0xcf313ac43fef601c9e46f82a687b6a3c655f47c31675c1791c49de20295be63d
16. 0x0f8977bd16865247451d1ee836b084081d3d0cd17d6a07353fad0c70393c7403
17. 0xe08e8bd91858a8a10c46ecde9c29a6288b960987303173c5e882afb46ce91fa0
18. 0xe6cfe00ae4e02bc12327e45b5f3334ea5850ba3bcc8e3101d30a8189856a95af

Day root

0x2c19037464cc4c493624565f01775d6446fc78eaa118fc6865bfe2bfca9ff78e

Chained

0xd1d7dee8322ceff8f4c8d83aa1fe4a4466825c2af1c97dbde3d35db61234f7be

Published

On Arc Testnet, block 69584042: 0x8bae2b1d097834c8b458513862d70d29ee807e98dc2e863b27250131c15b35ae