{
  "block": 106144441,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "4681.357360 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2026-05-18T01:05:03",
  "trx_id": "1922c6b2d67d6010d796743ca7c3a0c69f6fe265",
  "trx_in_block": 1,
  "virtual_op": 0
}

{
  "block": 105979739,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "1969.146955 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2026-05-12T07:12:18",
  "trx_id": "587c4ae87fd50514c7a14da2c54038aeb91d6ba8",
  "trx_in_block": 0,
  "virtual_op": 0
}

{
  "block": 105512067,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "4693.873116 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2026-04-26T00:24:30",
  "trx_id": "9d15fca8e4e52f49c1513b38fa8b60f8c62c89e6",
  "trx_in_block": 1,
  "virtual_op": 0
}

{
  "block": 102854483,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "2010.693774 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2026-01-23T10:01:51",
  "trx_id": "fb9285fb1dca219d148e196864e7c97c74998c3c",
  "trx_in_block": 0,
  "virtual_op": 0
}

{
  "block": 91300862,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "2174.912971 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2024-12-17T05:19:51",
  "trx_id": "da4427708e8a6805210f4181b65e97bfca600da1",
  "trx_in_block": 5,
  "virtual_op": 0
}

{
  "block": 79855054,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "2344.046503 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2023-11-13T21:02:18",
  "trx_id": "62a8a12a63464e65d4861d491d2b086404df6fc4",
  "trx_in_block": 1,
  "virtual_op": 0
}

{
  "block": 78349001,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "5281.325289 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2023-09-21T22:48:33",
  "trx_id": "c4335caff34c20120aeb6b164f764f94e233b582",
  "trx_in_block": 0,
  "virtual_op": 0
}

{
  "block": 69114183,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "5503.006727 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2022-11-03T12:28:30",
  "trx_id": "8f5d5c45469b3af4d515fe5f1658039f753b4505",
  "trx_in_block": 7,
  "virtual_op": 0
}

{
  "block": 60810275,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "5723.539958 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2022-01-17T11:40:39",
  "trx_id": "58744e4bb69392819a915d0a3e4c624345b439c4",
  "trx_in_block": 4,
  "virtual_op": 0
}

{
  "block": 54608619,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "5907.308616 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2021-06-14T01:33:51",
  "trx_id": "bacf55fcf5e09f63e24040a3c4089c7440403cd4",
  "trx_in_block": 4,
  "virtual_op": 0
}

{
  "block": 49356042,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "6094.730590 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-12-11T11:51:24",
  "trx_id": "6a4d4f1a0a675b1114c053db82c7b36e31fe16db",
  "trx_in_block": 3,
  "virtual_op": 0
}

{
  "block": 49207603,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "1912.543513 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-12-06T05:28:33",
  "trx_id": "ba7dd4a1bb110039fbe2722918959bb23b7ea028",
  "trx_in_block": 3,
  "virtual_op": 0
}

{
  "block": 49191139,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "6100.938444 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-12-05T15:29:24",
  "trx_id": "0e713817bb408799a6bdc599639f7786ed9401da",
  "trx_in_block": 1,
  "virtual_op": 0
}

{
  "block": 48259576,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "1920.017158 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-11-02T17:08:51",
  "trx_id": "36b41812b9e282b434bafeaa952abb02368fd224",
  "trx_in_block": 0,
  "virtual_op": 0
}

{
  "block": 46758195,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "6196.894099 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-09-10T20:26:18",
  "trx_id": "3a963cc4e83b8cbdd269559ec242555d5155217b",
  "trx_in_block": 8,
  "virtual_op": 0
}

{
  "block": 45482621,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "27378.518984 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-07-27T23:57:00",
  "trx_id": "6b5ed6f38a4fb87547c0d0b89af00e5696b4cb66",
  "trx_in_block": 5,
  "virtual_op": 0
}

{
  "block": 45482378,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "6240.928014 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-07-27T23:44:42",
  "trx_id": "e48f9160140041dce922bca8e2befe469474db40",
  "trx_in_block": 4,
  "virtual_op": 0
}

{
  "block": 45375349,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "27388.121929 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-07-24T05:47:27",
  "trx_id": "def8533ca00ee4bb731de4831db7a59807e0576c",
  "trx_in_block": 14,
  "virtual_op": 0
}

{
  "block": 44163425,
  "op": [
    "account_update",
    {
      "account": "hemangmehta",
      "json_metadata": "{\"profile\":{\"name\":\"hemangmehta\",\"location\":\"India\",\"website\":\"http://hemangmehta.me\"}}",
      "memo_key": "STM66xCS9dUCscg2d3cYoUVRyfP5PAf6ztX9VEKPKi6PPzhXCD6dN",
      "posting": {
        "account_auths": [
          [
            "bottracker.app",
            1
          ],
          [
            "busy.app",
            1
          ],
          [
            "dtube.app",
            1
          ],
          [
            "hapramp.app",
            1
          ],
          [
            "steemia.app",
            1
          ]
        ],
        "key_auths": [
          [
            "STM6sSr2Gjbx4iMFR8pH15HMoHHK36MLgLUp734Dbg3wbeoiDyC3t",
            1
          ]
        ],
        "weight_threshold": 1
      }
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-06-11T17:42:06",
  "trx_id": "55989e2bdce3f64e107d279c1363b71f3dd8a9a9",
  "trx_in_block": 0,
  "virtual_op": 0
}

{
  "block": 43217862,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "6303.743803 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-05-09T06:26:33",
  "trx_id": "0174f7bc2047de7391e25a17158b36d6a642e8cd",
  "trx_in_block": 15,
  "virtual_op": 0
}

{
  "block": 43194090,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "1953.311140 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-05-08T10:09:33",
  "trx_id": "26d9641f0e61e078a0ace276efc73a3f8578055a",
  "trx_in_block": 6,
  "virtual_op": 0
}

{
  "block": 41596820,
  "op": [
    "comment",
    {
      "author": "steemitboard",
      "body": "Congratulations @hemangmehta! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@hemangmehta/birthday2.png</td><td>Happy Steem Birthday! - You are on the Steem blockchain for 2 years!</td></tr></table>\n\n<sub>_You can view [your badges on your Steem Board](https://steemitboard.com/@hemangmehta) and compare to others on the [Steem Ranking](https://steemitboard.com/ranking/index.php?name=hemangmehta)_</sub>\n\n\n**Do not miss the last post from @steemitboard:**\n<table><tr><td><a href=\"https://steemit.com/steemitboard/@steemitboard/downvote-challenge-add-up-to-3-funny-badges-to-your-board\"><img src=\"https://steemitimages.com/64x128/https://steemitimages.com/0x0/![](https://cdn.steemitimages.com/DQmUuJkZdnSpHVWssxF82ntymqXg4Pvk6K6bYvckUYVRsnj/image.png)\"></a></td><td><a href=\"https://steemit.com/steemitboard/@steemitboard/downvote-challenge-add-up-to-3-funny-badges-to-your-board\">Downvote challenge - Add up to 3 funny badges to your board</a></td></tr></table>\n\n###### [Vote for @Steemitboard as a witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) to get one more award and increased upvotes!",
      "json_metadata": "{\"image\":[\"https://steemitboard.com/img/notify.png\"]}",
      "parent_author": "hemangmehta",
      "parent_permlink": "machine-learning-0-to-1-article-1",
      "permlink": "steemitboard-notify-hemangmehta-20200312t204515000z",
      "title": ""
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2020-03-12T20:45:15",
  "trx_id": "f0c3c3822f144c45ad2b07c92090bc7a2f13e08b",
  "trx_in_block": 10,
  "virtual_op": 0
}

{
  "block": 38929954,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "6386.681160 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-12-11T01:06:30",
  "trx_id": "3da66a46a9172a35a2e336c9531c47740e2fb984",
  "trx_in_block": 31,
  "virtual_op": 0
}

{
  "block": 35780429,
  "op": [
    "transfer",
    {
      "amount": "0.001 STEEM",
      "from": "dtube",
      "memo": "Time is running out, claim your DTube account now before anyone else can! Login at https://d.tube",
      "to": "hemangmehta"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-08-22T17:01:00",
  "trx_id": "41de0a1362098916badc2b2f6860b9f3fe02af05",
  "trx_in_block": 9,
  "virtual_op": 0
}

{
  "block": 31167820,
  "op": [
    "transfer",
    {
      "amount": "0.200 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-LPgTzh-sbd",
      "to": "yensesa-hot"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-03-15T06:41:12",
  "trx_id": "8fe9cad58b43a0770d670ecd9ac0feb9bea2f958",
  "trx_in_block": 17,
  "virtual_op": 0
}

{
  "block": 31165889,
  "op": [
    "transfer",
    {
      "amount": "0.100 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-LPgTzh-sbd",
      "to": "yensesa-hot"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-03-15T05:04:36",
  "trx_id": "32f2bbc6d95d6cef5c845a3b9ca889ee48bfc105",
  "trx_in_block": 20,
  "virtual_op": 0
}

{
  "block": 31165824,
  "op": [
    "transfer",
    {
      "amount": "0.100 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-RmQmmg-sbd",
      "to": "yensesa-hot"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-03-15T05:01:21",
  "trx_id": "63aa606b9e2eabc89180ebb0de68e9770c42a418",
  "trx_in_block": 15,
  "virtual_op": 0
}

{
  "block": 31165431,
  "op": [
    "transfer",
    {
      "amount": "0.010 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-LPgTzh-sbd",
      "to": "yensesa-hot"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-03-15T04:41:42",
  "trx_id": "72d0d658ad15baa9848c5cbbaeb6e1734e3e2bbb",
  "trx_in_block": 1,
  "virtual_op": 0
}

{
  "block": 31164094,
  "op": [
    "transfer",
    {
      "amount": "0.100 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-LPgTzh-sbd",
      "to": "yensesa-hot"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-03-15T03:34:51",
  "trx_id": "47f0596ecb6183780d9d6fc1ac7eeda004f2395c",
  "trx_in_block": 6,
  "virtual_op": 0
}

{
  "block": 31164071,
  "op": [
    "transfer",
    {
      "amount": "0.100 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-LPgTzh-sbd",
      "to": "yensesa-hot"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-03-15T03:33:42",
  "trx_id": "9c040097286a1a6d483047ba6a0e193608ae64cb",
  "trx_in_block": 5,
  "virtual_op": 0
}

{
  "block": 31115714,
  "op": [
    "transfer",
    {
      "amount": "0.500 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-RmQmmg-sbd",
      "to": "yensesa-hot"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-03-13T11:14:24",
  "trx_id": "80cefcf013ee3922f25e2b806a66a09bb5ab62a6",
  "trx_in_block": 31,
  "virtual_op": 0
}

{
  "block": 31110000,
  "op": [
    "comment",
    {
      "author": "steemitboard",
      "body": "Congratulations @hemangmehta! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@hemangmehta/birthday1.png</td><td>Happy Birthday! - You are on the Steem blockchain for 1 year!</td></tr></table>\n\n<sub>_You can view [your badges on your Steem Board](https://steemitboard.com/@hemangmehta) and compare to others on the [Steem Ranking](http://steemitboard.com/ranking/index.php?name=hemangmehta)_</sub>\n\n\n**Do not miss the last post from @steemitboard:**\n<table><tr><td><a href=\"https://steemit.com/drugwars/@steemitboard/drugwars-early-adopter\"><img src=\"https://steemitimages.com/64x128/https://cdn.steemitimages.com/DQmYGN7R653u4hDFyq1hM7iuhr2bdAP1v2ApACDNtecJAZ5/image.png\"></a></td><td><a href=\"https://steemit.com/drugwars/@steemitboard/drugwars-early-adopter\">Are you a DrugWars early adopter? Benvenuto in famiglia!</a></td></tr></table>\n\n###### [Vote for @Steemitboard as a witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) to get one more award and increased upvotes!",
      "json_metadata": "{\"image\":[\"https://steemitboard.com/img/notify.png\"]}",
      "parent_author": "hemangmehta",
      "parent_permlink": "machine-learning-0-to-1-article-1",
      "permlink": "steemitboard-notify-hemangmehta-20190313t062829000z",
      "title": ""
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-03-13T06:28:30",
  "trx_id": "d5c99371379908589206abfd7d4a06c3487f6312",
  "trx_in_block": 9,
  "virtual_op": 0
}

{
  "block": 31077553,
  "op": [
    "transfer",
    {
      "amount": "1.000 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-RmQmmg-sbd",
      "to": "yensesa-hot"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-03-12T03:25:21",
  "trx_id": "edcb4d3564bd2124667ca5d590e2f60e7c99e866",
  "trx_in_block": 0,
  "virtual_op": 0
}

{
  "block": 30755583,
  "op": [
    "vote",
    {
      "author": "hemangmehta",
      "permlink": "re-tytran-delegating-to-yensesa-i-had-been-accidentally-delegating-1000-sp-to-minnowpowerup-and-tipu-for-many-extra-months-and-ackza-just-20180706t022010700z",
      "voter": "tytran",
      "weight": 2600
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-02-28T22:56:06",
  "trx_id": "07f7ae3c51d1dabb41f3dd1da96d7bd30a0c5dc0",
  "trx_in_block": 14,
  "virtual_op": 0
}

{
  "block": 29712304,
  "op": [
    "transfer",
    {
      "amount": "0.100 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-RmQmmg-sbd",
      "to": "yensesa-hot"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-01-23T16:47:00",
  "trx_id": "5128e42ce0de25041cbffb2f69d1325c095a84de",
  "trx_in_block": 5,
  "virtual_op": 0
}

{
  "block": 29712190,
  "op": [
    "transfer",
    {
      "amount": "0.010 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-RmQmmg-sbd",
      "to": "yensesa-hot"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-01-23T16:41:18",
  "trx_id": "4f9218af0e572e8b8a9f602ea6b57f7f84f77d76",
  "trx_in_block": 27,
  "virtual_op": 0
}

{
  "block": 29272137,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "6583.707236 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2019-01-08T09:39:57",
  "trx_id": "2cca83185f83c86f7ad8d2883e2d5694da6b1860",
  "trx_in_block": 2,
  "virtual_op": 0
}

{
  "block": 29038188,
  "op": [
    "transfer",
    {
      "amount": "0.999 STEEM",
      "from": "yensesa-hot",
      "memo": "Yensesa Transfer to hemangmehta",
      "to": "hemangmehta"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-31T06:28:57",
  "trx_id": "fdcbd25e61d90a57ca9b406c3b02b24212a161a5",
  "trx_in_block": 9,
  "virtual_op": 0
}

{
  "block": 29037777,
  "op": [
    "transfer",
    {
      "amount": "1.000 STEEM",
      "from": "yensesa-hot",
      "memo": "Yensesa Transfer to hemangmehta",
      "to": "hemangmehta"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-31T06:08:24",
  "trx_id": "342d94d631e7bc134460b74a78ccb4ec385f0282",
  "trx_in_block": 13,
  "virtual_op": 0
}

{
  "block": 28725320,
  "op": [
    "transfer",
    {
      "amount": "1.299 SBD",
      "from": "yensesa-hot",
      "memo": "Yensesa Transfer to hemangmehta",
      "to": "hemangmehta"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-20T09:38:45",
  "trx_id": "a8b6ce1a7046ca8f3c38b326e506dbcb5d707def",
  "trx_in_block": 12,
  "virtual_op": 0
}

{
  "block": 28725262,
  "op": [
    "transfer",
    {
      "amount": "1.500 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-RmQmmg-sbd",
      "to": "yensesa-hot"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-20T09:35:51",
  "trx_id": "c4363154fc01db91c8f46cd882f6929661e56ffd",
  "trx_in_block": 14,
  "virtual_op": 0
}

{
  "block": 28701611,
  "op": [
    "transfer",
    {
      "amount": "0.001 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-RmQmmg-sbd",
      "to": "yensesa-hot"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-19T13:52:18",
  "trx_id": "413308c5691d224be6092d9a4e85ca9c3064701f",
  "trx_in_block": 0,
  "virtual_op": 0
}

{
  "block": 28701597,
  "op": [
    "transfer",
    {
      "amount": "0.500 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-RmQmmg-sbd",
      "to": "yensesa-hot"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-19T13:51:36",
  "trx_id": "244b35398797d5106f02b933fa5d421c114079b6",
  "trx_in_block": 14,
  "virtual_op": 0
}

{
  "block": 28701385,
  "op": [
    "transfer",
    {
      "amount": "0.001 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-RmQmmg-sbd",
      "to": "yensesa-hot"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-19T13:41:00",
  "trx_id": "b5fff3a9bce10b68aee880b7aedc285de6dcf141",
  "trx_in_block": 19,
  "virtual_op": 0
}

{
  "block": 28245221,
  "op": [
    "transfer",
    {
      "amount": "1.000 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-ORmjvt-sbd",
      "to": "yensesa"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-03T17:17:45",
  "trx_id": "5e24dd24c49111da07f4b52dd7b0ba70929bebad",
  "trx_in_block": 13,
  "virtual_op": 0
}

{
  "block": 28173549,
  "op": [
    "transfer",
    {
      "amount": "0.010 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-DvkEAs-sbd",
      "to": "yensesa"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-01T05:32:45",
  "trx_id": "49b116a5de007f3174c8302f437a9165c5fbbc4a",
  "trx_in_block": 34,
  "virtual_op": 0
}

{
  "block": 28173465,
  "op": [
    "transfer",
    {
      "amount": "0.010 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-DvkEAs-sbd",
      "to": "yensesa"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-01T05:28:33",
  "trx_id": "fddc886e1208f76afc7e4848f7c2984eb6513afc",
  "trx_in_block": 3,
  "virtual_op": 0
}

{
  "block": 28173413,
  "op": [
    "transfer",
    {
      "amount": "0.010 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-DvkEAs-sbd",
      "to": "yensesa"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-01T05:25:57",
  "trx_id": "d560de4b5e3b3a3485f936bd917a39ff36596141",
  "trx_in_block": 38,
  "virtual_op": 0
}

{
  "block": 28173352,
  "op": [
    "transfer",
    {
      "amount": "0.010 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-DvkEAs-sbd",
      "to": "yensesa"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-01T05:22:54",
  "trx_id": "07f430c30e576eb2fce03cb07677b6bef2c02422",
  "trx_in_block": 0,
  "virtual_op": 0
}

{
  "block": 28173271,
  "op": [
    "transfer",
    {
      "amount": "0.010 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-DvkEAs-sbd",
      "to": "yensesa"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-01T05:18:51",
  "trx_id": "dee14d3d1522d4f05d92271bda994fb1cd82ea73",
  "trx_in_block": 16,
  "virtual_op": 0
}

{
  "block": 28173243,
  "op": [
    "transfer",
    {
      "amount": "0.010 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-DvkEAs-sbd",
      "to": "yensesa"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-01T05:17:27",
  "trx_id": "9528f38dda770505f8ed8bb84256850d0bce2b62",
  "trx_in_block": 24,
  "virtual_op": 0
}

{
  "block": 28173225,
  "op": [
    "transfer",
    {
      "amount": "0.010 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-DvkEAs-sbd",
      "to": "yensesa"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-01T05:16:33",
  "trx_id": "b5d8b67d54c450efda7f3267633bc9dac75497b0",
  "trx_in_block": 13,
  "virtual_op": 0
}

{
  "block": 28173189,
  "op": [
    "transfer",
    {
      "amount": "0.010 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-DvkEAs-sbd",
      "to": "yensesa"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-01T05:14:45",
  "trx_id": "14fe1950bd5a2017f06baf91a69ae09580945737",
  "trx_in_block": 7,
  "virtual_op": 0
}

{
  "block": 28172648,
  "op": [
    "transfer",
    {
      "amount": "0.001 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-DvkEAs-sbd",
      "to": "yensesa"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-01T04:47:42",
  "trx_id": "5cf7b3a8c6d2fd0cf9410cefcee092c10eff6ff1",
  "trx_in_block": 42,
  "virtual_op": 0
}

{
  "block": 28172603,
  "op": [
    "transfer",
    {
      "amount": "0.010 SBD",
      "from": "hemangmehta",
      "memo": "yensesa-DvkEAs-steem",
      "to": "yensesa"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-12-01T04:45:27",
  "trx_id": "ee9f056ad8663c79bbc516e4e3442c7777da17cc",
  "trx_in_block": 42,
  "virtual_op": 0
}

{
  "block": 28146008,
  "op": [
    "transfer",
    {
      "amount": "0.100 SBD",
      "from": "hemangmehta",
      "memo": "testing stremoperation",
      "to": "yensesa"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-11-30T06:35:12",
  "trx_id": "67a117ea609870f8be4819b99f3a532f4e7db4c4",
  "trx_in_block": 1,
  "virtual_op": 0
}

{
  "block": 28072830,
  "op": [
    "transfer",
    {
      "amount": "1.000 SBD",
      "from": "hemangmehta",
      "memo": "testing",
      "to": "yensesa"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-11-27T17:35:12",
  "trx_id": "d914bb84132ba7a8a1bc5de7c143edb32faaffa3",
  "trx_in_block": 46,
  "virtual_op": 0
}

{
  "block": 28044248,
  "op": [
    "delegate_vesting_shares",
    {
      "delegatee": "hemangmehta",
      "delegator": "steem",
      "vesting_shares": "26747.459682 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-11-26T17:45:21",
  "trx_id": "27ea9f303f6c147a6224d3c5bb5ab1b7b74cde7c",
  "trx_in_block": 48,
  "virtual_op": 0
}

{
  "block": 27768823,
  "op": [
    "claim_reward_balance",
    {
      "account": "hemangmehta",
      "reward_sbd": "0.016 SBD",
      "reward_steem": "0.000 STEEM",
      "reward_vests": "42.388979 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-11-17T04:07:45",
  "trx_id": "c6df6df342f2c33ab1e040110665043ead900c68",
  "trx_in_block": 11,
  "virtual_op": 0
}

{
  "block": 26852902,
  "op": [
    "author_reward",
    {
      "author": "hemangmehta",
      "permlink": "machine-learning-0-to-1-article-1",
      "sbd_payout": "0.016 SBD",
      "steem_payout": "0.000 STEEM",
      "vesting_payout": "42.388979 VESTS"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-10-16T08:17:36",
  "trx_id": "0000000000000000000000000000000000000000",
  "trx_in_block": 4294967295,
  "virtual_op": 22
}

{
  "block": 26652800,
  "op": [
    "vote",
    {
      "author": "hemangmehta",
      "permlink": "machine-learning-0-to-1-article-1",
      "voter": "ange.nkuru",
      "weight": 1000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-10-09T09:24:24",
  "trx_id": "5d38904e7f67858138c1b08c8273e2d395e5f21d",
  "trx_in_block": 9,
  "virtual_op": 0
}

{
  "block": 26652664,
  "op": [
    "vote",
    {
      "author": "hemangmehta",
      "permlink": "machine-learning-0-to-1-article-1",
      "voter": "acknowledgement",
      "weight": 1000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-10-09T09:17:36",
  "trx_id": "0f163ccfa046e2685d7fd05ca663f80c449ee0c8",
  "trx_in_block": 5,
  "virtual_op": 0
}

{
  "block": 26652416,
  "op": [
    "vote",
    {
      "author": "hemangmehta",
      "permlink": "machine-learning-0-to-1-article-1",
      "voter": "desmond41",
      "weight": 5000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-10-09T09:05:12",
  "trx_id": "b965ffc56092b03984eb79db644254894d1372d1",
  "trx_in_block": 19,
  "virtual_op": 0
}

{
  "block": 26652220,
  "op": [
    "vote",
    {
      "author": "hemangmehta",
      "permlink": "machine-learning-0-to-1-article-1",
      "voter": "omotundegirls",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-10-09T08:55:24",
  "trx_id": "4bab40ea6569aa37a85a2972aae4cfd5cbec11b2",
  "trx_in_block": 4,
  "virtual_op": 0
}

{
  "block": 26652217,
  "op": [
    "vote",
    {
      "author": "hemangmehta",
      "permlink": "machine-learning-0-to-1-article-1",
      "voter": "momogrow",
      "weight": 100
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-10-09T08:55:15",
  "trx_id": "2bf11bd85927785efb9329809f79de996638141e",
  "trx_in_block": 3,
  "virtual_op": 0
}

{
  "block": 26652217,
  "op": [
    "vote",
    {
      "author": "hemangmehta",
      "permlink": "machine-learning-0-to-1-article-1",
      "voter": "mosunomotunde",
      "weight": 60
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-10-09T08:55:15",
  "trx_id": "11c3e58f58f5cc832d180e15d419ec3c0287fc97",
  "trx_in_block": 2,
  "virtual_op": 0
}

{
  "block": 26652215,
  "op": [
    "comment",
    {
      "author": "yensesa",
      "body": "You got 2.00% upvote from Yensesa. Thank you for your continues support of Yensesa Exchange and being a member of Yensesa Residual Income",
      "json_metadata": "",
      "parent_author": "hemangmehta",
      "parent_permlink": "machine-learning-0-to-1-article-1",
      "permlink": "machine-learning-0-to-1-article-1",
      "title": ""
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-10-09T08:55:09",
  "trx_id": "13b5583bbbe4d685f8b19e301e86f1b8e9a1ef06",
  "trx_in_block": 11,
  "virtual_op": 0
}

{
  "block": 26652213,
  "op": [
    "vote",
    {
      "author": "hemangmehta",
      "permlink": "machine-learning-0-to-1-article-1",
      "voter": "yensesa",
      "weight": 200
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-10-09T08:55:03",
  "trx_id": "833b5fa8a85fc9a1d8af75da7d898878f6b6b0cc",
  "trx_in_block": 8,
  "virtual_op": 0
}

{
  "block": 26651467,
  "op": [
    "vote",
    {
      "author": "hemangmehta",
      "permlink": "machine-learning-0-to-1-article-1",
      "voter": "hemangmehta",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-10-09T08:17:45",
  "trx_id": "78d22ac2853c0746cc8d8d3ebce4bf715d9bfbbe",
  "trx_in_block": 35,
  "virtual_op": 0
}

{
  "block": 26651464,
  "op": [
    "comment",
    {
      "author": "hemangmehta",
      "body": "Hi Guys,\n\nLast week, i have announced my [first series]() on machine learning. today we are starting with first article on machine learning.\n\n![new-piktochart_32924942.png](https://cdn.steemitimages.com/DQmc777XapF5HgDspcfWZTELfs7sb4RxoamAF19ZVszC3Zo/new-piktochart_32924942.png)\n\nIn this first article we will learn,\n\n1. What is machine learning\n2. How machine learning works\n3. How Machine learning different than programmatic approach, and we will see what is learning\n4. Types of machine learning algorithms\n5. Some real life examples of machine learning\n6. Let’s begin with small introduction on Machine learning.\n\nAs we all know, AI & ML is one of the hottest topic of 21st century. Everyone is talking about, and people are using it nicely in almost every domain.\n\nBut,\n\nDo you know when machine learning comes ?\nCan you guess?\nIn 2000 or In 1950 or even earlier in 1900 ?\nYou guys won't believe it comes since mid of 17th century. You can check history is ML here\n\nBasic idea behind research of machine of learning was to make machine intelligent as human.\n\nWe all knows how new born baby become smart human with time. In fact we all learn through reading and our experience, as we grow. Or you can say human has capability of self learning.\n\nHumans can do self learning through past experience & his knowledge.\n\nUntil ML come into picture, computer is able to learn only through hard coded program. It mean whatever you want to achieve, you need to write program for it, and on the basis of which computer able to performed.\n\nIn short computer were only follow instruction we provided. It didn’t able to learn by itself. Self learning is missing for computer like human have.\n\nMachine learning overcome self learning problem.\n\n**What is ML ?**\n\nMachine learning is responsible to make computer as intelligent as human, and enables it to self-learn without being explicitly programmed.\n\n**How ML works ?**\n\nSo let’s talk about how machine learning works, as we already know one can only do self-learning from his past experience only. we need to understand how computer can learn from his past experience ? so researcher find a way to do it. They have created data set(knowledge set) from past experience and write some programs / techniques to learn from that data. Those programs are nothing but algorithms.\n\nCurrently there are plenty of machine learning algorithms available in market. And researcher are continuously doing research on it. We will cover some core algorithms in these series which are most useful now a days.\n\nlet's see how ML is different than programmatic approach.\n\n**Programmatic Vs Machine Learning Solution**\n\nIn Programmatic solution, you need to give program and input data to your computer, so computer will use your program to generate output.\n\nBut in Machine learning approach, you need to give sample (input/output) data to computer as well as your input data(for which you want output). And computer will generate program or you can say Model as a output. And you can use that Model to solve subsequent task.\n\n![1.png](https://cdn.steemitimages.com/DQmZ3sU4n17GDK9m9WjyNsXXjUaMYDxhS5Gp5zxs7umcdWb/1.png)\n\nLet’s first understand what is Learning,\n\nLearning is the ability to improve once behaviour with experience. In short build a computer system which improve with experience.\n\nlet's check former definition of Machine learning as given by Tom Mitchell\n\nA computer program is said to learn from experience E, with respect to some class of task T and performance measure P. If it’s performance on task in T, as measure by P improves with experience E\n\nT - Task (like Prediction, Classification..)\nE - Experience also called sample data.\nP - Performance measurement. Let say you want to increase accuracy in prediction / problem solving. Corresponding to this you can define the Performance measure P.\n\nBased on this definition we can look at learning system as a box, to which we feed the experience or the data (E), and there is a problem or a task (T) that require solution. (we will also give background knowledge which will help the system) and this problem/ Task learning program comes up with Model or solution, and its corresponding performance can be measure.\n\nBelow is the semantic diagram of a ML system.\n\n![2.png](https://cdn.steemitimages.com/DQmUzEzomx6sWEVhdtzYzsR776FWHScgZ42W3jGc5oewyUz/2.png)\n\nInside the black box, there are two main components\n\n**Leaner:**\nIt takes experience/data and background knowledge, and build the models\n\n**Reasoner:**\nIt use that Model built by leaner, with given a task find the solutions to the task\n\n![3.png](https://cdn.steemitimages.com/DQmRAQfcCzmyK9fEZmoEDfWZLwojrqs6yGnmHpyDvpDupKH/3.png)\n\n**Steps to create a learner:**\n\n1. Choose/Prepare the training data\n2. Choose target function, how we want to represent the Model. This what we want to learn (For example if we write to try Machine learning system to play game of checkers, The target function would be given a board position what move to take)\n3. Choose how to represent the target function (linear / decision tree / or something else)\n4. Choose learning algorithm (which we will going to learn in next articles)\n5. First & third steps are the most important step in designing of a learning algorithm.\n\nLet’s take a look into one example of machine learning in details. As we already discussed ML is used in almost all domain. But let’s take a example of “diagnose a disease”\n\nInput: symptoms, lab measurement, test result, DNA tests etc..\nOutput: one of the set of possible diseases or “none disease”\n\nFor doing this one can data mine historical medical record to learn which future patients will respond best to which treatments.\n\nThere are mainly 4 types of machine learning algorithms as below -\n\n1. Supervised algorithm\n2. Unsupervised algorithm\n3. Semi-Supervised algorithm\n4. Reinforcement algorithm\n\nWe will look each type of algorithms in detail in next part of this series.\n\n**Is machine learning magic ?**\n\nOnce you start seeing how easily machine learning techniques can be applied to problems that seem really hard (like handwriting recognition), you start to get the feeling that you could use machine learning to solve any problem and get an answer as long as you have enough data. Just feed in the data and watch the computer magically figure out the equation that fits the data!\n\nSo remember, if a human expert couldn’t use the data to solve the problem manually, a computer probably won’t be able to either. Instead, focus on problems where a human could solve the problem, but where it would be great if a computer could solve it much more quickly.\n\n**Real life examples Of Machine Learning**\n\n- E-commerce giant like Amazon using ML to recommended products on the basis of user’s purchasing pattern\n- Facebook using ML to automatic recognize your friend’s face and ask you to tag them\n- Uber using ML to estimate time from source to destination\n- Google use ML in many ways like,\n    - in google maps to extract street names and house number from photo taken by street view cars,\n    - In gmail to detect spam email\n    - In youtube to recommended videos from your watching pattern\n- Bank are using ML to detect fraud\n\nThese are basic examples, but in today’s life we are using many machine learning applications daily and we even don’t know. Try to think about all the app you are using. 70-80% of them are using ML. For example Gmail, uber, facebook, twitter, etc...\n\n**Personal note for newbee:**\n\nML is not like other technologies, where you can just read theory and you can able to use it. If you want to learn ML in a right path, try to discover different problem and think about it’s solution. Because by knowing theory only, you can not become master in ML. so if you want to be a master in ML do practical more rather than reading. So try to solve as many problem you can.\n\nNext week i will come up with new article on\nTypes of machine learning algorithms in which we will see different types of algorithms available, which algorithm use in which condition, real-life examples etc.\n\nNext couple of weeks will be fantastic for both of us, stay in touch guys.\n\nThanks for all your support in advance.\n\n-Hemang",
      "json_metadata": "{\"tags\":[\"machine-learning\",\"yensesa\",\"learning\"],\"image\":[\"https://cdn.steemitimages.com/DQmc777XapF5HgDspcfWZTELfs7sb4RxoamAF19ZVszC3Zo/new-piktochart_32924942.png\",\"https://cdn.steemitimages.com/DQmZ3sU4n17GDK9m9WjyNsXXjUaMYDxhS5Gp5zxs7umcdWb/1.png\",\"https://cdn.steemitimages.com/DQmUzEzomx6sWEVhdtzYzsR776FWHScgZ42W3jGc5oewyUz/2.png\",\"https://cdn.steemitimages.com/DQmRAQfcCzmyK9fEZmoEDfWZLwojrqs6yGnmHpyDvpDupKH/3.png\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}",
      "parent_author": "",
      "parent_permlink": "machine-learning",
      "permlink": "machine-learning-0-to-1-article-1",
      "title": "Machine-Learning 0 to 1 - Article 1"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-10-09T08:17:36",
  "trx_id": "edd8d8b680079aa504a616a39bf125added056de",
  "trx_in_block": 25,
  "virtual_op": 0
}

{
  "block": 26651312,
  "op": [
    "vote",
    {
      "author": "hemangmehta",
      "permlink": "machine-learning-0-to-1",
      "voter": "hemangmehta",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-10-09T08:10:00",
  "trx_id": "148060ec704f9ba6dad95def2604da0372719b43",
  "trx_in_block": 0,
  "virtual_op": 0
}

{
  "block": 26651308,
  "op": [
    "comment",
    {
      "author": "hemangmehta",
      "body": "Hey Guys,\n\nAs i promised to write some good articles on machine learning & AI, today i am going to start with one of my fav. subject Machine Learning\n\nSince last couple of months, I came across many friends & students in my network who always talk about ML, wants to learn ML, but they don’t know where to start and what the best source of learning. So i have decided to write for them, who are new to ML, wanted to learn how ML actually working.\n\nSo today i am announcing my first series on Machine Learning 0 to 1.\n\n![new-piktochart_32924942.png](https://cdn.steemitimages.com/DQmc777XapF5HgDspcfWZTELfs7sb4RxoamAF19ZVszC3Zo/new-piktochart_32924942.png)\n\n\n**Who can join this series ?**\nIf you are software engineer/ student / business manager who wants to learn machine learning.\n\nLet me give you brief idea about what i will cover in this series,\n\n1. Introduction to Machine Learning\n2. Types of machine learning algorithms\n3. Supervised algorithm\n4. Linear Regression\n5. Logistic Regression\n6. Decision Tree\n7. Support Vector Machines.\n8. Naive Bayes.\n9. k-nearest neighbor algorithm.\n10. Introduction to unsupervised algorithm\n11. Introduction to Neural Network\n12. Project\n\nYou guys might be thinking, there are plenty of resources available on this topics, why i am writing same thing right ? But my pattern of writing will be something different.\n\nI believe, to understand any topic, you should have answer of three questions\n\nWHY?\nWHAT?\nHOW?\n\nIf you know these answers, means you understood it very well. Same pattern i will follow in this series. For each algorithm, you guys will get answer of,\n\n- What is it ?\n- Why it is useful?\n- Where it is useful? (Real life examples)\n- How to use it? (Pro-grammatically)\n- Sample code example\n- Useful links\n\nFor each algorithm you will learn theory as well as practical too.\n\nGuys believe me, next couple of weeks will be fantastic for us, If you are interested in AI & ML, looking for right path to start, this series is for you only.\n\n**Join Me**\n\nFollow me here OR\nYou can follow #machine-learning OR\nIf you have any doubts / questions, i will happy to help you.\n\n**Next Article Agenda**\n\nIn first article of this series, we will learn what is machine learning, history of machine learning, different types of machine learning algorithms, we will see some real life examples too. see you soon guys !\n\n\n-Hemang Mehta",
      "json_metadata": "{\"tags\":[\"machine-learning\",\"yensesa\",\"learning\"],\"image\":[\"https://cdn.steemitimages.com/DQmc777XapF5HgDspcfWZTELfs7sb4RxoamAF19ZVszC3Zo/new-piktochart_32924942.png\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}",
      "parent_author": "",
      "parent_permlink": "machine-learning",
      "permlink": "machine-learning-0-to-1",
      "title": "Machine Learning 0 to 1"
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-10-09T08:09:48",
  "trx_id": "b8a0e05cae54067afe3f2f62a48b4e254f6207e0",
  "trx_in_block": 22,
  "virtual_op": 0
}

{
  "block": 26247106,
  "op": [
    "vote",
    {
      "author": "yensesa",
      "permlink": "stake-for-voting-on-yensesa-will-be-changing-in-october-25th-2018",
      "voter": "hemangmehta",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-09-25T06:57:51",
  "trx_id": "fa5bc75d6bc6433fa03924242b938fccf94d6e17",
  "trx_in_block": 22,
  "virtual_op": 0
}

{
  "block": 26071671,
  "op": [
    "vote",
    {
      "author": "becomingmyself",
      "permlink": "i-m-still-who-i-ve-always-been-my-name-is-lexie-and-i-am-becoming-myself",
      "voter": "hemangmehta",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-09-19T04:38:12",
  "trx_id": "286de6d884afa4caaedd127042322bdadfc99b3a",
  "trx_in_block": 5,
  "virtual_op": 0
}

{
  "block": 26071669,
  "op": [
    "vote",
    {
      "author": "tlk",
      "permlink": "tlk-introducing-myself",
      "voter": "hemangmehta",
      "weight": 10000
    }
  ],
  "op_in_trx": 0,
  "timestamp": "2018-09-19T04:38:06",
  "trx_id": "63fffba953ebe1a827505bc4ddcbca9bbcb8e204",
  "trx_in_block": 34,
  "virtual_op": 0
}