Transaction
ID: 54d8f2126a...0b38
Inputs (1)
Spent
Address:
Output transaction:
Settlement height:
Value:
7.04 ERG
Tokens:
Loading assets...
Outputs (3)
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.001 ERG
Tokens:
Loading assets...
Spent
Address:
Spent in transaction:
Settlement height:
Value:
0.0011 ERG
Spent
Address:
Spent in transaction:
Settlement height:
Value:
7.04 ERG
Tokens:
Loading assets...
Transaction Details
Confirmations: 313,058
Total coins transferred: 7.04 ERG
Fees: 0.0011 ERG
Fees per byte: 0.000000376 ERG
Raw Transaction Data
{
"id": "54d8f2126aaa883f60f1376117b758aec0ed8e4a00c8987a5ea0575aceac0b38",
"blockId": "639fc2913e9132fd41e998bcf910d105167cc0884fcd254548bce0a1b6f9c517",
"inclusionHeight": 1452276,
"timestamp": 1738443898766,
"index": 6,
"globalIndex": 8532204,
"numConfirmations": 313058,
"inputs": [
{
"boxId": "eb69701eb5bc8d63f751ec17e9359a5d3231cc702343d71032678ca31c0aa768",
"value": 7040509814,
"index": 0,
"spendingProof": "ada70c68965db710998385f2b6142eb58e3e1f0121d3ac91a4783ba33ef2586057c2b9b830b58e2e2e7c498dc040b8e5424520f8fcea16a6",
"outputBlockId": "94eab6927b355cad0624a8565e6e74c9678d230a1baf0e42eeab611c8cd3c0d5",
"outputTransactionId": "f8446481878ccf96b2f91cd6b0f1ebdedfc8bc00f99cfe9efcccba8ed76254b6",
"outputIndex": 5,
"outputGlobalIndex": 46039460,
"outputCreatedAt": 1452180,
"outputSettledAt": 1452182,
"ergoTree": "0008cd028c945947b43157d9006ba85e05cddea5b527a9af821eacb2eb9e8fc55af6fd36",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(8c9459,aa05aa,...)))}",
"address": "9fayRrb8wGmJcCHMg1phwzCTaLf3sZAX7BiJrQhPFUAshceX71u",
"assets": [
{
"tokenId": "0040ae650c4ed77bcd20391493abe84c1a9bb58ee88e87f15670c801e2fc5983",
"index": 0,
"amount": 190592000,
"name": "bPaideia",
"decimals": 4,
"type": "EIP-004"
},
{
"tokenId": "c0d9e581c1099455f13b82e13a1c20c7308a736cf63acc1025ae1e17a58a72c9",
"index": 1,
"amount": 1,
"name": "Sigmanauts Mining DAO Membership",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "fcfca7654fb0da57ecf9a3f489bcbeb1d43b56dce7e73b352f7bc6f2561d2a1b",
"index": 2,
"amount": 13867615000,
"name": "ErgOne",
"decimals": 8,
"type": "EIP-004"
},
{
"tokenId": "34d449dc84a27d0f8fb2166d415a7223604f6426fb2d83ee099f2312182d575d",
"index": 3,
"amount": 48860790054209,
"name": "PHP",
"decimals": 8,
"type": "EIP-004"
},
{
"tokenId": "1fd6e032e8476c4aa54c18c1a308dce83940e8f4a28f576440513ed7326ad489",
"index": 4,
"amount": 25000000,
"name": "Paideia",
"decimals": 4,
"type": "EIP-004"
},
{
"tokenId": "a650291cd343d511a435b8720476736fcaf3e6c87f977b4e8123bcff666543be",
"index": 5,
"amount": 998,
"name": "Ergo DevDAO",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "3503ba6ce5d8bc1332229284c95fff15cf3c1d0b463fdfd6f3c9b57b7af09fe3",
"index": 6,
"amount": 1,
"name": "Good Things DAO Token",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "d577aa18ae22a1667f5509796de26b64bc4964407db10e80ee2d1b05f2bdf091",
"index": 7,
"amount": 1,
"name": "Sigmanauts Membership",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {}
}
],
"dataInputs": [],
"outputs": [
{
"boxId": "aa53b05d32005b15c6f59a26f2be8f8032bcdbe96af8a1973e4c6d08778e434d",
"transactionId": "54d8f2126aaa883f60f1376117b758aec0ed8e4a00c8987a5ea0575aceac0b38",
"blockId": "639fc2913e9132fd41e998bcf910d105167cc0884fcd254548bce0a1b6f9c517",
"value": 1000000,
"index": 0,
"globalIndex": 46043539,
"creationHeight": 1452274,
"settlementHeight": 1452276,
"ergoTree": "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",
"ergoTreeConstants": "0: Coll(126,-119,-126,-122,21,45,53,37,-18,-76,-34,-118,29,-125,58,61,92,-95,-6,120,-71,10,-24,61,-102,15,4,-63,66,31,66,-68)\n1: Coll(27,75,-117,120,-97,-35,74,52,-59,-15,-49,115,-76,-39,-102,92,-84,-72,-52,-70,117,38,95,110,-33,73,80,-119,59,22,47,7)\n2: Coll(31,-42,-32,50,-24,71,108,74,-91,76,24,-63,-93,8,-36,-24,57,64,-24,-12,-94,-113,87,100,64,81,62,-41,50,106,-44,-119)\n3: Coll(108,-95,21,91,-21,51,44,-107,-42,-43,66,80,-117,95,-43,-43,56,7,-116,-16,49,-51,-113,124,113,107,-15,-118,-39,-64,-40,-46,126,-25,-8,52,64,61,-38,-32,79,110,84,127,-105,-74,-57,19,3,61,70,118,102,123,-28,-112,18,29,-58,89,-11,35,5,66)",
"ergoTreeScript": "{\n val func1 = {(tuple1: (Coll[Box], Coll[Byte])) => tuple1._1.filter({(box3: Box) => blake2b256(box3.propositionBytes) == tuple1._2 }) }\n val func2 = {(tuple2: (Coll[Box], Coll[Byte])) =>\n tuple2._1.filter({(box4: Box) => box4.tokens.exists({(tuple6: (Coll[Byte], Long)) => tuple6._1 == tuple2._2 }) })\n }\n val coll3 = Coll[Byte]()\n val opt4 = getVar[Coll[Byte]](1.toByte)\n val func5 = {(coll5: Coll[Box]) => coll5.fold(0L, {(tuple7: (Long, Box)) => tuple7._1 + tuple7._2.value }) }\n val coll6 = placeholder[Coll[Byte]](3)\n val coll7 = coll6.slice(32, 64)\n val coll8 = placeholder[Coll[Byte]](0)\n val func9 = {(box9: Box) => box9.R4[AvlTree].get }\n val coll10 = placeholder[Coll[Byte]](2)\n val func11 = {(tuple11: (Coll[Box], Coll[Byte])) => tuple11._1.flatMap({(box13: Box) => box13.tokens }).fold(0L, {(tuple13: (Long, (Coll[Byte], Long))) =>\n val tuple15 = tuple13._2\n tuple13._1 + if (tuple15._1 == tuple11._2) { tuple15._2 } else { 0L }\n }) }\n val func12 = {(opt12: Option[Coll[Byte]]) => opt12.get.slice(1, 33) }\n val coll13 = Coll[Byte](\n 91.toByte, -49.toByte, -15.toByte, 2.toByte, 37.toByte, 67.toByte, 102.toByte, 120.toByte, 12.toByte, -43.toByte, 25.toByte, 18.toByte, 87.toByte, 5.toByte, 10.toByte, 110.toByte, -45.toByte, 58.toByte, -59.toByte, -47.toByte, 46.toByte, -17.toByte, 14.toByte, 48.toByte, 65.toByte, 57.toByte, -19.toByte, 93.toByte, -104.toByte, 31.toByte, 75.toByte, -6.toByte\n )\n val b14 = getVar[Byte](0.toByte).get\n val func15 = {(box15: Box) => box15.tokens(1) }\n val i16 = INPUTS.indexOf(SELF, 0)\n val func17 = {(l17: Long) =>\n if (l17 < 128L) { 1 } else {\n if (l17 < 16384L) { 2 } else {\n if (l17 < 2097152L) { 3 } else {\n if (l17 < 268435456L) { 4 } else {\n if (l17 < 34359738368L) { 5 } else {\n if (l17 < 4398046511104L) { 6 } else { if (l17 < 562949953421312L) { 7 } else { if (l17 < 72057594037927936L) { 8 } else { 9 } } }\n }\n }\n }\n }\n }\n }\n sigmaProp(anyOf(Coll[Boolean]({(b18: Byte) => if ((b18 == 3.toByte) || (b18 == 4.toByte)) {(\n val coll20 = blake2b256(SELF.propositionBytes)\n val box21 = func1((OUTPUTS, coll20))(0)\n val coll22 = CONTEXT.dataInputs\n val box23 = func2((coll22, placeholder[Coll[Byte]](1)))(0)\n val coll24 = opt4.getOrElse(coll3)\n val coll25 = func1((INPUTS, coll20))\n val l26 = func5(coll25)\n val box27 = func2((OUTPUTS, coll7))(0)\n val coll28 = func9(func2((coll22, coll8))(0)).getMany(Coll[Coll[Byte]](Coll[Byte](-119.toByte, 46.toByte, 111.toByte, 71.toByte, -95.toByte, 13.toByte, 92.toByte, -112.toByte, -72.toByte, 122.toByte, -44.toByte, -122.toByte, 51.toByte, 85.toByte, -50.toByte, -83.toByte, 0.toByte, -61.toByte, -30.toByte, -104.toByte, 50.toByte, 23.toByte, -18.toByte, 21.toByte, 83.toByte, 50.toByte, 83.toByte, -51.toByte, -102.toByte, 96.toByte, 37.toByte, -62.toByte), Coll[Byte](79.toByte, -40.toByte, -80.toByte, -42.toByte, -39.toByte, -126.toByte, 66.toByte, 114.toByte, 111.toByte, 87.toByte, -77.toByte, -33.toByte, -90.toByte, -122.toByte, 18.toByte, 103.toByte, -110.toByte, -72.toByte, -27.toByte, 5.toByte, 110.toByte, 29.toByte, 81.toByte, -74.toByte, -23.toByte, 13.toByte, 104.toByte, -128.toByte, -49.toByte, 45.toByte, -51.toByte, -59.toByte), Coll[Byte](-80.toByte, -71.toByte, 7.toByte, -85.toByte, -81.toByte, -83.toByte, -115.toByte, -1.toByte, -50.toByte, 47.toByte, -97.toByte, 29.toByte, -6.toByte, 21.toByte, 53.toByte, -64.toByte, 34.toByte, -35.toByte, -96.toByte, 83.toByte, 102.toByte, -12.toByte, -5.toByte, -46.toByte, 127.toByte, 88.toByte, 29.toByte, 19.toByte, 47.toByte, 75.toByte, 35.toByte, -10.toByte), Coll[Byte](-72.toByte, -61.toByte, 44.toByte, 11.toByte, -98.toByte, 66.toByte, -52.toByte, -122.toByte, -48.toByte, 48.toByte, -78.toByte, 97.toByte, -114.toByte, 90.toByte, 6.toByte, -64.toByte, -46.toByte, -21.toByte, 43.toByte, -96.toByte, 100.toByte, 31.toByte, 9.toByte, 6.toByte, -123.toByte, -71.toByte, -123.toByte, -19.toByte, -85.toByte, 16.toByte, -107.toByte, 111.toByte)), getVar[Coll[Byte]](2.toByte).getOrElse(coll3))\n val coll29 = {(opt29: Option[Coll[Byte]]) => opt29.get.slice(6, 38) }(coll28(3))\n val l30 = byteArrayToLong(coll28(2).get.slice(1, 9))\n val l31 = func11((coll25, coll10))\n val coll32 = Coll[Box](box21)\n val l33 = func11((coll32, coll10))\n val l34 = func11((coll25, coll29))\n val l35 = func11((coll32, coll29))\n val bool36 = box21.tokens.filter({(tuple36: (Coll[Byte], Long)) =>\n val coll38 = tuple36._1\n (coll38 != coll10) && (coll38 != coll29)\n }).forall({(tuple36: (Coll[Byte], Long)) => tuple36._2 >= func11((coll25, tuple36._1)) })\n val bool37 = coll25.flatMap({(box37: Box) => box37.tokens }).forall({(tuple37: (Coll[Byte], Long)) =>\n val coll39 = tuple37._1\n (coll39 == coll10) || box21.tokens.exists({(tuple40: (Coll[Byte], Long)) => tuple40._1 == coll39 })\n })\n anyOf(Coll[Boolean]({(b38: Byte) => if (b38 == 3.toByte) {(\n val coll40 = func9(box23).getMany(Coll[Coll[Byte]](Coll[Byte](34.toByte, 94.toByte, 63.toByte, -59.toByte, -47.toByte, -119.toByte, -11.toByte, 71.toByte, -39.toByte, -58.toByte, 38.toByte, -66.toByte, -67.toByte, -58.toByte, 113.toByte, 57.toByte, -117.toByte, 108.toByte, 0.toByte, 124.toByte, 120.toByte, 61.toByte, -60.toByte, 127.toByte, -112.toByte, 63.toByte, 36.toByte, -65.toByte, 127.toByte, 52.toByte, -124.toByte, 121.toByte), Coll[Byte](-68.toByte, 74.toByte, 90.toByte, -71.toByte, -28.toByte, 90.toByte, -73.toByte, 75.toByte, 121.toByte, -6.toByte, -20.toByte, -65.toByte, 103.toByte, 73.toByte, 108.toByte, -62.toByte, -65.toByte, -116.toByte, 43.toByte, 14.toByte, 85.toByte, -37.toByte, -24.toByte, -84.toByte, -49.toByte, -61.toByte, -99.toByte, 20.toByte, -119.toByte, 17.toByte, 116.toByte, -112.toByte), Coll[Byte](118.toByte, 124.toByte, -86.toByte, -128.toByte, -71.toByte, -114.toByte, 73.toByte, 106.toByte, -40.toByte, -87.toByte, -10.toByte, -119.toByte, -60.toByte, 65.toByte, 10.toByte, -28.toByte, 83.toByte, 50.toByte, 127.toByte, 15.toByte, -107.toByte, -23.toByte, 80.toByte, -124.toByte, -64.toByte, -82.toByte, 32.toByte, 99.toByte, 80.toByte, 121.toByte, 59.toByte, 119.toByte), coll13), coll24)\n val l41 = byteArrayToLong(coll40(0).get.slice(1, 9)) * {(box41: Box) => box41.R5[Coll[Long]].get(2) }(box27) + 1L\n val bool42 = coll10 == coll29\n allOf(Coll[Boolean](box21.value >= l26 - byteArrayToLong(coll40(3).get.slice(1, 9)), l33 >= l31 - l41 + byteArrayToLong(coll40(1).get.slice(1, 9)) + if (bool42) { l30 } else { 0L }, if (bool42) { true } else { l35 >= l34 - l30 }, bool36, bool37, func11((Coll[Box](OUTPUTS.filter({(box43: Box) => blake2b256(box43.propositionBytes) == coll40(2).get.slice(1, 33) })(0)), coll10)) >= l41, blake2b256(INPUTS(1).propositionBytes) == func12(coll28(1))))\n )} else { false } }(b14), {(b38: Byte) => if (b38 == 4.toByte) {(\n val coll40 = func9(box23).getMany(Coll[Coll[Byte]](Coll[Byte](-20.toByte, -14.toByte, -48.toByte, 75.toByte, -82.toByte, 72.toByte, -96.toByte, 10.toByte, -118.toByte, 110.toByte, 73.toByte, -64.toByte, 86.toByte, 114.toByte, 99.toByte, -55.toByte, -11.toByte, -46.toByte, 63.toByte, 38.toByte, -56.toByte, 35.toByte, 88.toByte, -95.toByte, 118.toByte, -85.toByte, -47.toByte, -16.toByte, 33.toByte, -40.toByte, -79.toByte, 48.toByte), coll13), coll24)\n allOf(Coll[Boolean](box21.value >= l26 - byteArrayToLong(coll40(1).get.slice(1, 9)), l33 >= l31 - byteArrayToLong(coll40(0).get.slice(1, 9)), if (coll10 == coll29) { true } else { l35 >= l34 }, bool36, bool37, blake2b256(INPUTS(1).propositionBytes) == func12(coll28(0)), func15(box27)._2 == func15(func2((INPUTS, coll7))(0))._2))\n )} else { false } }(b14)))\n )} else { false } }(b14), {(b18: Byte) => if (b18 == 9.toByte) { func9(func2((CONTEXT.dataInputs, coll8))(0)).getMany(Coll[Coll[Byte]](blake2b256(Coll[Byte](105.toByte, 109.toByte, 46.toByte, 112.toByte, 97.toByte, 105.toByte, 100.toByte, 101.toByte, 105.toByte, 97.toByte, 46.toByte, 99.toByte, 111.toByte, 110.toByte, 116.toByte, 114.toByte, 97.toByte, 99.toByte, 116.toByte, 115.toByte, 46.toByte, 97.toByte, 99.toByte, 116.toByte, 105.toByte, 111.toByte, 110.toByte, 46.toByte).append(func2((INPUTS, coll6.slice(0, 32)))(0).propositionBytes))), opt4.get)(0).isDefined } else { false } }(b14), {(b18: Byte) => if (b18 == 10.toByte) {(\n val coll20 = blake2b256(SELF.propositionBytes)\n val coll21 = func1((INPUTS, coll20))\n val coll22 = func1((OUTPUTS, coll20))\n val l23 = func5(coll21)\n allOf(Coll[Boolean](coll21.size >= 5, coll22.size == 1, coll22(0).tokens.forall({(tuple24: (Coll[Byte], Long)) => func11((coll21, tuple24._1)) == tuple24._2 }), l23 - func5(coll22) <= 2000000L, l23 >= 2000000L))\n )} else { false } }(b14), {(b18: Byte) => if (b18 == 7.toByte) { {(tuple20: (Coll[Byte], Box)) => if (i16 >= OUTPUTS.size) { false } else {(\n val box22 = OUTPUTS(i16)\n val l23 = box22.value\n val l24 = SELF.value\n val coll25 = box22.propositionBytes\n val coll26 = SELF.bytesWithoutRef\n val coll27 = SELF.propositionBytes\n val i28 = SELF.creationInfo._1\n val coll29 = box22.bytesWithoutRef\n val i30 = box22.creationInfo._1\n allOf(Coll[Boolean](l23 >= l24 - 2000000L, blake2b256(coll25) == func12(func9(tuple20._2).getMany(Coll[Coll[Byte]](tuple20._1), opt4.getOrElse(coll3))(0)), coll26.slice(func17(l24) + coll27.size + func17(i28.toLong), coll26.size) == coll29.slice(func17(l23) + coll25.size + func17(i30.toLong), coll29.size), anyOf(Coll[Boolean](i30 - i28 >= 504000, coll27 != coll25))))\n )} }((Coll[Byte](-57.toByte, -59.toByte, 55.toByte, -26.toByte, -58.toByte, 53.toByte, -109.toByte, 14.toByte, -53.toByte, 74.toByte, -50.toByte, -107.toByte, -91.toByte, 73.toByte, 38.toByte, -77.toByte, -85.toByte, 119.toByte, 105.toByte, -115.toByte, -97.toByte, 73.toByte, 34.toByte, -16.toByte, -79.toByte, -59.toByte, -114.toByte, -88.toByte, 113.toByte, 86.toByte, 72.toByte, 59.toByte), func2((CONTEXT.dataInputs, coll8))(0))) } else { false } }(b14))))\n}",
"address": "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",
"assets": [
{
"tokenId": "a650291cd343d511a435b8720476736fcaf3e6c87f977b4e8123bcff666543be",
"index": 0,
"amount": 5,
"name": "Ergo DevDAO",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {},
"spentTransactionId": "844913907b60f40452e8546e8d375b2002bd4b20be2667babed74f4ed1040159",
"mainChain": true
},
{
"boxId": "9cfdf6234bf3c503f33a998ee055c40bdc10e807c998271b74b1de919e45b0a0",
"transactionId": "54d8f2126aaa883f60f1376117b758aec0ed8e4a00c8987a5ea0575aceac0b38",
"blockId": "639fc2913e9132fd41e998bcf910d105167cc0884fcd254548bce0a1b6f9c517",
"value": 1100000,
"index": 1,
"globalIndex": 46043540,
"creationHeight": 1452274,
"settlementHeight": 1452276,
"ergoTree": "1005040004000e36100204a00b08cd0279be667ef9dcbbac55a06295ce870b07029bfcdb2dce28d959f2815b16f81798ea02d192a39a8cc7a701730073011001020402d19683030193a38cc7b2a57300000193c2b2a57301007473027303830108cdeeac93b1a57304",
"ergoTreeConstants": "0: 0\n1: 0\n2: Coll(16,2,4,-96,11,8,-51,2,121,-66,102,126,-7,-36,-69,-84,85,-96,98,-107,-50,-121,11,7,2,-101,-4,-37,45,-50,40,-39,89,-14,-127,91,22,-8,23,-104,-22,2,-47,-110,-93,-102,-116,-57,-89,1,115,0,115,1)\n3: Coll(1)\n4: 1",
"ergoTreeScript": "{sigmaProp(\n allOf(\n Coll[Boolean](\n HEIGHT == OUTPUTS(placeholder[Int](0)).creationInfo._1, OUTPUTS(placeholder[Int](1)).propositionBytes == substConstants(\n placeholder[Coll[Byte]](2), placeholder[Coll[Int]](3), Coll[SigmaProp](proveDlog(decodePoint(minerPubKey)))\n ), OUTPUTS.size == placeholder[Int](4)\n )\n )\n)}",
"address": "2iHkR7CWvD1R4j1yZg5bkeDRQavjAaVPeTDFGGLZduHyfWMuYpmhHocX8GJoaieTx78FntzJbCBVL6rf96ocJoZdmWBL2fci7NqWgAirppPQmZ7fN9V6z13Ay6brPriBKYqLp1bT2Fk4FkFLCfdPpe",
"assets": [],
"additionalRegisters": {},
"spentTransactionId": "e2dbb73e8a9692abcf8238b6b01720c272994f1e4360d88153718f859eeb7175",
"mainChain": true
},
{
"boxId": "69f1bdeac73dbc34f668ca42740b7f1bc1198a35d732a29cea1f6abca9cdf93d",
"transactionId": "54d8f2126aaa883f60f1376117b758aec0ed8e4a00c8987a5ea0575aceac0b38",
"blockId": "639fc2913e9132fd41e998bcf910d105167cc0884fcd254548bce0a1b6f9c517",
"value": 7038409814,
"index": 2,
"globalIndex": 46043541,
"creationHeight": 1452274,
"settlementHeight": 1452276,
"ergoTree": "0008cd028c945947b43157d9006ba85e05cddea5b527a9af821eacb2eb9e8fc55af6fd36",
"ergoTreeConstants": "",
"ergoTreeScript": "{SigmaProp(ProveDlog(ECPoint(8c9459,aa05aa,...)))}",
"address": "9fayRrb8wGmJcCHMg1phwzCTaLf3sZAX7BiJrQhPFUAshceX71u",
"assets": [
{
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"index": 0,
"amount": 190592000,
"name": "bPaideia",
"decimals": 4,
"type": "EIP-004"
},
{
"tokenId": "c0d9e581c1099455f13b82e13a1c20c7308a736cf63acc1025ae1e17a58a72c9",
"index": 1,
"amount": 1,
"name": "Sigmanauts Mining DAO Membership",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "fcfca7654fb0da57ecf9a3f489bcbeb1d43b56dce7e73b352f7bc6f2561d2a1b",
"index": 2,
"amount": 13867615000,
"name": "ErgOne",
"decimals": 8,
"type": "EIP-004"
},
{
"tokenId": "34d449dc84a27d0f8fb2166d415a7223604f6426fb2d83ee099f2312182d575d",
"index": 3,
"amount": 48860790054209,
"name": "PHP",
"decimals": 8,
"type": "EIP-004"
},
{
"tokenId": "1fd6e032e8476c4aa54c18c1a308dce83940e8f4a28f576440513ed7326ad489",
"index": 4,
"amount": 25000000,
"name": "Paideia",
"decimals": 4,
"type": "EIP-004"
},
{
"tokenId": "a650291cd343d511a435b8720476736fcaf3e6c87f977b4e8123bcff666543be",
"index": 5,
"amount": 993,
"name": "Ergo DevDAO",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "3503ba6ce5d8bc1332229284c95fff15cf3c1d0b463fdfd6f3c9b57b7af09fe3",
"index": 6,
"amount": 1,
"name": "Good Things DAO Token",
"decimals": 0,
"type": "EIP-004"
},
{
"tokenId": "d577aa18ae22a1667f5509796de26b64bc4964407db10e80ee2d1b05f2bdf091",
"index": 7,
"amount": 1,
"name": "Sigmanauts Membership",
"decimals": 0,
"type": "EIP-004"
}
],
"additionalRegisters": {},
"spentTransactionId": "ef98185823ca2ab3765c0fee400e680075b6e64f96e80b2f49cc430cf94d7a7f",
"mainChain": true
}
],
"size": 2929,
"isUnconfirmed": false
}