# migrations

dir: embeddings_queue
filename: 00001-embeddings.sqlite.sql
hash: d3755dfd232be8e8301f4d7fcfb3a486
sql:
CREATE TABLE embeddings_queue (
    seq_id INTEGER PRIMARY KEY,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    operation INTEGER NOT NULL,
    topic TEXT NOT NULL,
    id TEXT NOT NULL,
    vector BLOB,
    encoding TEXT,
    metadata TEXT
);


dir: sysdb
filename: 00001-collections.sqlite.sql
hash: 38352d725ad1c16074fac420b22b4633
sql:
CREATE TABLE collections (
    id TEXT PRIMARY KEY,
    name TEXT NOT NULL,
    topic TEXT NOT NULL,
    UNIQUE (name)
);

CREATE TABLE collection_metadata (
    collection_id TEXT REFERENCES collections(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (collection_id, key)
);


dir: sysdb
filename: 00002-segments.sqlite.sql
hash: 2913cb6a503055a95f625448037e8912
sql:
CREATE TABLE segments (
    id TEXT PRIMARY KEY,
    type TEXT NOT NULL,
    scope TEXT NOT NULL,
    topic TEXT,
    collection TEXT REFERENCES collection(id)
);

CREATE TABLE segment_metadata (
    segment_id TEXT  REFERENCES segments(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (segment_id, key)
);


dir: sysdb
filename: 00004-tenants-databases.sqlite.sql
hash: 048867ce8fcdefe4023c7110e4433591
sql:
CREATE TABLE IF NOT EXISTS tenants (
    id TEXT PRIMARY KEY,
    UNIQUE (id)
);

CREATE TABLE IF NOT EXISTS databases (
    id TEXT PRIMARY KEY, -- unique globally
    name TEXT NOT NULL, -- unique per tenant
    tenant_id TEXT NOT NULL REFERENCES tenants(id) ON DELETE CASCADE,
    UNIQUE (tenant_id, name) -- Ensure that a tenant has only one database with a given name
);

CREATE TABLE IF NOT EXISTS collections_tmp (
    id TEXT PRIMARY KEY, -- unique globally
    name TEXT NOT NULL, -- unique per database
    topic TEXT NOT NULL,
    dimension INTEGER,
    database_id TEXT NOT NULL REFERENCES databases(id) ON DELETE CASCADE,
    UNIQUE (name, database_id)
);

-- Create default tenant and database
INSERT OR REPLACE INTO tenants (id) VALUES ('default_tenant'); -- The default tenant id is 'default_tenant' others are UUIDs
INSERT OR REPLACE INTO databases (id, name, tenant_id) VALUES ('00000000-0000-0000-0000-000000000000', 'default_database', 'default_tenant');

INSERT OR REPLACE INTO collections_tmp (id, name, topic, dimension, database_id)
    SELECT id, name, topic, dimension, '00000000-0000-0000-0000-000000000000' FROM collections;
DROP TABLE collections;
ALTER TABLE collections_tmp RENAME TO collections;


dir: sysdb
filename: 00005-remove-topic.sqlite.sql
hash: b1367c826b8fba5f96f27befdc1d42d2
sql:
-- Remove the topic column from the Collections and Segments tables

ALTER TABLE collections DROP COLUMN topic;
ALTER TABLE segments DROP COLUMN topic;


dir: sysdb
filename: 00006-collection-segment-metadata.sqlite.sql
hash: 4eea7468935bf25d4604a0fed2366116
sql:
-- SQLite does not support adding check with alter table, as a result, adding a check
-- involve creating a new table and copying the data over. It is over kill with adding
-- a boolean type column. The application write to the table needs to ensure the data
-- integrity.
ALTER TABLE collection_metadata ADD COLUMN bool_value INTEGER;
ALTER TABLE segment_metadata ADD COLUMN bool_value INTEGER;


dir: metadb
filename: 00001-embedding-metadata.sqlite.sql
hash: 2b4cf52c4bb2676e21d6860a4409f856
sql:
CREATE TABLE embeddings (
    id INTEGER PRIMARY KEY,
    segment_id TEXT NOT NULL,
    embedding_id TEXT NOT NULL,
    seq_id BLOB NOT NULL,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    UNIQUE (segment_id, embedding_id)
);

CREATE TABLE embedding_metadata (
    id INTEGER REFERENCES embeddings(id),
    key TEXT NOT NULL,
    string_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (id, key)
);

CREATE TABLE max_seq_id (
    segment_id TEXT PRIMARY KEY,
    seq_id BLOB NOT NULL
);

CREATE VIRTUAL TABLE embedding_fulltext USING fts5(id, string_value);


dir: metadb
filename: 00002-embedding-metadata.sqlite.sql
hash: 12a570f7121b3a8ce750a2a7c36da20f
sql:
-- SQLite does not support adding check with alter table, as a result, adding a check
-- involve creating a new table and copying the data over. It is over kill with adding
-- a boolean type column. The application write to the table needs to ensure the data
-- integrity.
ALTER TABLE embedding_metadata ADD COLUMN bool_value INTEGER


dir: metadb
filename: 00003-full-text-tokenize.sqlite.sql
hash: f97ad6334aeaa8f419f01110b648b97a
sql:
CREATE VIRTUAL TABLE embedding_fulltext_search USING fts5(string_value, tokenize='trigram');
INSERT INTO embedding_fulltext_search (rowid, string_value) SELECT rowid, string_value FROM embedding_metadata;
DROP TABLE embedding_fulltext;


# embeddings_queue

created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\10_questions_Awakening_Health_Labs.pdf:0:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\10_questions_Awakening_Health_Labs.pdf", "page": 0, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\10_questions_Awakening_Health_Labs.pdf:0:0", "chroma:document": "10\nquestions\nAwakening\nHealth\nLabs\n1.\nWhat\nis\nthe\nprimary\ngoal\nof\nAwakening\nHealth\nLabs?\na)\nTo\nrevolutionize\nthe\ntransportation\nsector\nb)\nTo\nredefine\nhealthcare\nusing\nAI\nand\nrobotics\nc)\nTo\ndominate\nthe\nentertainment\nindustry\nd)\nTo\nrevolutionize\nthe\nfood\nand\nbeverage\nindustry\nANS:\nb)\nTo\nredefine\nhealthcare\nusing\nAI\nand\nrobotics\nResource\nlink:\nVideo\nhttps://drive.google.com/drive/folders/16WaNiQO_wbCyjTx1zqqEv3uEEzWbipzj\n2.\nWho\nare\nthe\nfounding\npartners\nof\nAwakening\nHealth\nLabs?\na)\nMicrosoft\nand\nApple\nb)\nSingularity\nStudio\nand\nHanson\nRobotics\nc)\nHanson\nand\nGoogle\nd)\nDeep\nFunding\nand\nCardano\nANS:\nc)\nSingularity\nStudio\nand\nHanson\nRobotics\nResource\nlink:\nInfographics\nhttps://discord.com/channels/909843832491896832/1100390675293413436/121091724185239\n5560\n3.\nWhat\nis\nthe\nname\nof\nthe\nAI-powered"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\10_questions_Awakening_Health_Labs.pdf:0:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\10_questions_Awakening_Health_Labs.pdf", "page": 0, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\10_questions_Awakening_Health_Labs.pdf:0:1", "chroma:document": "5560\n3.\nWhat\nis\nthe\nname\nof\nthe\nAI-powered\nhealthcare\nrobot\ndeveloped\nby\nAwakening\nHealth\nLabs?\na)\nSophia\nb)\nGrace\nc)\nAlexa\nd)\nDesdemona\nANS:\nb)\nGrace\nResource\nLink:\nVideo\nhttps://drive.google.com/drive/folders/16WaNiQO_wbCyjTx1zqqEv3uEEzWbipzj\n4.\nWhat\nare\nsome\nof\nthe\nfunctions\nof\nAwakening\nHealth\nLabs\u2019\nhealthcare\nrobot?\na)\nConducting\nsurgeries\nand\nmedical\nprocedures\nb)\nProviding\ncompanionship\nand\ncognitive\nstimulation\nc)\nCooking\nmeals\nand\ndoing\nhousehold\nchores"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\10_questions_Awakening_Health_Labs.pdf:1:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\10_questions_Awakening_Health_Labs.pdf", "page": 1, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\10_questions_Awakening_Health_Labs.pdf:1:0", "chroma:document": "d)\nDriving\ncars\nand\noperating\nmachinery\nANS:\na)\nConducting\nsurgeries\nand\nmedical\nprocedures\nResource\nLink:\nVideo\nhttps://drive.google.com/drive/folders/16WaNiQO_wbCyjTx1zqqEv3uEEzWbipzj\n5.\nWhat\nare\nthe\ntarget\naudience\nfor\nAHL's\nrobots\nfrom\nthe\nvideo?\na)\nThey\nare\nprimarily\nhealthy\nyoung\nadults.\nb)\nThey\ninclude\nthe\nelderly,\ndisabled,\nand\nthose\nneeding\ncare.\nc)\nThey\nare\ndoctors\nand\nother\nmedical\nprofessionals.\nd)\nThe\npassage\ndoesn't\nprovide\nany\ninformation\nabout\nthe\ntarget\naudience.\nb)\nThey\ninclude\nthe\nelderly,\ndisabled,\nand\nthose\nneeding\ncare.\nResource\nLink:\nVideo\nhttps://drive.google.com/drive/folders/16WaNiQO_wbCyjTx1zqqEv3uEEzWbipzj\n6.\nWhat\nsignificant\ntransformation\ndid\nAwakening\nHealth\nLabs\nundergo\nin\nOctober\n2023?\na)\nIt\nmerged\nwith\nForest\nHills\nLabs\nb)\nIt\nlaunched\na\nnew\nhealthcare\napp"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\10_questions_Awakening_Health_Labs.pdf:1:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\10_questions_Awakening_Health_Labs.pdf", "page": 1, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\10_questions_Awakening_Health_Labs.pdf:1:1", "chroma:document": "2023?\na)\nIt\nmerged\nwith\nForest\nHills\nLabs\nb)\nIt\nlaunched\na\nnew\nhealthcare\napp\ncalled\nEmbrace.\nc)\nIt\ndoubled\nthe\nproduction\nof\nits\nGRACE\nrobots.\nd)\nIt\nopened\nits\nfirst\nclinic\nfocused\non\nrobotic\nhealthcare.\npen_spark\nANS:\na)\nIt\nmerged\nwith\nForest\nHills\nLabs\nResource\nLink:\nResource\nInfographics\nhttps://discord.com/channels/909843832491896832/1100390675293413436/121091724185239\n5560\n7.\nWho\nleads\nthe\nteam\nof\nAI\nand\nrobotics\nexperts\nat\nAwakening\nHealth\nLabs?\na)\nElon\nMusk\nb)\nDr.\nBen\nGoertzel\nc)\nDr.\nDavid\nHanson\nd)\nAlex\nYang"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\10_questions_Awakening_Health_Labs.pdf:2:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\10_questions_Awakening_Health_Labs.pdf", "page": 2, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\10_questions_Awakening_Health_Labs.pdf:2:0", "chroma:document": "ANS:\nd)\nAlex\nYang\nResource\nLink:\nVideo\nhttps://drive.google.com/drive/folders/16WaNiQO_wbCyjTx1zqqEv3uEEzWbipzj\n8.\nWhat\nis\nEmbrace,\nand\nwhat\nindustry\ndoes\nit\naim\nto\nenhance?\na)\nA\ngaming\nconsole\nfor\nchildren\nb)\nA\ndigital\nclinical\napp\nfor\naesthetic\nclinics\nc)\nA\nsocial\nmedia\nplatform\nfor\ninfluencers\nd)\nA\nstreaming\nservice\nfor\nmovies\nand\nTV\nshows\nANS:\nb)\nA\ndigital\nclinical\napp\nfor\naesthetic\nclinics\nResource\nLink:\nVideo\nhttps://drive.google.com/drive/folders/16WaNiQO_wbCyjTx1zqqEv3uEEzWbipzj\n9.\nName\ntwo\nbenefits\nof\nEmbrace?\na)\nEnhanced\nefficiency\nand\npersonalized\ntreatments\nb)\nFaster\ninternet\nspeed\nand\nimproved\nphone\nconnectivity\nc)\nLower\nelectricity\nbills\nand\nreduced\ncarbon\nemissions\nd)\nAccess\nto\nfree\ncoffee\nand\nsnacks\nfor\npatients\nANS:\na)\nEnhanced\nefficiency\nand\npersonalized\ntreatments"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\10_questions_Awakening_Health_Labs.pdf:2:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\10_questions_Awakening_Health_Labs.pdf", "page": 2, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\10_questions_Awakening_Health_Labs.pdf:2:1", "chroma:document": "and\nsnacks\nfor\npatients\nANS:\na)\nEnhanced\nefficiency\nand\npersonalized\ntreatments\nResource\nLink:\nfrom\nthe\ninfographic\nhttps://discord.com/channels/909843832491896832/1100390675293413436/121091724185239\n5560\n10.\nWhere\ndoes\nAwakening\nHealth\nLabs\noperate\nfrom?\na)\nSan\nFrancisco\nb)\nParis\nc)\nHong\nKong\nd)\nTokyo\nANS:\nc)\nHong\nKong\nResource\nlink:\nfrom\nthe\ninfographic\nhttps://discord.com/channels/909843832491896832/1100390675293413436/121091724185239\n5560"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:0:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 0, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:0:0", "chroma:document": "HyperCycle:\nA Lightweight Agent-System-Based\nLedgerless Blockchain Architecture,\nSupporting Secure, Efficient, Scalable,\nCross-Chain AI Microservices,\nBen Goertzel1, Toufi Saliba2, Dann Toliver2Anton Kolonin1, Sergei Rodionov1\n1SingularityNET Foundation\n2TODA Network\nMay 12, 2023\nAbstract\nA novel ledgerless blockchain architecture called Hypercycle is pre-\nsented, oriented toward secure, inexpensive, high-speed large-scale on-\nchain execution of microservices, especially (but not exclusively) those\ncarrying out AI-related functions. This fully peer-to-peer HyperCycle\nblockchain is founded on the TODA/IP P2P communication protocol,\nthe TODA asset model, SingularityNET\u2019s Proof of Reputation system\nand the MeTTa AI programming language drawn from the OpenCog\nHyperon AGI framework."}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:0:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 0, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:0:1", "chroma:document": "Hyperon AGI framework.\nIn blockchain-world lingo, one can think of HyperCycle as mul-\ntilayer, layer-fluid or \u201dLayer 0++\u201d \u2013 meaning it starts at the level\nof basic secure communication protocol, then adds layers from there,\nand can play the roles associated with various traditionally conceived\nlayers (core protocol, base blockchain, sidechain,etc.) depending on\ncontext..\nA HyperCycle network consists of a population of autonomous\nagents, communicating in a secure p2p manner, each owning their own\ntransaction history and accumulating their own reputation, grouped\n1"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:1:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 1, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:1:0", "chroma:document": "together in rings in which they collaborate to execute consensus mech-\nanisms enabling execution of instantaneously deterministic nano-transactions\nbased on cryptographic proofs of computation performed in a p2p set-\nting with zero third party dependencies.\nHyperCycle agents are constructed as specific TODA files/pack-\nets (Sato-Servers), complete with the TODA transaction and cycle\ntrie mechanisms for secure strongly decentralized management of file\nand network history (the cycle trie being the inspiration for the name\n\u201dHyperCycle\u201d). Consensus mechanisms involved in HyperCycle rings\nmay be ledgerless e.g. based on hierarchically sharded ledgers, de-\npending on the particular requirements. However, if ledgers are used\nthey play a supplementary rather than foundational role (e.g. sup-"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:1:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 1, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:1:1", "chroma:document": "they play a supplementary rather than foundational role (e.g. sup-\nplemental usage might be for efficiently broadcasting proofs, or for\nledger-based long-term backup storage of a selected fraction of on-\nHyperCycle transactions or aggregate of transactions).\nBrief discussion is given here on the particulars of HyperCycle cus-\ntomization to key application areas like swarm AI, rating and reward\nin media networks, decentralized payments and computer processing,\nand public/private chain interoperability. A key strategy for enabling\nbroad usability will be the creation of MeTTa-based DSLs focused on\nsmart contract programming in specific vertical areas. Such DSLs may\nstraightforwardly be presented to the user as low or no code frame-"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:1:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 1, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:1:2", "chroma:document": "straightforwardly be presented to the user as low or no code frame-\nworks auto-generated from the underlying MeTTa code, effectively\nleveraging the HyperCycle framework behind the scenes.\nContents\n1 Introduction 3\n1.1 Enabling Fast, Inexpensive, Large-Scale On-Chain Micro-transactions 7\n1.2 HyperCycle Multi-Tokenomics . . . . . . . . . . . . . . . . . . 8\n1.3 HyperCycle Multi-Ring Structure . . . . . . . . . . . . . . . . 9\n1.4 Broad Usability via Smart Contract DSLs . . . . . . . . . . . 11\n2 Background: TODA/IP, Proof of Reputation and the MeTTa\nLanguage 12\n2.1 The TODA/IP ledgerless blockchain . . . . . . . . . . . . . . 12\n2.1.1 TODA/IP Transaction Processing: Decentralized and\nLocalized . . . . . . . . . . . . . . . . . . . . . . . . . 13"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:1:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 1, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:1:3", "chroma:document": "Localized . . . . . . . . . . . . . . . . . . . . . . . . . 13\n2.1.2 TODA: Interoperable Proof Structures . . . . . . . . . 16\n2"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:2:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 2, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:2:0", "chroma:document": "2.1.3 Key TODA Data Structures and the TODA Cycle . . . 17\n2.2 Proof of Reputation . . . . . . . . . . . . . . . . . . . . . . . . 19\n2.3 OpenCog Hyperon\u2019s MeTTa as a Smart Contract Language . 21\n3 HyperCycle 23\n3.1 Core HyperCycle Architecture Concepts . . . . . . . . . . . . 23\n3.2 HyperCycle Consensus Mechanisms . . . . . . . . . . . . . . . 25\n3.2.1 Lightweight Rings . . . . . . . . . . . . . . . . . . . . . 25\n3.2.2 Hierarchical Sharding Enhanced Consensus Mechanisms 25\n3.2.3 Further Notes on Security, Reliability and Performance\nTradeoffs . . . . . . . . . . . . . . . . . . . . . . . . . . 26\n3.3 Smarter Smart Contracts . . . . . . . . . . . . . . . . . . . . . 31\n3.3.1 Accelerated Smart Contract Execution . . . . . . . . . 31\n3.3.2 Smart Contract DSLs for HyperCycle: A Path to Broad"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:2:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 2, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:2:1", "chroma:document": "3.3.2 Smart Contract DSLs for HyperCycle: A Path to Broad\nUsability . . . . . . . . . . . . . . . . . . . . . . . . . . 33\n4 A Few Promising Early Applications 34\n4.1 Swarm AI: Evolutionary Learning, Algorithmic Chemistry, En-\nsemble ML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35\n4.2 Ratings and Rewards in Online Networks . . . . . . . . . . . . 35\n4.3 Decentralizing Payments and Processing Power . . . . . . . . 36\n4.4 Adaptively Blending Public and Private Chains . . . . . . . . 36\n5 Conclusion: Catalyzing the Future Of AI and Blockchain\nEcosystem 37\nNotes on Deep Cardano Integration 38\n1 Introduction\nOver the years since the Bitcoin whitepaper was released in 2008, a variety\nof different blockchain technologies and networks have been created, lever-"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:2:2

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 2, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:2:2", "chroma:document": "of different blockchain technologies and networks have been created, lever-\naging roughly Bitcoin-like underlying architecture, some cases progressive\nevolutions while others are regressive. However, New concepts such as smart\ncontracts, DAOs and multi-layer networks have been added to the picture,\nincreasing functionality but in some cases moving further rather than closer\nto the original vision of fully peer-to-peer (p2p) strongly secure networks.\n3"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:3:0

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 3, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:3:0", "chroma:document": "The complexity of the interdependencies between various desirable proper-\nties such as speed, security, scalability, usability, and decentralization have\nbecome much clearer in the interval since Bitcoin emerged.\nIt has also become clear, given the diversity of tools and chains that have\nemerged with different strengths and weaknesses, that there may not end up\nbeing One Blockchain Network To Rule Them All. Rather, we may be facing\nan ecosystem comprising a decentralized network of decentralized networks,\neach architected with underlying mechanisms that combine roughly the same\nunderlying math and computer science in ways specialized to suit particular\nclasses of use-cases.\nHyperCycle comes into this scene from a novel direction, and it can be"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:3:1

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 3, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:3:1", "chroma:document": "HyperCycle comes into this scene from a novel direction, and it can be\nviewed from multiple perspectives associated with the multiple \u201dlayers\u201d com-\nmonly identified in blockchain systems:\n\u2022Layer 0 : HyperCycle can be viewed and used as a \u201dLayer 0\u201d technol-\nogy, in the sense that\n\u2013It has aspects that sit inside the network packets and operates on\nthat level below any and all existing siloed AIs, blockchains and\nledgers.\n\u2013The reputation system underlying its Proof of Reputation consen-\nsus system is most logically construed as operating across blockchains\nand incorporating off-chain information as well.\n\u2013Its tools for accelerating smart contract execution work across\nmultiple VMs, via intervening on a lower layer than typical consensus-"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:3:2

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 3, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:3:2", "chroma:document": "multiple VMs, via intervening on a lower layer than typical consensus-\nbased mechanisms for validating smart contract execution results\n\u2022Layer 1 : HyperCycle can be viewed and used as a \u201dLayer 1\u201d, in that\nit can be used as an independent blockchain network for mediating p2p\ninteractions between participants to manage and exchange values to\nacquire AI computation but also to transact the value created via AI\ncomputations.\n\u2022Layer 2 : HyperCycle can be viewed and used as having \u201dLayer 2\u201d\naspects. It can interoperate very closely with other blockchains, so\nwhen desired it can interact with another chain much in the manner\nthat a sidechain does. Further, in some cases it can provide proofs\nof the correctness of its activity which can be verified by other chains,\n4"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:4:0

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 4, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:4:0", "chroma:document": "rendering its interaction \u201dLayer 2\u201d like in a comparable sense to various\nsorts of rollups.\n\u2022Layer 3 : HyperCycle can interoperate with existing Layer 2 sidechains\nin a similar manner to Layer 1 chains, if these Layer 2 chains have\nunique characteristics to be leveraged\nThis seamless \u201dlayer-fluidity\u201d is achieved using core algorithms and structures\ndrawn from the TODA asset model [GGT19] and the TODA/IP ledgerless\nblockchain [ST19] , along with the Proof of Reputation concept [AK21] and\nMeTTa AI programing language developed for use within the SingularityNET\nAI/blockchain network.\nAlong with Proof of Reputation, MeTTa and other tools used within\nHyperCycle, SingularityNET [Goe19] [GGH+17] [MG19] also provides some\nof the motivation underlying HyperCycle\u2019s creation. SingularityNET is a"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:4:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 4, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:4:1", "chroma:document": "of the motivation underlying HyperCycle\u2019s creation. SingularityNET is a\ndecentralized protocol and platform for communication and coordination be-\ntween AI agents and their users, originally implemented on Ethereum and\nnow in the midst of a port to Cardano.\nA core issue experienced with the Ethereum based version of Singulari-\ntyNET has been that, due to the temporal and financial cost of using the\nblockchain, AI developers are driven toward design patterns in which indi-\nvidual agents carry out massive and complex AI processes internally at a\nmodular level, using the blockchain only for relatively infrequent communi-\ncations or for setting up appropriate secure persistent channels. The Cardano\nversion is expected to palliate this problem somewhat, but not sufficiently"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:4:2

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 4, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:4:2", "chroma:document": "version is expected to palliate this problem somewhat, but not sufficiently\nto allow the highly granular use of blockchain within AI-system operations\nthat would be preferred. HyperCycle will solve this problem to a signifi-\ncantly greater degree. Via radically decreasing the cost and increasing the\nspeed of blockchain transactions, HyperCycle allows design patterns involv-\ning lighter-weight AI agents interoperating more frequently in more diverse\nways, and therefore opening new doors for intelligence to emerge from coop-\nerative dynamics Of course these improvements will come with some tradeoffs\nwhich are acceptable in the SingularityNET application context but would\nbe unappealing in some other applications.\nThe deployment of SingularityNET on HyperCycle will require HyperCy-"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:4:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 4, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:4:3", "chroma:document": "The deployment of SingularityNET on HyperCycle will require HyperCy-\ncle to demonstrate rich and fine-grained interoperability with the Cardano\nand Ethereum blockchains, because many useful SingularityNET agents de-\npend for their operation on interactions with other third-party processes\n5"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:5:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 5, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:5:0", "chroma:document": "Figure 1: HyperCycle Computation Nodes Interacting\nrunning on these other chains. The HyperCycle design in fact supports in-\nteroperability with these and other chains to an unprecedented degree. We\nhave taken serious looks at Bitcoin, Cardano, Ethereum, Cosmos, Polkadot,\nAlgorand, Avalanche and NEM, and believe it should be tractable for Hyper-\nCycle smart contracts to operate in close communication and coordination\nwith smart contracts running on all these chains and others. That said, the\noverheads and limitations and strengths of cross-chain operation will be dif-\nferent in each case. In the Appendix to this document we discuss some of\nthe particular aspects of interoperation with Cardano blockchain, which is\none of the cases we\u2019ve thought through in the most detail so far."}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:5:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 5, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:5:1", "chroma:document": "one of the cases we\u2019ve thought through in the most detail so far.\nFigure 1 showcases a single HyperCycle Computation Node subcontract-\ning 2 other nodes simultaneously and zero dependencies. Yet it is crucial to\nbe reminded that no finality is ever reached without having something global\nbeing impacted. We cover that in details in Toda/IP.\n6"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:6:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 6, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:6:0", "chroma:document": "1.1 Enabling Fast, Inexpensive, Large-Scale On-Chain\nMicro-transactions\nOne important class of use-cases for which HyperCycle is intended is where:\n\u2022You have a large number of transactions that need to happen in a\nshort period of time such as HPC centers (a single HPC center can\ndemand over 26 million micro transactions per second to have finalities\nin milliseconds, highest security and near zero cost)\n\u2022You don\u2019t need to see everyone else\u2019s transactions but only those related\nto you\n\u2022While prompts must happen in real time, they must be preceded by\ncommitted transaction in real-time1\nWhile this is not the only class of situations HyperCycle can be applied to,\nit is the particular sort of use-case that has been most firmly in mind while"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:6:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 6, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:6:1", "chroma:document": "it is the particular sort of use-case that has been most firmly in mind while\ndeveloping the design, and it is envisioned as playing a large role in shaping\nthe initial implementation.\nThe HyperCycle design fulfills these requirements (as well as those of\nother use-cases) via leveraging\n\u2022TODA/IP\u2019s ability to coordinate a large number of secure blockchain\ntransactions without the necessity of maintaining any replicated ledgers.\nEffectively zero-dependencies\n\u2022The SingularityNET weighted liquid rank reputation system\u2019s capabil-\nity to assess the contribution of an agent to a network and thus the\nreliability of that agent to assist with various network functions.\n1In one of the TODA asset model, which is heavily leveraged within HyperCycle, you"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:6:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 6, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:6:2", "chroma:document": "can \u201dperform\u201d any transaction you like, any time you want, even ridiculous completely\nmade up ones, but anyone looking at that transaction can completely verify its integrity,\nbased only on the information in the transaction\u2019s proof of provenance. So no bad trans-\naction can provide evidence that it is a good transaction, and every good transaction has\nstandalone evidence proving its veracity to anyone who takes time to look. However, the\ntransaction may still take time to reach finality globally, but in the meantime and within\nthe cycle and its reach to other cycle, the finality is immediate, which is unprecedented\nat this time this document was last updated. The question then becomes, in a given ap-"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:6:3

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 6, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:6:3", "chroma:document": "plication, whether to accept a partially complete transaction or insist on a fully complete\none before proceeding.\n7"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:7:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 7, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:7:0", "chroma:document": "\u2022The MeTTa AI language developed in the OpenCog Hyperon AGI\nproject, made blockchain-savvy via integration with the rholang/RSpace\ntools developed in the RChain project2\n\u2022The ability to interoperate closely with smart contracts defined for\nother chains, including Plutus, Solidity and others\n1.2 HyperCycle Multi-Tokenomics\nFirst let\u2019s start by answering \u201dwhy\u201d does it need a token? Effectively what\nis the token\u2019s absolute utility? The native HyPC native token is absolutely\nneeded for each machine to initiate any work, not by consensus but by lit-\nerally \u201dabsolute need\u201d for the packet to make it across, which is where the\ntoken resides in what\u2019s called Sato-Server, which was one of the successful\nevolutions that came out of Toda/IP towards a global data structure called"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:7:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 7, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:7:1", "chroma:document": "evolutions that came out of Toda/IP towards a global data structure called\nEarth64. While this may sound unnecessary, further below folks would get\nthe aha moment. . Given than the cryptographic proofs are embedded inside\nthe 64KB Toda file aka the Sato-Server that is actually the native HyPC to-\nken, it means the proof of provenance of that token must also be included. It\nis crucial for the AI computation node to know with certainty that it will get\npaid if does the work. Therefore the payment is actually embedded in the set\nof instruction with the inquiry to do the work, if the payment is validated,\nthen the VM sends the instructions with the data that needs to be processed\nto the AI machine. This means the AI machine is actually managed by the"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:7:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 7, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:7:2", "chroma:document": "to the AI machine. This means the AI machine is actually managed by the\nVM. In-order for the provenance to be of significance, the AI machine needs\nto ensure that he payment comes from a source first that has been identified\nby homo-sapiens as money. (why homo-sapiens? Well they are the ones that\nrun this planet and must identify this as money, otherwise AI won\u2019t be able\nto buy electricity or hardware to sustain itself and evolve) Hence the token\nlistings is the very first step towards the provenance of that proof. The fact\nthat someone paid money for the token and everyone pays money for the to-\nken is sufficient for the machine to utilize that proof of provenance as valid.\nMainly because the machine needs to know if it\u2019s done the work requested"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:7:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 7, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:7:3", "chroma:document": "Mainly because the machine needs to know if it\u2019s done the work requested\n(modular AI), can it actually use this token to pay for electricity or what-\nnot. This gets even more interesting when the HyPC is not only used as the\nmethod of payment but to also carry payments of other currency inside it\nalong with the actual identity of the machine.. Hence HyPC value is due to\n2https://rholang.io/\n8"}


created_at: 2024-06-29 05:41:35
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:8:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 8, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:8:0", "chroma:document": "its utility and the more demand of that utility the more it is likely to increase\nin value as its supply is finite but the demand is not likely to ever slow down.\nThere are 3 main utilities of the token, 2 of them are technical:\n1- Each node must have 1024 HyPC to initially surface in the network\nand have an identity that can carry proofs of computations, reputations and\nUptime which can enable the node to multiply without any additional licenses\nor tokens. Up to 1024 node.\n2- Each node must be able to communicate with other nodes values and\ntherefore when a computer has finished processing, the cryptographic proof\nof its work is sufficient to re-consume the token received.\n3- Can embed other tokens or currency within it and therefore gives flex-"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:8:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 8, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:8:1", "chroma:document": "3- Can embed other tokens or currency within it and therefore gives flex-\nibility for AI compute to have any currency for it to effectively operate.\nThe tokenomics of HyperCycle also have unique aspects. There is no\ntransaction fee per se for the AI compute transactions running on the net-\nwork, but a similar role is played by royalties , the payment obligations as-\nsociated with which are split up automatically and deterministically among\nparticipants in the network to ensure all parties are properly incentivized.\nRoyalties and exchange of value between nodes involves the native HyPC\ntoken, but in particular applications can include a variety of other value in-\nstruments, say USD, EURO, WON, AGIX, ADA, ETH, USDC, Ava, Algo,\netc. For instance,"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:8:2

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 8, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:8:2", "chroma:document": "etc. For instance,\n\u2022HyPC native tokens, paying for the overall AI computation including\ntools and mechanisms enabling various HyperCycle rings to provide\nconsensus, information propagation and other useful functions\n\u2022AGIX tokens to pay for the AI functionality required to maintain an\nuncorrupted reputation system.\n\u2022ETH, BTC, ADA or others can be used to pay for the long-term storage\nof partial snapshots of the HyperCycle network state.\n1.3 HyperCycle Multi-Ring Structure\nA HyperCycle network (as loosely depicted in Figure 2) comprises a collection\nof \u201drings\u201d, each containing a set of HyperCycle agents, each of which contains\nand controls a record of its own transaction history, and each of which is able\nto enter into various sorts of relationships with other agents (e.g. sending or"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:8:3

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 8, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:8:3", "chroma:document": "to enter into various sorts of relationships with other agents (e.g. sending or\nreceiving transactions, or participating in validating transactions). Unlike in\n9"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:9:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 9, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:9:0", "chroma:document": "standard ledger based blockchains, and drawing key structures and methods\nfrom TODA and TODA/IP (such as the TODA transaction trie and the cycle\ntrie that gave HyperCycle its name), in HyperCycle individual agents and\ntheir purposeful interactions are the core of the system \u2013 no replicated ledgers\nare needed, and where they are used they are best considered efficiency-\noriented augmentations to the metadata that agents retain regarding their\nown histories and properties.\nFigure 2: Conceptual illustration of two HyperCycle rings and their connec-\ntion to the Any Ledger Blockchain mainchain.\nEach ring in the network performs its own consensus judgments, and is\nconnecting to pseudo-randomly selected set of nodes in the network that is"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:9:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 9, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:9:1", "chroma:document": "connecting to pseudo-randomly selected set of nodes in the network that is\ntriggered by time and a deterministic function on how to meet and what\nabout. Effectively every-time a an HyPC Packet is passed from one device\nto the next, and while it is obvious that the requester must submit the\ncryptographic signature of the HyPC to the receiver node, at the time the\nrequester will be ignored if didn\u2019t launch the token through a deterministic\nmap from the data structure Earth64 (no single machine needs to save it\nor host it anywhere but everyone can compute it fairly quickly) This also\nmeans when a transaction is initiated launching the tokens through a trie\nmakes it impossible for double spent. It is simply not possible. The imagine\n10"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:10:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 10, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:10:0", "chroma:document": "below shows how 2 separate temporal events happen at slight different times\nand size but they both must deterministically merge as they must upward\nnavigate the BST Earth64 data structure tree forming a trie every single\ntime. Effectively the packet is sent and as it propagates through the network\nwith the hash of the dealing all together (irreversible)\nThe SingularityNET reputation system, deployed among the nodes in-\nvolved in HyperCycle rings, provides a dynamically updated reputation mea-\nsure to each node. Proof of Reputation means that the choice of validators\nwithin HyperCycle rings will be biased toward higher-reputation network\nnodes (who then get fees in HyperCycle tokens for helping with consensus\ndecisions)... Reliance on reputation in this way helps avoid various sorts of"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:10:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 10, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:10:1", "chroma:document": "decisions)... Reliance on reputation in this way helps avoid various sorts of\nattacks in a relatively straightforward and elegant way. To seed the repu-\ntation system we could e.g. give initial reputation boosts to AGIX or ADA\nstakers, and also to early purchasers of HyperCycle tokens.\nSection 4 briefly reviews a few example applications ideally suited for\nHyperCycle \u2013 swarm AI, rating and reward in media networks, decentral-\nized payments and computer processing, and public/private chain inter-\noperability \u2013 and roughly indicates the sort of consensus mechanism and\nHyperCycle/any-mainchain interaction likely to be needed in each case.\nThe HyperCycle design also supports flexible public/private deployment.\nGiven a fully implemented HyperCycle, SingularityNET applications (along"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:10:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 10, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:10:2", "chroma:document": "Given a fully implemented HyperCycle, SingularityNET applications (along\nwith many other sorts of applications) will be deployable with subnetworks\non both public and private HyperCycle rings.\n1.4 Broad Usability via Smart Contract DSLs\nFinally, all this underlying complexity can nevertheless be presented to appli-\ncation developers in a simple and usable manner, appropriate to the domain\nareas in which their applications are operating. Toward this end, integration\nof the OpenCog Hyperon [BGT21] framework\u2019s MeTTa programming lan-\nguage [Pot21] into Plutus is also envisioned, as a strategy for enabling broad\nusability via the creation of MeTTa-based DSLs focused on smart contract\nprogramming in specific vertical areas, which will often correspond to partic-"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:10:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 10, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:10:3", "chroma:document": "programming in specific vertical areas, which will often correspond to partic-\nular specialized rings. Such DSLs may straightforwardly be presented to the\nuser as low or no code frameworks auto-generated from the underlying MeT-\nTa/Plutus code, effectively leveraging the HyperCycle framework behind the\nscenes.\n11"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:11:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 11, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:11:0", "chroma:document": "2 Background: TODA/IP, Proof of Reputa-\ntion and the MeTTa Language\nIn this section we review a few key aspects of the existing blockchain networks\nand designs that are critical to the HyperCycle design. Then Section 3 will\npull all the pieces together.\n2.1 The TODA/IP ledgerless blockchain\nTODA/IP [ST19] is a protocol for secure communication and coordination in\ndecentralized networks, which relies on the same underlying data structures\nand encryption mechanisms as standard functioning cryptography that is\nrunning this planet including the innovations by blockchain platforms, but\ncombines these in a radically different way to enable tremendously greater\nscalability.\nTODA/IP significantly bypasses well known problems w/ replicated ledgers,\nsuch as"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:11:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 11, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:11:1", "chroma:document": "such as\n\u2022Coordination overhead associated with distributing information from a\nlarge network to a number of replicated ledgers\n\u2022Communications overhead required for sending, receiving, and process-\ning messages (e.g. according to gossip protocols which, while resilient,\nare known for their considerable communications overhead)\n\u2022Decreased performance with scale. Replicated ledgers require that ev-\nery fully participating node must process every single transaction which\noccurs. This means that as more transactions occur, and more nodes\nparticipate, overall transaction times suffer severely. This type of pro-\ntocol offers security, neutrality and censorship resistance at the cost of\nscalability.\nBasically, in typical replicated ledger blockchain systems, every peer receives"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:11:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 11, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:11:2", "chroma:document": "Basically, in typical replicated ledger blockchain systems, every peer receives\nall transaction data, all hash reference values, and all block headers. Every\nnode needs to be supplied with enough information to be able to re-create the\nentire chain. This is how blockchain explorers are able to provide browsing\nthrough the history of major blockchains.\nSharding palliates all these problematic factors, but only partially. To\nmake sharding work really well requires hierarchical sharding architectures\n12"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:12:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 12, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:12:0", "chroma:document": "which give algorithmically more satisfactory performance but at cost of adding\nsignificant complexity and overhead. TODA/IP is in some ways similar in\nspirit to sharding, but keeps things simpler and lower-overhead by adopt-\ning a more fully decentralized approach, in which each local data-chunk is\nresponsible for its own historical information (or in some cases that of its\nclose neighbors). This changes the nature of the communication protocols\na fair bit, but removes the main source of complexity and cost in ledger-\nbased blockchains, which is the maintenance of a population of ledgers each\nstoring broad-based information about the network as opposed to simply a\npopulation of inter-transacting agents each with their own local data and\ninterests."}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:12:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 12, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:12:1", "chroma:document": "interests.\nA network that is ledgerless in its foundational operation can still make\nuse of ledgers when appropriate. For instance if one wants a rapidly search-\nable backup of a certain subset of network transactions, storing all these in\na replicated ledger makes a lot of sense. HyperCycle is envisioned to make\nuse of the Cardano mainchain for this purpose, along with other purposes\nlike help maintaining certain sorts of security guarantees. In this approach,\na judiciously chosen subset of HyperCycle transactions (at least within se-\nlected rings) are periodically snapshotted and pushed to Cardano mainchain\nwhere they will be stored in Cardano\u2019s replicated ledger for backup, and\nrapid search and access.\nIt is also possible to use TODA/IP together with hierarchical sharding,"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:12:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 12, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:12:2", "chroma:document": "It is also possible to use TODA/IP together with hierarchical sharding,\nwhen one wants to achieve particular sorts of balance between security guar-\nantees and performance. In this case the overhead of sharding is still there,\nbut is significantly less than in cases where a sharded ledger is required to\ndo all the work.\n2.1.1 TODA/IP Transaction Processing: Decentralized and Lo-\ncalized\nThe core structural element of TODA/IP is its association of individual\nrecords with their own localized ledgers. This makes these records semi-\nautonomous agents in a sense that is not remotely the case for entries stored\nin the ledger of a traditional distributed/replicated ledger-based blockchain.\nThe core dynamical aspect of TODA/IP, correspondingly, is the way se-"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:12:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 12, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:12:3", "chroma:document": "The core dynamical aspect of TODA/IP, correspondingly, is the way se-\ncure transaction processing occurs in a manner that is wholly decentralized\nand is also \u201dlocalized\u201d in the sense that it doesn\u2019t require access to any\noverall ledger of all transactions that have ever occurred in the blockchain,\n13"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:13:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 13, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:13:0", "chroma:document": "but requires only interaction of a small set of parties who are closer to the\ntransaction at issue. In this dynamics, records are acting as \u201dnodes\u201d in a\nblockchain network \u2013 specifically, each record may correspond to a number\nof different nodes, each acting in a different portion of the tree.\nLet us now briefly unpack how this works, in a simple case of a ring with\na \u201dplain vanilla\u201d fully-replicated-ledger-less TODA/IP consensus system.\nGiven a record Rowned by wallet A, that record can be sent to wallet B\nthrough the generation of a transaction request, which is signed by wallet A,\nthen signed by wallet B, then distributed and signed by a set of validators.\nA cycle in TODA/IP consists of a round of transactions requests and ensuing"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:13:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 13, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:13:1", "chroma:document": "A cycle in TODA/IP consists of a round of transactions requests and ensuing\nvalidations (for those requested transactions that are valid).\nBut how are the validators chosen? In the simplest TODA/IP implemen-\ntations, basically, any active device so long it is geographically dispersed it\nwill get a fair probabilistic chance in the pseudo-random selection be chosen\nas a validator. This does not mean there is some magical proof of disperse-\nment but instead reliance on the laws of physics and how network speed can\nnever exceed the speed of light, yet while the selection itself gives fair chance\nto any node that matches the criteria of the pseudorandom function, the role\nthey need to play requires a race amongst selected nodes and if they hap-"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:13:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 13, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:13:2", "chroma:document": "they need to play requires a race amongst selected nodes and if they hap-\npen to be concentrated they null each other. More on that in the Toda/IP\npaper. A pseudorandom function is used to select devices in the network\nfor participation in a computation validating a given transaction, based on\ntheir geographic dynamic disbursement. Devices cannot apply more compu-\ntation than the modest amount required for the pseudorandom selection of\nwork that is assigned to them in each block of time while the work is to run\nthe system itself it is in parallel that the TM machines run these network\nof hashes to ensure the payment systems work while the AI to AI treat the\nentire TM as a black-box.\nBecause there is no way to achieve extra privileges in the network via ex-"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:13:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 13, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:13:3", "chroma:document": "Because there is no way to achieve extra privileges in the network via ex-\ntra work, there is no intrinsic need for \u201cmining farms\u201d or similar mechanisms\nas the work that is done is always work needed to run the entirety of the\nsystem from the TM perspective and on the other side and in parallel the\nwork needed to run the AI computation is on a case by case. There\u2019s always\na requester and a responder. Please note, those are machines requesting work\nand machines responding, when we say machine we mean a Hypercycle com-\nputation node that is comprised by all its elements including but not limited\nto, VM, AI Machine and TM. Also, while this approach enables economical\nincentives for devices to be on the network, it requires no disincentive for\n14"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:14:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 14, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:14:0", "chroma:document": "not participating in the computation. The amount of work involved here is\nindependent of the size of the network; and the work is spread out through\nthe system, rather than concentrated as it would be in typical replicated\nledger-based blockchains.\nEach validator provides four important functions:\n\u2022Confirming the validity of the transaction (structural soundness and\nproof correctness)\n\u2022Prevent sending a packet twice in this cycle\n\u2022Help build the consensus proofs for the transaction\n\u2022Provide matching proofs for A and B\nOne of the validators will also be chosen to create a new record containing\nthe signed transaction validation message the validation process returns to\nthe recipient. This new file will then be sent from the chosen validator to"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:14:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 14, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:14:1", "chroma:document": "the recipient. This new file will then be sent from the chosen validator to\nitself, resulting in the appending of this file to the validator\u2019s record.\nAfter the transaction is done, the Merkle root of the data structure con-\nstructed as the transaction proceeds is a cryptographically secure represen-\ntation of the entire transaction.\nIt makes logical sense for a TODA/IP-based network to charge modest\ntransaction fees to cover the network, electricity and depreciation cost of\ncarrying out the work involved in transaction validation. These fees would\nbe naturally split among sender, recipient and validators, in a manner that\nmay be different for different TODA/IP implementations. There are obvious\nvariations such as preferentially assigning the validator role (and the modest"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:14:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 14, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:14:2", "chroma:document": "variations such as preferentially assigning the validator role (and the modest\nrevenue obtainable therefrom) to nodes that have provided rapid validation\nin the past. This leads on to the integration of TODA/IP with reputation\nsystems that we will discuss in Section 3 below and that forms a key aspect\nof the HyperCycle design.\nThe nodes in a TODA/IP network may be divided into \u201drings\u201d, each of\nwhich groups a certain set of records, and which are hierarchically connected.\nA single record can then live in multiple concentric rings. Different rings can\nhave different consensus mechanisms for approving transactions. Transac-\ntions made to the record in different rings can then be merged consistently\nwithin the record.\n15"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:15:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 15, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:15:0", "chroma:document": "A TODA/IP system may hold a collection of rings, what we might call a\n\u201dring-set\u201d, can be configured to all share a common \u201dTODATree\u201d (fully sat-\nurated and balanced BST) structure, which provides a universal file address\nsystem that may roughly be thought of as a decentralized analogue to IPV6.\nIn the context of a TODATree, each record has a unique address number\nthat it retains over its lifetime, defined via the branch of the TODATree it\noccupies. A highest level TODATree of height 256 has been posited to su-\npervene over all TODA/IP implementations, with each of the 296branches\nat level 160 associated with a particular TODA/IP implementation. Hy-\nperCycle will then involve a TODA/IP ring-set associated with a particular\nTODATree level 160 subtree."}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:15:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 15, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:15:1", "chroma:document": "TODATree level 160 subtree.\n2.1.2 TODA: Interoperable Proof Structures\nWhile TODA/IP is a full ledgerless blockchain system, TODA simply de-\nscribes a data structure.3The core of the TODA design is the \u201dTODA File\u201d,\nwhich essentially is a digital data file that comes along with a per-file ledger\nattached as metadata. The binding between a file\u2019s internal data and its\nledger allows a file to behave like a \u201dunique digital object\u201d (a sort of NFT)\nrather than a more abstract, interchangeable symbolic token.\nEvery transaction that a file is involved with causes a corresponding\nrecord to be attached to the file\u2019s associated ledger. These transaction records\ncontain among other information the addresses of the other parties involved"}


created_at: 2024-06-29 05:41:35
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:15:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 15, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:15:2", "chroma:document": "contain among other information the addresses of the other parties involved\nin the various transactions. The functioning of TODA is founded on the\nway these transaction records enable each file to have a single canonical well\nformed proof of provenance.\nThe transaction detail describing a particular transaction for a particular\nfile specifies several aspects such as:\n\u2022the destination address: who will become the new owner\n\u2022a metadata hash, which allows users to attach arbitrary metadata to\nthe transaction\n\u2022an encumbrance, which is a special restriction on how the transaction\nwill occur\n3This section is based on [GGT19]; later designs supersede this but are generally com-\npatible with it.\n16"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:16:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 16, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:16:0", "chroma:document": "\u2022a signature on this data, to certify that the current owner really wishes\nthe transaction to occur\nThe transaction detail becomes an intrinsic part of the proof of a file. By\nonly requiring the hash of the metadata to be included in a transaction, the\nsize of proofs is kept under control, and users are given the power to choose\nnot to share the metadata of a transaction with someone else.\nThese qualities provide TODA files with the ability to move transparently\nbetween rings, regardless of the consensus mechanisms or networks involved,\nprovided only that the capacity to create corresponding structures exists.\nThis allows records that make use of these data structures to interoperate\nnatively across unrelated systems.\n2.1.3 Key TODA Data Structures and the TODA Cycle"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:16:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 16, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:16:1", "chroma:document": "2.1.3 Key TODA Data Structures and the TODA Cycle\nOperation on a TODA file depend centrally on two Merkle trie data struc-\ntures: The File Trie and the Cycle Trie. Explicating how these tries work\nis the best way to go to the next level of detail in the description of TODA\noperations.\nThe file trie is a Merkle trie which associates file IDs with transaction\ndetail hashes for transactions involving that file. A transaction proof is the\nMerkle proof associating a single file detail with a file ID. The transaction\ndetails associated with a file\u2019s ID by its owner\u2019s file trie are the only valid\nrepresentation of operations on that file.\nThe file trie for a given address in a given cycle contains all of the oper-\nations performed over files owned by that address in that cycle. Files owned"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:16:2

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 16, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:16:2", "chroma:document": "ations performed over files owned by that address in that cycle. Files owned\nby that address that are not present in that file trie for that cycle are said to\nhave null proofs. Because the transaction details referenced in the transaction\nproofs associated with a file are the only valid representation of operations\non a file, a file with a null proof is demonstrably not operated on within the\ncontext of the file trie.\nEach file trie is contained within a cycle trie. The cycle trie can be built\nin a highly distributed way using e.g. TODA/IP, as we describe below, but\nit can also be built in a fully centralized setting, or something in between.\nThe values contained in this cycle trie are nothing other than the file trie"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:16:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 16, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:16:3", "chroma:document": "The values contained in this cycle trie are nothing other than the file trie\nMerkle roots corresponding to the transactions occurring in the cycle. Each\nfile only requires the small slice of the overall cycle trie that contains it.\nAs the cycle proceeds, the Merkle trie is built containing proofs for each\ntransaction. For a given transaction its two proofs (one from the sender, one\n17"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:17:0

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 17, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:17:0", "chroma:document": "Figure 3: Tries Merging\n18"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:18:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 18, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:18:0", "chroma:document": "from the receiver) will be known only to the sender and the receiver, but all\nare part of the cycle trie.\nNot all nodes in a ring, in general, will be available to participate in a\ngiven cycle. However, cycles in any ring that a file belongs to are relevant\nto that file whether the node corresponding to that file is transacting in that\ncycle or not. In order to be able to continually prove that your file\u2019s ledgers\nare complete and not missing any information, you need to collect a bit of\ndata every cycle that contains proof that your address did not contribute to\nthe cycle. Luckily, these trimmed proofs tend to be especially short and don\u2019t\nhave to wait for each other, nor wait for block, they can all run in parallel"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:18:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 18, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:18:1", "chroma:document": "have to wait for each other, nor wait for block, they can all run in parallel\nand those that arrive at a certain time to join and concatenates with others\nkeep going in a one way computation to reduce every pair to one following\ntheir deterministic Earth64 static map (Figure 3)\nA protocol-level way to deal with these zero-transaction proofs is to say\nthat the nodes assembling the cycle trie during a given cycle are responsible\nfor maintaining proofs of their non-participating neighboring nodes (where a\n\u201dneighbor\u201d is a node that a given node has transacted with before). As many\nneighboring nodes who didn\u2019t contribute to a cycle will generally share the\nsame proof, this is a relatively trivial set of data to maintain. There are also"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:18:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 18, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:18:2", "chroma:document": "other routes to managing these proofs which are more at the service level,\ni.e. specific rings may maintain specialized ledgers or other mechanisms for\nmanaging these proofs that in some contexts may provide greater efficiency\nthan relying on neighboring nodes. For instance, with sufficient trickery it\nmay be possible to store \u201dproofs of null proofs\u201d together with cycle roots\nwithout actually storing whole cycle tries; there is some research yet to be\ndone in this direction.\n2.2 Proof of Reputation\n4\nThe final key ingredient woven into the HyperCycle design is the \u201dProof\nof Reputation\u201d consensus mechanism that was incubated within Singulari-\ntyNET by a team led by Anton Kolonin and fleshed out fully in collaboration\nwith Oladotun Aluko [AK21]."}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:18:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 18, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:18:3", "chroma:document": "with Oladotun Aluko [AK21].\nSingularityNET\u2019s \u201dweighted liquid rank\u201d reputation system\n[KGDI18] is a general mechanism for assigning reputation to members of\n4This section is based on [AK21]\n19"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:19:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 19, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:19:0", "chroma:document": "a decentralized network, integrating implicit and explicit ratings, and incor-\nporating various subtleties such as adjusting the effect of member A\u2019s impact\non member B\u2019s reputation based on member A\u2019s reputation. The length of\na network member\u2019s history, the amount of currency they have spent, the\npredictiveness of their own ratings, and many other factors can play into\nreputation calculations. The framework also includes a role for machine\nlearning driven \u201dreputation integrity analysis\u201d agents to identify potential\nreputation fraud. Several simulations of the use of weighted liquid rank in\nvarious situations have been implemented and evaluated, e.g. reputation in\necommerce marketplaces [KGP+19].\nThe core idea of Proof of Reputation (PoR) is to use weighted liquid"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:19:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 19, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:19:1", "chroma:document": "The core idea of Proof of Reputation (PoR) is to use weighted liquid\nrank based reputations as the basis of a consensus mechanism for blockchain\nnetworks. The PoR framework uses the interaction of nodes in a network over\ntime to determine the amount of reputation associated with each node in the\nnetwork. A node\u2019s reputation is calculated by blending together a normalized\nset of ratings and the corresponding reputation values of the node providing\nthe rating at a given point in time, rather than simply the value of the direct\nrating given by other nodes. A node\u2019s behavior also directly influences its\noverall reputation value by appropriate formulae. The PoR mechanism then\nuses the reputation to determine a set of consensus nodes responsible for"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:19:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 19, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:19:2", "chroma:document": "uses the reputation to determine a set of consensus nodes responsible for\nmaintaining the network\u2019s shared state. Reputation values are ongoingly\nupdated as interactions between nodes in the network progress over time.\nA few of the core principles underlying this scheme are:\n\u2022. The reputation value computed for a node is based on the reputation\nvalue of the node providing the rating \u2013 this is the \u201dliquid\u201d aspect\n\u2022The temporal scoping of reputation, according to which reputation val-\nues accumulated by members in the longer past contribute less to the\ncurrent reputation value (while at the same time allowing agent history\nto contribute appropriately to reputation)\n\u2022The openness of all reputation values to all members of the community\nso that audits can be performed."}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:19:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 19, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:19:3", "chroma:document": "so that audits can be performed.\nIn accordance with principles of openness and decentralization, PoR stores\nnode reputation values on a sidechain rather than in a centralized database.\nIn a HyperCycle context, this ends up meaning that each HyperCycle net-\nwork spawns a side HyperCycle network managing the reputation values for\n20"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:20:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 20, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:20:0", "chroma:document": "the parent HyperCycle network. This doesn\u2019t spawn an endless recursion\nbecause the nodes in the child network (containing reputation values) have\nthe same owners as nodes in the parent network, so the consensus mechanism\nin the child mirrors that in the parent.\nAt the start of every PoR consensus round, consensus group members\nneed to be selected and added into a consensus group. Members of the con-\nsensus group are chosen from the nodes with the highest reputation values,\ne.g. with collective reputation scores that exceed 50% of the total reputation\nvalues of the network. A leader from the group is then selected, who serves\nthe functions of:\n\u2022Packaging all valid transactions from the list of pending transactions\nto a block"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:20:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 20, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:20:1", "chroma:document": "to a block\n\u2022Calculating the new reputation values for all network nodes using data\nfrom transactions in the transaction list\n\u2022Broadcasting the commit message to the consensus group\nTo verify a transaction, the consensus group waits until a certain min-\nimum number of of consensus members has sent a confirmatory message.\nThis process constitutes a consensus group vote, in which each node has a\nweighted vote proportional to its reputation value from the previous round.\nThis general mechanism is consistent with a variety of different underlying\nblockchain architectures, e.g. it could be used with account-based, UTxO or\nEUTxO frameworks. HyperCycle layers PoR based consensus into a system\nbased on EUTxO and TODA, resulting in a non-replicated-ledger-dependent"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:20:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 20, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:20:2", "chroma:document": "based on EUTxO and TODA, resulting in a non-replicated-ledger-dependent\nblockchain framework supporting powerful functional-programming smart\ncontracts but incorporating the particular security that PoR brings against\nvarious sorts of attacks.\n2.3 OpenCog Hyperon\u2019s MeTTa as a Smart Contract\nLanguage\nThe MeTTa (Meta Type Talk) language [Pot21] developed in the context\nof the OpenCog Hyperon AGI project [BGT21] has a number of properties\nthat make it favorable for use as the core smart contract language within\nHyperCycle. The very flexible nature of MeTTa\u2019s semantics (e.g. gradual\n21"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:21:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 21, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:21:0", "chroma:document": "dependent typing, and a homotopy type theory oriented treatment of equal-\nity) means that it does not impose unnecessary or arbitrary commitments\non handling of TODA/IP messaging or TODA data structures; MeTTa pro-\ngrams can be as open-ended as they need to be for dealing with a variety of\nHyperCycle ring structures.\nFurther, MeTTa is ideally suited for the development of application-\nspecific smart-contract DSLs, after the rough pattern of the Idris language\u2019s\ndependent-type-based DSL creation facility. Toward this end we envision\nmaking MeTTa available as the default, so that e.g. finance-focused, medicine-\nfocused or evolutionary-learning-focused smart contract languages for on-\nchain execution could be scripted in MeTTa and then run via Plutus. It will"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:21:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 21, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:21:1", "chroma:document": "chain execution could be scripted in MeTTa and then run via Plutus. It will\nthen be possible to create generic tools for mapping MeTTa DSLs into appro-\npriate low or no code development UIs, resulting in a HyperCycle framework\nwith a high degree of usability for developers working in niches for which\nDSLs have been created.\nA compiler is being created that translates MeTTa source into source-code\nin the rholang language originally developed for use in the Rchain blockchain\n[?]. This allows MeTTa to leverage rholang\u2019s powerful concurrency proper-\nties, such as linear or near-linear scaling in use of multiprocessors (includ-\ning on GPUs) for many algorithms. It also allows MeTTa smart contracts\nto leverage tokenomics internally to manage computational resource alloca-"}


created_at: 2024-06-29 05:41:35
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:21:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 21, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:21:2", "chroma:document": "to leverage tokenomics internally to manage computational resource alloca-\ntion. This fits naturally with the AI-DSL developed for use between Sin-\ngularityNET agents, which allows AI processes to specify their functional,\ncompute-resource and other properties using dependent type expressions. It\nprovides a framework in which smart contracts running on HyperCycle nodes\ncan automatically manage their resources and automatically outsource work\nto each other and collaborate to solve problems, in a manner that integrates\ntokenomics richly into the functional-programming and process-calculus in-\nfrastructure making the multi-agent system work.\nHyperCycle is designed to allow smart contracts in a wide variety of lan-\nguages. Composition of contracts, infusion of tokenomics into the flow of"}


created_at: 2024-06-29 05:41:35
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:21:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 21, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:21:3", "chroma:document": "guages. Composition of contracts, infusion of tokenomics into the flow of\ncontract execution and various other features will not work as efficiently or\nas sophisticatedly in other languages as in MeTTa. However, these features\nare not always critical, and there is a large advantage to being able to lever-\nage smart contracts already written for use on other existing blockchains,\nand interoperate these with each other as well as with MeTTa scripts. The\ntightness of the interoperation with smart contracts in other languages will\nhave some case-to-case aspects; it is clear for instance that the functional na-\n22"}


created_at: 2024-06-29 05:41:35
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:22:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 22, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:22:0", "chroma:document": "ture of Haskell will enable a quite tight integration between MeTTa contracts\nand Cardano\u2019s Plutus contracts.\n3 HyperCycle\nHyperCycle is a novel blockchain architecture which is formed by assem-\nbling key pieces from the existing blockchain networks and designs reviewed\nabove: TODA/IP and other aspects of the TODA framework, reputation\nfor consensus and other aspects of system regulation, and MeTTa for smart\ncontracts. The \u201dassembly\u201d process involved is not entirely straightforward,\nhowever, and requires some modifications and extensions to all of the ingre-\ndients. The result of this process is a blockchain with unprecedented capa-\nbility for handling high-speed, large-scale on-chain agent interactions such\nas is required for on-chain deployment of population-based AI algorithms,"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:22:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 22, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:22:1", "chroma:document": "as is required for on-chain deployment of population-based AI algorithms,\ntokenomics-powered interactive media, and numerous other applications.\n3.1 Core HyperCycle Architecture Concepts\nThe HyperCycle network is a population of \u201dagents\u201d which correspond to\nHyperCycle computation nodes exchanging TODA files through the network\npacket with zero third party dependency and therefore able to execute trans-\nactions in a p2p setting in a sub-second finality. Each agent has one single\nowner in every block of time, and one owner may control multiple agents.\nThe agents are organized into TODA-style rings, and a given TODA file\nmay correspond to multiple agents living within different rings. As usual\nwith TODA, each ring may have its own particular consensus mechanism."}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:22:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 22, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:22:2", "chroma:document": "with TODA, each ring may have its own particular consensus mechanism.\nThere are no fees per se for HyperCycle transactions, however the net-\nwork dynamics can incorporate royalties for ever successful AI computation.\nHaving said that the HyPC token has value and can be the mean of exchange\nit also has the capability to carry values inside it such as ETH, USD, ADA,\nAGIX and others.\nEach HyperCycle ring has its own independent operation, although some\nrings may rely on external blockchain for some functions, e.g. long-term\n\u201dexternal memory\u201d style backup. In this case, a ring would have its own\npolicy determining which of its internal transactions gets pushed back in\nsnapshots to for example BTC, ETH, ADA or others mainchain, and how\noften these snapshots are pushed back.\n23"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:23:0

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 23, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:23:0", "chroma:document": "Agent reputations and agent-owner reputations are calculated dynami-\ncally via a weighted liquid rank reputation system that operates at the overall\nHyperCycle network level. Three dimensions of reputation are maintained\nfor each agent: Reliability, cooperativeness and honesty. An agent with poor\nuptime or slow processing may be rated unreliable even if it has never done\nanything fraudulent or problematic. An agent with strong technical proper-\nties may still refuse to participate in constructing proofs, which will merit it\na low cooperativeness rating, but this is still important to distinguish from\na fraudster agent which will get a low honesty rating.\nUse of reputation and social connectivity dynamics should in many cases"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:23:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 23, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:23:1", "chroma:document": "Use of reputation and social connectivity dynamics should in many cases\nenable dynamic evolution of HyperCycle rings, and in rings that use hierar-\nchical sharding (to be discussed below), potentially of shards as well.\nEach ring, in each cycle of its operation, has certain cycle head parties and\ncertain cycle tail parties, in the rough sense of the Hydra protocol. Agents in\nthe ring with high reliability, cooperativeness and honesty may be thought\nof as essentially the same as conventional Hydra head parties. Any member\nof the ring who wants to participate actively in consensus during the next\ncycle can broadcast a message saying so, and will then be considered as a\ncycle head for the next cycle.\nIn HyperCycle, things will proceed more like in TODA/IP, but with added"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:23:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 23, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:23:2", "chroma:document": "In HyperCycle, things will proceed more like in TODA/IP, but with added\nelement to expedite transactions and that\u2019s the ratio a node has which incor-\nporates proof of computation, uptime and reputation-based weighting. While\neach TM (Traansaction Machine) node namely the Toda/IP component is\nidentical to other nodes Validators for each transaction proposed within a\ncycle will be chosen via a deterministic/pseudorandom function from the\nnetwork with sifficient probable redundancy while the work required is suffi-\ncient to be provided by any single node. In fact it\u2019s a race of who gets there\nfirs with the right proof and that\u2019s within each cycle and many cycles are\nchained within each block of time. While a block of time is being concluded,"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:23:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 23, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:23:3", "chroma:document": "chained within each block of time. While a block of time is being concluded,\nmany cycles might be half way through their chained deterministic pathway\nin the computation necessary to achieve the proof required. s for the selection\nof validators, it has nothing to do with the reputation, however the selection\nof many AI providers,, the preference is naturally geared to the agents with\nstronger reputations.\n24"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:24:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 24, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:24:0", "chroma:document": "3.2 HyperCycle Consensus Mechanisms\n3.2.1 Lightweight Rings\nThe simplest sort of HyperCycle ring is what we call a \u201dlightweight ring,\u201d\nwhich is wholly ledgerless in the manner of the \u201dsimple TODA/IP ring\u201d\nroughly described in Section 2.1 above.\nThe \u201dlightweight ring\u201d consensus mechanism can be adequate for rings\ninvolving individual transactions of huge financial value, but is also well\nsuited for rings oriented toward micro-transactions. The key is to have a tiny\ntransaction in the least amount of time so we can have many transactions as\neach transaction can lead to emergence of intelligence, the more the system\nis efficient the more emergence is gained. Examples would be:\n\u2022AI agents that are participating in population-based AI algorithms like"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:24:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 24, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:24:1", "chroma:document": "\u2022AI agents that are participating in population-based AI algorithms like\ngenetic algorithms, genetic programming, ensemble machine learning,\nswarm AI\n\u2022Small-scale tokenomic transactions such as rating articles or comments\non a tokenomics-enabled media site\n\u2022Small-scale financial micropayments for any purpose\n3.2.2 Hierarchical Sharding Enhanced Consensus Mechanisms\nFor cases where a greater level of protocol-level security and reliability is re-\nquired, rings with alternative consensus mechanisms are needed. For instance\none may create a \u201dhierarchical sharding enhanced ring\u201d (HS enhanced ring),\nin which the basic localized transactions between agents are supplemented\nby a hierarchically sharded ledger which contains proofs corresponding to the\nring."}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:24:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 24, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:24:2", "chroma:document": "ring.\nThe HS ledger may be used in various different ways, yielding multiple\nvariations of the HS consensus mechanism. One option is to use the HS\nledger purely as a backup, in case the more fully decentralized peer-to-peer\napproach used in the lightweight consensus mechanism fails. This allows\nguarantees beyond what is possible with the lightweight consensus, but in\nthe case where the agents in the ring are mostly effective and cooperative, it\nonly modestly increases operational cost and speed.\nAnother option is to use the HS ledger more aggressively. E.g. if zero-\ntransaction proofs are stored in the HS ledger then there\u2019s an option to omit\n25"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:25:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 25, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:25:0", "chroma:document": "them from file metadata, thus keeping proofs smaller. However this has risk\nto significantly increase the time cost of doing proofs because of the need for\nagents to download relevant portions of the HS ledger every time they do\nproofs. The severity of this issue will depends on the habitual distributions\nof the inter-agent transactions in the network \u2013 in particular, on the degree\nto which the transactions among the parties in the ring fall into a hierarchical\nstatistical pattern that can be reflected in the hierarchical sharding structure.\nBroadly speaking, the use of the HS ledger provides greater security\nagainst various sorts of attacks, and thus an HS ring is going to be appro-\npriate for agents carrying out larger, less micro-level/disposable transactions"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:25:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 25, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:25:1", "chroma:document": "priate for agents carrying out larger, less micro-level/disposable transactions\nthan those appropriate for a lightweight ring. However this added security\nwill come at an added performance cost \u2013 which can be reduced via clever\ndesign and efficient implementation, but will often remain a significant factor.\nOne general principle we see in these aspects of the HS consensus mech-\nanism is that the TODA design providing core elements to HyperCycle does\nnot obviate the key tradeoffs underlying all blockchain design, such as the\nperformance versus security tradeoff \u2013 but does provide a framework in which\nvarious different tradeoff choices appropriate for different applications can be\nelegantly inserted, via use of ring-specific consensus mechanisms associated"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:25:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 25, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:25:2", "chroma:document": "elegantly inserted, via use of ring-specific consensus mechanisms associated\nwith their own auxiliary data structures and dynamics building on the core\nHyperCycle mechanisms.\n3.2.3 Further Notes on Security, Reliability and Performance Trade-\noffs\nIn this section we dig a little more deeply into the technical tradeoffs involved\nin the choice between the lightweight and HS ring consensus mechanisms\ndescribed above. The discussion may also shed a bit of a broader light on\nsome of the tradeoffs involved in choosing a lightweight ring versus a heavier\nring for a particular application.\nIncentivizing Agent Participation in Consensus Firstly, one key ques-\ntion underlying the operation of a ring under the lightweight consensus mech-"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:25:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 25, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:25:3", "chroma:document": "tion underlying the operation of a ring under the lightweight consensus mech-\nanism is: What incentivizes agents to contribute the knowledge they possess,\nwhich is necessary for other agents\u2019 in the proof construction process they\nneed to undertake to construct the Cycle Trie? In some cases this is not a\nrelevant question, e.g. if all the agents in the ring are strongly externally\nincentivized to contribute to the overall outcome of the process the ring is\n26"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:26:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 26, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:26:0", "chroma:document": "carrying out (e.g. AI agents collaborating to solve a problem of mutual in-\nterest to all the agent owners). But in other cases the rapid cooperation\nof agents in providing distributed data to feed proofs to form the cycle trie\ncould be a potential critical bottleneck for lightweight consensus operation.\nThe basic answers to this question are: payment and reputation. The\nnetwork design involves modest transaction fees and some of these can be di-\nrected to agents providing necessary proof data for cycle trie formation. Also,\nmaintaining acceptable ongoing reputation will be necessary for agent-owners\nto continue participating in HyperCycle networks. While low-reputation\nagent-owners may always try to re-enter the network using a different iden-"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:26:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 26, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:26:1", "chroma:document": "agent-owners may always try to re-enter the network using a different iden-\ntity, there will be machine learning driven \u201dreputation police\u201d mechanisms\ndesigned to spot this sort of behavior. Further, agents with higher reputation\nare more likely to be chosen to participate in consensus and thus are more\nlikely to profit from their participation in the network ongoingly. One can\nalso juice up the benefits of having high reputation even further by making\ntransaction fees lower for high reputation agents, so that agents have a direct\nfinancial incentive for playing nice.\nAnother possible mechanism, to be introduced with great care and only\nin particularly appropriate sorts of applications, is to allow sufficiently high"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:26:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 26, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:26:2", "chroma:document": "reputation agents to add transactions to the cycle trie even if they don\u2019t have\nall the necessary proof data at hand. This is not a route one would want to\ntake in a network hosting large financial transactions, but in a ring comprising\nAI agents acting cooperatively to solve problems together on-chain, it may\nbe perfectly acceptable.\nPayment and reputation are \u201dsoft incentives\u201d rather than hard protocol\nguarantees, so in the lightweight consensus scenario there is still a possibility\nthat some agents will simply refuse to provide data that they hold locally,\nand eat the reputation loss. In this case, an external conflict resolution\nmechanism will need be invoked, e.g. if agent-owners involved have made\ndeposits (either within HyperCycle or in some other associated chain) they"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:26:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 26, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:26:3", "chroma:document": "deposits (either within HyperCycle or in some other associated chain) they\nwill lose all or part of the deposits they have made. But even so, there\u2019s a\npossibility that some transactions will not be able to happen.\nCosts and Benefits of HS Enhanced Consensus The HS consensus\nmechanism sketched above is, in a sense, a hybrid between the fully ledger-\nless TODA/IP system and a more traditional ledger-based blockchain with\nhierarchical sharding (see e.g. [CDD+19] for a fleshed-out description of hier-\n27"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:27:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 27, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:27:0", "chroma:document": "archical sharding in the Thinkey blockchain, but many other examples exist).\nIn this case we have information which, ideally, everybody in a ring should\nhave (with consensus): the cycle roots of the TODA/IP system. In the\nHS approach we increase the odds of achieving this ideal by increasing this\ncommon information a bit beyond what\u2019s there in the lightweight consensus\nmechanism. All head parties in a ring still construct the data structures for\nthe record. But along with building the cycle tree, the non-zero transaction\npackets are added to the hierarchically sharded ledger. This system has al-\nmost the same level of privacy as in the lightweight consensus protocol (from\ncommon information it will be impossible to infer who owns items). But it"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:27:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 27, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:27:1", "chroma:document": "common information it will be impossible to infer who owns items). But it\nautomatically solves the two requirements mentioned above, and dramati-\ncally reduces the proof sizes (since proofs for zero transactions will not be\nrequired in proofs of provenance).\nThe big downside of the HS approach is that, in order to guarantee its\nability the needed proof for any file received, an agent needs to be able\nto download the relevant portion of the HS ledger. In the worst case it\nneeds to download the whole ledger, which could become huge as the system\ngrows. On the other hand, if the transactions in the network have a strong\ntendency to be localized, so that most transactions occur between agents\nin the same small shard, then this may be only a moderate-sized problem."}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:27:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 27, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:27:2", "chroma:document": "in the same small shard, then this may be only a moderate-sized problem.\nStill, at least in unfavorable scenarios, the HS consensus mechanism loses the\nlightweight consensus mechanism\u2019s favorable property of accelerating rather\nthan decelerating as the number of network participants increases.\nTwo Strong (But Optional) Requirements One way to think about\nthe value added by the HS approach (or other similar mechanisms) is to look\nat the following two requirements, both of which are clearly desirable for\nmaximal network security:\n1. If a user adds a non zero transaction then this transaction can make its\nway to the final ?cycle root? (cycle root for which we have consensus)\nif and only if user has merkle proof for this transaction. It means"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:27:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 27, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:27:3", "chroma:document": "if and only if user has merkle proof for this transaction. It means\nthat it is not possible by design to fail (because of network error or\nintentionally) to send the element of merkle proof to the user.\n2. Users should always be able to reliably recover all cycle roots and all\nmerkel proof for all zero transactions.\n28"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:28:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 28, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:28:0", "chroma:document": "In a \u201dlow security margin\u201d ring, such as one using the lightweight con-\nsensus mechanism outlined above, we can have nonzero probability of failure\nfor these two properties. However, when failed it can be completed in sub-\nsequent block. Failure here does not mean loss. On the other hand, it is\ndifficult to see how one would satisfy the second requirement in particular\nwithout doing something roughly \u201ddecentralized ledger like\u201d \u2013 i.e. without\nhaving decentralized consensus about proofs of zero transactions, and making\nthese available for everybody. The first requirement is in some way trickier\nbut is also less complex to address in ledger-like scenarios.\nHowever, it is not a given that these requirements must necessarily be met"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:28:1

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 28, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:28:1", "chroma:document": "However, it is not a given that these requirements must necessarily be met\nin all rings via guarantees at the protocol level. Maximizing security guar-\nantees generally also maximizes cost (computational and otherwise), which\nlimits the scope of potential use cases. Bitcoin is currently primarily con-\ncerned with the use-case of transacting large quantities of speculative assets,\nand for this purpose a decentralized ledger based approach is relatively apro-\npos. But other sorts of use-case, e.g. in agent-based AI or social media or\nmicropayments, have dramatically different sets of requirements which point\ntoward different trade-off choices, and some of these choices may sensibly\ninvolve giving up some protocol-level security guarantees in pursuit of per-"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:28:2

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 28, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:28:2", "chroma:document": "involve giving up some protocol-level security guarantees in pursuit of per-\nformance or other beneficial aspects.\nDefusing the Power of Forking It\u2019s also worth noting that essentially\nevery TODA/IP-based system will possess, by design, certain sorts of secu-\nrity that elude all modern decentralized ledger-based systems \u2013 for instance,\nsecurity against the chaos and economic perversity habitually incurred by\nblockchain forks. All too often the fallback solution to a major security is-\nsue in a ledger-based blockchain is \u201dwe can always fork the chain.\u201d This has\nworked disturbingly well for various cryptocurrencies on a financial basis. For\ninstance, the results of the Bitcoin forks leading to Bitcoin Cash and Bit-\ncoin SV, and the Ethereum fork leading to Ethereum Classic, were broadly"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:28:3

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 28, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:28:3", "chroma:document": "coin SV, and the Ethereum fork leading to Ethereum Classic, were broadly\nprofitable: Both pre and post fork tokens retained significant financial value,\nso that the forks increased the market caps of the underlying blockchains.\nHowever, in other cases the ability to fork in this way is highly undesirable;\nfor instance real-world assets like loyalty points, fiat, video game assets, etc.\nThe fundamental uniqueness of TODA/IP records obviates the power of this\nsort of forking. In a HyperCycle network with HS consensus, forking the HS\nledger still doesn\u2019t fork the records underlying the HyperCycle agents. There\n29"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:29:0

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 29, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:29:0", "chroma:document": "can of course be software upgrades but there is no structural bias for these\nto lead to perverse forking situations as seem to routinely occur with today\u2019s\nstandard distributed ledger-based blockchains.\nFile Uniqueness as a Key to Understanding Tradeoffs HyperCycle\ninherits from TODA the centrally important invariant that each file must\nhave a single canonical well formed proof of provenance . The key questions,\nwhich we have been discussing in this section as regards lightweight versus HS\nconsensus mechanisms are ones like: How quickly can that proof be obtained?\nHow complex is obtaining it? How much of a security margin is there for my\nfiles? What recourse is there if something blows up (either through lack of\navailability or through widespread equivocation)?"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:29:1

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 29, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:29:1", "chroma:document": "availability or through widespread equivocation)?\nThe answer to each of those questions is ultimately \u201cit depends\u201d, because,\nas with TODA, there is a goal of having HyperCycle workable in a variety of\ndifferent use-cases with different practical requirements, while maintaining\ncanonical uniqueness for the underlying files. This requires flexibility be-\ncause the security margin you want for high value assets always costs a fair\nbit in terms of finance and/or performance, and continues to cost over time\n(nothing is free), whereas for efficient micro-transactions you have to accept\nsignificantly lower security margins to get the efficiency required. What we\ncan ask for is not a single detailed blockchain design that addresses all possi-"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:29:2

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 29, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:29:2", "chroma:document": "ble requirements with the exact same set of algorithms, but rather a flexibly\nmodularized blockchain design that can be elegantly customized to meet\na broad set of requirements \u2013 for instance via diverse pluggable consensus\nmechanisms, as in the case of HyperCycle.\nFile Uniqueness Across Multiple Rings These tradeoff-related issues\narise in particularly subtle ways when considering the dynamics of TODA/IP-\nbased systems across multiple rings. The uniqueness of a file in a multi-ring\nnetwork is guaranteed only if we have consensus about cycle roots, in an\nappropriately defined sense. In the simplest case, if there is agreement about\nall cycle roots across all rings in the network, we can pass files between rings\nunproblematically. However, this strong situation is not the only one of"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:29:3

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 29, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:29:3", "chroma:document": "unproblematically. However, this strong situation is not the only one of\ninterest. For instance, if I have a file which was in another ring Afor some\nperiod of time, then to appropriately carry out proofs regarding AI require\ncycle roots from ring Afor this period of time, but not necessarily beyond\nthis period of time. The proof data from Aover the relevant period of time\n30"}


created_at: 2024-06-29 05:41:35
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:30:0

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 30, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:30:0", "chroma:document": "is included in the file\u2019s proof of provenance, and serves to link the cycle roots\nofAinto the greater network of rings.\n3.3 Smarter Smart Contracts\nHyperCycle\u2019s approach to smart contracts is both subtle and inclusive, in-\nvolving a unique native smart contract framework including a python VM,\nMeTTa (and its compilation to rholang), along with an efficient smart con-\ntract execution framework that works for many different smart contract lan-\nguages, and a framework for allowing interoperation of smart contracts writ-\nten in different languages and potentially interacting with different third-\nparty blockchains including EVM but it will not be only EVM.\n3.3.1 Accelerated Smart Contract Execution\nHandling of smart contract execution in HyperCycle is a deep topic that we"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:30:1

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 30, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:30:1", "chroma:document": "Handling of smart contract execution in HyperCycle is a deep topic that we\nwill touch only lightly here. Rather than relying on generic consensus mecha-\nnisms, greater efficiency can be achieved by use of specialized mechanisms for\nvalidating smart contract output in a secure and decentralized way. In this\nmanner we can achieve multiple orders of magnitude speedup over existing\nblockchains.\nIn the standard Ethereum-blockchain approach, each smart contract is\nrun on every full node in the network, thus providing consensus on what\nthe output of the contract should be according to the network\u2019s consensus\nalgorithm. This is secure (or at least as secure as the network\u2019s consensus\nalgorithm) but obvious extremely inefficient. If 2000 nodes need to approve"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:30:2

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 30, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:30:2", "chroma:document": "algorithm) but obvious extremely inefficient. If 2000 nodes need to approve\nto give consensus on a smart contract\u2019s output, then the smart contract is\nbasically running at least 2000x slower than if it were just a program running\non a single machine.\nA few workarounds for this smart contract execution bottleneck have\nbeen proposed, most famously zero-knowledge rollups, which are part of the\nEthereum roadmap, and are being pursued for Cardano by a number of\nparties. However the simplest (not that simple) form of zk-rollup based\nsystem relies on a small number of centralized provers, and decentralized\nalternatives become increasingly complex relying on special marketplaces of\nnodes providing zk proofs.\nThe HyperCycle framework supports considerably simpler and lower-"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:30:3

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 30, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:30:3", "chroma:document": "The HyperCycle framework supports considerably simpler and lower-\noverhead methods of radically accelerating smart contract execution. Basi-\n31"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:31:0

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 31, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:31:0", "chroma:document": "cally, a deterministic yet pseudorandom choice and a few other clever meth-\nods are used to enable each smart contract to be run only on a small set of\nnodes rather than the whole array. This kind of flexible mechanism becomes\nespecially convenient in a system that has no globally shared or replicated\nledger and correspondingly no universal list of nodes who would be called on\nto validate all smart contracts.\nIn the HyperCycle approach, when one node has a smart contract it wants\nto run, it uses a specially designed pseudorandom function to select a small\nnumber (say N=3) of other nodes to run the smart contract as well. In the\nsimplest case of a smart contract that just gives a single output and doesn\u2019t\nreport interim results: These other N nodes each run the contract and report"}


created_at: 2024-06-29 05:41:35
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:31:1

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 31, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:31:1", "chroma:document": "report interim results: These other N nodes each run the contract and report\nan encrypted version of the result of the execution ... if the results from the\nN nodes are all proved equivalent then the result is decrypted and delivered\nto the original requestor node, and payment is delivered to the N nodes.\nIn the case of a smart contract that produces interim results, and does\npotentially expensive processing in-between the results, then the process of\ncomparison among the Nnodes doing execution may be done at appropriate\ncheck-points during smart contract execution, as regards interim results. If\none is working with a pre-existing smart contract interpreter (such as the\nPlutus interpreter) then enabling this variation will generally require some\nminor tweaks to interpreter."}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:31:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 31, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:31:2", "chroma:document": "minor tweaks to interpreter.\nThis approach can work across various different VMs, and can handle\nsmart contracts running on VM Awhich then spawn smart contracts running\non VM B, and more complex scenarios.\nFor most smart contracts, whose output is drawn from a reasonably large\nspace of possibilities, a choice like N=3 will reduce the odds that all N\nexecutor nodes fallaciously return the same result very very close to zero.\nRoughly speaking the result of this approach is that to run a smart con-\ntract in a provably almost-surely-correct way only takes say 3 x(plus some\nnot-that-huge overhead) as much compute effort as running comparable code\non just one machine. Which is a very modest slowdown compared to run-\nning smart contracts on existing ledger based blockchain networks (orders of"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:31:3

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 31, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:31:3", "chroma:document": "ning smart contracts on existing ledger based blockchain networks (orders of\nmagnitude less). While maintinin the same security guarantees and arguably\nmore.\nIn cases where aren\u2019t too many operating devices able to run the smart\ncontract, or where the smart contract is being validated only within a rela-\ntively small ring of participants, and therefore guessing who\u2019s being selected\nbecomes easier by attackers, additional obfuscation mechanisms may be valu-\n32"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:32:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 32, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:32:0", "chroma:document": "able. To provide extra defense against collusion by the pseudorandomly cho-\nsen nodes, one can transform the functions (contracts) being sent to each\nnode so that each node is executing a slightly different computation, and the\nnodes then have no tractable way to connect their assignments with those\nof other nodes. This additional complexity, however, is not necessary if one\nis dealing with small, quickly-executed smart contracts that can easily be\nrun on any pseudorandomly selected machine in a large ring of HyperCycle\nnodes.\n3.3.2 Smart Contract DSLs for HyperCycle: A Path to Broad\nUsability\nWhile all efforts will still be to incorporate EVM for compatibility reasons,\npython and the MeTTa language\u2019s semantics are highly advantageous for"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:32:1

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 32, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:32:1", "chroma:document": "python and the MeTTa language\u2019s semantics are highly advantageous for\nsmart contracts involving AI operations, as many of these can be concisely\nexpressed using MeTTa\u2019s abstract type constructs. MeTTa\u2019s compilation\nto rholang provides a highly concurrency-friendly and tokenomics-infused\nimplementation for MeTTa contracts. MeTTa\u2019s interoperablity with other\nsmart contract languages from other chains will allow practical applications\nto be glued together fairly quickly in many cases. However, equally important\nfor the future of HyperCycle are MeTTa\u2019s facilities for creating DSLs.\nSmart contract coding can be a subtle and sensitive matter, and it\u2019s\npreferable to have a framework in which most application developers don\u2019t"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:32:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 32, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:32:2", "chroma:document": "preferable to have a framework in which most application developers don\u2019t\nneed to think much about the nitty-gritty of smart contracts and can focus on\ntheir application. In the Ethereum world, the closest thing that has emerged\nto usability so far is a \u201dcut and paste culture\u201d in which most developers\nwork by copying others\u2019 Solidity code and tweaking it to suit their needs.\nHowever, this is not how things work in a software ecosystem powered by\ntools with reasonable composability. MeTTa provides powerful composabil-\nity but has the disadvantage of being relatively opaque to most developers\nwithout background in functional programming, process calculus or related\nmathematics.\nAn alternate approach to achieving usable smart contract development"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:32:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 32, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:32:3", "chroma:document": "An alternate approach to achieving usable smart contract development\nis the creation of domain specific languages presenting developers with only\nthose blockchain-related functions they really need in their domain of ap-\nplication. Cardano\u2019s Marlowe DSL-for-decentralized-finance pioneered this\napproach [SNST20], but also highlights some relevant caveats. While Mar-\nlowe is extremely elegant it seems not to provide out-of-the-box the most\n33"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:33:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 33, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:33:0", "chroma:document": "convenient approach for achieving scalability in modern DeFi applications.\nSo, for example, a Cardano DeFi application like SundaeSwap ended up\ndirectly leveraging Plutus (which is generic) rather than Marlowe (which is\nfinance-specific but not tailored for the particular sort of financial operations\nat the heart of SundaeSwap) [Sun]. One thing this illustrates is the need\nfor a framework enabling rapid updating and modification of DSLs within\nan extremely flexible base language. This course was not open for Marlowe\nwhen it was initially created as its launch preceded that of Plutus, but it is\nimportant to consider in a HyperCycle context.\nMeTTa provides an elegant approach for the creation of application and\nalgorithm specific DSLs, similar to the use of Idris for DSL creation [Bra13]"}


created_at: 2024-06-29 05:41:35
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:33:1

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 33, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:33:1", "chroma:document": "algorithm specific DSLs, similar to the use of Idris for DSL creation [Bra13]\n[Bra20] but with greater flexibility. The simple and generic nature of the\nprocess of DSL creation in MeTTa opens the door for tools that auto-generate\nlow-code or no-code UI frameworks for MeTTa-specified DSLs. This arguably\nis the most (and perhaps only) viable approach for making smart contract\ndevelopment adequately simple and usable for the average product developer\nwithout sacrificing security, robustness or capability.\nDepending on the DSL, in many cases it may be possible to create low-\ncode or no-code frameworks that implicitly conduct authoring in the DSL.\nThe abstract nature of MeTTa lends itself well to the templatization of this"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:33:2

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 33, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:33:2", "chroma:document": "The abstract nature of MeTTa lends itself well to the templatization of this\nsort of process, e.g. to the creation of general-purpose tools for mapping\nsufficiently simple application-specific DSLs into corresponding low/no-code\nframeworks. In this approach the only people who need to deal with the\ntechnicalities of MeTTa are those who are writing DSLs for new domains, or\ncreating custom applications sufficiently recondite that they don\u2019t correspond\nto any of the DSLs in the library.\n4 A Few Promising Early Applications\nWe now give a few brief notes about a few key HyperCycle application areas\nthat we are eager to explore once a sufficiently mature version of HyperCycle\nis available. We note this is somewhat of a haphazard sampling of the pos-"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:33:3

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 33, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:33:3", "chroma:document": "is available. We note this is somewhat of a haphazard sampling of the pos-\nsibility space, reflecting our current landscape of pursuits which may omit\nprojects and verticals that will be at the top of our minds by the time Hy-\nperCycle is ready for scalable usage. Also, of course, much of the beauty of\nlaunching an extremely flexible and general purpose platform is that others\ncan then utilize it in ways and domains one never conceived.\n34"}


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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 34, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:34:0", "chroma:document": "4.1 Swarm AI: Evolutionary Learning, Algorithmic Chem-\nistry, Ensemble ML\nOne major use-case driving the design of HyperCycle is the desire to run\npopulation-based AI methods on-chain \u2013 for instance, run a genetic algo-\nrithm where each population member is an on-chain agent and crossover and\nmutation are on-chain transactions. \u201dAlgorithmic chemistry\u201d applications as\nexplored in e.g. [Goe16] [Bul20]present similar issues and opportunities.\nThere is also great potential in the area of decentralized ensemble machine\nlearning \u2013 building model ensembles confronting a common data-analysis\nor reinforcement learning problem, in a setting where each model in the\nensemble may be owned and managed by a different party. To carry this"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:34:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 34, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:34:1", "chroma:document": "ensemble may be owned and managed by a different party. To carry this\nout in a transparent and fully decentralized way, one would like as much\nas possible of the process of passing around datasets, assessing and merging\nresults, running tests and so forth to be done on-chain.\nThese are applications where for almost all problems a lightweight consen-\nsus approach will be preferable \u2013 maximal real-time security is not key. If an\nattack or failure causes some interim AI results to be lost, this is unfortunate\nbut rarely tragic. DSLs here will serve as AI algorithm configuration lan-\nguages enabling developers to quickly experiment with different approaches\non different datasets in different applications.\n4.2 Ratings and Rewards in Online Networks"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:34:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 34, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:34:2", "chroma:document": "4.2 Ratings and Rewards in Online Networks\nManaging ratings and rewards in online networks is another case where\nlightweight consensus is desirable. In this case it is important that attacks or\nsystem failures not cause participants\u2019 ratings or rewards to get permanently\nlost, but it\u2019s OK if making participants whole after infrequent unfortunate\nincidents takes some time and depends on a slow-paced conflict resolution\nmechanism. Having maximally strong real-time security guarantees is less\nimportant than having a system with very low cost of operation, and which\nis quick and effective under all normal circumstances. It is also easy to\nenvision a low or no code DSL enabling developers to quickly specify the\ncustomization of the weighted liquid rank reputation framework and various"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:34:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 34, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:34:3", "chroma:document": "customization of the weighted liquid rank reputation framework and various\nrelated reward tokens in their applications.\n35"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:35:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 35, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:35:0", "chroma:document": "4.3 Decentralizing Payments and Processing Power\nTwo somewhat subtler use-cases are decentralized payments processing, and\nthe tokenized decentralized management of processing power done in the\nNuNet platform5.\nIn each of these cases, one has a spectrum of transactions, some of which\nare small in magnitude (micropayments on an online media platform, uti-\nlization of small amounts of background processing on an individual\u2019s smart-\nphone) and naturally match with a lightweight consensus protocol, others\nof which are more serious (larger payments, utilization of large chunks of\nprocessing such as supercomputer time or significant portions of a corpo-\nrate computing network) and would merit the added protocol-level security\nguarantees of something like HS enhanced consensus."}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:35:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 35, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:35:1", "chroma:document": "guarantees of something like HS enhanced consensus.\nThese applications would appear to benefit from multiple rings with dif-\nferent consensus mechanisms, and the ability for some agents to participate\nin transactions in both rings. So for instance if I pay a few US cents equiv-\nalent to a blogger based on the time I spent reading their article, this goes\nthrough a lightweight consensus based ring; but if I pay a larger chunk of\nfunds to an AI server farm to train a big ML model, this should go through\nan HS enhanced ring.\nDSL-wise, the Marlowe framework would seem to have many positive\nlessons to teach here, and one envisions a Marlowe-like language embedded\nin Plutus via MeTTa leveraging unique HyperCycle transactional features\nbased on TODA and reputation."}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:35:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 35, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:35:2", "chroma:document": "based on TODA and reputation.\n4.4 Adaptively Blending Public and Private Chains\nTODAQ, one of the companies in the TODA ecosystem, has pioneered the use\nof centralized TODA rings for enterprise applications. A private TODA ring\nof this nature can interoperate naturally with public TODA rings running on\ndecentralized consensus mechanisms like TODA/IP, due to the elastic nature\nof the TODA design in which the sovereignty of the individual data file is\nkey.\nA similar approach can be taken for enterprise deployments of Hyper-\nCycle. In fact there are multiple approaches to be pursued for different\nenterprise contexts.\n5http://nunet.io\n36"}


created_at: 2024-06-29 05:41:35
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:36:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 36, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:36:0", "chroma:document": "One strategy is to develop a HyperCycle ring with outright centralized\ncontrol and data management: Basically going beyond the HS enhanced\nconsensus and just using a centralized database as a HyperCycle ledger, with\nonly as much replication as is needed for practical efficiency (no need to use\nreplication to assure decentralized control in this case).\nAnother strategy, opposite in some ways, is to recognize that within the\nbounds of a particular enterprise, it may be that lightweight consensus mech-\nanisms can safely be used, because it\u2019s safe to assume that all the agents op-\nerating in the HyperCycle ring are going to be honest and cooperative with\nrespect to each other. This approach can enable greater efficiency within the"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:36:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 36, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:36:1", "chroma:document": "respect to each other. This approach can enable greater efficiency within the\nwalled garden of an enterprise than can ever be possible in the \u201dwild west\u201d\nof a public blockchain.\nThe choice between these two strategies depends on the degree of cen-\ntralization and internal trust in the particular enterprise in question. The\nHyperCycle infrastructure supports a variety of custom possibilities beyond\nthe two specific extreme strategies indicated here. Broadly, the point to be\nmade in this regard is that the flexibility to define rings with custom con-\nsensus mechanisms, and then transact agents and files across these rings,\nallows hybrid public/private blockchain deployments to be created to meet\nthe needs of essentially any enterprise. After an initial batch of enterprise"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:36:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 36, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:36:2", "chroma:document": "the needs of essentially any enterprise. After an initial batch of enterprise\ndeployments has been successfully executed, as a side-effect set of template\n\u201denterprise consensus mechanisms\u201d will have been created, which will handle\nthe vast majority of enterprise situations.\n5 Conclusion: Catalyzing the Future Of AI\nand Blockchain Ecosystem\nBlockchain technologies are still in their early days, so it would be a mistake\nto assume that the technologies that have become popular today represent\nmature solutions that compellingly solve the problems they address. Some\nleading blockchain frameworks do embody algorithms, structures and pro-\ncesses that appear solid enough to persist into the decentralized networks\nof the future. However, there are also major aspects of current blockchains,"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:36:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 36, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:36:3", "chroma:document": "of the future. However, there are also major aspects of current blockchains,\nsuch as the default use of distributed replicated ledgers and the reliance on\ncrude consensus mechanisms like Proof of Stake, that appear to us to owe\ntheir current dominance largely to historical accident rather than fundamen-\n37"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:37:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 37, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:37:0", "chroma:document": "tal superiority or appropriateness.\nWe have articulated here a novel blockchain design, HyperCycle, which\nwe believe has the power and the flexible customizability to serve as the basis\nfor the decentralized networks of the future, at least in the particular arena of\nlarge-scale systems of software agents providing microservices to themselves\nand external consumers.\nWe refer to HyperCycle as \u201dmultilayer\u201d, \u201dlayer-fluid\u201d or \u201dLayer 0++\u201d\nbecause it has the flexibility to service the entire ecosystem of microservices\n(focusing on though not restricted to AI) via adopting whatever role is ap-\npropriate in a given context: low-level secure communication protocol, base\nblockchain, sidechain, etc.. The goal here is to present the highest security"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:37:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 37, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:37:1", "chroma:document": "blockchain, sidechain, etc.. The goal here is to present the highest security\nat the lowest possible network level, thus minimizing frictional cost and en-\nabling unprecedented capabilities. Via leveraging cutting-edge cryptography\ntogether with essential ideas, structures and processes from the TODA/IP\nblockchain and the Proof of Reputation and OpenCog Hyperon frameworks,\nwe believe a radically different future for blockchain technology can be cre-\nated with relatively modest (though still far from trivial) engineering effort.\nOur most immediate use-cases for HyperCycle are the SingularityNET\ndecentralized AI network, and the various spinoff projects emergent from the\nSingularityNET ecosystem (e.g. NuNet for decentralized processing power,"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:37:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 37, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:37:2", "chroma:document": "SingularityNET ecosystem (e.g. NuNet for decentralized processing power,\nMindplex for decentralized media, Rejuve for decentralized medical data and\nresearch, SophiaVerse for decentralized metaverses, SingularityDAO for DeFi,\netc.). However, HyperCycle\u2019s scope extends far beyond these particular ap-\nplication and we foresee a very broad range of utilization once HyperCycle\u2019s\nengineering and deployment are complete. Indeed we believe HyperCycle\ntechnology has the potential to play a key role in projecting blockchain be-\nyond the niches it currently serves and into the dominant role in the global\ntech and financial ecosystem that various pundits have long foreseen.\nAppendix: Notes on Deep Cardano Integra-\ntion6\nHyperCycle involves a unique combination of mechanisms enabling efficient,"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:37:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 37, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:37:3", "chroma:document": "HyperCycle involves a unique combination of mechanisms enabling efficient,\nlightweight decentralized coordination of software processes; at its foundation\nit is not dependent nor piggybacking on other blockchains that it interacts\n6This section largely contains information drawn from [Fou21] and [CCF+20]\n38"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:38:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 38, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:38:0", "chroma:document": "with, as it already has an extremely solid foundation for all aspects of se-\ncure, decentralized software coordination. However, integrations can provide\ncertain functionality that may very well be the most effective way to make\nvarious applications work at a particular point in time, giving available tool-\ning and user bases.\nHyperCycle is capable of robust cross-chain interaction, and in particular\nit\u2019s possible to run a variety of virtual machines within the HyperCycle con-\ntext, enabling accelerated execution of smart contract languages associated\nwith a variety of different blockchains. The tightness of the coupling possible\nbetween HyperCycle and another chain, however, depends on the particular\nnature of the other chain. One chain with which especially tight coupling"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:38:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 38, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:38:1", "chroma:document": "nature of the other chain. One chain with which especially tight coupling\nappears possible is Cardano.\nCardano\u2019s smart contract language, Plutus, is a pure functional language,\nwhich makes it relatively straightforward to tightly connect Plutus contracts\nand HyperCycle MeTTa contracts, having them share state and exchange\ninformation while in the course of executing. At a technical level this is\nachieved via connecting the Rust MeTTa runtime with the Haskell PLutus\nruntime using foreign function interfaces. The lack of reliance on imperative\nprogramming in both MeTTa and Plutus makes it generally straightforward\nfor contracts in either language to flexibly and rapidly subcontract work to\ncontracts in the other language.\nThe potential for close linkage between HyperCycle and Cardano arises"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:38:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 38, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:38:2", "chroma:document": "The potential for close linkage between HyperCycle and Cardano arises\nat a nore fundamental level than smart contracts, though, and is rooted in\nCardano\u2019s Extended UTXO transaction model, which enables shared state\nbetween Cardano and other chains using the (still in development at time of\nwriting) Hydra protocol. \u2019 HyperCycle can also leverage the UTxO model\nsuch as Cardano blockchain and also the Plutus smart contract language and\nnumerous other related features, however substituting a different consensus\nmechanism and a different system for data management.\nThe Extended UTXO Transaction Model\nCardano\u2019s Extended UTxO (EUTxO) model [Fou21] extends the standard\nUTxO model underlying Bitcoin in a manner explicitly designed to support"}


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topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:38:3

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 38, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:38:3", "chroma:document": "UTxO model underlying Bitcoin in a manner explicitly designed to support\nsmart contracts operating according to functional programming principles.\nTransactions in a UTxO ledger contain a set of inputs and outputs, where\noutputs lock an amount of cryptocurrency, such that only authorized inputs\nof subsequent transactions can connect and consume those funds. The set\n39"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:39:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 39, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:39:0", "chroma:document": "of outputs that are dangling (unconnected) pending validation, at any given\npoint in time, are the unspent transaction outputs (UTxOs) . In addition to\nthe locked currency, each output also comes with a validator predicate; and\neach input comes with a redeemer value. To determine whether a given input\nof the currently validated transaction is permitted to connect to a currently\ndangling and unspent output, the software determines whether the validator\npredicate the output applies to the redeemer.\nThe EUTxO model extends the UTxO model in two ways:\n\u2022Instead of restricting the validator/redeemer mechanism to dealing with\nsignatures, addresses in the EUTxO model can contain arbitrary logic\nin the form of scripts. When a node validates a transaction, the trans-"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:39:1

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 39, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:39:1", "chroma:document": "in the form of scripts. When a node validates a transaction, the trans-\naction will look up the script provided by the output\u2019s address and will\nexecute the script if the transaction is allowed to use the output as an\ninput.\n\u2022Outputs in EUTxO can carry essentially arbitrary data in addition\nto an address and value. This allows scripts to carry, for instance,\ninformation regarding the state of long-running smart contracts.\nThe script associated with a transaction can access the data being carried\nby the output, the transaction being validated, and some additional pieces\nof data called redeemers, which the transaction provides for every input;\nbased on this it can carry out complex logic to determine whether the given"}


created_at: 2024-06-29 05:41:35
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:39:2

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 39, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:39:2", "chroma:document": "based on this it can carry out complex logic to determine whether the given\ninput is permissible or not. The fact that the validator can inspect the entire\nvalidated transaction enables validators to enforce that contract invariants\nare maintained across entire chains of transactions.\nKey to the EUTxO model is that the success or failure of transaction\nvalidation depends only on the transaction itself and its inputs, and not\non anything else on the blockchain. This works naturally with the \u201dpure\nfunctional programming\u201d nature of the Haskell language which underlies the\nPlutus smart contract system; in a pure functional language like Haskell,\ncomputation is broken down into functions whose outputs depend only on\ntheir inputs, without side-effect as are rampant in imperative language. The"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:40:0

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 40, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:40:0", "chroma:document": "to the blockchain. A transaction can still fail if some other transaction con-\ncurrently consumes an input that the transaction is expecting; however, if all\ninputs are still present throughout the period of execution, the transaction\nis mathematically guaranteed to succeed.\nThe differences between this UTxO-based model and the account-based\nmodels used in Ethereum and other similarly structured blockchains necessi-\ntate significantly different design patterns on the DApp level. EUTxO makes\nit much more tractable and straightforward to create DApps with provable\nguarantees regarding their behavior; and EUTxO also provides a superior\nfoundation for powerful extensions to the core Cardano model like Hyper-\nCycle. However, it does require a way of thinking about transactions and"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:40:1

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 40, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:40:1", "chroma:document": "Cycle. However, it does require a way of thinking about transactions and\ndata representation that feels initially unfamiliar to developers who got their\nstart with account-based blockchains. EUTxO smart contracts can be for-\nmally modeled using a type of state machine called a constraint emitting\nmachine (CEM). Based on Mealy machines, each one consists of a set of\nstates Sc, a set of inputs i=Ic, a predicate final c:Sc\u2192Bool identifying\nfinal states, and a step relation s\u2192(s,tx), which takes a state son an input\nito a successor state s? under the requirements that the constraints tx are\nsatisfied. Cardano implements CEMs on a EUTxO ledger (the mainchain)\nby representing a sequence of CEM states as a sequence of transactions, each"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:40:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 40, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:40:2", "chroma:document": "by representing a sequence of CEM states as a sequence of transactions, each\nof which has a state-machine input icand a state-machine output oc, where\nthe latter is locked by a validator ? c, implementing the step relation (excep-\ntions being the initial and final state, which have no state-machine input or\noutput respectively).\nHydra: Cardano\u2019s Interoperability Solution\n7\nHydra [CCF+20] is the algorithmic and software framework enabling\nthe Cardano mainchain to communicate efficiently and elegantly with other\nblockchains. It supports implementation of what would informally be called\nsidechains, and also of proxies to other fully autonomous and separate blockchains.\nWhen Hydra is used to connect to another blockchain network, the \u201dhead"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:40:3

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 40, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:40:3", "chroma:document": "When Hydra is used to connect to another blockchain network, the \u201dhead\nparties\u201d are a distinguished set of high-performance and high-availability par-\nticipants in this other network. \u201dTail parties\u201d are other participants in this\nother network, who may be unreliable in terms of being online at any given\n7This section draws mainly on [CCF+20] and [Kia20]\n41"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:41:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 41, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:41:0", "chroma:document": "time or being computationally capable of carrying out operations critical for\ndecentralized network functioning. The Hydra architecture can be divided\naccordingly into the head protocol, the tail protocol, and the cross-head-and-\ntail communication protocol. These components are supported by additional\nunderlying protocols for routing, reconfiguration and virtualization.\nThe head protocol is the only portion of Hydra currently developed to a\nstate of reasonable maturity. It allows the heads parties in a blockchain net-\nwork connected to the Cardano mainchain to rapidly process large numbers of\ntransactions with minimal storage requirements by way of a multiparty state\nchannel. The tail protocol, which has not yet been publicly described in a de-"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:41:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 41, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:41:1", "chroma:document": "channel. The tail protocol, which has not yet been publicly described in a de-\ntailed way, enables the network heads to provide scalability for large numbers\nof additional network participants who may use the system from low-power\ndevices, such as mobile phones, and who may be offline for extended periods\nof time. The cross-head-and-tail communication protocol leverages virtual-\nization to allow heads and tails to communicate without going through the\nmedium of the Cardano mainchain.\nHydra\u2019s state channels are isomorphic to Cardano in the sense that they\nmake use of the same transaction format and contract code as the underlying\nCardano blockchain. This means smart contracts can be directly moved\nback and forth between Hydra state channels and the Cardano blockchain"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:41:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 41, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:41:2", "chroma:document": "back and forth between Hydra state channels and the Cardano blockchain\n\u2013 so that Hydra state channels effectively yield parallel, off-chain siblings of\ntransactions on the Cardano blockchain.\nHead Protocol Transactions\nIn terms of transaction management, what is happening behind the scenes in\nusing the Hydra head protocol to connect a network to Cardano mainchain\nis: The head parties involved cooperate to commit a set of UTxOs comprising\nthe initial head state, which these head parties then evolve by handling smart\ncontracts and transactions among themselves without mainchain interaction.\nTransaction validation, including script execution, proceeds according to the\nexact same rules as onchain, leveraging the exact same validation code. In"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:41:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 41, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:41:3", "chroma:document": "exact same rules as onchain, leveraging the exact same validation code. In\ncase of disputes or in case some party wishes to terminate the offchain pro-\ntocol, the head parties decommit the current state of the head back to the\nblockchain - which then necessarily results in an updated blockchain state\nthat is consistent with the offchain protocol evolution on the initially commit-\nted UTxO set. The mainchain doesn\u2019t need to know the detailed transaction\nhistory that led to the committed state, it only. needs to know the commit-\n42"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:42:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 42, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:42:0", "chroma:document": "ted state. Further, the decommit process is designed such that, when the\nlatest state in the head is very large, the head state can be decommitted via\nparallel decommitment of small chunks. UTxOs can also be added to and\nremoved from a running head without closing it.\nTo get a Hydra head started, any party may take the role of an initiator\nand ask a set of parties to participate. Each party then establishes pairwise\nauthenticated channels to all other parties in the head. This public-key ma-\nterial is used both for the authentication of head-related onchain transactions\nthat are restricted to head members and for multisignature-based event con-\nfirmation in the head. The initiator then submits an initial transaction to the"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:42:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 42, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:42:1", "chroma:document": "firmation in the head. The initiator then submits an initial transaction to the\nmainchain that contains the head parameters. This automatically initializes\na state machine for the head instance that manages the transfer of UTxOs\nbetween mainchain and head.. Each head member then attaches a commit\ntransaction, which locks on the mainchain the UTxOs that the party wants\nto commit to the head.\nFrom this point on, the head enters into an open state The head members\ncontinue running the offchain head protocol, which evolves the initial UTxO\nset independently of the mainchain. In the case that some head members\nfail to post a commit transaction, the head can be aborted by going directly\nfrom initial to final.\nOnce a head is in the closed state, the underlying state machine grants"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:42:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 42, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:42:2", "chroma:document": "Once a head is in the closed state, the underlying state machine grants\nparties a contestation period, during which each head party has one chance\nto contest the closure by providing the certificate for a newer head UTxO\nset. Contesting leads back to the state closed. After the contestation period\nhas elapsed, the state machine may proceed to the final state.\nWhat\u2019s happening on the mainchain while a Hydra head is evolving of-\nfchain is that the mainchain Hydra protocol:\n1. Upon head initiation, locks the mainchain UTxOs committed to the\nhead\n2. Keeps these locks and waits while the head is active\n3. Facilitates the settlement of the final head state back to the mainchain\nafter the head is closed.\nThe end result is a dynamic that replaces the initial head UTxO set by the"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:42:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 42, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:42:3", "chroma:document": "The end result is a dynamic that replaces the initial head UTxO set by the\nfinal head UTxO set on the mainchain in a manner that respects but does\nnot persist the complete set of head transactions.\n43"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:43:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 43, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:43:0", "chroma:document": "Some careful record-keeping is required here behind the scenes, in order\nto ensure that each head member posts exactly one commit transaction and\nthat the open transaction faithfully collects all commit transactions. To en-\nable this, a single non-fungible \u201dparticipation token\u201d is issued to each head\nmember, connoting a capability and obligation to participate in the head\nprotocol.. This token must flow through the commit transaction of the re-\nspective head member and to be valid the open transaction must collect the\nfull set of participation tokens.\nHydra Tail Protocol\nThe Hydra head protocol has many powerful use-cases on its own. For in-\nstance, a natural strategy for porting the SingularityNET marketplace to\nCardano is to map the multiparty escrow contract in the Ethereum-based"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:43:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 43, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:43:1", "chroma:document": "Cardano is to map the multiparty escrow contract in the Ethereum-based\nSingularityNET implementation into a Hydra head. The SingularityNET\nagents participating in the multiparty token and API access transaction are\nthen the head parties and the head instance remains active until the escrow\nis cleared and the multiparty transaction is done.\nHowever, if one wants to associate a sizeable external network with Car-\ndano using Hydra as the interface in a \u201dsidechain\u201d like design pattern, it\u2019s\nnecessary to also handle the situation where there are many participants in\nthe HyperCycle \u201dsidechain\u201d that are intermittently online or have unreliable\nor minimal computational power. This situation leads to a variety of security\nchallenges, as there are various scams a network participant can carry out"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:43:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 43, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:43:2", "chroma:document": "challenges, as there are various scams a network participant can carry out\nmore effectively when not required to be consistently online to participate in\ntransactions. The Hydra Tail protocol [Kia20] has two key mechanisms for\naddressing this:\n\u2022requiring participants to put collateral on the mainchain, which will be\nlost if fraud is attempted\n\u2022instantiating a Challenge-Response-Protocol on the mainchain, with\nwhich clients can dispute claims by any participant\nThis is one aspect of the default Hydra tail protocol that will likely be heav-\nily customized for HyperCycle, via leveraging Proof of Reputation and TO-\nDA/IP mechanisms. Given HyperCycle\u2019s particular requirements and the\nunderspecification of the Hydra tail protocol, the approach we are planning"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:43:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 43, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:43:3", "chroma:document": "underspecification of the Hydra tail protocol, the approach we are planning\nto take in the HyperCycle design is to create a custom version of the Hydra\n44"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:44:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 44, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:44:0", "chroma:document": "tail protocol that suits HyperCycle\u2019s needs, incorporating mechanisms and\nideas beyond those that have previously occurred in the Cardano sphere.\nReferences\n[AK21] Oladotun Aluko and Anton Kolonin. Proof-of-reputation: An\nalternative consensus mechanism for blockchain systems. Inter-\nnational Journal of Network Security & Its Applications (IJNSA)\nVol, 13, 2021.\n[BGT21] Alexey Potapov Ben Goertzel and Hyperon Team. Opencog hy-\nperon. 2021. https://wiki.opencog.org/w/Hyperon .\n[Bra13] Edwin Brady. The idris programming language. In Central Eu-\nropean Functional Programming School , pages 115\u2013186. Springer,\n2013.\n[Bra20] Edwin Brady. Idris 2: Quantitative type theory in action. Techni-\ncal report, Technical Report. University of St Andrews, Scotland,\nUK. https://www. type ?, 2020."}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:44:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 44, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:44:1", "chroma:document": "UK. https://www. type ?, 2020.\n[Bul20] Marius Buliga. Artificial chemistry experiments with chem-\nlambda, lambda calculus, interaction combinators. arXiv preprint\narXiv:2003.14332 , 2020.\n[CCF+20] Manuel MT Chakravarty, Sandro Coretti, Matthias Fitzi, Peter\nGazi, Philipp Kant, Aggelos Kiayias, and Alexander Russell. Hy-\ndra: Fast isomorphic state channels. IACR Cryptol. ePrint Arch. ,\n2020:299, 2020.\n[CDD+19] Shan Chen, Weiguo Dai, Yuanxi Dai, Hao Fu, Yang Gao, Jianqi\nGuo, Haoqing He, and Yuhong Liu. Thinkey: A scalable\nblockchain architecture. arXiv preprint arXiv:1904.04560 , 2019.\n[Fou21] Cardano Foundation. Understanding the extended utxo model.\n2021. https://docs.cardano.org/plutus/eutxo-explainer .\n[GGH+17] Ben Goertzel, Simone Giacomelli, David Hanson, Cassio Pen-"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:44:2

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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:45:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 45, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:45:0", "chroma:document": "open market and inter-network for ais. Thoughts, Theories Stud.\nArtif. Intell. Res. , 2017.\n[GGT19] Adam Gravitis, Norman Goh, and Dann Toliver. Toda primer.\n2019. https://engineering.todaq.net/TODA_Tech_Primer_\nv1.0.pdf .\n[Goe16] Ben Goertzel. Cogistry: Accelerating al-\ngorithmic chemistry via cognitive synergy.\n2016. https://blog.opencog.org/2016/10/25/\ncogistry-accelerating-algorithmic-chemistry-via-cognitive-synergy/ .\n[Goe19] Ben Goertzel. Singularitynet whitepaper 2.0. 2019. https://\npublic.singularitynet.io/whitepaper.pdf .\n[KGDI18] Anton Kolonin, Ben Goertzel, Deborah Duong, and Matt Ikle.\nA reputation system for artificial societies. arXiv preprint\narXiv:1806.07342 , 2018.\n[KGP+19] Anton Kolonin, Ben Goertzel, Cassio Pennachin, Deborah"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:45:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 45, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:45:1", "chroma:document": "[KGP+19] Anton Kolonin, Ben Goertzel, Cassio Pennachin, Deborah\nDuong, Matt Ikl\u00b4 e, Nejc Znidar, and Marco Argentieri. A rep-\nutation system for multi-agent marketplaces. arXiv preprint\narXiv:1905.08036 , 2019.\n[Kia20] Aggelos Kiayias. Enter the hydra: scaling dis-\ntributed ledgers, the evidence-based way. 2020.\nhttps://iohk.io/en/blog/posts/2020/03/26/\nenter-the-hydra-scaling-distributed-ledgers-the-evidence-based-way/ .\n[MG19] Gabriel Axel Montes and Ben Goertzel. Distributed, decentral-\nized, and democratized artificial intelligence. Technological Fore-\ncasting and Social Change , 141:354\u2013358, 2019.\n[Pot21] Alexey Potapov. Metta language specification. 2021. https:\n//wiki.opencog.org/w/Hyperon .\n[SNST20] Pablo Lamela Seijas, Alexander Nemish, David Smith, and Simon"}


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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:45:2

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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle.pdf:46:0

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf", "page": 46, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle.pdf:46:0", "chroma:document": "[ST19] Toufi Saliba and Dann Toliver. Toda/ip protocol 2.3: An internet\ncommunication protocol. 2019. https://github.com/Toufi/\nWhitepaper/blob/master/TodaIP_ProtocolV2.3.pdf .\n[Sun] Sundaeswap fundamentals, author=Pi Lanningham and Sun-\ndaeSwap Team, note = https://sundaeswap.finance/papers/\nSundaeSwap-2021-06-01-Fundamentals.pdf , year=2022.\n47"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:0:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 0, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:0:0", "chroma:document": "HyperCycle\nCore\nv0.92\nAuthored\nby\nToufi\nSaliba\nand\nRobert\nMoir\nPhD\nwith\ncollaboration\nby\nDann\nToliver,\nGreg\nColvin\nPhD,\nErnie\nCasilla,\nand\nBarry\nRowe\nPhD\nHyperCycle\nprovides\nthe\nessential\nmissing\ncomponents\nrequired\nfor \nthe\nInternet\nof\nAI,\nwhere\nnetworked\nAI\nagents\ncan\ncooperate\nand \ncompete\nat\nthe\nmicro\nlevel\nin\nways\nthat\nbring\nabout\nemergent \nintelligence\nat\nthe\nmacro\nlevel.\nFacilitating\ncooperation\nbetween\nmicro-level\ncomputational\nmodules \nrequires\nsecure\npeer-to-peer\ncontracting\nand\nsubcontracting.\nThis \nallows\nmodules\nto\ncompensate\neach\nother\nin\nmilliseconds\nfor \ncomputations\ndue\nto\nnot\nrelying\non\nthird-party\nintermediaries, \nenabling\nthe\ndirect\ntransmission\nof\nextremely\nsmall\nvalues.\nThis\nvision\nof\nan\nInternet\nof\nAI\nhas\nbeen\na\nlongstanding\ngoal\nfor \ncomputer\nand\nAI\nscientists."}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:0:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 0, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:0:1", "chroma:document": "an\nInternet\nof\nAI\nhas\nbeen\na\nlongstanding\ngoal\nfor \ncomputer\nand\nAI\nscientists.\nAdvancements\nin\nnetwork\nspeed, \ncryptography,\nand\nrelated\nfields\nhave\nled\nto\nthe\ndevelopment\nof\na \nsystem\nthat\nenables\ndirect,\npeer-to-peer\ntransactions,\nleading\nto \nsubstantial\nreductions\nin\ntime\nand\nenergy\ncosts.\n1"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:1:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 1, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:1:0", "chroma:document": "1\nIntroduction\n.............................................................................................................................\n2\n2\nHigh\nLevel\nView\nof\nthe\nHyperCycle\nNetwork\n........................................................................\n3\n2.1\nThe\nTODA\nProtocols\nand\nEarth\n64\nSato-Servers\n...............................................................\n3\n2.2\nThe\nHyperCycle\nVirtual\nMachine\n.....................................................................................\n4\n2.3\nSecurely\nGetting\nFunds\ninto\nthe\nHyperCycle\nNetwork\n.....................................................\n5\n2.4\nEnabling\nCooperative\nAI\nfor\nBeneficial\nEmergence\n.........................................................\n6\n2.5\nCompetitive\nAI\nfor\nProgressive\nEvolution"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:1:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 1, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:1:1", "chroma:document": "6\n2.5\nCompetitive\nAI\nfor\nProgressive\nEvolution\n........................................................................\n7\n3\nHyperCycle\nComputation\nNodes\n...........................................................................................\n8\n3.1\nNode\nStructure\nand\nFunction\n...........................................................................................\n8\n3.2\nStarting\nOperation\nof\na\nNode\n.........................................................................................\n10\n3.3\nRevenue\nFrom\na\nNode\n/\nIncentive\nModel\n......................................................................\n11\n3.4\nNode\nScore\n=\nUptime\n\u00b7\nComputation\n\u00b7\nReputation\n........................................................\n11\n3.5\nNode\nSplitting"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:1:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 1, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:1:2", "chroma:document": "........................................................\n11\n3.5\nNode\nSplitting\n................................................................................................................\n12\n4\nHyperCycle\nTransactions\n.....................................................................................................\n14\n5\nConclusion\n............................................................................................................................\n16\n6\nFAQ\n........................................................................................................................................\n17\nA\nAppendix\n...............................................................................................................................\n19\nA.1\nVarious\nKinds\nand\nForms\nof\nHyperCycle"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:1:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 1, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:1:3", "chroma:document": "19\nA.1\nVarious\nKinds\nand\nForms\nof\nHyperCycle\nTokens\n..........................................................\n19\nA.2\nProvenance\nSystem\n......................................................................................................\n20\nA.3\nHCN\nVirtual\nMachine\n.....................................................................................................\n21\nA.4\nHCN\nTransaction\nMachines\n...........................................................................................\n22\nA.5\nHCN\nAI\nMachine\n............................................................................................................\n23\nA.6\nHCN\nNode\nManager\n......................................................................................................\n23\nA.7\nHCN\nMerklizer"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:1:4

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 1, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:1:4", "chroma:document": "23\nA.7\nHCN\nMerklizer\n...............................................................................................................\n24\nGlossary\n.....................................................................................................................................\n26\n2"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:2:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 2, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:2:0", "chroma:document": "1\nIntroduction\nComputational\ntheories\nof\nthe\nmind\nemerged\nin\nthe\nmiddle\ndecades\nof\nthe\n20th\ncentury.\nWe\nbuild\non\nthese,\nmodeling\nthe\nmind\nas\na\nsecure,\ndistributed,\nscalable\nand\nfully\ndecentralized\nnetwork\nof\ninteracting\nnodes.\nThe\nnodes\nof\nthis\nnetwork\nasynchronously\nexchange\ncomputations,\nlaying\nthe\ngroundwork\nfor\ncomplex\nforms\nof\nthought\nand\nawareness.\nBuilding\nthe\nInternet\nof\nAI\non\na\nglobal\nscale\nrequires\nsolving\nsome\nof\nthe\nhardest\nproblems\nin\nparallel\nand\ndistributed\ncomputing\nin\na\nway\nthat\nensures\nthat\nthe\nsystem\nwill\nbe\nsecure,\nefficient,\nconfidential,\nscalable\nand\ninteroperable\nby\ndesign.\nAccomplishing\nthis\nrequires\nan\neconomic\nand\ncomputational\nprotocol\nthat\nallows\nnodes\non\nthe\ninternet\nto\ntransact\npeer-to-peer\n(P2P).\nA\nstep\ntoward\nthis\nwas\nmade\nin\n2008\nwith\nthe\ninvention\nof"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:2:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 2, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:2:1", "chroma:document": "peer-to-peer\n(P2P).\nA\nstep\ntoward\nthis\nwas\nmade\nin\n2008\nwith\nthe\ninvention\nof\nledger-based\nblockchains,\nstarting\nwith\nBitcoin\nas\na\nbyzantine-fault-tolerant\nprotocol\nfor\nachieving\ndistributed\nconsensus\non\nupdates\nto\nan\nimmutable\nledger.\nA\nfurther\nadvance\ncame\nin\n2014\nwith\nthe\nEthereum\nblockchain,\nwhich\nallows\narbitrary\ncode\nto\nbe\nstored\nand\nrun\non\nan\nimmutable\nledger.\nHyperCycle\nadvances\non\nthese\nremarkable\nachievements\nto\ncreate\na\nplatform\nwhere\nAI\nagents\nwith\ncomplementary\ncapabilities\ncan\nseamlessly\ntransact,\nenabling\nthem\nto\ncollectively\ntackle\nproblems\nof\never-increasing\nsize\nand\ncomplexity ,\nempowered\nby\nagent-to-agent\nmicrotransactions\nfor\nmicroservices\nwith\nsub-second\nfinality.\nThe\nTODA\nprotocols\nprovide\nthe\nessentials.\nThe\nledgerless\nTODA/IP\nconsensus\nprotocol\nminimizes\nnetwork"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:2:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 2, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:2:2", "chroma:document": "the\nessentials.\nThe\nledgerless\nTODA/IP\nconsensus\nprotocol\nminimizes\nnetwork\ntraversal\nand\nperforms\nonly\nthe\nminimum\ncomputation\nnecessary\nto\nsecure\nthe\nnetwork\nitself.\nAnd\nbuilding\non\nTODA/FP,\nEarth\n64\nSato-Servers\nensure\nthat\nthe\nassets\nthey\ncarry\nare\nsecured\nindependently\nof\nany\nblockchain\nledger.\nThus\nthroughput\nis\nmaintained\nand\nsecurity\ngrows\nas\nnodes\nare\nadded\u2015the\nnetwork\ncan\ngrow\nto\nglobal\nscale.\nThis\neliminates\nthe\nkey\ncommunications\nbottlenecks\nof\nprevious\nledger-based\nblockchain\nprotocols\nto\nprovide\ntrue\nparallel\nand\ndistributed\ncomputing\nwhile\nconserving\nthe\nmaximum\namount\nof\ndecentralization.\n3"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:3:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 3, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:3:0", "chroma:document": "By\nleveraging\nthe\nnovel\ncapacities\nof\nthe\nTODA\nprotocols,\nHyperCycle\npaves\nthe\nway\nfor\nthe\nrealization\nof\na\nglobal\ncomputational\nmind,\nwhere\nthe\ncollective\nintelligence\nof\nAI\nagents\nharmoniously\nconverges\nto\naddress\nmultifaceted\nchallenges.\nAnd\nbecause\nit\nis\ngoverned\nby\nthe\nentire\nopen-source\nnetwork\nof\ndevelopers\nand\noperators,\nas\nopposed\nto\na\nsingle\nentity\nor\nsmall\ngroup,\nthe\nemergent\neffects\nof\nexpansive\nconnectivity ,\ncompetition\nand\ncooperation\namong\nself-actualizing\nnodes\nhas\na\nhigher\nprobability\nof\nenabling\nthe\nglobal\ncommunity\nto\nreach\nits\ngoals,\nincluding\nthe\nsafer\nintroduction\nof\nnew\nforms\nof\nintelligence.\n2\nHigh\nLevel\nView\nof\nthe\nHyperCycle\nNetwork\nIn\nthis\nsection\nwe\nconsider\nhow\nHyperCycle\nComputation\nNodes\n(HCNs)\nlink\ntogether\nusing\ntheir\nvirtual\nmachines,\nhow\nmoney\nsecurely"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:3:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 3, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:3:1", "chroma:document": "Nodes\n(HCNs)\nlink\ntogether\nusing\ntheir\nvirtual\nmachines,\nhow\nmoney\nsecurely\nenters\nand\ntravels\nthrough\nthe\nnetwork\nusing\ncryptographic\nproofs\nof\nprovenance,\nand\nhow\nthe\nnetwork\nis\ndesigned\nto\nevolve\nprogressively\nand\nfacilitate\nthe\nemergence\nof\nnew\nforms\nof\nintelligence.\nWe\nstart\nby\ndescribing\nthe\nTODA\nProtocols\nand\nthe\nEarth\n64\nSato-Server\ndata\nstructure\nthat\nare\nthe\nkey\ntechnological\ninnovations\nthat\nmake\nthe\nHyperCycle\nnetwork\npossible.\n2.1\nThe\nTODA\nProtocols\nand\nEarth\n64\nSato-Servers\nWith\ncurrent\nnetwork\nand\nhardware\ncapabilities\neach\nagent\ncan\nmake\nmultiple\nrequests\nto\nother\nagents\nconcurrently,\nwho\ncan\ndo\nthe\nsame\nrecursively,\nallowing\na\nsingle\nnode\nto\neffectively\ncommunicate\nwith\nhundreds\nof\nmillions\nof\nnodes.\nHowever,\ntechnological\ninnovations\nare\nrequired\nto\naccomplish\nthis\nat"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:3:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 3, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:3:2", "chroma:document": "of\nnodes.\nHowever,\ntechnological\ninnovations\nare\nrequired\nto\naccomplish\nthis\nat\nnetwork\nspeeds\nfor\nasset\ntransfer\nand\nnot\njust\ndata.\nHyperCycle\nleverages\nthe\nTODA\nProtocols\nand\nthe\nEarth\n64\ndata\nstructure\nfor\ndigital\nasset\nmanagement\nand\ndistributed\nconsensus\nto\nmake\nthis\npossible.\nOn\nthe\ndigital\nasset\nside\nof\nthe\nTODA\nProtocols\nis\nTODA/FP,\nthe\nTODA\nFile\nProtocol,\nprovided\nby\nT.R.I.E.\nand\nTODAQ.\nTODA/FP\ndefines\na\ncryptographic\ndata\nstructure\nthat\nallows\nthe\nstate\nof\nan\nasset\nto\nbe\nmanaged\nindependently\nof\nits\nintegrity,\nwhere\n\u2018integrity\u2019\nrefers\nto\nnon-equivocation\nor\nthe\nfact\nthat\nan\nasset\nhas\nnot\nbeen\nspent\nmore\nthan\nonce.\nThus,\na\nsystem\nmanaging\nthe\nintegrity\nof\nan\nasset\ndoes\nnot\nneed\nto\nhold\nor\neven\nknow\nthe\nstate\nof\nthat\nasset,\nminimizing\nthe\ncost\nof\noperating\nthe\nintegrity\nsystem\nand"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:3:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 3, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:3:3", "chroma:document": "state\nof\nthat\nasset,\nminimizing\nthe\ncost\nof\noperating\nthe\nintegrity\nsystem\nand\nallowing\nthe\nasset\u2019s\nstate\nto\nbe\nheld\non\nany\ndevice.\n4"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:4:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 4, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:4:0", "chroma:document": "TODA/FP\nguarantees\nthat\nas\nlong\nas\nthe\nintegrity\nmanagement\nsystem\ndoes\nnot\nequivocate\n(generate\nmultiple\nhistories),\nthen\nthe\nasset\ncannot\nequivocate\neither.\nThis\nintegrity-at-a-distance\nproperty\nis\nmathematically\nproven,\nand\nthe\nrequired\ndata\nfor\nthe\nlightweight\nproof\nis\ncarried\nalong\nwith\nthe\nasset\nduring\nits\ntransfer\nfrom\none\nparty\nto\nanother.\nThe\nEarth\n64\nSato-Server\ndata\nstructure\nbuilds\non\nthe\ncapabilities\nof\nTODA/FP\nand\nadds\ntwo\nkey\nfeatures:\nthe\nability\nto\nhave\nintegrity-system\nindependence,\nthereby\nallowing\nthe\nintegrity\nprovider\nof\nan\nasset\nto\nchange\nover\ntime;\nand\nthe\ncapacity\nto\ncreate,\nsplit,\nand\nmerge\nnodes\nof\nthe\nSato\nTree,\nthe\nbinary\ntree\nof\nSato-Servers\nthat\nEarth\n64\ndefines.\nThe\nresult\nis\nan\nasset-level,\nportable\nblockchain\nthat\nis\nlow-cost,\nscalable\nand\ncan\nbe\nplugged"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:4:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 4, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:4:1", "chroma:document": "asset-level,\nportable\nblockchain\nthat\nis\nlow-cost,\nscalable\nand\ncan\nbe\nplugged\ninto\ndifferent\nsystems\nto\nmanage\nits\nintegrity.\nHyperCycle\nmakes\nuse\nof\nthe\nsplitting\ncapacity\nof\nSato-Servers\nfor\nboth\nthe\nnative\nHyPC\ntoken\nand\nHyperCycle\nComputation\nNode\nlicenses.\nIn\nthe\ncase\nof\nHyPC\ntokens,\nthey\ncan\nbe\nsubdivided\ninto\nsmaller\ndenominations.\nFor\nHyperCycle\nComputation\nNode\nlicenses,\nthey\ncan\nbe\nsplit\ninto\nsmaller\nlicenses\nor\ncan\ngenerate\nadditional\nlicenses,\nas\nis\ndescribed\nin\nSection\n3.5\nbelow.\nOther\nimportant\nfeatures\nof\nthe\nSato-Server\ndata\nstructure\nare\nthat\nit\nis\na\ncontainer\nfor\narbitrary\ndigital\nitems\nof\nvalue\nand\nthat\neach\none\nhas\na\nglobally\nunique\nidentifier.\nHyperCycle\nuses\nthe\ncontainer\nfeature\nto\nallow\nnative\nHyPC\ntokens\nto\nhold\nother\nvalue\ninstruments,\nincluding\nfiat\nand\ncrypto"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:4:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 4, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:4:2", "chroma:document": "native\nHyPC\ntokens\nto\nhold\nother\nvalue\ninstruments,\nincluding\nfiat\nand\ncrypto\ncurrencies.\nEach\nSato-Server\nalso\nhas\na\nunique\nbinary\nidentifier\nthat\ncorresponds\nto\na\nunique\nregion\non\nthe\nsurface\nof\nthe\nEarth,\nachieving\nglobal\nuniqueness\nusing\nthe\ninternationally\nrecognized\nGCS\ncoordinate\nsystem.\nOn\nthe\ndistributed\nconsensus\nside,\nTODA/IP\nemploys\na\ndistributed\ncomputation\nas\nproof\nof\nwork,\nwhich\nscales\nlinearly\nwith\nthe\nnetwork's\nsize,\nensuring\na\nconstant\nworkload\nfor\neach\nnode.\nTo\naccomplish\nthis,\nthe\ndata\nstructure\nis\ndesigned\nto\nmaintain\nonly\nthe\nessential\ndata\nrequired\nto\nsecure\nthe\nnetwork's\nstate,\nminimizing\nthe\ndistributed\ncomputation\nneeded\nfor\nsecurity.\nTransacting\nnodes\nstore\nthe\nstate\nof\ntheir\nown\nassets,\neliminating\nthe\nneed\nfor\na\nglobal\nledger,\nreplacing\nit\nat\nthe\nnetwork\nlevel"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:4:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 4, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:4:3", "chroma:document": "eliminating\nthe\nneed\nfor\na\nglobal\nledger,\nreplacing\nit\nat\nthe\nnetwork\nlevel\nwith\na\nsingle\nglobal\nMerkle\nroot\nhash\nper\nnetwork\ncycle.\nIn\neach\ncycle,\nparties\ninvolved\nin\ntransactions\ncan\nengage\nin\nan\nunlimited\nnumber\nof\ntransactions\nacross\nas\nmany\nassets.\nHowever,\nonly\none\ntransaction\nper\nasset\nis\npermitted\nper\ncycle.\nWithin\nthe\ncontext\nof\nHyperCycle,\nthese\ncycles\noperate\nat\nvarying\nscales\nwithin\nthe\nnetwork.\nSmaller\nsystems\nof\ntransactions\ncan\nachieve\nfinality\nrelative\nto\na\nportion\nof\nthe\nnetwork\nin\nfractions\nof\na\nsecond,\nwhile\nfinality\nat\nthe\nnetwork-wide\nlevel\nis\nachieved\nin\neach\nnetwork\nblock\nevery\n6-30\nseconds.\nThe\nformer\nguarantees\nthe\nnecessary\nperformance\nfor\nhigh-speed\ntransactions,\nwhile\nthe\nlatter\nensures\nthe\ncertainty\nrequired\nfor\nasset\nre-spending.\nThe\ncombination\nof\nthe\nTODA"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:4:4

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 4, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:4:4", "chroma:document": "the\ncertainty\nrequired\nfor\nasset\nre-spending.\nThe\ncombination\nof\nthe\nTODA\nProtocols\nand\nEarth\n64\nSato-Servers\nenables\ntrue\nP2P\ntransactions\nbetween\nHyperCycle\nnodes.\nNetwork\npackets\nserve\nas\ncarriers\nfor\ninstructions,\ndata,\nand\npayment.\nSince\ntransactions\ncan\nachieve\nsub-second\nfinality,\neconomic\nsignals\nand\n5"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:5:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 5, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:5:0", "chroma:document": "data\ncoexist\nefficiently,\nallowing\nthe\nnetwork\nto\nrapidly\nevolve\nnew\nproblem-solving\napproaches\nwhile\nmeeting\nthe\nperformance\nexpectations\nof\nthe\nglobal\nmarket.\n2.2\nThe\nHyperCycle\nVirtual\nMachine\nHyperCycle\nComputation\nNodes\nact\nas\nthe\n\u201cneurodes\u201d\nwhose\ninterconnections\nform\nthe\naxons\nand\nsynapses\nof\nthe\nglobal\nHyperCycle\nbrain.\nThis\ndemands\na\npowerful\nvirtual\nmachine\nat\nthe\nheart\nof\nevery\nnode.\nThis\nVirtual\nMachine\nenables\nefficient,\nsecure\ncommunication\nbetween\nthe\ninternal\ncomponents\nof\nan\nHCN\nand\nbetween\nthe\nnodes\nthemselves.\nThe\ninternal\ncomponents\nof\na\nComputation\nNode\ninclude\none\nor\nmore\nTransaction\nMachines\n(TMs)\nand\none\nor\nmore\nAI\nMachines\n(AIMs),\nloosely\ncoupled\nvia\nthe\nHyperCycle\nVirtual\nMachine\n(VM).\nInternally\nto\na\nnode,\nthe\nTransaction\nand\nAI\nMachines\ncommunicate\nsolely\nvia"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:5:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 5, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:5:1", "chroma:document": "Internally\nto\na\nnode,\nthe\nTransaction\nand\nAI\nMachines\ncommunicate\nsolely\nvia\nthe\nVM.\nAll\ncomponents\nare\nmodular\u2015any\ncomponents\nproviding\nthe\nnecessary\ninterfaces\ncan\nbe\nused\nas\nHCN\nmachines.\nExternally,\nthe\nVM\nfor\neach\nHCN\nmanages\nall\nnon-financial\ncommunication\nwith\nother\nnodes\nby\ntalking\nto\ntheir\nVMs.\nFinancial\ncommunication\nis\nhandled\nindependently\nby\nthe\nTMs\nand\nthe\nTODA/IP\nnetwork.\nFor\nan\nHCN\nto\nserve\nas\nan\neffective\nneurode,\nthe\nVM\nmust\nbe\ndynamically\nprogrammable\nand\nreprogrammable.\nHCN\nVMs\ncommunicate\npayment\ninstructions,\ndata,\nand\ncode\nto\nother\nVMs.\nVMs\nare\npaid\nto\nexecute\nthe\ncode\non\nthe\ndata,\ncontrolling\nthe\nexecution\nof\nthe\nAIMs\nand\ninitialing\nfurther\ncommunication.\nThe\ninitial\nHCN\nVirtual\nMachine\nwill\nbe\nbased\non\nPython.\nMillions\nof\nprogrammers\nuse\nPython,\nincluding\nmany\nof"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:5:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 5, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:5:2", "chroma:document": "will\nbe\nbased\non\nPython.\nMillions\nof\nprogrammers\nuse\nPython,\nincluding\nmany\nof\nour\npotential\nusers:\ndevelopers\nin\nthe\nblockchain,\ndata\nanalytics,\nmachine\nlearning\nand\nAI\ncommunities.\nPython\nis\nmodular\nand\ncomposable,\nand\nit\nsupports\nembedding\ncode\nfrom\nother\nlanguages,\nincluding\nmany\npowerful\nlibraries.\nAnd\ncrucially,\nPython\nis\na\ndynamic\nlanguage\nthat\ncan\nexecute\nits\nown\nsource\ncode,\nwhich\ncan\nbe\nsent\nas\nmessages\nto\nand\nfrom\nHCN\nVMs.\nIn\nthis\nway\none\nVM\ncan\nin\nfact\ndynamically\ntransmit\ncode\nto\nbe\nexecuted\non\nanother\nVM.\nSo\nwhat\nwe\nhave\nhere\nis\na\nnetwork\nof\ncomputational\nagents\ncommunicating\ncode,\ndata,\nand\npayments\nto\nother\nagents.\nThis\nempowers\nnodes\nto\nestablish\nspontaneous\ntransactional\nrelationships\non-the-fly.\nThis\ndynamic\nformation\nof\nad\nhoc\nconnections\nbetween\nnodes\nensures\nflexible"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:5:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 5, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:5:3", "chroma:document": "This\ndynamic\nformation\nof\nad\nhoc\nconnections\nbetween\nnodes\nensures\nflexible\nand\nefficient\ncollaboration\nin\nthe\nnegotiation\nand\nexecution\nof\ncontracts\nand\nsubcontracts\nfor\nartificial\nintelligence\nwork.\n2.3\nSecurely\nGetting\nFunds\ninto\nthe\nHyperCycle\nNetwork\n6"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:6:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 6, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:6:0", "chroma:document": "Provable\nprovenance\nof\nfunds\nis\ncrucial\nwithin\nthe\nHyperCycle\nnetwork,\nas\nit\nrequires\nthe\nconversion\nof\nvarious\ncurrencies\ninto\nnative\ntokens\n(HyPC)\nwhile\nmaintaining\ntraceability.\nThe\nprovenance\nof\nfunds\nencompasses\ntwo\nelements.\nThe\nfirst\ninvolves\nexisting\nmethods\nfor\nestablishing\nthe\nprovenance\nof\nfiat\ncurrency,\nbridging\nto\nstablecoins\nor\ncryptocurrencies,\nand\ntransferring\ntokens\nacross\nblockchains.\nHyperCycle\ndoes\nnot\nrely\non\nany\nspecific\nmethod\nbut\nrequires\na\nprovable\nprovenance\nfrom\nthe\nfund\nsource.\nThe\nEthereum\nblockchain\nis\nvery\nuseful\nin\nthis\nrespect,\nbecause\nusers\ncan\nconvert\nfiat\ninto\nETH\nor\nERC20\ntokens,\nwhich\ncan\neasily\nbe\nconverted\ninto\nHyPC.ETH\nor\nc_HyPC.ETH\n(NFTs\ncorresponding\ndirectly\nto\nunique\nnative\ntokens),\nproviding\na\nborderless\nmeans\nof\npayment\nfor\nservices\non\nthe"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:6:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 6, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:6:1", "chroma:document": "native\ntokens),\nproviding\na\nborderless\nmeans\nof\npayment\nfor\nservices\non\nthe\ninternet\nusing\nthe\nEthereum\nblockchain.\nThis\nkind\nof\ntoken\nbridging\nis\nof\ncourse\nnot\nexclusive\nto\nEthereum,\nand\ncan\nbe\nextended\nto\nother\nprogrammable\nblockchains\nsuch\nas\nPolygon,\nCardano,\nor\nBNB\nChain.\nThe\nsecond\nelement\nof\nprovable\nprovenance\nrelates\nto\nthe\nnative\nHyPC\ntoken\nin\nits\ntrue\nform\nas\na\nSato-Server,\nwhich\nemploys\nTODA/IP\nto\nensure\nintegrity\nby\npreventing\ndouble\nspending\nwhile\nmaintaining\na\nproof\nof\nprovenance\nwithin\nthe\ndata\nstructure.\nThis\nensures\ntraceability\nof\nfunds\nthroughout\nthe\nentire\ntransaction\nhistory\nin\nthe\nHyperCycle\nnetwork.\nTogether,\nthese\ntwo\nelements\nestablish\nprovable\nprovenance\nfor\nall\nfunds\nwithin\nthe\nHyperCycle\nnetwork.\nTo\nillustrate,\nAlice\ncould\nuse\none\nof\nmany\nexchanges\nto\nconvert"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:6:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 6, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:6:2", "chroma:document": "network.\nTo\nillustrate,\nAlice\ncould\nuse\none\nof\nmany\nexchanges\nto\nconvert\nUSD\ninto\nETH,\nwhich\ncan\nbe\nused\nto\npurchase\nHyPC.ETH\nusing\na\ndecentralized\nexchange.\nShe\ncan\nthen\nuse\nour\nswap\ncontract\nto\nconvert\nher\nHyPC.ETH\ninto\nc_HyPC.ETH\non\nthe\nEthereum\nnetwork\nwith\nwhich\nto\nform\nher\nnode's\nidentity\nor\nto\npay\nfor\nAI\nservices\non\nHyperCycle.\nSimilarly\nBob\ncould\nconvert\nBTC\nor\neven\nhypothetically\nUSD\nor\nGBP\n(\u00a3)\nto\nGBPT\nusing\na\nregulated\nbridge\nfrom\nthe\ncentral\nbank\nof\nEngland,\nand\nuse\na\ndecentralized\nexchange\nto\nswap\nGBPT\nfor\nHyPC.ETH.\nWhatever\nthe\norigin\nof\nfunds,\nthere\nwill\nbe\na\nprovable\nprovenance\nonto\nthe\nblockchain\nand\ninto\nand\nthroughout\nthe\nHyperCycle\nnetwork.\nIt\nis\nimportant\nto\nappreciate\nthat\neach\nnative\nHyPC\nSato-Server\nholds\nboth\nvalue\nand\nfunctions\nas\na\ncontainer,\nwhich\nallows\nHyPC\nto"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:6:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 6, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:6:3", "chroma:document": "Sato-Server\nholds\nboth\nvalue\nand\nfunctions\nas\na\ncontainer,\nwhich\nallows\nHyPC\nto\ncarry\npayments\nin\nother\ntoken\ntypes\nthrough\nvarious\nbridges.\nIncorporating\nan\nEthereum\nVirtual\nMachine\n(EVM)\ninto\nHyperCycle\nnodes\nas\na\nTM\nmakes\nthis\neven\neasier,\nfacilitating\nthe\nuse\nof\nETH\nand\nother\nERC20\ntokens\nfor\npayments\nin\nthe\nHyperCycle\nnetwork\nand\nallowing\nrevenue\nto\nbe\ncashed\nout\ndirectly\nto\nEthereum.\n2.4\nEnabling\nCooperative\nAI\nfor\nBeneficial\nEmergence\nNodes\nin\nthe\nHyperCycle\nnetwork\npossess\ndual\nfunctionality:\nrunning\nAI\nmachines\nto\noffer\nservices\nand\nmaking\nrequests\nfor\nservices\nfrom\nother\nnodes.\nThis\nunique\ncapacity\nenables\nnodes\nto\ncontract\nand\nsubcontract\nwork,\nallowing\nad\nhoc\nnetworks\nof\nnodes\nto\ndynamically\nsolve\nproblems\nmore\neffectively\nand\nefficiently,\nfostering\nbeneficial\nemergence"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:6:4

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 6, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:6:4", "chroma:document": "solve\nproblems\nmore\neffectively\nand\nefficiently,\nfostering\nbeneficial\nemergence\nthrough\nnovel\nnode\ncombinations.\n7"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:7:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 7, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:7:0", "chroma:document": "Work\nrequests\nin\nHyperCycle\ninvolve\nthree\nelements:\nthe\nHyPC\nSato-Server\ncontaining\nthe\npayment,\ninstructions\nfor\nthe\ntask,\nand\nthe\nrelevant\ndata.\nWhen\nBob\nreceives\na\nrequest\nfrom\nAlice,\nhe\nvalidates\nthe\nSato-Server\nto\nensure\nits\nauthenticity.\nTo\nclaim\nthe\npayment,\nBob\nneeds\nto\nprovide\nproof\nof\ncompleting\nthe\ntask\nusing\nthe\ninstructions\nand\ndata\nprovided\nby\nAlice.\nFor\nexample,\nsuppose\nAlice\nwants\nto\nprocess\npresentations\nby\nconverting\nimages\nand\naudio\nto\ntext.\nWhile\nshe\nhas\nAI\nmachines\nfor\ntext\nprocessing,\nshe\nlacks\nimage\nOCR\nand\naudio-to-text\ncapabilities.\nAlice\ndiscovers\nthat\nBob\ncan\nperform\nthese\ntasks\nand\ncontracts\nhim,\npaying\nwith\nHyPC\nmicropayments\nper\nimage\nand\naudio\nfile.\nIf\nBob\nencounters\nchallenges,\nsuch\nas\nLatin\ntext\nor\nnoisy\naudio,\nhe\ncan\nsubcontract\nto\nother\nnodes\non\nthe"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:7:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 7, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:7:1", "chroma:document": "such\nas\nLatin\ntext\nor\nnoisy\naudio,\nhe\ncan\nsubcontract\nto\nother\nnodes\non\nthe\nnetwork.\nBob\nsearches\nand\nfinds\nCharlie\nfor\nLatin\nOCR\nand\nDana\nfor\naudio\nenhancement,\nand\nsubcontracts\nthe\nproblematic\ncases\nto\nthem.\nThis\ncollaborative\napproach\nallows\nBob\nto\novercome\nobstacles\ninstead\nof\nbeing\nblocked\nby\nthem,\nallowing\nthe\nnetwork\nto\nprovide\na\ncomprehensive\nsolution\nto\nAlice's\ninitial\nrequest.\nHyperCycle\nenables\ndynamic\nproblem-solving\nthrough\ncooperative\nnetworking\nof\ncompute\nunits\nand\nAI.\nIt\nprovides\na\ndistinct\nand\nnovel\ncapability\nfor\nthe\nInternet\nof\nAI,\nallowing\narbitrary\nnodes\nto\nsecurely\ncontract\nand\nsubcontract\nat\na\nmicrotransaction\nlevel.\nThis\nenhances\nglobal-scale\nemergence\nin\ncollaborative\ntask\ncompletion,\ndistinguishing\nit\nfrom\ncentralized\nenvironments\nlike\nAWS,\nGCP,\nand\nAzure.\n2.5"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:7:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 7, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:7:2", "chroma:document": "distinguishing\nit\nfrom\ncentralized\nenvironments\nlike\nAWS,\nGCP,\nand\nAzure.\n2.5\nCompetitive\nAI\nfor\nProgressive\nEvolution\nTo\npromote\na\nnaturally\nprogressive\nevolution\nof\nthe\nInternet\nof\nAI\nthat\nbenefits\nhumanity\nas\na\nwhole,\nfostering\ncompetition\nbetween\nnodes\nproviding\nsimilar\nservices\nis\ncrucial.\nThe\nHyperCycle\nnetwork's\nad\nhoc\nP2P\nstructure\nfacilitates\nthis\nprocess\nby\ndesign.\nSuppose\nAlice\ndiscovers\na\nnew\nnode\ncalled\nHannah,\nwhich\noutperforms\nBob's\ndevices\nin\naccuracy\nand\nperformance\nbut\non\na\nsmaller\nscale.\nBy\nallocating\na\nsmall\namount\nof\nwork\nto\nHannah's\nservice,\nAlice\nenables\nHannah\nto\ngrow\nand\nimprove\nover\ntime.\nThis\nencourages\nBob\nto\ninnovate\nand\nenhance\nhis\nown\nservice\nquality,\ngenerating\nmarket\nsignals\nthat\ncontribute\nto\nimprovement\nof\nthe\noverall\ncapabilities\nand\nfunctioning\nof\nthe"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:7:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 7, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:7:3", "chroma:document": "contribute\nto\nimprovement\nof\nthe\noverall\ncapabilities\nand\nfunctioning\nof\nthe\nnetwork.\nEnabling\ncompetition\namong\nnodes\noffering\nsimilar\nservices\nis\nnot\na\nnew\nconcept,\nhaving\nexisted\nin\nthe\nAI\nfield\nfor\ndecades\nand\nin\neconomics\nfor\ncenturies.\nHowever,\nwhat\nsets\nHyperCycle\napart\nis\nthe\nability\nto\nachieve\nthis\nbeneficial\nevolutionary\nbehavior\nwith\nhigh\nspeed,\nscalability,\nand\nminimal\nfriction.\nThis\nachievement\nis\nmade\npossible\nby\naddressing\nlong-standing\nbottlenecks,\nparticularly\ndesigning\nsecure\ncommunication\nprotocols\nthat\nscale\nlinearly\nand\ntransmit\nmicrovalues\nat\nnegligible\ncost.\nIn\nthis\nregard,\nthe\nfoundational\ntechnology\nof\nTODA/IP\n8"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:8:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 8, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:8:0", "chroma:document": "plays\na\npivotal\nrole,\nas\nit\novercomes\nthese\nchallenges\nto\nenable\nHyperCycle's\nbeneficial\nself-evolution\nand\nemergence.\n3\nHyperCycle\nComputation\nNodes\nThe\nInternet\nof\nAI\nnecessitates\nseamless\nnetworking\nbetween\nAI\nsoftware\ncomponents.\nHyperCycle\nComputation\nNodes\nact\nas\nspecialized\nagents,\nenabling\ncommunication\nand\ntransactions.\nThis\nsection\nexplores\nthe\nstructure\nand\nfunctionality\nof\nHCNs,\naccommodating\nany\nAI\nsoftware\nand\nfacilitating\ninter-node\ncommunication.\nWe\ndiscuss\noperating\nrequirements\nand\nrevenue\ncollection,\nas\nwell\nas\nwealth\ncreation\nusing\nunique\nnode\nidentities\nthat\ntrack\nnodes\u2019\nquality\nof\nservice\nand\nallow\nhigh-quality\nmachines\nto\nsplit\nlicenses,\ncreating\ntwo\nnodes\nfrom\none.\nLet\u2019s\nbegin\nby\ntaking\na\nlook\nat\nwhat\nis\ninside\nan\nHCN.\n3.1\nNode\nStructure\nand\nFunction\nAs\nmentioned"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:8:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 8, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:8:1", "chroma:document": "a\nlook\nat\nwhat\nis\ninside\nan\nHCN.\n3.1\nNode\nStructure\nand\nFunction\nAs\nmentioned\nabove,\nan\nHCN\ncomprises\nthree\nessential\ncomponents:\nthe\nVM,\nTM,\nand\nAIM.\nThe\nVM\ninteracts\nwith\nthe\nTM\nfor\nhandling\npayments\nand\nthe\nAIM\nfor\nAI\ncomputation.\nThese\ncomponents\ncollaborate\nto\nenable\nnodes\nto\nperform\ntheir\nfunction\nwithin\nthe\nnetwork.\nUpon\ninstalling\nthe\nnode\nsoftware,\nthe\noperator\nselects\nAI\nmodules\nto\nrun\nwithin\nthe\nAIM,\nutilizing\nDocker\ncontainer\nimages\nor\nother\nmeans\nof\nsoftware\nprovisioning.\nUsing\nthe\nVM,\nthe\nnode\ninforms\nother\nnetwork\nnodes\nabout\nits\nsoftware\ncapabilities\nand\ncorresponding\npricing.\nWhen\nAlice\nrequests\ncomputation\nfrom\nBob,\nshe\nsends\nan\nHyPC\nSato-Server\ncontaining\na\npayment\ncommitment\nalong\nwith\ninstructions,\nand\ndata.\nThe\npayment\ncommitment\nis\nirreversible\nbut\nremains"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:8:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 8, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:8:2", "chroma:document": "with\ninstructions,\nand\ndata.\nThe\npayment\ncommitment\nis\nirreversible\nbut\nremains\nunspendable\nby\nBob\nuntil\na\nproof\nof\ncomputation\nis\nprovided,\nwhich\nmay\nbe\nthe\ncomputed\noutput.\nBob's\nVM\nverifies\nthe\npayment\nvalidity\nwith\nhis\nTM,\nsends\nthe\ninstructions\nand\ndata\nto\nthe\nAIM\nfor\ncomputation,\nand\nreceives\nthe\nresult.\nBob\nsends\nthe\noutput\nto\nAlice\nand,\nwith\nproof\nof\ncomputation\nin\nhand,\nhis\nTM\ncan\nrelease\nthe\npayment.\nBob\u2019s\nVM\ncan\nsubcontract\nparts\nof\nthe\nwork\nto\nCharlie\nand\nDana\nin\na\nsimilar\nway,\nas\ndepicted\nin\nthe\nimage\nbelow.\n9"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:9:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 9, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:9:0", "chroma:document": "Using\nTODA/IP,\nthe\nTMs\ncan\ntransact\nwith\neach\nother\nin\nparallel\nto\nthe\ncommunications\nhandled\nby\nthe\nVM.\nThe\ndiagram\nbelow\nvisually\ndepicts\nthe\nnetwork's\nfunctionality\nfrom\nthe\nTMs\u2019\nperspective.\nIn\nthis\nsystem,\npayments\nand\npayment\ncommitments\nare\nestablished\nwithin\nthe\nTODA/IP\nnetwork\n(node\n\u2192\nnode\nand\nnode\n\u2192\nTODA/IP\nmessages\nin\nthe\ndiagram)\nto\nensure\nthe\nintegrity\nof\nthe\nexchanged\nSato-Servers.\nThese\npayments\nand\ncommitments\nattain\nfinality\nwithin\na\nsegment\nof\nthe\nnetwork\nby\nbeing\nincluded\nin\na\nTODA/IP\ncycle\n(TODA/IP\n\u2192\nTODA/IP\nmessages),\nthereby\nguaranteeing\nimmutability\nand\nfinality\nwithin\nthe\nvalidating\nnode\nset.\nSubsequently,\nproof\nof\ninclusion\nin\na\ncycle\nis\ncommunicated\nto\nthe\nparticipating\nnodes\n(TODA/IP\n\u2192\nnode\nmessages),\ninstilling\nconfidence\nin\nthe\nfirmness\nof\nthe\ntransaction."}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:9:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 9, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:9:1", "chroma:document": "\u2192\nnode\nmessages),\ninstilling\nconfidence\nin\nthe\nfirmness\nof\nthe\ntransaction.\nThese\ncycles\nare\neventually\nconsolidated\ninto\na\nnetwork\nblock\n(interior\nspiral\nmessages\nin\nthe\ndiagram)\nat\nregular\nintervals\nof\n6-30\nseconds,\nthereby\nestablishing\nthe\nultimate\nfinality\nof\npayments\nwithin\nthe\nnetwork\nand,\nbecause\neach\nasset\ncan\nonly\ntransact\nonce\nper\nblock,\npermitting\nthe\nreuse\nof\nfunds.\n10"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:10:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 10, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:10:0", "chroma:document": "Negotiations\nbetween\nnodes\nfor\ncontract\nterms\noccur\ninfrequently,\nand\nonce\nestablished,\nwork\nrequests\ncan\nbe\nsent\nas\nP2P\ntransactions\nusing\nSato-Servers\nwithout\nthird-party\ninvolvement.\nRenegotiation\nbecomes\nnecessary\nonly\nwhen\nthere\nare\nchanges\nin\nservices,\navailability,\ncapacity,\nor\nprices.\nAI\nAgents\nin\na\nunified\nsetting\ncan\nalso\nswitch\ntheir\nmode\nto\ndirectly\nwork\non\na\nsingle\ntask\nfor\nas\nlong\nas\nneeded\nand\nswitch\nback.\n11"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:11:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 11, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:11:0", "chroma:document": "3.2\nStarting\nOperation\nof\na\nNode\nTo\nset\nup\na\nfunctioning\nHCN,\nseveral\npreliminary\nsteps\nare\nrequired.\nFirstly,\nthe\nnode\nneeds\npowerful\nhardware\nto\nrun\ncompetitive\nAI\nsoftware.\nSecondly,\na\nHyperCycle\nlicense\nis\nnecessary\nfor\nnetwork\noperation.\nLastly,\n1024\nHyPC\nare\nneeded\nto\nestablish\neach\nnode's\nidentity.\nThe\ncombination\nof\na\nlicense\nand\na\ncertain\nquantity\nof\nHyPC\nappropriate\nfor\nthat\nlicense\nis\ncalled\na\nProvenance\nMarriage.\nThis\nmarriage\nactivates\na\nlicense\nand\nenables\na\ncryptographic\nquality\nof\nservice\nscore\nand\nother\nbenefits,\nas\ndescribed\nin\nSections\n3.4\nand\n3.5\nbelow.\nThese\nthree\npieces\u2014hardware,\nlicense,\nand\nnative\ntokens\u2014are\nessential\nfor\na\nfully\noperational\nnode\non\nthe\nHyperCycle\nnetwork.\nActivating\na\nHyperCycle\nlicense\ninvolves\na\nProvenance\nMarriage,\nwhere\nthe\nlicense\nis\ncombined"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:11:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 11, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:11:1", "chroma:document": "license\ninvolves\na\nProvenance\nMarriage,\nwhere\nthe\nlicense\nis\ncombined\nwith\nthe\nrequired\nHyPC\nidentity\ntoken,\nforming\nthe\nnode\u2019s\nidentity.\nThe\nmarried\nlicense\nand\ntoken\nare\nthen\nintegrated\ninto\nthe\nnode\nsoftware.\nThis\nallows\nthe\nnode\nto\ncommunicate\nits\ncapacities,\ncryptographically\nrecord\nits\nbehavior,\nand\ncommence\noperations.\nAlthough\nall\nthree\npieces\nare\nnecessary\nfor\na\nfully\noperating\nnode,\nthey\ncan\nbe\nobtained\nseparately\nby\ndifferent\nparties.\nCollaborations\nbetween\nowners\nof\nhardware,\nlicenses,\nand\ntokens\nenable\njoint\nnode\noperation\nat\nreduced\ncosts\nfor\neach\nindividual.\nPools\nor\nmarketplaces\nfacilitate\nbringing\ntogether\npieces\nowned\nby\ndifferent\nparties,\nproviding\nHyPC\ntokens,\nlicenses,\nor\nhardware\nto\nthose\nseeking\nspecific\nelements\nto\ninitiate\nand\nsustain\na\nnode.\nIt\nis\nimportant\nto"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:11:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 11, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:11:2", "chroma:document": "seeking\nspecific\nelements\nto\ninitiate\nand\nsustain\na\nnode.\nIt\nis\nimportant\nto\nnote\nthat\nthese\npools\nor\nmarketplaces,\nwhile\nvital\nto\nthe\necosystem,\nare\ndistinct\nfrom\nthe\nHyperCycle\ncore\nand\nare\nnot\nowned\nor\nmanaged\nby\nHyperCycle.\n3.3\nRevenue\nFrom\na\nNode\n/\nIncentive\nModel\nAn\nHCN\ngenerates\nrevenue\nby\nproviding\nAI\nservices\nand\nreceiving\npayment\nin\nHyPC\ntokens,\npossibly\nalong\nwith\nother\nvalue\ninstruments\nsuch\nas\nAGIX,\nADA,\nEUR\nand\nUSDC.\nThe\nHyperCycle\nnetwork\noperates\nwithout\ntransaction\nfees\nbut\ncollects\na\n1%\nroyalty\non\nnode\nrevenue\nto\nsupport\nnetwork\nparticipants,\nincluding\nHyperCycle\nitself.\nRoyalties\nare\ndistributed\nsecurely\nand\nfairly\nusing\nan\nautomated,\ndeterministic\nalgorithm.\nNodes\ngain\nvalue\nthrough\na\nscore\nthat\nreflects\ntheir\nuptime,\ncomputation,\nand\nreputation.\nA\nhigher\nscore"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:11:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 11, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:11:3", "chroma:document": "a\nscore\nthat\nreflects\ntheir\nuptime,\ncomputation,\nand\nreputation.\nA\nhigher\nscore\nincreases\nthe\nlikelihood\nof\nreceiving\nwork,\nthereby\nincreasing\nthe\nnode's\nvalue.\n12"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:12:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 12, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:12:0", "chroma:document": "When\na\nlicensed\nnode\nachieves\na\nscore\nof\n2,\nit\ncan\nsplit\ninto\ntwo\nlicensed\nnodes,\nfurther\nenhancing\nits\nworth.\n3.4\nNode\nScore\n=\nUptime\n\u00b7\nComputation\n\u00b7\nReputation\nThe\nHyperCycle\nnetwork\nrelies\non\nnodes\nproving\ntheir\navailability\n(proof\nof\nuptime),\naccurate\nand\ntimely\ncomputations\n(proof\nof\ncomputation),\nand\nresponsible\nbehavior\n(proof\nof\nreputation)\nto\nensure\nhigh\nquality\nnetwork\nfunctionality\nand\nsecurity.\nData\nrecords\nfor\nuptime,\ncomputation,\nand\nreputation\nare\nregularly\nrecorded\nin\ncryptographic\nform\nwithin\nnetwork\nintervals\nranging\nfrom\n6\nto\n30\nseconds.\nThese\nrecords\nserve\nas\nevidence\nof\na\nnode's\nbehavior\nby\ndemonstrating\ntheir\ninclusion\nin\nthe\ncryptographic\nrecords\nfor\neach\n6-30\nsecond\nblock\nof\nnetwork\ntime.\nUsing\nthe\nrecorded\ndata,\nany\none\ncan\ncalculate,\nand\nthe\nnetwork\ncan"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:12:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 12, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:12:1", "chroma:document": "time.\nUsing\nthe\nrecorded\ndata,\nany\none\ncan\ncalculate,\nand\nthe\nnetwork\ncan\ndeterministically\nconfirm,\nratios\nfor\nthree\nproperties:\nuptime;\ncomputation;\nand\nreputation.\nThese\nratios\nprovide\ninsights\ninto\na\nnode's\nperformance\nfor\neach\ncorresponding\nproperty.\nEach\nratio\nis\ncalculated\nas\na\nmeasured\npercentage\nfor\nthe\nproperty\nmultiplied\nby\na\nscaling\nfactor:\nproperty_ratio\n=\nproperty_%\n*\nscaling_factor\nwhere\nthe\ninitial\npercentage\nfor\neach\nproperty\nstarts\nat\n100%\nbut\ncan\nvary\nover\ntime,\nwhile\nthe\nscaling\nfactor\nstarts\nat\n1\nand\ngradually\nincreases\nto\na\nmaximum\nof\n2\nafter\n18\nmonths.\nAs\na\nresult,\neach\nratio\nstarts\nout\nat\n1\nbut\ncan\nrange\nin\nvalue\nfrom\n0\nup\nto\n2.\nThe\noverall\nscore\nfor\na\nnode\nis\nobtained\nby\nmultiplying\nthe\nthree\nratios\u2015Uptime,\nComputation,\nand\nReputation\u2015together,\nwith\nthe\nmaximum"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:12:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 12, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:12:2", "chroma:document": "the\nthree\nratios\u2015Uptime,\nComputation,\nand\nReputation\u2015together,\nwith\nthe\nmaximum\nscore\ncapped\nat\n2:\nscore\n=\nmin\u200a(\u200aUptime\n*\nComputation\n*\nReputation,\n2\u200a)\nConsequently,\nthe\nnode's\nscore\nalso\ninitially\nstarts\nat\n1\nand\ntakes\nvalues\nin\nthe\nrange\nfrom\n0\nto\n2\nover\ntime.\nThe\nrole\nof\nthe\nscaling\nfactor\nin\nthe\ncomputation\nof\neach\nratio\nis\nto\nincrease\nthe\nmaximum\npossible\nvalue\nof\neach\nratio\nas\ntime\nprogresses.\nIn\nparticular,\nafter\nsix\nmonths\nthe\nscaling\nfactor\nfor\neach\nratio\nreaches\nup\nto\n\u221b\n2\n\u2248\n1.26.\nThis\nenables\na\nnode's\nscore\nto\nreach\n2\nafter\na\nsix-month\nperiod\nif\nit\nmaintains\nthe\nmaximum\nvalues\nfor\nuptime,\ncomputation,\nand\nreputation\nduring\nthat\ntime.\nEven\nif\na\nnode\ndoesn't\nachieve\na\nperfect\nscore\nfor\none\nor\nmore\nproperties,\nit\nstill\nhas\nthe\nopportunity\nto\nincrease\nits\nscore\nto\n2\nbecause\nthe"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:12:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 12, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:12:3", "chroma:document": "properties,\nit\nstill\nhas\nthe\nopportunity\nto\nincrease\nits\nscore\nto\n2\nbecause\nthe\nmaximum\nvalue\nof\neach\nratio\ncontinues\nto\nrise\npast\n\u221b\n2\nafter\nsix\nmonths.\nThis\nprovides\nvarious\npaths\nfor\nthe\nproduct\nof\nthe\nratios\nto\nreach\n2.\n13"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:13:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 13, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:13:0", "chroma:document": "This\ncontinual\nincrease\nin\nthe\nratios\u2019\nmaximum\nvalue\ngives\nparticular\nsignificance\nto\nthe\nuptime.\nThe\nComputation\nand\nReputation\nratios\nare\ntransaction-based,\nmeaning\nthat\nthey\ndepend\non\nexternal\nfactors\nbeyond\nwhat\nthe\nnode\ncan\nfully\ncontrol.\nOn\nthe\nother\nhand,\nhaving\nexcellent\nuptime\non\nits\nown\ncan\ngrow\nthe\nUptime\nratio\nto\n2,\nwhich\ncan\nallow\nthe\nnode\u2019s\nscore\nto\naccumulate\nfaster\nif\na\nvery\nhigh\nuptime\nis\nmaintained.\nA\nscore\nabove\n1\nprovides\nbenefits\nto\na\nnode,\nincluding\nincreased\nconfidence\nfrom\nthe\nnetwork,\nhigher\nlikelihood\nof\nselection\nfor\nwork\nassignments,\nand\nthe\npotential\nto\nsplit\ninto\ntwo\nlicensed\nnodes\nwhen\nthe\nscore\nreaches\n2.\nAlthough\nwe\nare\nworking\non\na\ncutting\nedge\nreputation\nsystem,\nthe\nscoring\nscheme\nremains\nflexible,\nallowing\nfor\nthe\nadoption\nof\nimproved\nmethods\nto\nmeasure"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:13:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 13, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:13:1", "chroma:document": "remains\nflexible,\nallowing\nfor\nthe\nadoption\nof\nimproved\nmethods\nto\nmeasure\nUptime,\nComputation,\nand\nReputation\nin\nthe\nfuture.\n3.5\nNode\nSplitting\nLicensed\nnodes\nin\nHyperCycle\nhave\nthe\nunique\nability\nto\nundergo\ntwo\ntypes\nof\nsplitting\nto\nexpand\ntheir\ncapabilities.\nThe\nsplitting\ncapacity\nof\nHyperCycle\nlicenses\nand\nHyPC\nnative\ntokens\nrelies\non\nthe\nsplitting\ncapacity\nof\nthe\nSato-Servers\nthat\ninstantiate\nthem.\nConsequently,\nas\neach\nnode\nundergoes\nsplitting,\nthe\nprocess\nfollows\nthe\nnested\nmodel\nof\nthe\nEarth\n64\ndata\nstructure,\nwhile\nalso\nmaintaining\na\nhuman-readable\nprefix\nindicating\nthe\nsize\nof\nthe\nnode.\nThe\nfirst\ntype\nof\nsplitting\nbegins\nwith\na\nMaster\nNode\nlicense\nof\nAI\nDistributed\nComputation\nPower\n(AIDCP),\ndenoted\n10AIDCP_1024.\nSuch\na\nlicense\ncan\nbe\nsplit\ninto\n512\nindividual\nnode\nlicenses\nby"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:13:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 13, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:13:2", "chroma:document": "10AIDCP_1024.\nSuch\na\nlicense\ncan\nbe\nsplit\ninto\n512\nindividual\nnode\nlicenses\nby\nits\nowner\nwithout\nany\nconditions,\nmeaning\nthat\nit\ncan\nbe\nsplit\ninto\nits\nconstituent\nlicenses\nwhether\nor\nnot\nit\nhas\nundergone\na\nProvenance\nMarriage\nwith\nsufficient\nHyPC\ntokens\nto\nactivate\nit.\nMore\ndetails\non\nthis\nsplitting\nprocess\ncan\nbe\nfound\nin\nAppendix\nA.1.\nThe\nsecond\ntype\nof\nsplitting\nallows\nindividual\nlicensed\nnodes\nto\nmultiply\nin\nnumber.\nAn\nindividual\nlicense\nthat\nhas\nnot\nsplit\nyet\nis\ndenoted\n1AIDCP_1024\nor\nAIDCP_1024.\nTo\nbe\nable\nto\nsplit,\nthe\nnode\nmust\nhave\nundergone\na\nProvenance\nMarriage\nwith\na\n1024\nHyPC\nidentity\ntoken,\nbecause\nthis\nsplitting\nis\ntied\nto\nthe\nvalue\nof\nthe\nnode\nquality\nscore,\nwhich\nmust\nreach\n2\nto\nallow\na\nsplit.\nWhen\nthis\nhappens,\nthe\nnode\ncan\nsplit\ninto\ntwo\nAIDCP_512\nnodes,\neach\nwith\n512"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:13:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 13, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:13:3", "chroma:document": "When\nthis\nhappens,\nthe\nnode\ncan\nsplit\ninto\ntwo\nAIDCP_512\nnodes,\neach\nwith\n512\nHyPC\nto\nback\ntheir\nidentity.\nThe\nnew\nchild\nnodes\ncreated\nfrom\nthe\nparent\nnode\nstart\nwith\na\nscore\nof\n1\nand\ninitially\nshare\nidentical\ngenetics\nobtained\nfrom\ntheir\nparent\u2019s\nidentity\n(married\nlicense\nand\nidentity\ntokens),\nbut\nthey\nare\ndecoupled\nfrom\neach\nother\nin\noperation.\nConsequently,\ntheir\nscores\nevolve\nindependently,\nallowing\none\nnode\nto\nchoose\nto\nsplit\nwhile\nthe\nother\nmay\nopt\nnot\nto.\nHowever,\nif\nboth\nchild\nnodes\nachieve\na\nscore\nof\n2\nand\ndecide\nto\nsplit,\nthe\ntotal\nnumber\nof\nlicensed\nnodes\nbecomes\nfour,\nall\nAIDCP_256\nnodes.\nWith\neach\niteration\nof\nthe\nindividual\nnode\nsplitting\nprocess,\nthe\nnumber\nof\nnodes\ncan\ndouble.\n14"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:14:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 14, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:14:0", "chroma:document": "The\nprocess\nof\nsplitting\na\nsingle\nnode\nlicense\ncan\niterate\nup\nto\n10\ntimes,\nresulting\nin\nthe\ngeneration\nof\nup\nto\n1024\nlicensed\nnodes\nfrom\nthe\noriginal\none.\nAt\nthe\nend\nof\n10\niterations,\neach\nof\nthe\nnodes,\ndenoted\nAIDCP_1,\nonly\nneeds\n1\nHyPC\nto\nback\nits\nidentity.\nThe\nEarth\n64\ndata\nstructure\nguarantees\nthat\nno\nfurther\nsplitting\nis\npossible\nbeyond\nthis\npoint.\nNote\nthat\nit\nfollows\nfrom\nthis\ndesign\nthat\nthe\nmultiplication\nprocess\ndoes\nnot\nrequire\nacquiring\nadditional\nHyPC\nor\nlicenses;\nnodes\nsimply\nadhere\nto\nHyperCycle\u2019s\nadaptation\nof\nthe\nSato-Server\nsplitting\nprotocol\naccordingly.\nThere\nare,\nhowever,\nplanned\nvariations\nof\nthis\nprotocol\nto\nallow\nexpedited\nsplitting.\nDespite\nhaving\nfewer\ncoupled\nHyPC\nfor\ntheir\nidentities,\nthe\nsplit\nindividual\nnodes\nmaintain\nthe\nsame\ncapabilities\nas\ntheir\nparents."}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:14:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 14, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:14:1", "chroma:document": "the\nsplit\nindividual\nnodes\nmaintain\nthe\nsame\ncapabilities\nas\ntheir\nparents.\nNode\nowners\nand\noperators\nare\nincentivized\nto\nprovide\nhigh-quality\nservice\nto\nthe\nnetwork,\nallowing\ntheir\nnodes'\nscores\nto\nconsistently\nincrease\nto\n2\nat\napproximately\nsix-month\nintervals.\nBy\nactively\nparticipating\nin\nthe\nnode\nsplitting\nprocess,\noperators\ncan\nparticipate\nin\nthe\nwealth\ncreation\nopportunities\nfacilitated\nby\nHyperCycle.\nIt\nis\nimportant\nto\nappreciate\nthat\na\nsingle\nnode\ncan\nrepresent\neither\na\nsmall\ncomputer\nor\nan\nentire\nHPC\ncluster.\nGrowth\nbeyond\nthis\npoint\nis\nindefinitely\npossible,\nbut\nit\nmay\nnot\nfollow\nthe\nsame\ngenetic\nmarkers\nindicated\nin\nthis\nproject\nat\nthis\ntime.\nThis\nlimitation\nis\ninfluenced\nby\nfactors\nsuch\nas\ndeterministic/finite\nlookups,\nefficiencies,\ncrypto-economics,\nincentives,\nrelative"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:14:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 14, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:14:2", "chroma:document": "lookups,\nefficiencies,\ncrypto-economics,\nincentives,\nrelative\nutilities,\nand\nothers.\nUltimately,\nthe\ngrowth\nof\nthe\nnetwork\nis\nbounded\nby\nthe\nlaws\nof\nphysics\n(space,\ntime)\nand\nthe\nprotocol\nneeds\nto\nbe\nin\nline\nwith\nthe\nfull\ngrowth\npotential\nthis\nentails.\n15"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:15:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 15, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:15:0", "chroma:document": "4\nHyperCycle\nTransactions\nIn\nthis\nsection\nwe\nconsider\nhow\nthe\ndifferent\ncomponents\nof\nan\nHCN\u2015the\nVM,\nTM\nand\nAIM\u2015work\ntogether\nto\nexecute\ntransactions\nin\nthe\nHyperCycle\nnetwork.\nIn\nany\nsuch\nsituation,\nno\nmatter\nhow\ncomplex\na\ntransaction\nis\nand\nhow\nmany\nlayers\nof\nsubcontracting\nit\ninvolves,\nthe\ntransaction\ncan\nbe\nbroken\ndown\ninto\npieces\ninvolving\none\nnode\nrequesting\nwork\nfrom\nanother\nnode,\nand\nsending\nit\na\npayment\ncommitment\nalong\nwith\ndata\nand\nthe\ninstructions\nfor\nhow\nto\nprocess\nit.\nThe\nnode\nrequesting\nwork\nis\ncalled\nthe\nrequestor\nand\nthe\nnode\nperforming\nthe\nwork\nthe\nexecutor,\nand\nany\nnode\ncan\nbe\na\nrequestor\nor\nan\nexecutor\nor\nboth.\nSuppose\nfor\nsimplicity,\nthen,\na\nsimple\ntransaction\ninvolving\ntwo\nparties.\nLet\nAlice\nbe\nthe\nrequestor\nand\nBob\nbe\nthe\nexecutor.\nThe\ndata\nflow\ndiagram\nbelow"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:15:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 15, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:15:1", "chroma:document": "Let\nAlice\nbe\nthe\nrequestor\nand\nBob\nbe\nthe\nexecutor.\nThe\ndata\nflow\ndiagram\nbelow\nillustrates\nat\na\nhigh\nlevel\nthe\nkinds\nof\nmessages\nexchanged\nbetween\ndifferent\ncomponents\nto\nexecute\na\ntransaction\nin\nthe\nHyperCycle\nnetwork.\nWe\nwill\nfocus\non\nthe\ncore\nelements\nof\na\ntransaction,\nbut\ncommunication\nwith\none\nor\nmore\nservice\nnetworks\nmay\nbe\nneeded\nfor\nAlice\nand\nBob\nto\nfind\neach\nother\nbased\non\nthe\nservices\nneeded\nand\noffered\nand\nat\nwhat\nprices.\n16"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:16:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 16, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:16:0", "chroma:document": "Following\ndiscovery,\nAlice\nand\nBob\nnegotiate\ntheir\nterms\nfor\nthe\ntransaction,\nwhich\ncan\ninvolve\nexchanging\nmessages,\nsuch\nas\nwhat\nAlice\nis\nwilling\nto\npay\n(\nmakePayOffer\n)\nand\nwhat\nBob\nis\nwilling\nto\ncompute\n(\nmakeComputeOffer\n).\nOnce\nthis\nhas\nbeen\nworked\nout,\nthe\ntransaction\ncan\nbegin.\nNote\nthat\nthis\nnegotiation\nstep\nneed\nnot\nhappen\nevery\ntime\nAlice\nwants\nBob\nto\ndo\nsome\nwork,\nas\nlong\nas\nthe\nassumptions\non\nwhich\nthe\nagreement\nis\nbased\nare\nstill\nvalid.\nTo\nexecute\na\ntransaction,\nAlice\nmust\nverify\nshe\nhas\nsufficient\nfunds\nto\npay\nfor\nBob\u2019s\nwork,\nso\nher\nVM\nasks\nher\nTM\nto\ntell\nher\nwhat\nher\nbalance\nis\n(\ngetBalance\n),\nand\nthe\nTM\nreplies\n(\nsendResult\n).\nIf\nthis\npasses,\nshe\ncan\nexecute\nthe\ntransaction\nby\nhaving\nher\nVM\ninstruct\nher\nTM\nto\nproceed\n(\nexecuteTx\n)\nand\nsending\nthe\ninstructions\nand\ndata\nto"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:16:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 16, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:16:1", "chroma:document": "her\nTM\nto\nproceed\n(\nexecuteTx\n)\nand\nsending\nthe\ninstructions\nand\ndata\nto\nBob\u2019s\nVM\n(\nexecuteComputeTx\n).\nHer\nTM\nwill\nsend\nBob\u2019s\nTM\nthe\npayment\ndata\n(\npaymentData\n)\nas\na\nSato-Server\nand\nwill\nregister\non\nthe\nTODA/IP\nnetwork\nher\ncommitment\nto\npay\nBob\nif\nhe\nreturns\nher\nthe\noutput\n(\ncommitPayment\n).\nOn\nBob\u2019s\nend,\nhis\nVM\nreceives\nthe\ndata\nand\ninstructions\nfrom\nAlice,\nhis\nTM\nreceives\nthe\npayment\ndata\nfrom\nAlice,\nand\nhis\nTM\nreceives\na\nproof\nfrom\nthe\nTODA/IP\nnetwork\nthat\nAlice\nhas\ncommitted\npayment\nto\nhim\nprovided\nthat\nhe\nreturns\nher\nthe\noutput\n(\ncommitmentProof\n).\nUpon\nreceipt\nof\nthe\ninstructions\nand\ndata,\nBob\u2019s\nVM\nwaits\nfor\na\nmessage\nfrom\nhis\nTM\n(\nfundsReceived\n)\nindicating\nthat\nthe\npayment\nhas\nbeen\nconfirmed\nto\nbe\ncommitted\nto\nhim.\nAt\nthis\npoint\nBob\nproceeds\nby\nsending\nthe\ndata\nand\ninstructions"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:16:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 16, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:16:2", "chroma:document": "to\nhim.\nAt\nthis\npoint\nBob\nproceeds\nby\nsending\nthe\ndata\nand\ninstructions\nto\nhis\nAIM\n(\nexecuteCompute\n),\nwho\ndoes\nthe\nrequested\ncomputation\nand\nreturns\nthe\nresult\nto\nBob\u2019s\nVM\n(\nreturnOutput\n),\nwhich\nwe\nassume\nincludes\nan\nattestation\nthat\nthis\nis\nthe\ncorrect\noutput\nthat\nBob\ncan\nuse\nto\nsatisfy\nthe\nconditions\nfor\nthe\nrelease\nof\npayment.\nAt\nthis\npoint,\nBob\u2019s\nVM\nreturns\nthe\noutput\nto\nAlice\nand\nsends\nthe\nattestation\nto\nhis\nTM\n(\nexecuteTx\n)\nto\nrelease\nthe\npayment.\nBob\u2019s\nTM\nupdates\nthe\nstate\nof\nthe\npayment\ndata\nwith\nthe\nproof\nthat\ncomputation\nhas\ncompleted,\nsending\nthis\nto\nthe\nTODA/IP\nnetwork\nto\nregister\nthis\nfact\n(\ncomputationProof\n).\nAt\nthe\nend\nof\nthe\nTODA/IP\ncycle\n(less\nthan\na\nsecond),\nBob\u2019s\nTM\nreceives\nback\na\nproof\nfrom\nthe\nTODA/IP\nnetwork\nthat\nthe\npayment\nis\ncomplete\n(\npaymentProof\n).\nThis"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:16:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 16, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:16:3", "chroma:document": "from\nthe\nTODA/IP\nnetwork\nthat\nthe\npayment\nis\ncomplete\n(\npaymentProof\n).\nThis\ncompletes\nthe\ntransaction.\nAlice\nhas\nreceived\nthe\nrequested\noutput\nand\nBob\nhas\nreceived\nthe\npayment.\nBut\nnote\nthat\nBob\ncannot\nspend\nthe\nfunds\nreceived\nfrom\nAlice\nuntil\nthe\nend\nof\nthe\nnext\nnetwork\nblock,\nwhich\ntakes\n6-30\nseconds.\n17"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:17:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 17, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:17:0", "chroma:document": "5\nConclusion\nHyperCycle\nComputation\nNodes\nfunction\nas\nthe\ncore\nagents\nwithin\nthe\nHyperCycle\nnetwork.\nTheir\nability\nto\nrapidly\ncontract\nand\nsubcontract\neach\nother\nP2P\nin\nadaptive\nways\nfosters\ncooperation\nand\ncompetition\nfor\nbeneficial\nemergence\nand\nprogressive\nself-evolution.\nTheir\nmarriage\nof\na\nHyperCycle\nlicense\nand\nthe\nnative\nHyPC\ntoken,\nalong\nwith\nvalue\ngeneration\nand\nproof\nmechanisms\nthat\nresult\nfrom\nthe\nmarriage,\nincentivize\nnode\noperators\nto\ncontribute\nto\nthe\nnetwork's\nquality\nof\nservice,\nfunctionality,\nand\nsecurity.\nBeyond\nthe\ncore\nfunctionality,\nthe\nHyperCycle\necosystem\noffers\nvast\npossibilities.\nSingularityNET\nenvisions\nan\nAI\nmarketplace\nwhere\nhumans\nand\nmachines\ncan\ntransact,\nadvancing\nmachine\nintelligence\nand\npaving\nthe\nway\nfor\ntrue\nAGI.\nAdditionally,\nmarketplaces\nfor"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:17:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 17, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:17:1", "chroma:document": "intelligence\nand\npaving\nthe\nway\nfor\ntrue\nAGI.\nAdditionally,\nmarketplaces\nfor\nhigh-performance\ncomputing\ncan\nleverage\nHyperCycle\nas\na\nservice,\nsupporting\nadvancements\nin\nvarious\nfields\nsuch\nas\nscience,\nmedicine,\nand\ntechnology.\nThe\necosystem\nalso\nincorporates\npractical\nservices\nlike\npools\nand\nmarketplaces\nthat\nenable\nnodes\nto\nefficiently\nadvertise\nand\nnegotiate\nservices.\nWith\nHyperCycle's\ndecentralized\ninteroperability\nbuilt\nupon\nTODA/IP\nand\nEarth\n64\n's\nSato-Servers,\nthe\npotential\nfor\nsynergistic\nemergence\nis\nboundless.\nThe\ndiverse\nparticipants\nin\nthe\nHyperCycle\necosystem\ncan\ncollaboratively\nbuild\nan\ninternet\nof\nAI\nand\nhigh-performance\ncomputing,\nserving\nthe\nbetterment\nof\nhumanity\nand\nthe\nplanet\nas\na\nwhole.\n18"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:18:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 18, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:18:0", "chroma:document": "6\nFAQ\nQuestion\n:\nDoes\nthe\nnode\noperator\nhave\nto\nprovide\nthe\nlicense\nand\nidentity\ntokens\nor\ncan\nthese\nbe\ndelegated\nto\nthe\noperator?\nAnswer\n:\nThe\nlicense\nand\nidentity\ntokens\ncan\nbe\nowned\nby\nthe\nnode\noperator\nbut\ncan\nalso\nbe\ndelegated\nto\nthe\nnode\noperator\nby\nothers.\nLicense\nand\nc_HyPC\npools\ncan\nbe\nused\nfor\nthis\npurpose,\nfor\nexample.\nQuestion\n:\nDoes\nthe\nsplitting\nof\na\nnode\ninto\ntwo\nchild\nnodes\nmean\nthat\nthree\nnodes\nare\nnow\nin\noperation?\nAnswer\n:\nNo,\nthe\nsplitting\nof\na\nnode\ngenerally\nmeans\nthat\nthe\nparent\nnode\nno\nlonger\nexists.\nThere\nare,\nhowever,\ndeployment\nexceptions\nwhere\nthe\nparent\nnode\nbecomes\na\nworker\nfor\nthe\nchildren,\nbut\nthe\nparent\nnevertheless\ndoes\nnot\nfunction\nas\na\nnode\non\nthe\nnetwork.\nQuestion\n:\nWhen\na\nnode\nsplits\nafter\nhaving\nincreased\nits\nscore\nto\n2,\nare\nmore\ntokens\nrequired\nto\nrun"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:18:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 18, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:18:1", "chroma:document": "splits\nafter\nhaving\nincreased\nits\nscore\nto\n2,\nare\nmore\ntokens\nrequired\nto\nrun\none\nof\nthe\nnew\nnodes?\nAnswer\n:\nNo,\nthis\nis\nthe\nreason\nfor\nneeding\n1024\nHyPC\nto\nback\nthe\nidentity\nof\na\nnode.\nWhen\na\n1024\nHyPC\nnode\nsplits\ninto\ntwo,\nthe\ntwo\nnew\nnodes\nhave\n512\nHyPC\neach\nbacking\ntheir\nidentity.\nEach\ntime\na\nnode\nsplits\nin\nthis\nway,\nthe\nnew\nnodes\nhave\nhalf\nthe\namount\nof\nHyPC\nbacking\ntheir\nidentity\nas\nthe\nparent\ndid,\ndown\nto\nthe\nlevel\nat\nwhich\nthe\nnodes\nhave\na\nsingle\nHyPC\nbacking\ntheir\nidentity,\nat\nwhich\npoint\nno\nfurther\nsplits\nare\npossible.\nQuestion\n:\nCan\nmultiple\nnodes\nbe\nrunning\non\na\nsingle\ndevice?\nAnswer\n:\nYes,\nthe\nprotocol\nallows\nmany\nnodes\nto\nrun\non\na\nsingle\ndevice.\nThere\nare\nphysical\nlimitations\nto\nhow\nmany\nnodes\ncan\nrun\non\na\nsingle\ndevice,\nin\nterms\nof\nprocessing\npower,\nmemory,\nnetworking,"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:18:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 18, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:18:2", "chroma:document": "can\nrun\non\na\nsingle\ndevice,\nin\nterms\nof\nprocessing\npower,\nmemory,\nnetworking,\nenergy\nconsumption,\netc.,\nbut\nthe\nprotocol\nplaces\nno\nsuch\nlimitations\non\nhow\nmany\nnodes\nper\ndevice.\nQuestion\n:\nCan\na\n512\nnode\npackage\nrun\non\na\nsingle\ncomputer?\nAnd\nis\n512\nx\n1024\n=\n524,288\nHyPC\nneeded\nto\nrun\nthis\nfully?\nAnswer\n:\nAs\nexplained\nin\nthe\nprevious\nanswer,\nyes,\n512\nnodes\ncan\nrun\non\na\nsingle\nnode\nprovided\nthat\nthe\nmachine\nis\npowerful\nand\nefficient\nenough\nto\ndo\nso.\nTo\nrun\nthe\nfull\npackage\nwith\nthe\nfull\nbenefits\nof\nthe\nmeasurement\nof\nuptime,\ncomputation\nand\nreputation,\nyes,\neach\nof\nthe\n512\nnodes\nmust\nbe\nbacked\nby\n1024\nHyPC.\nThe\nnode\ncan\noperate\nin\nthis\nway\nwithout\nsplitting\nthe\npackage\ninto\nindividual\nnodes,\nallowing\nall\nof\nthe\nnodes\ntogether\nto\naccumulate\nthe\nsame\nnode\nscore.\nQuestion\n:\nHow\nis\nproof\nof"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:18:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 18, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:18:3", "chroma:document": "nodes\ntogether\nto\naccumulate\nthe\nsame\nnode\nscore.\nQuestion\n:\nHow\nis\nproof\nof\nreputation\nof\na\nnode\ncalculated\nand\nused\nin\nthe\nnetwork?\nAnswer\n:\nAnton\u2019s\nteam\nis\nworking\non\na\ncutting\nedge\nreputation\nsystem,\non\nwhich\nthere\nare\npublished\nwhitepapers.\nWhichever\nsystem\nis\ndeveloped,\nit\nwill\ntake\nsix\nmonths\nto\nincrease\na\nnode\u2019s\nreputation\nfrom\n1\nto\n\u221b\n2,\nwhich\nis\nthe\ncrucial\nfactor\nin\ndetermining\nthe\nmultiplicative\nnode\nscore\nthat\ndetermines\nwhether\na\nnode\ncan\nspit\ninto\ntwo.\nThe\ndetailed\nreputation\ncalculation\nitself,\nhowever,\nmay\nbe\ngiven\n19"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:19:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 19, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:19:0", "chroma:document": "more\nweight\nby\ncertain\nsections\nof\nthe\nnetwork\nfor\nthe\npurposes\nof\nbuilding\ntrust\nand\nfaster\ntransactions,\nbut\nthese\nfeatures\nare\nindependent\nof\nnode\nsplitting.\nQuestion\n:\nHow\ndoes\na\nnode\nknow\nthat\nanother\nnode\nhas\nmultiplied?\nAnd\nhow\ndoes\nthe\nreputation\nof\nthe\nparents\nfactor\ninto\nthe\nreputation\nof\nthe\nchildren?\nAnswer\n:\nEvery\nnode\nthat\nhas\nsplit\nis\nthe\ndescendant\nof\na\nnode\nformed\nfrom\nthe\nmarriage\nof\na\nsingle\nlicense\nand\n1024\nHyPC.\nWhen\nthe\nnode\nsplits,\nit\ncarries\nthe\ngenetics\nof\nthe\nparent\nas\ncryptographic\nproof\nof\nthe\nmarriage\nand\nof\nthe\nconception\nof\nthe\ntwo\nchild\nnodes,\nwhich\nany\nnode\ncan\ninspect\nto\nobtain\na\nproof\nthat\na\nnode\nhas\nmultiplied.\nIn\nterms\nof\nreputation,\nthe\nparents\nwill\nalso\nhave\nproof\nthat\nthey\nreached\na\nscore\nof\n2\nbefore\nmultiplying,\nand\ntherefore\na\nreputation\nof\n\u221b\n2"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:19:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 19, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:19:1", "chroma:document": "they\nreached\na\nscore\nof\n2\nbefore\nmultiplying,\nand\ntherefore\na\nreputation\nof\n\u221b\n2\nbefore\nmultiplying.\nSo\neven\nthough\nthe\nnew\nchildren\nonly\nhave\na\nscore\nand\nreputation\nof\n1,\nit\nis\nknown\nthat\ntheir\nparents\nwere\nhighly\nreliable\nnodes,\nwhich\ncan\npotentially\nbe\nutilized\nin\nsome\nsections\nof\nthe\nnetwork\nto\ngive\nhigher\ntrust\nto\nthe\nnew\nchildren.\nQuestion\n:\nWhat\nare\nHyperCycle\u2019s\nsources\nof\nrevenue?\nAnswer\n:\nHyperCycle\nhas\ntwo\nsources\nof\nrevenue.\nOne\nis\nfrom\nthe\nsale\nof\nsoftware\nlicenses\nand\ntokens,\nwhich\nare\nintended\nto\ngrow\nthe\ncompany.\nThe\nsecond\nis\nthe\n1%\nroyalty\non\nall\nnode\nrevenue\nthat\nis\ncollected\nto\nincentivize\nall\nparties\nin\nthe\necosystem\nincluding\nHyperCycle.\nThe\nroyalty\nsystem\nis\na\nsubset\nof\npayments,\nto\nyield\nmore\nprogressive\nevolution\nof\nthe\necosystem\nas\na\nwhole\nrather\nthan\npurely\nto"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:19:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 19, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:19:2", "chroma:document": "more\nprogressive\nevolution\nof\nthe\necosystem\nas\na\nwhole\nrather\nthan\npurely\nto\ngenerate\nprofit\nfor\nHyperCycle.\nQuestion\n:\nIs\nthe\nuse\nof\nHyPC\nto\nback\nthe\nidentity\nof\na\nnode\nscalable\nwith\na\nlarge\nrise\nin\nthe\ntoken\nprice?\nAnswer\n:\nFor\nthe\nidentity\ntokens,\nthe\nrise\nin\nprice\nof\nHyPC\nincreases\nthe\ncost\nto\nset\nup\na\nnode\nin\nproportion\nto\nthe\nincrease\nin\nHyPC\nprice.\nFor\nlarge\nincreases\nin\nprice\nthis\ncan\nstill\nbe\neconomical\nfor\nlarge\ncentralized\noperations\nrelying\non\na\nsmall\nnumber\nof\nnodes,\nbut\nis\nless\nso\nfor\nthose\nrequiring\na\nlarge\nnumber\nof\nnodes.\nIn\neither\ncase,\noperations\ndo\nbenefit\nfrom\nbeing\nearly\nadopters\nof\nthe\necosystem.\n20"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:20:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 20, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:20:0", "chroma:document": "A\nAppendix\nA.1\nVarious\nKinds\nand\nForms\nof\nHyperCycle\nTokens\nThe\ndifferent\ntypes\nof\ntokens\nand\ntheir\nrepresentations\nrequire\nclarification\nto\navoid\nconfusion.\nIn\nthis\ncontext,\nit\nis\nimportant\nto\nunderstand\nthat\ntrue\nnode\nlicenses\nand\nnative\ntokens\nare\nSato-Servers,\nand\nas\nsuch\nexist\nindependently\nof\nany\nblockchain.\nHowever,\nfor\nconvenience\nand\nnetwork\nfunctionality,\nthese\nSato-Servers\nhave\nNFT\ntoken\nrepresentations\non\nvarious\nblockchains.\nThe\nnode\nlicenses\navailable\non\nledgers\nlike\nADA,\nMATIC,\nand\nETH\nare\nmerely\nNFTs\nthat\npoint\nto\na\nspecific\nlicense\nusing\nthe\nglobally\nunique\nbinary\naddress\nof\nthe\ncorresponding\nSato-Server.\nMastern\nNode\nlicenses,\nwith\nthe\nprefix\n10AIDCP_1024\n,\nrepresent\nSato-Servers\ncontaining\na\npackage\nof\n512\nnode\nlicenses.\nThese\nMaster\nNode\nlicenses\ncan\nbe\ndivided\ndown\n9"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:20:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 20, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:20:1", "chroma:document": "package\nof\n512\nnode\nlicenses.\nThese\nMaster\nNode\nlicenses\ncan\nbe\ndivided\ndown\n9\niterations\n(9\nlevels\nof\nthe\nEarth\n64\ntree),\nresulting\nin\nindividual\nnode\n1AIDCP_1024\nlicenses.\nFurther\nsplits\noccur\nwhen\nthe\nquality\nscore\nof\nsuch\na\nnode\nreaches\n2,\ngenerating\ntwo\n1AIDCP_512\nlicenses.\nThis\nprocess\ncan\niterate\nup\nto\n10\ntimes,\nultimately\nreaching\n1AIDCP_1\nlicenses\nat\nthe\nbottom\nof\nthe\nEarth\n64\ntree,\nwhich\ncannot\nbe\nsplit\nfurther.\nNote\nthat\nthe\nidentifiers\nfor\nthe\nlicenses\non\nchain\nalso\ncontain\na\nsuffix\nthat\ngives\nthe\ninteger\nequivalent\nof\nthe\nbinary\nSato-Server\naddress\nthat\ninstantiates\nthe\nlicense.\nFor\nexample,\nthe\nMaster\nnode\nwith\nidentifier\n10AIDCP_1024.17593259786358\nhas\n17593259786358\nas\nthe\naddress\nof\nits\nSato-Server.\nThis\ninteger\ndetermines\nthe\nlicense\u2019s\nprecise\nlocation\nin\nthe\nEarth\n64"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:20:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 20, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:20:2", "chroma:document": "This\ninteger\ndetermines\nthe\nlicense\u2019s\nprecise\nlocation\nin\nthe\nEarth\n64\nbinary\ntree\nand\nits\nunique\ngeographical\nlocation\non\nEarth\u2019s\nsurface.\nThere\nis\na\nsimilar\ndivide\nbetween\non-chain\nrepresentations\nof\nthe\nnative\ntoken\nand\ntheir\ntrue\nform\nas\nSato-Servers.\nFungible\nHyPC.X\ntokens\nand\nnon-fungible\nc_HyPC.X\ntokens\nrepresent\nthe\nnative\ntoken\non-chain.\nc_HyPC.X\ntokens\ndirectly\ncorrespond\nto\nspecific\nSato-Servers\nbased\non\ntheir\nglobally\nunique\nbinary\nSato-Server\naddress,\nmaking\nthem\nthe\ntrue\non-chain\nrepresentation\nof\nthe\nnative\ntoken.\nThe\nfungible\nHyPC.X\ntokens\nserve\nonly\nas\na\nconvenience\ntoken.\nThe\nsymbol\n\"HyPC\",\nwithout\nthe\n\u201c.X\u201d,\nis\nreserved\nfor\nthe\nnative\ntoken\nSato-Servers\nthemselves\nand\nthe\nunit\nof\nvalue\nthey\ncarry.\nEach\nHyPC\ntoken\ncarries\na\nspecific\nvalue\ndetermined\nby\nits\nlocation\nin\nthe"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:20:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 20, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:20:3", "chroma:document": "Each\nHyPC\ntoken\ncarries\na\nspecific\nvalue\ndetermined\nby\nits\nlocation\nin\nthe\nEarth\n64\ntree.\nAs\nsuch,\nthe\nvalue\nof\neach\nHyPC\ntoken\nSato-Server\nis\nalways\na\npower\nof\n2\nHyPC,\nranging\nfrom\n2\n-18\nto\n2\n19\n.\nThus,\nthe\nsmallest\ndenomination\nof\nHyPC\nis\nabout\n4\nmillionths\nof\nan\nHyPC.\nThis\npartially\nsatisfies\nthe\n6\ndecimal\npoints\nin\nthe\ncurrent\npointers\nof\nHyPC.ETH,\nHyPC.BNB,\nHyPC.ADA,\nHyPC.ALGO,\nHyPC.AVA\nand\nHyPC.MATIC.\nBecause\nc_HyPC.X\ntokens\ndirectly\npoint\nto\nHyPC\n21"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:21:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 21, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:21:0", "chroma:document": "tokens,\ntheir\nvalue\nis\nalso\nrestricted\nto\na\nquantity\nof\nHyPC\nthat\nis\na\npower\nof\n2.\nAs\nof\nthis\nwriting,\nonly\nc_HyPC.X\ntokens\nwith\n2\n19\n=\n524,288\nHyPC\nare\navailable,\nthough\nsmaller\ndenominations\nand\na\nlimited\non-chain\nsplitting\nability\nof\nthese\ntokens\nare\nplanned.\nIt\nis\nworth\nnoting\nthat\nthe\n\u201cc\u201d\nin\n\u201cc_HyPC\"\nstands\nfor\n\u201ccontainerized\u201d.\nThis\nis\nbecause\nthe\nHyPC\nSato-Servers\nnot\nonly\ncarry\na\ncertain\nvalue\nin\nHyPC\nbased\non\ntheir\nlocation\nin\nthe\nEarth\n64\ntree\nbut\nalso\nare\ncontainers\nthat\ncan\nhold\nother\ntokens.\nA.2\nProvenance\nSystem\nA\nkey\npart\nof\nthe\ntechnical\nside\nof\nHyperCycle\nis\nthe\nprovenance\nsystem\nthat\nprovides\nrigorous,\ncryptographic\nprovenance\nfor\nthe\nnative\ntoken\nand\nthe\nlicenses.\nAs\ndescribed\nin\nthe\nprevious\nsubsection,\nthere\nare\npointers\nto\nthe\nnode\nlicenses\nand\nnative\ntoken\navailable"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:21:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 21, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:21:1", "chroma:document": "subsection,\nthere\nare\npointers\nto\nthe\nnode\nlicenses\nand\nnative\ntoken\navailable\nfor\npurchase\nand\nmanagement\nin\nlimited\nways\non\nchain.\nThe\nnode\nlicense\npointers\nare\nNFTs\nand\nthe\nnative\ntokens\nhave\nfungible\nand\nnon-fungible\nversions.\nAs\nof\nthe\ntime\nof\nwriting\nthe\nfungible\nnative\ntoken\npointers\nare\nhosted\non\nEthereum\n(ETH)\nand\nBinance\n(BNB),\nwhile\nthe\nnon-fungible\nnative\ntoken\npointers\nare\nonly\navailable\non\nEthereum;\nthe\nlicense\npointers\nare\navailable\non\nCardano\n(ADA).\nIn\nthe\nnear\nfuture\nlicenses\nwill\nalso\nbe\navailable\non\nPolygon\n(MATIC).\nAvailability\nof\nfungible\ntoken\npointers\nwill\nbe\nextended\nto\nPolygon,\nCadano\nand\nBitcoin\n(BTC).\nThe\nlicenses\navailable\non\nchain\nat\nthe\ntime\nof\nwriting\nare\nonly\nthe\nMaster\nNode\n10AIDCP_1024\nlicenses,\nwhich\nare\na\npackage\nof\n512\nsingle\nnode\n1AIDCP_1024\nlicenses."}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:21:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 21, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:21:2", "chroma:document": "licenses,\nwhich\nare\na\npackage\nof\n512\nsingle\nnode\n1AIDCP_1024\nlicenses.\nSmaller\nlicense\npackages\nand\nlimited\non-chain\nsplitting\nof\nlicense\npointers\nis\nplanned.\nThe\nc_HyPC.ETH\ntokens\navailable\nare\nNFTs\nrepresenting\npackages\nof\n512\n\u00d7\n1024\n=\n524,288\n=\n2\n19\nHyPC\nwhich\nis\nthe\namount\nof\nHyPC\nrequired\nto\nback\nthe\nidentity\nfor\n10AIDCP_1024\nlicenses\nin\na\nProvenance\nMarriage.\nSmaller\nvalue\nc_HyPC.X\ntokens\nand\nlimited\non-chain\nsplitting\nof\nnative\ntoken\npointers\nare\nalso\nplanned.\nThe\nfungible\nform\nof\nthe\nnative\nHyperCycle\ntoken\nwas\ninitially\nreleased\non\nEthereum\n(ETH)\nas\nan\nERC20\ntoken\nwith\nticker\nsymbol\nHyPC,\nwhich\nwe\ndenote\nby\nHyPC.ETH.\nThere\nis\nalso\na\nBinance\nversion\nHyPC.BNB\navailable.\nTo\nuse\nthis\nHyPC\nin\nthe\nHyperCycle\nnetwork\nit\nfirst\nneeds\nto\nbe\nconverted\ninto\nits\nnon-fungible,\non-chain\nc_HyPC"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:21:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 21, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:21:3", "chroma:document": "network\nit\nfirst\nneeds\nto\nbe\nconverted\ninto\nits\nnon-fungible,\non-chain\nc_HyPC\nform,\nwhich\ncan\nbe\ndone\non\nEthereum\nusing\na\nswap\ncontract\nwe\nhave\ndeveloped.\nThis\ncontract\nensures\nthat\nthe\namount\nof\nHyPC.X\nin\ncirculation\n(either\nin\nfungible\nor\nnon-fungible\nform)\nis\nalways\nfixed\nby\nlocking\nup\nunits\nof\nHyPC.ETH\nin\nthe\ncontract\nto\nmake\na\nc_HyPC.ETH\navailable.\nThe\nc_HyPC.ETH\ncan\nlater\nbe\nredeemed,\nreturning\nownership\nof\nthe\nc_HyPC.ETH\nto\nthe\ncontract\nand\nreleasing\nthe\nlocked\nHyPC.ETH.\n22"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:22:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 22, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:22:0", "chroma:document": "In\nterms\nof\nproofs\nof\nprovenance,\nthe\nprovenance\nsystem\nrelies\non\nthe\nproofs\nof\nprovenance\nprovided\nby\nthe\nvarious\nblockchains\ninvolved\nand\nas\nwell\nas\nthe\nEarth\n64\nSato-Server\ndata\nstructure\nand\nTODA/IP.\nProvable\nprovenance\nof\nlicenses\nand\nnative\ntokens\nin\nthis\ncontext\nis\nensured\nby\nthe\ncryptographic\nproperties\nof\nthe\nSato-Server\ndata\nstructure,\nwhich\nensures\nthat\neach\nstate\nupdate\nof\nan\nasset\nis\nhash-chained\nto\nthe\nprevious\nones\nand\nthat\nthese\nstate\nchanges\nare\nunique\n(no\ndouble\nspend).\nThis\nlatter\ncapability\nis\nensured\nby\nincorporating\ninto\nSato-Servers\nelements\nof\nthe\nTODA/FP\n(TODA\nFile\nProtocol),\ndeveloped\nby\nTODAQ\nand\nT.R.I.E.,\nto\nallow\nthe\nintegrity\n(single\nspend)\nof\nassets\nto\nbe\nmaintained\nby\nan\nexternal\nintegrity\nprovider\nwithout\nthe\nintegrity\nprovider\nneeding\nto\nknow\nthe\nstate\nof"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:22:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 22, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:22:1", "chroma:document": "integrity\nprovider\nwithout\nthe\nintegrity\nprovider\nneeding\nto\nknow\nthe\nstate\nof\nthe\nasset\n(so-called\nintegrity-at-a-distance).\nThe\nintegrity\nprovider\nfor\nthe\nSato-Servers\nutilized\nin\nHyperCycle\nis\nTODA/IP,\nwhich\nensures\nthat\nefficient,\nsecure\ndistributed\nconsensus\nis\nreached\non\nthe\nstate\nof\nall\nassets\nit\nmaintains\nglobally,\nwithout\nneeding\nto\nknow\nthe\nasset\nstate\n(ledgerless).\nIn\nthis\nway,\nSato-Servers,\nTODA/FP\nand\nTODA/IP\nall\nwork\nharmoniously\ntogether\nto\nprovide\nefficient\nand\nsecure\nproofs\nof\nprovenance\nfor\nlicenses\nand\nnative\ntokens\nin\nthe\nHyperCycle\nsystem.\nNote\nthat\nonce\nHyPC\ntokens\nand\nlicenses\nare\nconverted\nto\ntheir\nSato-Servers\nthey\ncan\nbe\nheld\ndirectly\non\nthe\ndevice\nthat\nis\noperating\na\nnode.\nMoreover,\nthe\nHyPC\nused\nin\ntransactions\ncan\nbe\nexchanged\ndirectly\nbetween\nnodes\nP2P\nat\nthe"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:22:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 22, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:22:2", "chroma:document": "HyPC\nused\nin\ntransactions\ncan\nbe\nexchanged\ndirectly\nbetween\nnodes\nP2P\nat\nthe\nnetwork\nlevel,\nwithout\ninvolving\na\nthird\nparty\nin\nthe\ntransaction.\nWe\nhave\ncovered\nthe\non-chain\nprovenance\nand\nthe\nSato-Server\nprovenance.\nThe\nonly\nmissing\npiece\nis\nthe\ntransition\nbetween\nthe\ntwo.\nThis\ntransition\nfrom\nthe\non-chain\nrepresentation\nof\na\nlicense\nor\nc_HyPC.ETH\ntoken\nto\nits\nSato-Server\nis\nmanaged\nby\nEarth\n64\nto\nensure\nthis\nprocess\nhappens\nwith\nintegrity.\nThis\nprocess\nrelies\non\nthe\nsame\nmethod\nthat\nSato-Servers\nin\ngeneral\nare\nconverted\nfrom\nan\non-chain\nform\nto\nSato-Server\ndata\nform.\nA.3\nHCN\nVirtual\nMachine\nThe\nheart\nof\nthe\nHCN\nsoftware\nis\nthe\nvirtual\nmachine\n(VM),\nwhich\nprovides\nprogrammable\nfunctionality\nfor\nexecuting\na\nwide\nrange\nof\ntransactions\ninvolving\ndifferent\narrangements\nof\nnodes.\nThe\nVM\nalso"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:22:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 22, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:22:3", "chroma:document": "range\nof\ntransactions\ninvolving\ndifferent\narrangements\nof\nnodes.\nThe\nVM\nalso\nprovides\nseamless\ncommunications\nbetween\ninternal\ncomponents\nwithin\na\nnode\nand\nexternal\nconnections\nwith\nother\nnodes\nto\nnegotiate\nand\nexecute\ncontracts.\nInternally\nto\na\nnode,\nthe\nVM\nis\nresponsible\nfor\ncommunicating\nwith\nthe\nTM\nto\nensure\nthat\nthe\nappropriate\nconditions\nare\nmet\nto\nsend\nor\nreceive\npayments\nand\nthat\nsufficient\nproof\ncan\nbe\nprovided\nto\nthe\nTODA/IP\nnetwork\nto\nrelease\npayment\nafter\ncomputational\nwork\nhas\nbeen\ndone.\nThe\nVM\nalso\ncommunicates\nwith\nthe\nAIM,\nto\nexecute\ncomputational\nwork,\nto\ndetermine\nbased\non\nresponses\nfrom\nthe\nAIM\nwhether\nsubcontracting\nwork\nto\nother\nnodes\nneeds\nto\noccur,\nand\nto\ncollect\nattestations\nof\ncomputational\nwork\nto\nconstruct\nthe\nproofs\nneeded\nby\nthe\nTM.\n23"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:23:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 23, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:23:0", "chroma:document": "Externally,\nthe\nVM\nis\nresponsible\nfor\na\nrange\nof\ndifferent\ncommunications.\nThis\nincludes\npublishing\nthe\nnode\u2019s\nservice\ncapacities\nand\nprices,\nand\ndiscovering\nthose\nof\nother\nnodes.\nThis\ncan\nhappen\nP2P\nbetween\nnodes\nbut\nmay\nalso\nmake\nuse\nof\nsecondary\nnetworks.\nIt\nalso\nincludes\nconducting\nnegotiations\nwith\nother\nnodes\nto\nestablish\nthe\nservice\nrequirements\nfor\nsome\nkind\nof\ncomputation\nthat\na\nnetwork\nof\nnodes\nwants\nto\nparticipate\nin.\nThis\nis\nessential\nto\nestablish\nreliable\nsubcontracting\nbetween\nnodes\nto\nensure\nthat\na\nlarger\ncomputation\ncan\nbe\ncompleted\nfor\na\ngiven\nuser\nor\nuser\ngroup.\nAnd\nthe\nVM\nmust\nalso\nconduct\nthe\nactual\ntransactions\nbetween\nnodes,\nby\ntransferring\ninstructions\nand\ndata\nbetween\nnodes,\nwhile\nensuring\nthat\nthe\nTMs\nin\nquestion\nsend\nand\nreceive\nthe\nrequired\npayment\ninformation"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:23:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 23, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:23:1", "chroma:document": "that\nthe\nTMs\nin\nquestion\nsend\nand\nreceive\nthe\nrequired\npayment\ninformation\nand\nproofs.\nHigh-performance\ncomputation\nis\nparamount,\nand\nzero\nknowledge\nremains\nprohibitively\nexpensive,\nso\nwe\nattest\nto\nthe\ncorrect\nexecution\nof\nthe\nVM\nand\nAIM\nvia\nthe\nsecure\nenclave\ntechnology\navailable\non\nsome\nmodern\nCPUs\nand\nGPUs.\nThis\ntechnology\nsupports\nnative-speed\nexecution\non\nencrypted\ndata,\nat\nthe\ncost\nof\na\nchain\nof\ntrust\ngoing\nback\nto\nthe\nmanufacturer.\nGoing\nforward,\nour\nown\nand\nother\nrecent\nresearch\nshows\nthe\nway\ntowards\nleveraging\nhardware\nand\nzero-knowledge\nfor\ntrustless\nsecurity\nwith\nlow\nimpact\non\nperformance.\nA.4\nHCN\nTransaction\nMachines\nThe\nHCN\nsoftware\nincorporates\none\nor\nmore\nTransaction\nMachines\n(TMs)\nthat\nestablish\ntheir\nown\nconnectivity\nto\na\ntransaction\nnetwork,\nwhile\nbeing\nloosely\ncoupled"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:23:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 23, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:23:2", "chroma:document": "their\nown\nconnectivity\nto\na\ntransaction\nnetwork,\nwhile\nbeing\nloosely\ncoupled\nor\ndecoupled\nfrom\nother\ncomponents\nwithin\nthe\nnode.\nA\nTM\nfacilitates\ninteractions\nwith\nvarious\nnetwork\ncomponents,\nincluding\nthe\nTMs\nof\nother\nnodes\nand\nnetwork\nprotocols\nsuch\nas\nthe\nTODA/IP\nheartbeat.\nNotably,\nthe\nmanagement\nof\nthe\ntransaction\nnetwork\noperates\nindependently\nof\nthe\ncommunications\nwithin\nand\nbetween\nnodes.\nWithin\nan\nHCN,\na\nTM\nserves\nas\na\ncritical\ninterface\nfor\nthe\nVM.\nIt\nenables\nthe\nVM\nto\nsend\npayment\ncommitments\nto\nthe\nTM\nfor\nvalidation\nand\nallows\nthe\nVM\nto\nconstruct\nSato-Server\nstate\nupdate\ndata.\nThe\nTM\ncan\nthen\npackage\nand\nsign\nthis\ndata.\nThese\nfunctionalities\nempower\nthe\nVM\nto\nnegotiate\nagreements\nwith\nnetworked\nnodes\nand\nensure\nthe\nappropriate\nestablishment\nof\npayment\ncontracts\nfor\nrequested"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:23:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 23, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:23:3", "chroma:document": "and\nensure\nthe\nappropriate\nestablishment\nof\npayment\ncontracts\nfor\nrequested\nwork.\nThe\nTM,\nin\nconjunction\nwith\nSato-Servers\nand\nTODA/IP,\nensures\nsecure\nand\nefficient\nP2P\npayments\nwithin\nthe\nHyperCycle\nnetwork.\nBy\nleveraging\nSato-Servers\nand\nthe\ndecentralized\nconsensus\nprotocol\nof\nTODA/IP,\nthe\nsystem\noffers\nflexibility\nin\nfacilitating\npayments,\nsupporting\nthe\nexchange\nof\nvarious\ncurrencies\nwithin\nHyPC\nSato-Servers\nwhile\nupholding\nrobust\nsecurity\nand\nincurring\nminimal\ncosts.\nHyperCycle\nnodes\nwill\nbe\nable\nto\nsecure\ntheir\ntransactions\nby\nusing\na\nTM\nkitted\nwith\na\nzero-knowledge\nEVM\nto\nhandle\ncommunications\nand\ntokens\non\nEthereum-compatible\nblockchains.\n24"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:24:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 24, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:24:0", "chroma:document": "The\nTMs\nof\na\nnode\nwill\nnot\nbe\naddressable\ndirectly\nto\nprevent\nDDoS\nattacks.\nConsequently,\naddressing\nwill\nmake\nuse\nof\nthe\nnode\nlicense\nID\n(Sato-Server\naddress),\nwhich\nmust\nthen\nbe\nappropriately\nresolved\ninto\na\nURL\nby\nthe\npayment\nnetwork.\nA.5\nHCN\nAI\nMachine\nThe\nArtificial\nIntelligence\nMachine\n(AIM)\nacts\nas\na\nversatile\ninterface\nresponsible\nfor\nmanaging\nand\nexecuting\nAI\nmodules\nfor\ncomputational\ntasks.\nThese\nAI\nmodules\nare\ncontained\nwithin\nseparate\ncontainers,\nproviding\nmodularity\nand\nflexibility\nto\nthe\nsystem.\nThe\ncontainers\ncan\nuse\nDocker\nor\nother\ncontainerized\nsoftware\nplatforms.\nThe\nAIM\nwill\nseamlessly\nintegrate\nwith\nthe\nVM\nto\nreceive\ninstructions\nand\ndata\nfor\nprocessing\nand\nreturn\noutputs,\ndespite\nthe\nfact\nthat\nthe\nVM\nand\nAIM\nare\nloosely\ncoupled\nby\ndesign.\nThe\nAIM\nhas\nthe\ncapability\nto"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:24:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 24, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:24:1", "chroma:document": "the\nVM\nand\nAIM\nare\nloosely\ncoupled\nby\ndesign.\nThe\nAIM\nhas\nthe\ncapability\nto\ndynamically\nswitch\nbetween\ndifferent\nmodes,\nincluding\n\"inference\nmode\"\nand\n\"training\nmode.\"\nIn\ninference\nmode,\nthe\nAIM\nwill\nprocess\ninstructions\nand\ndata\nusing\npre-packaged\ncontainers,\nwhile\nin\ntraining\nmode,\nit\nwill\norchestrate\nad\nhoc\nclusters\nof\nmachines\nfor\nintensive\ncomputations\nover\nextended\nperiods.\nTo\nmaximize\nperformance,\nthe\nAIM,\nunder\nthe\ncontrol\nof\nthe\nVM,\nwill\nleverage\navailable\nnetworking\nhardware,\nsuch\nas\nRDMA/Infiniband\nconnections\nand\nDPUs.\nAs\npart\nof\nthe\nlarger\necosystem,\nbeyond\nthe\ncore,\nAI\nmodule\nmarketplaces\nare\nalready\nbeing\nestablished\nby\necosystem\npartners\nsuch\nas\nSingularityNET.\nNode\noperators\nwill\nhave\nthe\nability\nto\ndownload\nsoftware\ncontainers\nfrom\nthese\nmarketplaces\nand\ninstall\nthem\non"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:24:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 24, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:24:2", "chroma:document": "to\ndownload\nsoftware\ncontainers\nfrom\nthese\nmarketplaces\nand\ninstall\nthem\non\ntheir\nnodes.\nOnce\ninstalled,\noperators\ncan\nadvertise\ntheir\nAI\nmicroservices\navailability\nwithin\nthe\nnetwork,\nspecifying\nthe\noffered\nservices\nand\ncorresponding\npricing.\nThese\nmarketplaces\nmay\ntake\nvarious\nforms,\nincluding\ncentralized\nsites\nor\ndistributed\nP2P\nmarketplaces\nbased\non\ndistributed\nhash\ntables.\nWe\nemphasize\nthat\nthe\nmarketplace\ninfrastructure\noperates\nindependently\nfrom\nthe\ncore\nHyperCycle\nsystem,\nallowing\nfor\nprogressive\nenhancements\nand\nimproved\nservices\nover\ntime.\nA.6\nHCN\nNode\nManager\nThe\nNode\nManager\n(NM)\nplays\na\nvital\nrole\nin\nthe\nnode\nsoftware,\nserving\nas\na\ncentral\ncomponent\nfor\neffective\nnode\nmanagement.\nIts\nprimary\npurpose\nis\nto\noversee\nthe\nsoftware\ninstalled\non\na\nnode,\nincluding\nthe"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:24:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 24, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:24:3", "chroma:document": "primary\npurpose\nis\nto\noversee\nthe\nsoftware\ninstalled\non\na\nnode,\nincluding\nthe\nconfigurations,\nversions,\nand\noptions\nof\nthe\nVM,\nTM\nand\nAIM.\nThis\nensures\nseamless\noperation\nand\noptimal\nperformance\nof\nthe\nnode.\nThe\nNM\ncan\nbe\nconfigured\nto\nallow\nsecure\nremote\nsystem\nmanagement.\nThe\nNM\nwill\nincorporate\nan\nadministration\npanel\nthat\nprovides\na\nuser-friendly\ninterface\nfor\nnode\noperators.\nThis\nadministration\npanel\nwill\nserve\nas\na\ncentralized\nhub\nfor\nmanaging\nand\n25"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:25:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 25, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:25:0", "chroma:document": "configuring\nvarious\naspects\nof\nthe\nnode.\nIt\nwill\nempower\noperators\nwith\nconvenient\ntools\nto\nstreamline\nadministrative\ntasks,\nmonitor\nperformance,\nand\ncustomize\nsettings\naccording\nto\ntheir\nrequirements.\nA.7\nHCN\nMerklizer\nHyperCycle\nemploys\na\nmethod\ncalled\nTilling,\nwhich\nprocesses\nthe\nstate\ninformation\nof\nthe\nnetwork\nin\na\ndecentralized\nway\nusing\ndistributed\ncomputing,\nproviding\nefficient\nnetwork\nstate\nsummaries\nand\nthe\nability\nto\nprove\nprior\nstate\nat\nlater\ntimes.\nAs\npart\nof\nthe\nTilling\nprocess,\nthe\nnode\nsoftware\nincludes\na\ncritical\ncomponent\ncalled\nthe\nMerklizer,\ndesigned\nto\nuphold\nthe\nintegrity\nof\nthe\nnode's\nstate\nrecords\nover\ntime.\nThe\nMerklizer\nperforms\nperiodic\ntasks,\nfollowing\nthe\nsystem\n6-30\nsecond\nblock\nproduction\ntime,\nby\ngenerating\na\nrecord\nof\nthe\nnode\u2019s\nmachine\nstate.\nThis\nlocal"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:25:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 25, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:25:1", "chroma:document": "production\ntime,\nby\ngenerating\na\nrecord\nof\nthe\nnode\u2019s\nmachine\nstate.\nThis\nlocal\nmachine\nstate\nrecord,\nrepresented\nas\na\nMerkle\nroot\nhash,\nis\nthen\nshared\nwith\nthe\nnetwork.\nThe\nlocal\nstate\ndata\nand\nits\ninclusion\nin\nthe\npublished\nhash\nform\nthe\nbasis\nfor\nproofs\nrelated\nto\nuptime,\ncomputation,\nand\nreputation,\ncrucial\nfor\nmaintaining\na\nhighly\nsecure\nand\nreliable\nnetwork.\nAs\nthese\nlocal\nrecords\non\neach\nnode\nneed\nto\nbe\nmaintained\nfor\nthe\nwhole\nnetwork,\na\nservice\nto\nensure\nthe\nintegrity\nof\nall\nof\nthese\nrecords\nis\nrequired.\nThis\nis\nthe\nfunction\nof\nthe\nMerkle\nService,\nanother\ncomponent\nof\nthe\nTilling\nprocess,\nwhich\nconsolidates\nall\nof\nthe\nhashes\npublished\nby\neach\nnode\nby\ncollecting\nall\nthe\nlocal\nhashes\nand\ncombining\nthem\ninto\na\nsingle\nglobal\nMerkle\nroot\nhash,\nincorporating\nand\nsummarizing\nthe\nentire"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:25:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 25, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:25:2", "chroma:document": "into\na\nsingle\nglobal\nMerkle\nroot\nhash,\nincorporating\nand\nsummarizing\nthe\nentire\nnetwork\nstate\nin\neach\nblock\u2019s\ntime\nwindow.\nThe\nMerkle\nService\ncommunicates\nthis\nglobal\nroot\nhash\nto\nevery\nnode\nin\nthe\nnetwork,\nalong\nwith\nconcise\nMerkle\nproofs\ndemonstrating\nthe\ninclusion\nof\neach\nactive\nnode\nin\nthe\nglobal\nroot\nhash.\nThe\nMerklizer\noperates\nby\naggregating\ndata\nfrom\nvarious\ncomponents\nof\nthe\nnode,\nincluding\nthe\nVM,\nTM,\nAIM\nand\nNM.\nIt\ncombines\nthis\ndata\nin\na\nstructured\nmanner,\nfacilitating\nfuture\nverification\nof\nthe\ninclusion\nof\nspecific\ndata\nfor\neach\ncomponent\nin\na\ngiven\nlocal\nMerkle\nroot\nhash\nfor\na\nparticular\ncycle.\nTo\nensure\nthe\nability\nto\ngive\nsuch\nproofs\nwhen\nneeded\nin\nthe\nfuture,\nthe\nMerklizer\nalso\nmaintains\na\ncomprehensive\ndatabase\nof\nhistorical\nstate\nand\nMerkle\nproof\ndata.\nIn\nthe\nultimate"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:25:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 25, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:25:3", "chroma:document": "database\nof\nhistorical\nstate\nand\nMerkle\nproof\ndata.\nIn\nthe\nultimate\ndesign,\nthe\nMerkle\nService\nwill\nbe\nrun\nas\na\ndecentralized\nP2P\nnetworking\nprotocol,\nutilizing\nTODA/IP\nfor\nefficient\nand\nsecure\ndistributed\nconsensus\non\nthe\nglobal\nroot\nhash.\nThis\ndecentralized\napproach\nensures\nscalability\nas\nthe\nnetwork\nexpands\nwhile\nmaintaining\na\nconsistent\nglobal\nblock\ntime.\nIt\nis\ncrucial\nto\nunderstand\nthat\nthe\nglobal\nblock\ntime\nrefers\nto\nthe\nfull\nimmutability\nand\nfinality\nof\ntransactions\nat\nthe\nnetwork\nlevel,\nwhich\ndetermines\nthe\nre-spendability\nof\ntokens.\nThis\nis\nseparate\nfrom\nthe\ncycles\nand\nthreads\nleading\nup\nto\nthe\nblocks.\nTransactions\nachieve\nimmediate\nirrefutability,\nwhile\nthe\nlocalized\nrecord\nthat\nestablishes\ntheir\nimmutability\nand\nfinality\nis\npublished\nat\nthe\nend\nof\neach\ntransaction\ncycle,\nwhich"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:25:4

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 25, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:25:4", "chroma:document": "and\nfinality\nis\npublished\nat\nthe\nend\nof\neach\ntransaction\ncycle,\nwhich\nhappens\nnearly\ninstantaneously.\nMany\nsuch\ncycles\nare\nfolded\ninto\na\nsingle\nglobal\nblock,\nwhich\nwhen\npublished\nfinalizes\nfor\nthe\nnetwork\nas\na\nwhole\n26"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:26:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 26, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:26:0", "chroma:document": "all\nthe\ntransactions\ncontained\nwithin\nthe\nblock,\nconfirming\nthe\nre-spendability\nof\nall\nthe\ntokens\ntransacted.\nThe\nenvisioned\ndesign\nemphasizes\nthe\nMerklizer's\nand\nMerkle\nService\u2019s\npivotal\nrole\nin\nmaintaining\nthe\nnetwork's\nstate\nrecords\nand\nensuring\nthe\nintegrity\nof\ntransactions,\nproviding\na\nrobust\nand\nreliable\ninfrastructure\nfor\nthe\nnetwork's\noperation.\nWhile\nall\nthis\npart\nof\nthe\nTilling\nprocess,\nit\nis\ncrucial\nto\nunderstand\nthe\nnecessity\nof\nstructuring\nthe\ndata\nin\neach\nmoment\nof\nnetwork\ntime\nand\nnot\nafter\nthe\nfact.\nThis\nis\nbecause\nto\ngain\nthe\nsecurity\nbenefits\nof\nnetwork\nstate\nrecords,\nthe\nrecords\nfor\neach\nmoment\nmust\nbe\nprovably\ncoupled\nto\na\nparticular\nnetwork\ntime\n(cycle\nand/or\nblock),\nwhich\ncannot\nbe\ndone\nafter\nthe\nfact.\nThe\nstate\ndata\nprovides\nrecords\nof\nevents\nas\nthey\nhappen,\nleaving"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:26:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 26, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:26:1", "chroma:document": "the\nfact.\nThe\nstate\ndata\nprovides\nrecords\nof\nevents\nas\nthey\nhappen,\nleaving\ntraces\nin\nthe\nnetwork\nthat\nare\nstructured\nin\nthe\nTilling\nprocess.\nThis\nstructure\nprovided\nby\nTilling\nwill\nbe\ncrucial\nin\nfurther\nsecuring\nthe\nnetwork\nas\ntime\ngoes\non.\n27"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:27:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf", "page": 27, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\HyperCycle_Core_v0.92.pdf:27:0", "chroma:document": "Glossary\nagent\na\nHyperCycle\nComputation\nNode\nthat\ncan\nact\nautonomously\nto\nnegotiate\ncontracts\nfor\ncomputational\nwork\nwith\nother\nagents\nper\ncomputational\ntask\nusing\nmicropayments\nAIDCP\nAI\nDistributed\nComputation\nPower\n\u2022\na\nmeasure\nof\nthe\nsize\nof\na\nnode\nlicense\npackage\nthat\ncan\nrange\nfrom\n10xAIDCP\n(512\nlicense\npackage)\nto\n1xAIDCP\n(single\nnode\nlicense)\nAIM\nAI\nMachine\n\u2022\na\ncomponent\nof\na\nHyperCycle\nComputation\nNode\nthat\nis\nresponsible\nfor\ntaking\ninstructions\nand\ndata\nfrom\nthe\nVM,\nprocessing\nthe\ndata\nappropriately,\nand\nreturning\nthe\nresult\nto\nthe\nVM\nComputation\nas\na\ncapitalized\nterm\nrefers\nto\nthe\nratio\nthat\nmeasures\na\nnode\u2019s\nperformance\nin\nterms\nof\nits\nconsistency\nand\nreliability\nfor\nproviding\ncomputation\nservices\ncontainer\na\nterm\nused\nvariously\nto\nrefer\nto\na\nSato-Server\ncontainer,\nthat\nis"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:27:1

metadata:
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:27:2

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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:28:0

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created_at: 2024-06-29 05:41:35
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\HyperCycle_Core_v0.92.pdf:28:1

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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:2:1

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 2, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:2:1", "chroma:document": "and\nmore.\nJam\nGalaxy\nintends\nto\nrevolutionize\nthe\nmusic\nindustry\nin\nthe\nsame\nway\nas\nDALL-E\nand\nStable\nDiffusion\nhave\naffected\nthe\ngraphic\narts,\nand\nChatGPT\nand\nits\nilk\nare\naffecting\nthe\nworld\nof\nwriting;\nJam\nGalaxy\nwill\naffect\nthe\nmusic\nindustry\nwith\na\nspeed\nthat\nwill\nshock\neven\nthe\nmost\njaded\nobservers.\nThe\nlast\nbig\ndisruption\nthe\nmusic\nbusiness\nsaw\n\u2013\nthe\nadvent\nof\nthe\ninternet\nand\ndigital\nmusic\n\ufb01les\n\u2013\nat\n\ufb01rst\nseemed\nlike\nit\nwas\ngoing\nto\ndemocratize\nthe\nmusic\nbusiness\nand\nmassively\nbene\ufb01t\nartists\nand\nfans,\nbut\nin\nthe\nend\nhad\na\nmore\ncomplex\neffect.\nIt\u2019s\nnow\nunprecedentedly\neasy\nto\nproduce\nnew\nmusic\nand\nput\nit\nonline\n\u2013\nbut\ndistribution\nand\nindustry\neconomics\nare\ncontrolled\nby\na\nsmall\nnumber\nof\nstreaming\nservices\nthat\nare\neven\nmore\nhegemonic\nthan\nthe\nrecord\nlabels\nand\nradio\nstations\nof\nold."}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:2:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 2, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:2:2", "chroma:document": "that\nare\neven\nmore\nhegemonic\nthan\nthe\nrecord\nlabels\nand\nradio\nstations\nof\nold.\nHow\ncan\nwe\nensure\nthe\nnew\nmusic\nrevolution\nthat\u2019s\nunfolding\n\u2013\nthe\nAI-music\nrevolution\n\u2013\nends\nup\ndifferently\nthan\nthe\nshift\nto\ndigital,\nand\nbrings\nus\na\nnext-phase\nmusic\nworld\nthat\nmeets\nthe\nneeds\nof\nartists\nand\nlisteners,\nmore\nthan\ncorporate\nintermediaries?\nBlockchain\ntechnology\nprovides\nthe\ntools\nneeded\nto\nensure\nthe\nAI\nrevolution\ntransforms\nthe\nmusic\nworld\nin\na\npositive\nway.\nIt\nmust\nalso\nbe\nappropriately\ndeployed\nand\ndeeply\nintegrated\nwith\nAI,\nin\na\nmanner\nakin\nto\nthe\nway\nmusicians\nand\nfans\nthink.\nThis\nis\nwhere\nthe\nJam\nGalaxy\nplatform\nand\ntoolsets\nstand\nout!\nCarefully\ndesigned\nby\na\nteam\nof\nAI\nspecialists,\nsoftware\nengineers\nand\nmusicians\n\u2013\nbridging\nthe\ngap\nbetween\nthe\ntechnological\nand\nthe\nartistic\n\u2013\nJam\nGalaxy"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:2:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 2, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:2:3", "chroma:document": "\u2013\nbridging\nthe\ngap\nbetween\nthe\ntechnological\nand\nthe\nartistic\n\u2013\nJam\nGalaxy\nis\nuniquely\npositioned\nto\nlead\nthe\nmusic\nworld\ninto\nthe\nfuture.\n3"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:3:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 3, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:3:0", "chroma:document": "Jam\nGalaxy\u2019s\nrevolutionary\nplatform\nallows\nmusicians,\nproducers\nand\nany\nother\nindustry\nparticipants\nto\nearn\ntokens\nby\nproviding\nmusical\ncreations\nto\nlisteners,\nand\nalso\nby\nproviding\ntheir\ncreations\nas\ninputs\nto\nmusical\nAIs\nrunning\non\nthe\nplatform.\nWhen\nan\nAI\ntrained\non\nan\nartist\u2019s\noutput\ncreates\na\npiece\nof\nmusic,\nor\nassists\nwith\nthe\ncreation\nof\na\npiece\nof\nmusic,\nthen\nthe\nartist\ngets\ntokens\nas\nreward\nfor\nthis\nusage\nof\nthe\nAI.\nTraditional\nand\nAI-era\nstrategies\nfor\nmonetization\nare\nthus\nseamlessly\nwoven\ninto\na\ncommon\ntokenomic\nmodel.\nThe\nresult\nis\na\nframework\nin\nwhich\nmusicians\ncan\nincrease\ntheir\nincome,\nreach\na\nwider\naudience,\nparticipate\nin\nthe\ngrowth\nof\nthe\nnext\nphase\nof\ntechnology,\nand\nkeep\nin\ntouch\nwith\ntheir\nsupporters\nin\na\nmore\nimpactful\nway\nthan\never\nbefore.\nThe\nMusic\nIndustry\nat\na"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:3:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 3, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:3:1", "chroma:document": "supporters\nin\na\nmore\nimpactful\nway\nthan\never\nbefore.\nThe\nMusic\nIndustry\nat\na\nCrossroads\nThe\ncrux\nof\nthe\nmusic\nmarket\ntoday,\nfrom\na\nconsumer\nperspective,\nlies\nin\nstreaming\napps.\nAccording\nto\nthe\nGlobal\nWeb\nIndex,\ntoday\n67%\nof\nall\nadults\nmake\nuse\nof\na\nmusic\nstreaming\nservice.\nWith\n48.3%\nof\nthe\nglobal\nmusic\nsubscriber\nmarket,\nSpotify\nis\nfar\nahead\nof\nits\nnearest\ncompetitor,\nApple\nMusic,\nwhich\nhas\nonly\n13.7%.\nAn\nadditional\n13.4%\nof\nusers\nlisten\nto\nmusic\non\nTencent\nMusic,\n13.3%\nlisten\nto\nmusic\non\nAmazon\nMusic,\nand\n8.9%\nlisten\nto\nmusic\non\nYouTube\nMusic\n(MIDIA\n2023).\nMusic\nsales\nhit\na\nnew\nhigh\nof\n26.2B\nin\n2022,\nup\n9.1%\nfrom\na\nprevious\nhigh\nin\n2021\n\u2013\nand\nthis\nwas\nmainly\ndue\nto\nthe\nstreaming\nindustry,\nwhich\nsaw\npaying\nsubscribers\nrise\n17.8%\nfrom\n523\nmillion\nin\n2021\nto\n616\nmillion\nin\n2022."}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:3:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 3, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:3:2", "chroma:document": "paying\nsubscribers\nrise\n17.8%\nfrom\n523\nmillion\nin\n2021\nto\n616\nmillion\nin\n2022.\nSixty-seven\npercent\nof\nrevenue\ncurrently\ncomes\nfrom\nstreaming.\nThe\nmusic\nstreaming\nindustry\nis\nestimated\nto\ngenerate\n$27.14B\nin\nrevenue\nin\n2023\nand\nincrease\nat\na\ncompound\nannual\ngrowth\nrate\n(CAGR\n2022-2026)\nof\n15.67%\nby\n2027.\nThere\nare\nvarious\nestimates\nthat\nsuggest\nthe\ntotal\nnumber\nof\nusers\nwould\nreach\n1.2\nbillion\nby\nthe\nyear\n2027\n(Statista\n1\n).\n1\nhttps://www.statista.com/statistics/653926/music-streaming-service-subscriber-share/\n4"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:4:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 4, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:4:0", "chroma:document": "Global\nrecorded\nmusic\nrevenues\nby\nsegment\n2022\n2\nStreaming\napps\nprovide\nconsiderable\nconvenience\nand\na\nlarge\nvolume\nof\nmusic\nto\nlisten\nto.\nHowever,\nit\nis\nobserved\nthat\nthese\napps\ndo\nnot\nallow\nfor\nartistic\ncreativity,\nas\nthey\nare\nmainly\nbased\non\ncorporate\npro\ufb01t,\nand\nthey\nlimit\nthe\nusers\u2019\nlistening\nexperience.\nBefore\nthe\nadvent\nof\nstreaming\nservices,\nrecord\nlabels\nacted\nas\nmiddlemen\nto\nseparate\nboth\nthe\nrevenue\nand\ncontrol\nof\nmusic\nfrom\nartists\nand\nfans.\nUnfortunately,\nand\nsimilar\nto\nmany\nother\nindustries,\nthis\nposition\nof\n\u201cmiddleman\u201d\nwas\nconsistently\nthoroughly\nabused.\nNow\nthat\nstreaming\napps\nhave\njoined\nrecord\nlabels\nin\nthe\nmiddleman\nrole,\nthe\npattern\nof\nabusive\nbehavior\nhas\nnot\ndecreased,\nnor\nhas\nthe\npropensity\nto\nfoster\nwide-ranging\nartistic\ncreativity\nand\ndiversity.\nThe\nvast\nmajority\nof"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:4:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 4, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:4:1", "chroma:document": "to\nfoster\nwide-ranging\nartistic\ncreativity\nand\ndiversity.\nThe\nvast\nmajority\nof\nartists\nmake\nless\nthan\n$23\nper\nmonth\nfrom\nstreaming.\nThere\nis\nno\nsimple\nway\nfor\nfans\nto\npay\nartists\ndirectly,\nand\nwhile\ncurrent\nWeb3\ntechnology\nis\n2\nIFPI_Global_Music_Report_2022-State_of_the_Industry.pdf\n5"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:5:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 5, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:5:0", "chroma:document": "focused\non\ndecentralization\nand\nuser\nownership,\nit\nis\nnot\nuser-friendly\nfor\nmost\nmusicians\nor\nlisteners.\nLive\nmusic\nperformance\nhas\nalso\nsuccumbed\nmore\nand\nmore\nto\ncentralization\nas\ntechnology\nhas\ndeveloped.\nOligopolistic\ncontrol\nby\ncompanies\nsuch\nas\nTicketmaster/LiveNation,\niHeart\nMedia,\nand\nLiberty\nMedia\nhas\nled\nto\nsigni\ufb01cant\nprice\nincreases\nand\nrampant\nprice\ngouging.\nLive\nNation\nhas\n70%\nof\nthe\nticketing\nand\nlive\nevent\nvenue\nmarket;\ntherefore,\nto\navoid\nusing\nthem\nis\nnearly\nimpossible\nfor\nboth\nartists\nand\nfans.\niHeartMedia\ncontrols\nover\n860\nlive\nbroadcast\nstations\nin\n160\nmarkets\nacross\nAmerica\nand\nis\nthe\nlargest\nradio\nstation\ngroup\nowner\nin\nthe\nUnited\nStates,\nboth\nby\nnumber\nof\nstations\nand\nby\nrevenue.\nAs\nthese\ncompanies\ngive\npreferential\nairplay\nand\nvenue\naccess/promotion\nto\ntheir\nown"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:5:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 5, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:5:1", "chroma:document": "companies\ngive\npreferential\nairplay\nand\nvenue\naccess/promotion\nto\ntheir\nown\nsigned\nartists,\neveryone\nelse\nsuffers,\nand\nmore\ndiverse\nvoices\nare\nsqueezed\nout\nof\nmusic\ndistribution.\nOverall,\nthe\ncurrent\nmusic\nindustry\ndoes\na\nmediocre-at-best\njob\nfor\nboth\nmusicians\nand\nfans,\nwith\na\nbroken\nbusiness\nmodel,\ninadequate\ntech\ntools,\nand\na\nlack\nof\nfairness\nand\naccessibility.\nThis\nis\nthe\nsituation\ninto\nwhich\nAI\nmusic\ntools\nare\ncurrently\nentering,\nwith\na\nbang\n\u2013\na\ngrand\nentrance\nthat\ncan\ngo\none\nof\ntwo\nways:\n\u2605\nCentralized,\nexploitative\nand\nrepetitive:\nIf\nAI\nbased\nmusic\nis\ncontrolled\nby\nrecord\nlabels\nand\nstreaming\nservices,\nit\nwill\nserve\nto\ncement\ntheir\ndomination\neven\nmore\nthoroughly,\nand\nwill\nlead\nto\na\ndegradation\nof\nmusician\nand\nfan\ninterests,\nas\nwell\nas\na\nprecipitous\ndecline\nin\nartistic\nquality\nand"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:5:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 5, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:5:2", "chroma:document": "and\nfan\ninterests,\nas\nwell\nas\na\nprecipitous\ndecline\nin\nartistic\nquality\nand\ndiversity\nof\nmusic.\nMusic\ngenerated\nand\nproduced\nby\nbottom-line-driven\ncorporations\nwill\nbe\ncomposed\nin\nthe\nmanner\nof\nonline\nads\n\u2013\ndevised\nto\nfeed\nlisteners\u2019\naddictions\nrather\nthan\nto\nchallenge\nthem\nor\nexpand\ntheir\nminds.\nRecord\nlabels\nand\nstreaming\nservices\nwill\nsee\nno\npoint\nin\npaying\nhuman\nartists\nwho\nhave\nfrailties\nand\nlives\nand\nneeds\nand\nunpredictable\nhabits,\nwhen\nthey\ncan\nuse\nfully\nowned\nand\ncontrolled\nAI\nartists\ninstead.\n\u2605\nDecentralized,\ndemocratic\nand\ndiverse\n:\nOn\nthe\nother\nhand,\nwhat\nif\nAI\ntools\nare\nplaced\nin\nthe\nhands\nof\nartists\nand\nfans,\nin\na\nframework\nencouraging\nthem\nto\nbe\ndeployed\nand\ndeveloped\nin\na\n\ufb02exible\nand\ndiverse\nway?\nWe\ncan\nthen\nsee\na\nmusic\nworld\nwith\ndramatically\ngreater\ndiversity,\nin\nwhich\nAI"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:5:3

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 5, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:5:3", "chroma:document": "We\ncan\nthen\nsee\na\nmusic\nworld\nwith\ndramatically\ngreater\ndiversity,\nin\nwhich\nAI\nis\nused\nto\nmake\nit\neasier\nfor\neach\nartist\nto\nachieve\ntheir\nvision.\nAnd\nwe\ncan\nsee\nthis\nhappen\nwith\na\ndemocratic\nand\ndecentralized\neconomic\nmodel,\nin\nwhich\nAI\nmodels\ntrained\non\nartists\u2019\nwork\nfeed\nportions\nof\ntheir\nrevenue\nback\nto\nthese\nartists\nin\nan\nhonest\nand\nstraightforward\nway.\n6"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:6:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 6, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:6:0", "chroma:document": "Blockchain\ntechnology\neliminates\nthe\nneed\nfor\nexploitative\nmiddlemen\nand\nmakes\nit\neasy\nfor\nfans\nto\ncompensate\nartists\ndirectly,\nor\nfor\nfans\nto\ncompensate\nAIs\nwhich\nthen\ncompensate\nthe\nartists\nwho\nfueled\ntheir\narti\ufb01cial\ncreativity.\nThe\npotential\nof\nan\namazing\nnew\nmusic\nindustry\nlies\nbefore\nus.\nThe\nJam\nGalaxy\nPlatform\nThe\nJam\nGalaxy\nplatform\nprovides\na\nconcrete\npath\nto\na\ndecentralized,\ndemocratic\nand\ndiverse\nmusic\nindustry.\nThrough\nan\ninterlinked\ncollection\nof\nonline\ntools,\nmusicians\nand\nfans\nare\nfree\nto\ncreate\nand\nconnect\nin\na\nmultitude\nof\nways.\nBeginning\nwith\na\npowerful\ninitial\nset\nof\nfunctions\n\u2013\nthe\nCreator,\nPublisher\nand\nFan\nportals,\nNFT\nMarketplace,\nand\nthe\nJam\nGalaxy\nStreaming\nApp\nand\nMetaverse\n\u2013\nit\nis\ndesigned\nto\nevolve\nand\ngrow\nalong\nwith\nits\ncommunity\nand\nthe\nadvance\nof\nAI\nand"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:6:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 6, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:6:1", "chroma:document": "designed\nto\nevolve\nand\ngrow\nalong\nwith\nits\ncommunity\nand\nthe\nadvance\nof\nAI\nand\nblockchain\ntechnologies\nand\nmusic\nitself.\nCreator\nPortal\nMusicians\ncan\nsimply\nupload\nmusic\nto\nthe\nJam\nGalaxy\nMusic\nCommons,\nfrom\nwhich\npoint\nthey\ncan\npublish\nit\nin\nvarious\nways\nand\nmake\nit\navailable\nto\nfans.\nThis\nportal\nalso\nallows\nfor\nincredible\ninnovation,\nleveraging\nAI\ntools\nand\nmodels\ntrained\non\ndatasets.\nAI\nfunctions\nin\nthe\nworks,\nvia\ncollaboration\nwith\nparent\norganization\nSingularityNET\nand\nsister\n\ufb01rms\nMusaic\nand\nIncantio,\ninclude\nthe\nability\nto:\n7"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:7:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 7, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:7:0", "chroma:document": "\u2605\nGenerate\na\ndifferent\nending\nfor\na\nsong\n3\n\u2605\nCreate\na\nsmooth\nloop\nfrom\na\nsnippet\nof\na\nsong\n\u2605\nCreate\na\nsegue\nfrom\none\npassage\nof\nmusic\ninto\nanother\n\u2605\nGenerate\nnew\nmusic\nbroadly-similar\nto\na\nset\nof\nchosen\nsongs/snippets\n\u2605\nGenerate\nmusic\nbased\non\nverbal\ncues\n\u2605\nConvert\na\nmouthed\nbeatbox\nrhythm\nto\na\ndrum\ntrack,\nthen\ncompose\nmusic\nover\nit\n\u2605\nCreate\nbacking\nmusic\nfor\nsolo\ntrack,\nin\nspeci\ufb01ed\nstyle\n\u2605\nGenerate\nmusic\nto\nmatch\na\nscene\nin\na\nmovie\nor\nvideo-game,\nwith\nverbal\nstylistic\nguidance\n\u2605\nGenerate\ntracks\nand\nsound\nelements\nbased\non\nthe\nmusician-user\u2019s\npersonal\nsample\nlibrary\n\u2605\nGenerate\na\nfully\nmixed-down\n/\nproduced\nsong\nfrom\na\nbunch\nof\nraw\ntracks\nof\nindividual\ninstruments\n\u2605\nTake\na\nsketched-out\nsong\nplayed\non\na\nsingle\ninstrument\nand\nturning\nit\ninto\na\nfully-orchestrated\nsong\nwith\na\nselected\nset\nof"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:7:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 7, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:7:1", "chroma:document": "instrument\nand\nturning\nit\ninto\na\nfully-orchestrated\nsong\nwith\na\nselected\nset\nof\ninstruments\nand\nstyle\n3\nAll\nmusic\ncreated\nthrough\nthis\nPortal\ndoes\nnot\nviolate\nany\nclaimed\nrights\nof\nother\nartists.\n8"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:8:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 8, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:8:0", "chroma:document": "Publisher\nPortal\nOnce\nan\nartist\nhas\ncreated\na\npiece\nof\nmusic,\nthe\nnext\nsteps\nare\nto\ndisseminate\nand\nmonetize.\nThis\nmay\ntake\nmultiple\nforms,\ne.g.\n\u2605\nSimply\npushing\nthe\nmusic\nout\nto\nfans\nvia\nthe\nJam\nGalaxy\nstreaming\napp\n\u2013\nearning\ntokens\nvia\nbeing\nlistened\nto,\nand\nreceiving\nextra\ntokens\nvia\nfan\ndonations\n\u2605\nCreating\nNFTs\nfrom\npieces\nof\nthe\nmusic,\nand\noffering\nthem\nfor\nsale\non\nthe\nJam\nGalaxy\nmarketplace\n\u2605\nAdding\nvideo\nand/or\nartwork\nto\none\u2019s\nmusic,\nwhich\nmay\nbe\nsourced\nfrom\nhuman\nnetwork\nmembers\nor\ngenerated\nvia\nAI\n\u2605\nOffering\nmusic\nfor\nsync\nlicensing\nto\nvideo\ncreators\nvia\nJam\nGalaxy\u2019s\nsister\n\ufb01rm\nIncantio\nwhich\nis\ndedicated\nprecisely\nto\nthis\nStreaming\nApp\nand\nFan\nPortal\nThe\nJam\nGalaxy\nstreaming\napp,\nthe\nprimary\nface\nof\nJam\nGalaxy\nto\nthe\ntypical\nmusic\nlistener,\nwill\nallow\nusers\nto\n\u2605\nListen\nto"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:8:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 8, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:8:1", "chroma:document": "of\nJam\nGalaxy\nto\nthe\ntypical\nmusic\nlistener,\nwill\nallow\nusers\nto\n\u2605\nListen\nto\nmusic\nuploaded\nand\ncreated\nby\nJam\nGalaxy\nartists\n\u2605\nSign\nup\nas\nSuperFans\nof\nartists\nand\nreap\nassociated\nrewards\n\u2605\nUse\nAI\ntools\nto\nremix\nand\notherwise\nmodify\nartists\u2019\nmusic\n(in\naccordance\nwith\neach\nartist\u2019s\nindividual\npreferences\nand\nlicensing\nterms)\nand\npotentially\npublish\ntheir\ncreations\nto\nthe\ncommunity\nThe\nfan\nportal\nwill\nallow\nthe\nmusic\nfan\nto\nmonitor\nand\nmanipulate\ntheir\nholdings\nof\nvarious\nJam\nGalaxy\ntokens,\nand\nparticipate\nin\nJam\nGalaxy\ngovernance\nactions.\nNFT\nMarketplace\nThe\nJam\nGalaxy\nNFT\nmarketplace\nis\na\nspace\nwhere\nartists\nand\ncreators\ncan\nmint,\nshowcase,\nand\nsell\ntheir\nunique\ndigital\nassets\nto\na\nglobal\naudience.\nThis\ncan\ninclude\nanything\nfrom\nsongs\nand\nplaylists,\nto\ndigital\ncollectibles,\nart,\nand\neven"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:8:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 8, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:8:2", "chroma:document": "anything\nfrom\nsongs\nand\nplaylists,\nto\ndigital\ncollectibles,\nart,\nand\neven\ntangible\nassets\nlike\nconcert\ntickets\nand\nmerchandise.\nBy\ncharging\na\nsmall\nfee\nfor\nminting\nand\nfeaturing\nNFTs\non\nour\nplatform,\nwe\naim\nto\nprovide\na\ncurated\nand\ntrustworthy\nmarketplace\nfor\nbuyers\nand\nsellers\nalike.\nHere\nare\nsome\nkey\nfeatures\nof\nour\nNFT\nmarketplace:\n\u2605\nCurated,\ntrusted\nmarketplace\nfor\ncreators\nand\ncollectors\n\u2605\nMinting\nand\ntrading\nsupporting\na\nvariety\nof\ntop\ncryptocurrencies\n\u2605\nGlobal\nreach\n\u2605\nUser-friendly\nplatform\nthat\nallows\nartists\nto\npromote\ntheir\nwork\nand\nfans\nto\neasily\n\ufb01nd\nwhat\nthey\nlike\n9"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:9:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 9, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:9:0", "chroma:document": "\u2605\nSupport\nfor\nNFTs\nof\nall\ntypes,\nincluding\nsongs,\nplaylists,\ndigital\ncollectibles,\ntangible\nassets,\nart\nand\nmemorabilia,\nmerchandise,\nand\nconcert\ntickets.\nJam\nWorld\nMetaverse\nThrough\nJam\nGalaxy\u2019s\ncollaboration\nwith\nSingularityNET\nand\nits\nSophiaverse\nand\nMindplex\nprojects,\nJam\nGalaxy\nhas\naccess\nto\npowerful\nmetaverse\ntechnologies,\nwhich\ncan\nbe\nused\nto\nprovide\nunique\nweb3\nand\nAI\nenabled\nperformance\nspaces\nfor\nJam\nGalaxy\nartists.\nMixed\nlive/metaverse\nand\naugmented-reality-based\nperformances\nwill\nalso\nbe\npart\nof\nthe\nstory\n\u2013\nand\nactive\nexperimentation\nwith\nthese\nhas\nalready\nbegun,\ne.g.\nwith\na\nmixed\nlive/metaverse\nperformance\nby\nJam\nGalaxy\nBand\nmembers\nat\nthe\nEnter\nthe\nMetaverse\nevent\nin\nLondon\nin\nNovember\n2022.\nTokenomics\nJam\nGalaxy\nuses\na\nnative\nutility\ntoken,\nJGXY,\nas\na\nmedium\nof\nexchange"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:9:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 9, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:9:1", "chroma:document": "Jam\nGalaxy\nuses\na\nnative\nutility\ntoken,\nJGXY,\nas\na\nmedium\nof\nexchange\nbetween\nparticipants\non\nits\ndecentralized\nplatform.\n10"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:10:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 10, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:10:0", "chroma:document": "The\neconomic\ndynamics\nassociated\nwith\nthe\nJGXY\ntoken\nare\nspeci\ufb01cally\ndesigned\nto\nbene\ufb01t\nartists\nand\nfans\nin\na\nbreadth\nof\nways.\nThis\nensures\nthat\nartists\nand\nfans\ncontinue\nto\nbene\ufb01t\nas\nAI\nand\nallied\ntechnologies\ntransform\nthe\nmusic\nworld\nover\nthe\ncoming\nyears.\nThe\nJGXY\nrewards\nsystem\nprovides\ntier-based\naccess\nto\nJam\nGalaxy\nartist\nwebsites\nand\nmusic\nservices\nand\nencourages\nplatform\nuse.\nEvery\nmember\ngets\nbasic\naccess,\nto\nlisten\nto\nmusic,\nsearch\nfor\nmusic\nand\nview\ninformation\nabout\nmusic.\nMembers\nwho\ncontribute\nmore\nto\nthe\ncommunity\nreceive\ntoken\nrewards\n\u2013\nexamples\nof\ncontributions\nwould\nbe\nmusicians\nwho\nupload\nsongs\nfor\nlistening\nor\nfor\ntraining\nAI\nmodels,\nor\nlisteners\nwho\nrate\nsongs\nfor\nthe\nbene\ufb01t\nof\nAI\nmodels\nor\nrecommend\nsongs\nto\nothers.\nTokens\nare\nthen\nused\nto\nenable\nadditional"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:10:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 10, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:10:1", "chroma:document": "models\nor\nrecommend\nsongs\nto\nothers.\nTokens\nare\nthen\nused\nto\nenable\nadditional\nactivities\non\nthe\nplatform.\nTokens\ncan\nbe\ndirected\nby\nlisteners\nto\nartists\nwho\nare\nparticularly\nloved.\nThey\ncan\nbe\nused\nto\nbuy\nAI\nservices\nto\nhelp\nproduce,\nremix\nor\notherwise\ncustomize\nmusic;\nor\nto\nbuy\nNFTs\non\nthe\nJam\nGalaxy\nMarketplace.\nThey\ncan\nalso\nbe\nstaked\nin\n\u201cSuperfan\nStaking\nPools\u201d\nassociated\nwith\nparticular\nartists,\nwhich\nyields\nadditional\ntokens\nas\nstaking\nrewards,\nalong\nwith\nprivileges\nsuch\nas\nearly\naccess\nto\nnew\nreleases,\naccess\nto\nexclusive\nevents,\nor\nprivate\naudiences\nwith\nartists.\nJGXY\nalso\nserves\na\ngovernance\nrole:\nholders\nof\nJGXY\ncan\nvote\non\nimportant\ndecisions\nthat\naffect\nthe\nfuture\nof\nthe\nJam\nGalaxy\nplatform,\nsuch\nas\nthe\nprioritization\nof\nthe\nportal\nroadmap\nand\nthe\ndevelopment\nof\nAI\nmusic"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:10:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 10, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:10:2", "chroma:document": "as\nthe\nprioritization\nof\nthe\nportal\nroadmap\nand\nthe\ndevelopment\nof\nAI\nmusic\ntools.\nThrough\nvoting\nproposals,\nJGXY\nholders\nhave\nthe\npower\nto\ngovern\nimportant\naspects\nof\nthe\nplatform,\nincluding\nreputation\nveri\ufb01cation\nand\ngovernance.\nThis\npro-rata\nvoting\nsystem\nensures\nthat\neach\nholder\u2019s\nvote\nis\nweighted\naccording\nto\ntheir\nholdings,\ngiving\neveryone\na\nfair\nsay\nin\nthe\ndirection\nof\nthe\nplatform.\n11"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:11:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 11, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:11:0", "chroma:document": "We\nwill\nlaunch\nwith\nthe\nsingle\nJGXY\ntoken,\nfor\nthe\nsake\nof\nsimplicity,\nbut\nonce\nthe\nplatform\nhas\nbuilt\nreasonable\ntraction\nthe\ntokenomics\nwill\nmove\non\nto\nthe\nnext\nphase\nin\nwhich\neach\nartist\ncan\ncreate\ntheir\nown\ntoken.\nThese\nartist\ntokens\nwill\nthen\nbe\ngrouped\ninto\ntoken-baskets\ncalled\nJamSets,\nwhich\ncan\nbe\nbought\nand\nsold\nand\ntraded\nusing\nJGXY\non\nthe\nJam\nGalaxy\nfan\nportal.\nAI\ntools\nwill\nbe\nleveraged\nto\nassist\nwith\nprediction\nand\nmarket-making\nfor\nJamSets,\nleveraging\nthe\ntools\ndeveloped\nin\nsister\ncompany\nSingularityDAO\nfor\nAI-based\ntrading\nof\nbaskets\nof\nutility\ntokens\n(SingDAO\u2019s\nbaskets\nare\ncalled\nDynaSets,\nand\nare\nessentially\nidentical\nto\nJamSets\nwith\nthe\nonly\ndifference\nbeing\nthat\nthe\nconstituent\ntokens\nof\nthe\nlatter\ncorrespond\nto\nmusical\nartists).\nThe\nJam\nGalaxy\nBand\nMost\ngreat"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:11:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 11, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:11:1", "chroma:document": "of\nthe\nlatter\ncorrespond\nto\nmusical\nartists).\nThe\nJam\nGalaxy\nBand\nMost\ngreat\ninnovations\nemerge\nwhen\nenterprising\npeople\ncreate\nthe\ntools\nthat\nthey\nthemselves\nare\neager\nto\nuse.\nThe\nJam\nGalaxy\nplatform\nis\nno\nexception\n\u2013\nthe\nfounders\nare\n12"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:12:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 12, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:12:0", "chroma:document": "both\nlifelong\nmusicians\nand\nmusic\nfans\nand\nwill\nbe\namong\nthe\nmost\nenthusiastic\nusers\nof\nall\nthe\nplatform\u2019s\nfunctions.\nAlong\nwith\ntheir\nbroader\nmusic\ncareers\nthe\ncore\nfounders\nhave\nalso\nformed\nan\nAI-centered\nband,\nthe\nJam\nGalaxy\nBand,\nwhich\nis\nthe\n\ufb01rst-ever\nrock\nband\nfronted\nby\na\nhumanoid\nrobot,\nDesdemona,\ncreated\nby\nHanson\nRobotics\nas\nthe\nyounger\nsister\nof\nSophia,\nthe\n\ufb01rst\nrobot\ncitizen.\nDesdemona\u2019s\nlyrics\nand\nsinging\nare\nAI-created,\nas\nare\naspects\nof\nthe\nband\u2019s\nmusic.\nThe\nband\nserves\nas\na\ncontinual\nplayground\nfor\ntesting\nand\nexperimenting\nwith\nvarious\nAI\nand\nblockchain\ntechnologies\nbefore\nrolling\nthem\nout\nwidely\non\nthe\nJam\nGalaxy\nplatform.\n13"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:13:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 13, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:13:0", "chroma:document": "14"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:14:0

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 14, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:14:0", "chroma:document": "TranscendingtheCompetition\nAlong\nwith\nthe\nmajor\nstreaming\napps,\na\nhandful\nof\nother\nprojects\nhave\nentered\ninto\nthe\nmarketplace\nrecently,\noffering\ntools\nthat\nleverage\nblockchain\nand\nrelated\ntechnologies\nin\nan\naim\nto\nimprove\nthe\neconomics\nof\nthe\nmusic\nindustry.\nThere\nare\nalso\na\nfew\nnew\nplatforms\nproviding\nmusicians\nwith\nvarious\nAI\ntools.\nWhere\nJam\nGalaxy\nstands\nout\nis\nin\nthe\nadvanced\nnature\nof\nits\nunderlying\nAI\nand\nthe\nsophistication\nof\nits\ntoken\nmodel.\nJam\nGalaxy\nweaves\nthese\ntwo\naspects\ntogether,\nempowering\nmusicians\nto\ngrow\ntheir\nart\nand\ntheir\nrevenue\ncooperatively\nalong\nwith\nAI\ntechnology.\nThe\nbetter-known\nand\nmore\ninteresting\nof\nthe\ncompetitive\nand\ncomplementary\nprojects\nout\nthere\ntoday\nare:\n\u2605\nSpotify\n-\nDaniel\nEk\nand\nMartin\nLorentzon\ncreated\nSpotify\non\nApril\n23,\n2006.\nAs\nof\nSeptember"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:14:1

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 14, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:14:1", "chroma:document": "Ek\nand\nMartin\nLorentzon\ncreated\nSpotify\non\nApril\n23,\n2006.\nAs\nof\nSeptember\n2022,\nit\nhad\n456\nmillion\nmonthly\nactive\nusers\nand\n195\nmillion\npaying\nmembers.\n\u2605\nNewM\n-\nA\nmusic\nstreaming\nplatform\nand\nmarketplace,\nuses\nNon-Fungible\nToken\n(NFT)\ntechnology\nand\nDecentralized\nFinance\n(DeFi)\nto\neliminate\nmiddlemen\nand\nallow\nartists\nto\nshare\nand\nmonetize\ntheir\nworks\nwhile\nretaining\nownership\nrights\nand\nconnecting\ndirectly\nwith\nfans\nand\ninvestors.\n\u2605\nAudius\n-\nAudius\n(Ethereum\n&\nSolana)\nis\na\nmusic\nstreaming\nservice\nthat\noperates\nby\npaying\nartists\nrevenue\nfrom\nstreams\nthat\ncome\nfrom\nindividual\nusers\nthat\nplay\ntheir\nmusic\ndirectly.\nAudius\ncreates\nthe\ntimestamps\nof\nthe\ncontent,\nallowing\nartists\nand\nfans\nto\nbecome\ndecentralized\nnode\noperators\nthat\nfurther\nsupport\nthe\nnetwork.\n\u2605\nBandcamp\n-\nArtists\ncan\nuse"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
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C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:14:2

metadata:
{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 14, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:14:2", "chroma:document": "node\noperators\nthat\nfurther\nsupport\nthe\nnetwork.\n\u2605\nBandcamp\n-\nArtists\ncan\nuse\nBandCamp\nto\nadvertise\nand\nsell\ntheir\nmusic\nand\nitems.\nArtists\ncan\nsell\ndigital\ndownloads,\nvinyls,\ncassette\ntapes,\nand\nmerchandise.\nThe\n2007-founded\nmusic\ndistribution\nand\nmerchandising\ncompany\nhas\ngrown\nsigni\ufb01cantly.\n\u2605\nAmper\n-\nAmper\nuses\nCreative\nAI\nto\nhelp\nteams\nmake\nand\ncustomize\nmusic.\nAmper\u2019s\nexclusive\ncollection\nof\nover\na\nmillion\nsamples\nand\nthousands\nof\ninstruments\ncombines\nmusic\ntheory\nand\nAI\ninnovation\nto\nproduce\nhigh-quality\nmusic.\n\u2605\nAsync\n-\nAsync\nis\na\nplatform\nfor\nmusicians,\nartists\nand\ncreators\nof\nany\nkind\nto\ncollaborate\nand\njoin\na\nwider\ncommunity.\n\u2605\nYellowHeart\n-\nYellowheart\nis\nleveraging\nWeb3\nto\nreinvent\nticketing\nand\nsupport\nmusicians\nin\nthe\nprocess.\n15"}


created_at: 2024-06-29 05:41:35
topic: persistent://default/default/cf46ee96-ed77-4dde-9ce6-ff790c649483
encoding: FLOAT32
id:
C:\Users\VICTORCORREAGOMES\Documents\DataScienceMachineLearning\Projetos\OpenAI\data\Jam_Galaxy_Lightpaper_V1.2.pdf:15:0

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{"source": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf", "page": 15, "id": "C:\\Users\\VICTORCORREAGOMES\\Documents\\DataScienceMachineLearning\\Projetos\\OpenAI\\data\\Jam_Galaxy_Lightpaper_V1.2.pdf:15:0", "chroma:document": "Each\nof\nthese\nprojects\nis\nexcellent\nin\nits\nown\nway,\nbut\neach\nis\nalso\nmissing\nsome\nof\nthe\nkey\ningredients\nthat\nmake\nJam\nGalaxy\nthe\ncomplete\nsolution\nfor\nthe\nfuture\nof\nthe\nmusic\nworld.\nSTREAMING\nCOMMUNITY\nBUILDING\nINDUSTRY\nNETW ORKING\nPUBLISHING\n(NFTS)\nOW N\nMETAVERSE\nAI\nTOOLS\nINCENTIVE\nPROGRAM\nJam\nGalaxy\nY\nY\nY\nY\nY\nY\nY\nSpotify\nY\nY\nN\nN\nN\nN\nN\nNewM\nY\nY\nN\nY\nN\nN\nN\nAudius\nY\nY\nN\nN\nN\nN\nY\nBandcamp\nY\nY\nN\nN\nN\nN\nN\nAmper\nN\nN\nN\nN\nN\nY\nN\nAsync\nN\nY\nY\nY\nN\nN\nN\nYellowHeart\nY\nY\nN\nN\nN\nN\nY\n16"}


