{"payload":{"feedbackUrl":"https://github.com/orgs/community/discussions/53140","repo":{"id":786148553,"defaultBranch":"main","name":"RAGE","ownerLogin":"GATERAGE","currentUserCanPush":false,"isFork":false,"isEmpty":false,"createdAt":"2024-04-13T15:26:49.000Z","ownerAvatar":"https://avatars.githubusercontent.com/u/166870669?v=4","public":true,"private":false,"isOrgOwned":true},"refInfo":{"name":"","listCacheKey":"v0:1713022010.601464","currentOid":""},"activityList":{"items":[{"before":"9ddf4fa17a9bd4d6efe161bb1791ff92145116dc","after":"21f9215e6f4be12cfe7aed2278905b522cfa8904","ref":"refs/heads/main","pushedAt":"2024-06-05T16:35:23.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update mastermind.md\n\nMASTERMIND: The central controller orchestrating the entire system.\r\nModules: Each functional module (Autonomize, SimpleCoder, NonMonotonic Reasoning, Prediction Model, Logical Reasoning, Epistemic Logic, BDI Model, General Logic, Socratic Questioning) provides specific capabilities.\r\nIntegration: The modules are integrated to form a cohesive system, managed by MASTERMIND.\r\nOrchestration Logic: Ensures smooth communication and functionality between modules.\r\nMain Entry Point: The main function that initializes the system and starts the orchestration process.","shortMessageHtmlLink":"Update mastermind.md"}},{"before":"09afd081394ac6c5f2ff2450bf80767a6fc3268d","after":"9ddf4fa17a9bd4d6efe161bb1791ff92145116dc","ref":"refs/heads/main","pushedAt":"2024-06-05T16:34:25.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update mastermind.md\n\n          +-------------------+\r\n          |    MASTERMIND     |\r\n          +-------------------+\r\n                   |\r\n      +------------+------------+\r\n      |                         |\r\n+-----+-----+             +-----+-----+\r\n| Autonomize |            | SimpleCoder|\r\n+-----+-----+             +-----+-----+\r\n      |                         |\r\n+-----+-----+             +-----+-----+\r\n| NonMonotonic |          | Prediction |\r\n| Reasoning    |          | Model      |\r\n+-----+-----+             +-----+-----+\r\n      |                         |\r\n+-----+-----+             +-----+-----+\r\n| Logical     |           | Epistemic  |\r\n| Reasoning   |           | Logic      |\r\n+-----+-----+             +-----+-----+\r\n      |                         |\r\n+-----+-----+             +-----+-----+\r\n|    BDI     |            |  General   |\r\n|   Model    |            |   Logic    |\r\n+-----+-----+             +-----+-----+\r\n      |                         |\r\n      +-------------------------+\r\n                |\r\n          +-----+-----+\r\n          | Socratic  |\r\n          | Questioning|\r\n          +-----+-----+\r\n                |\r\n          +-----+-----+\r\n          | Orchestration|\r\n          |   Logic     |\r\n          +-----+-----+\r\n                |\r\n          +-----+-----+\r\n          |  Main Entry|\r\n          |   Point    |\r\n          +------------+","shortMessageHtmlLink":"Update mastermind.md"}},{"before":"92349ae61372bf241c1b76c20efc44b95978002e","after":"09afd081394ac6c5f2ff2450bf80767a6fc3268d","ref":"refs/heads/main","pushedAt":"2024-06-05T16:02:59.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update mastermind.md\n\nMASTERMIND Integration\r\n\r\nMASTERMIND.py: Orchestrates the interaction between various components and manages the overall workflow.\r\n\r\n    Functionality:\r\n        Initializes the system and sets up the environment.\r\n        Coordinates between modules including prediction, reasoning, and logic to process data and execute tasks.\r\n        Manages state and ensures the consistency of operations across the system.\r\n\r\nModules:\r\n\r\n    prediction.py:\r\n        Forecasts future states or outcomes based on historical data and predictive models.\r\n\r\n    nonmonotonic.py:\r\n        Implements non-monotonic reasoning to adapt beliefs and knowledge bases with new, contradicting information.\r\n\r\n    socratic.py:\r\n        Facilitates question-and-answer style learning or problem-solving.\r\n\r\n    reasoning.py:\r\n        Provides infrastructure for various types of reasoning, including deductive, inductive, and abductive reasoning.\r\n\r\n    logic.py:\r\n        Implements formal logic systems and operations for reasoning and decision-making processes.\r\n\r\n    epistemic.py:\r\n        Manages the knowledge and beliefs within the system.\r\n\r\n    autonomize.py:\r\n        Enhances the autonomy of agents or components, allowing for self-directed operation and decision-making.\r\n\r\n    bdi.py:\r\n        Implements the Beliefs, Desires, Intentions (BDI) agent framework, modeling the cognitive structure of agents.\r\n\r\n    terminai.py: Separates OpenAI API interaction from the assistant into command-mode, adding API keys to .env on first run and creating a standalone test file.\r\n\r\n    SimpleCoder.py: Provides coding aids, templates, and functions to simplify development tasks.\r\n\r\nconfig.json: Offers the default allowed agency for MASTERMIND.","shortMessageHtmlLink":"Update 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+---------------------+       +---------------------+\r\n                |  Data Retrieval     |       |  Embedding Module   |       |  Vector Store       |\r\n                |  Module (RAGE)      |       |  (RAGE)             |       |  Management (RAGE)  |\r\n                +---------------------+       +---------------------+       +---------------------+\r\n                                                                |                             |\r\n                                                                v                             v\r\n                                                        +---------------------+       +---------------------+\r\n                                                        |  Knowledge Base     |       |  Learning Engine    |\r\n                                                        |  (aGLM)             |       |  (aGLM)             |\r\n                                                        +---------------------+       +---------------------+","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"e1e812d01968ae0f69d588576a5a086d86de0132","after":"4ed96233935a613f48c3df3827407978707ca64a","ref":"refs/heads/main","pushedAt":"2024-06-05T15:51:56.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\n```plaintext\r\n+---------------------+       +---------------------+       +---------------------+       +---------------------+\r\n|  External Data      |       |  Vectara Platform   |       |  Feedback Systems   |       |  Security Tools     |\r\n|  Sources (APIs)     |       |  (Data Processing   |       |  (Learning &        |       |  (Compliance)       |\r\n|                     |       |  & Embedding)       |       |  Adaptation)        |       |                     |\r\n+---------------------+       +---------------------+       +---------------------+       +---------------------+\r\n         |                                 |                             |                             |\r\n         v                                 v                             v                             v\r\n+---------------------+       +---------------------+       +---------------------+       +---------------------+\r\n|  Data Retrieval     |       |  Embedding Module   |       |  Feedback Processor |       |  Security Protocols |\r\n|  Module (RAGE)      |       |  (RAGE)             |       |  (aGLM)             |       |  (RAGE & aGLM)       |\r\n+---------------------+       +---------------------+       +---------------------+       +---------------------+","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"1d7fec9325a72dfacadb3f6f5b40330b027075e8","after":"e1e812d01968ae0f69d588576a5a086d86de0132","ref":"refs/heads/main","pushedAt":"2024-06-05T15:48:39.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"f30666ea5d2c4ecc160128798d1f6308e64885de","after":"1d7fec9325a72dfacadb3f6f5b40330b027075e8","ref":"refs/heads/main","pushedAt":"2024-06-05T15:44:31.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\n+------------------------------------------------------+\r\n|                    MASTERMIND                        |\r\n|  +-----------------------------------------------+   |\r\n|  |  Coordination Module                         |   |\r\n|  |  Prediction Engine                           |   |\r\n|  |  Reasoning Module                            |   |\r\n|  |  Non-Monotonic Reasoning                     |   |\r\n|  |  Logic and Epistemic Management              |   |\r\n|  |  Autonomize Framework                        |   |\r\n|  |  BDI Agent Framework                         |   |\r\n|  +-----------------------------------------------+   |\r\n+---------------------|-------------------------------+\r\n                      |\r\n                      v\r\n+------------------------------------------------------+\r\n|                        aGLM                          |\r\n|  +-----------------------------------------------+   |\r\n|  |  Learning Engine                             |   |\r\n|  |  Knowledge Base                              |   |\r\n|  |  Interaction Handler                         |   |\r\n|  |  Feedback Processor                          |   |\r\n|  +-----------------------------------------------+   |\r\n+---------------------|-------------------------------+\r\n                      |\r\n                      v\r\n+------------------------------------------------------+\r\n|                        RAGE                          |\r\n|  +-----------------------------------------------+   |\r\n|  |  Data Retrieval Module                       |   |\r\n|  |  Data Preprocessing Module                   |   |\r\n|  |  Embedding Module                            |   |\r\n|  |  Vector Store Management                     |   |\r\n|  +-----------------------------------------------+   |\r\n+------------------------------------------------------+","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"723e90ae3e57d5499fbb35f92266af4725437191","after":"f30666ea5d2c4ecc160128798d1f6308e64885de","ref":"refs/heads/main","pushedAt":"2024-06-05T15:44:08.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\n+------------------------------------------------------+\r\n|                    MASTERMIND                        |\r\n|  +-----------------------------------------------+   |\r\n|  |  Coordination Module                         |   |\r\n|  |  Prediction Engine                           |   |\r\n|  |  Reasoning Module                            |   |\r\n|  |  Non-Monotonic Reasoning                     |   |\r\n|  |  Logic and Epistemic Management              |   |\r\n|  |  Autonomize Framework                        |   |\r\n|  |  BDI Agent Framework                         |   |\r\n|  +-----------------------------------------------+   |\r\n+---------------------|-------------------------------+\r\n                      |\r\n                      v\r\n+------------------------------------------------------+\r\n|                        aGLM                          |\r\n|  +-----------------------------------------------+   |\r\n|  |  Learning Engine                             |   |\r\n|  |  Knowledge Base                              |   |\r\n|  |  Interaction Handler                         |   |\r\n|  |  Feedback Processor                          |   |\r\n|  +-----------------------------------------------+   |\r\n+---------------------|-------------------------------+\r\n                      |\r\n                      v\r\n+------------------------------------------------------+\r\n|                        RAGE                          |\r\n|  +-----------------------------------------------+   |\r\n|  |  Data Retrieval Module                       |   |\r\n|  |  Data Preprocessing Module                   |   |\r\n|  |  Embedding Module                            |   |\r\n|  |  Vector Store Management                     |   |\r\n|  +-----------------------------------------------+   |\r\n+------------------------------------------------------+","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"c0ffb1e5c469e266d85418e333bfb30aa2800544","after":"723e90ae3e57d5499fbb35f92266af4725437191","ref":"refs/heads/main","pushedAt":"2024-06-05T15:43:27.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\n+------------------------------------------------------+\r\n|                    MASTERMIND                        |\r\n|  +-----------------------------------------------+   |\r\n|  |  Coordination Module                         |   |\r\n|  |  Prediction Engine                           |   |\r\n|  |  Reasoning Module                            |   |\r\n|  |  Non-Monotonic Reasoning                     |   |\r\n|  |  Logic and Epistemic Management              |   |\r\n|  |  Autonomize Framework                        |   |\r\n|  |  BDI Agent Framework                         |   |\r\n|  +-----------------------------------------------+   |\r\n+---------------------|-------------------------------+\r\n                      |\r\n                      v\r\n+------------------------------------------------------+\r\n|                        aGLM                          |\r\n|  +-----------------------------------------------+   |\r\n|  |  Learning Engine                             |   |\r\n|  |  Knowledge Base                              |   |\r\n|  |  Interaction Handler                         |   |\r\n|  |  Feedback Processor                          |   |\r\n|  +-----------------------------------------------+   |\r\n+---------------------|-------------------------------+\r\n                      |\r\n                      v\r\n+------------------------------------------------------+\r\n|                        RAGE                          |\r\n|  +-----------------------------------------------+   |\r\n|  |  Data Retrieval Module                       |   |\r\n|  |  Data Preprocessing Module                   |   |\r\n|  |  Embedding Module                            |   |\r\n|  |  Vector Store Management                     |   |\r\n|  +-----------------------------------------------+   |\r\n+------------------------------------------------------+","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"81d2d15ea50265192a6708c60a171bb42b698a1f","after":"c0ffb1e5c469e266d85418e333bfb30aa2800544","ref":"refs/heads/main","pushedAt":"2024-06-05T15:38:51.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\nhttps://chatgpt.com/g/g-f3jA46eUE-aglm","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"c9c8648667a23182a91c5f552373d7c6352a8e26","after":"81d2d15ea50265192a6708c60a171bb42b698a1f","ref":"refs/heads/main","pushedAt":"2024-06-05T15:38:02.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\nhttps://chatgpt.com/g/g-f3jA46eUE-aglm","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"5be7ada0adab2549bedadce8142058cd6eaeba4f","after":"c9c8648667a23182a91c5f552373d7c6352a8e26","ref":"refs/heads/main","pushedAt":"2024-06-05T15:32:10.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\nAccurate Generalized Linear Model (AGLM)","shortMessageHtmlLink":"Update 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aGLM.md\n\nhttps://github.com/pythaiml/automindx/tree/main","shortMessageHtmlLink":"Update aGLM.md"}},{"before":"26a7907f70f2f8001f7c586fcc5f4b613839b5e5","after":"3b78a48c1ba74a08f4f3b0f227c8b69ced427420","ref":"refs/heads/main","pushedAt":"2024-06-05T15:25:30.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update mastermind.md\n\nhttps://github.com/mastermindML/","shortMessageHtmlLink":"Update mastermind.md"}},{"before":"7166351889366ac4e2d20ebe12a6161f611a14b8","after":"26a7907f70f2f8001f7c586fcc5f4b613839b5e5","ref":"refs/heads/main","pushedAt":"2024-04-24T01:07:47.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update README.md\n\n```bash\r\ngit clone https://github.com/gaterage/aglm","shortMessageHtmlLink":"Update README.md"}},{"before":"77ddde0a4240b435e75d30790e7c299371b2ccab","after":"7166351889366ac4e2d20ebe12a6161f611a14b8","ref":"refs/heads/main","pushedAt":"2024-04-24T01:05:35.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update README.md\n\n<a href=\"https://github.com/Faicey\">UIUX</a> = User Interface User eXperience<br />\r\n<a href=\"https://github.com/AUTOMINDx\">IAML</a> = Intelligent Autonomous Machine Learning<br />\r\n<a href=\"https://github.com/mastermindml/aglm\">aGLM</a> = Autonomous General Learning Model<br />\r\n<a href=\"https://github.com/webmindml/.github\">webmindML</a> =  WebGPU reference links<br />\r\n<a href=\"https://github.com/xtends/.github\">xtends</a> = machine learning extensions<br />\r\n<a href=\"https://github.com/gaterage\">RAGE</a> = Retrieval Augmented Generative Engine<br />","shortMessageHtmlLink":"Update README.md"}},{"before":"36529bf95d764a7927a6081f383131f262fc5a79","after":"77ddde0a4240b435e75d30790e7c299371b2ccab","ref":"refs/heads/main","pushedAt":"2024-04-24T00:15:54.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update 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ragepaper.md"}},{"before":"cea8fb74eefaf5f686113d9ffb347b081b17ff5a","after":"5890253a11a29e786a7bd33e3d1106b8b22eb06c","ref":"refs/heads/main","pushedAt":"2024-04-13T18:07:13.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\n###################################################################\r\nusing below as template to complete above\r\n###################################################################\r\n\r\nRAGE Whitepaper: Retrieval Augmented Generative Engine\r\nExecutive Summary\r\nRAGE (Retrieval Augmented Generative Engine) represents a transformative advancement in artificial intelligence, integrating state-of-the-art retrieval mechanisms with advanced generative models to offer unparalleled accuracy, relevance, and adaptability. This whitepaper delves into RAGE's architecture, functionalities, and the significant advantages it brings to AI-driven applications, highlighting its synergistic integration with MASTERMIND and aGLM (Autonomous General Learning Model).\r\n\r\n1. Introduction\r\nBackground: Examines the evolution from static AI models to dynamic, learning systems.\r\nNeed for RAGE: Identifies challenges in AI applications that demand up-to-date information and contextual responses.\r\nPurpose of this Whitepaper: Details RAGE's capabilities and illustrates its impact across various industries.\r\n2. Technology Overview\r\nWhat is RAGE?: Provides a definition and a high-level overview.\r\nCore Components:\r\nMASTERMIND: Manages workflow and reasoning within the AI system.\r\naGLM: Focuses on dynamic, autonomous updates from retrieved data.\r\nRAGE Retrieval System: Enhances generative AI models with real-time data processing capabilities.\r\n3. Architecture\r\nSystem Architecture: Describes how RAGE integrates with MASTERMIND and aGLM, supported by diagrams.\r\nData Flow: Details the data acquisition, processing, and utilization within the system.\r\nIntegration Points: Discusses interaction with external systems and APIs.\r\n4. Functionalities\r\nReal-time Data Retrieval: Explores the mechanisms and technologies used to fetch current data.\r\nData Processing and Embedding: Uses Vectara’s platform for preprocessing and embedding via the Boomerang model.\r\nDynamic Learning and Adaptation: Describes how RAGE and aGLM learn from interactions and data updates.\r\nSecurity and Compliance: Outlines the security measures and compliance standards upheld by RAGE.\r\n5. Use Cases and Applications\r\nIndustry-specific Applications: Highlights examples from healthcare, finance, customer service, and research.\r\nPerformance Metrics: Discusses how RAGE improves response times, accuracy, and user satisfaction.\r\n6. Comparative Analysis and Advanced Integration Strategy\r\nMachine.dreaming: Introduces this advanced technique for fine-tuning larger models using parsed events from RAGE's accumulated knowledge stored in Vectara databases.\r\nIntegration with Together.ai: Enhances collaborative capabilities and optimizes model training within dynamic environments.\r\n7. Future Prospects\r\nScalability: Discusses how RAGE can adapt and scale with increasing data and complexity.\r\nInnovations on the Horizon: Forecasts future enhancements and features planned for RAGE.\r\n8. Implementation Strategy\r\nGetting Started with RAGE: Outlines steps for integrating RAGE into existing systems.\r\nConfiguration and Customization: Provides guidelines for tailoring RAGE to meet specific business needs.\r\n9. Conclusion\r\nSummarizes the transformative potential of RAGE and its expected impact on the future of AI applications.\r\n\r\n10. Appendices\r\nGlossary: Defines technical terms and acronyms used throughout the document.\r\nReferences: Lists scholarly articles, technical documents, and other resources cited in the whitepaper.\r\nContact Information: Offers ways to reach the development team for queries and collaborations.\r\nThis revised whitepaper integrates all updates and enhancements to provide a comprehensive overview of the RAGE framework, its unique capabilities, and its strategic application across a variety of industries. This document is designed to be a valuable resource for stakeholders considering the integration of advanced AI solutions within their operations.\r\n\r\nChapter 1: Introduction\r\nBackground\r\n\r\nThe evolution of artificial intelligence from static models to dynamic, adaptive systems marks a pivotal shift in technology's ability to interact with and understand the world. Traditional AI systems, reliant on static datasets, often fail to incorporate new information dynamically and struggle to provide contextually relevant responses in real-time scenarios. As a response to these limitations, the development of systems that can learn and adapt in real time has become crucial.\r\nNeed for RAGE\r\n\r\nCurrent AI applications face significant challenges in maintaining relevance and accuracy due to the rapid pace at which data and world events unfold. Traditional models lack the capability to update their knowledge bases without extensive retraining phases, which are not only time-consuming but also resource-intensive. There is a critical need for a system that can integrate continuous data flow and adapt its responses accordingly, ensuring both relevance and timeliness. This necessity is particularly acute in fields like finance, healthcare, and customer service, where real-time information can significantly impact outcomes.\r\nPurpose of this Whitepaper\r\n\r\nThis whitepaper aims to detail the capabilities and applications of the RAGE (Retrieval Augmented Generative Engine) framework and to demonstrate its impact across various industries. By integrating advanced retrieval techniques with dynamic learning systems, RAGE represents a significant advancement in AI technology. The document will explore how RAGE synergizes with components like MASTERMIND and aGLM (Autonomous General Learning Model) to create a highly adaptive and intelligent system. The purpose of this exploration is to illustrate the transformative potential of RAGE and its capacity to redefine industry standards through enhanced AI applications.\r\n\r\nThe whitepaper is structured to provide a comprehensive overview of RAGE’s architecture, functionalities, and strategic advantages. Through detailed descriptions of its core components, system architecture, and dynamic learning capabilities, the document will highlight how RAGE addresses the limitations of traditional AI systems and sets a new benchmark for intelligence and adaptability in technology.\r\nConclusion of Introduction\r\n\r\nAs industries continue to evolve and generate vast amounts of data, the need for advanced AI systems that can manage and utilize this information effectively becomes ever more pressing. RAGE is designed to meet these challenges head-on, offering a robust solution that enhances the capacity of AI to engage with and respond to an ever-changing world. Through this whitepaper, stakeholders across industries will gain insights into how integrating RAGE into their operations can drive innovation, enhance operational efficiency, and maintain competitive advantage in a data-driven landscape.\r\n\r\nChapter 4: Dynamic Learning and Intelligent Adaptation\r\n\r\nThe RAGE (Retrieval Augmented Generative Engine) framework, augmented by the Autonomous General Learning Model (aGLM), forms a powerful combination that embodies a continuously evolving learning intelligence. This chapter delves into how RAGE, as a dynamic learning agent, integrates with aGLM to facilitate ongoing adaptation and intelligent behavior across diverse applications.\r\nDynamic Learning in RAGE\r\n\r\nDynamic learning is at the core of RAGE's design philosophy, enabling the system to not just respond to queries with high accuracy but also to adapt and improve over time based on interaction data and feedback. This capability is critically supported by the integration of aGLM, which provides a robust mechanism for autonomous updates and learning enhancements.\r\n\r\n1. Continuous Data Integration and Learning\r\n\r\n    Real-time Data Processing: RAGE retrieves and processes data in real-time, allowing it to constantly update its knowledge base with the latest information.\r\n    Integration with aGLM: The data processed by RAGE is fed into aGLM, where it is used to refine and update learning models dynamically. This ensures that the intelligence of the system evolves with each interaction, adapting to new information and changing contexts.\r\n\r\n2. Feedback Mechanisms\r\n\r\n    User Feedback: Direct feedback from users is analyzed to gauge the effectiveness of the responses provided by RAGE. This feedback influences subsequent model training sessions, guiding the aGLM to focus on areas requiring improvements or adjustments.\r\n    Automated Learning Adjustments: RAGE employs algorithms that automatically adjust learning parameters in response to patterns observed in data interactions and user feedback, enhancing the system’s ability to learn from its environment effectively.\r\n\r\nIntelligent Adaptation\r\n\r\nThe combination of RAGE and aGLM facilitates an advanced level of intelligent adaptation, enabling the system to handle complex scenarios and provide solutions that are not only relevant but also contextually enriched.\r\n\r\n1. Context-Aware Responses\r\n\r\n    Understanding Nuance: By leveraging the continuously updated data and learning from past interactions, RAGE can understand and respond to nuances in user queries. This ability makes it particularly effective in scenarios where context heavily influences the nature of the response.\r\n    Adaptive Response Generation: As RAGE evolves, it becomes more adept at predicting user needs and adjusting its responses accordingly, ensuring high relevance and personalization.\r\n\r\n2. Predictive Capabilities\r\n\r\n    Anticipating User Needs: With aGLM's learning capabilities, RAGE can anticipate user needs based on historical interaction data and broader contextual analysis. This predictive capability enables proactive responses, enhancing user engagement and satisfaction.\r\n    Trend Analysis and Forecasting: RAGE can identify trends from the continuous stream of data it processes, using this information to make forecasts and predictions, which are invaluable for industries like finance, marketing, and public safety.\r\n\r\nUse Cases Demonstrating Learning Intelligence\r\n\r\n1. Customer Support Automation\r\n\r\n    In customer service, RAGE's learning intelligence enables it to understand user sentiment, preferences, and history, allowing it to provide tailored advice, recommend solutions, and anticipate future inquiries or issues.\r\n\r\n2. Healthcare Diagnosis and Treatment\r\n\r\n    RAGE can assist in diagnosing medical conditions by analyzing symptoms described in real-time consultations and comparing them with a vast database of medical knowledge. Over time, it learns to make more accurate diagnoses and suggests personalized treatment plans.\r\n\r\n3. Financial Market Analysis\r\n\r\n    In finance, RAGE analyzes real-time market data to offer predictions and risk assessments. Its ability to learn dynamically enables it to adapt its analysis based on market conditions and historical trends, providing traders with insights that are current and predictive.\r\n\r\nConclusion\r\n\r\nRAGE, enhanced by the dynamic learning capabilities of aGLM, represents a significant step forward in the development of intelligent AI systems. This synergy not only improves the accuracy and relevance of responses but also enables the system to adapt over time, learning from each interaction to become increasingly sophisticated. As RAGE continues to evolve, it promises to redefine the possibilities of artificial intelligence, making it an invaluable asset across various sectors.","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"f64971b8ddc03943b5c9e945185434eb9d9f0e9d","after":"cea8fb74eefaf5f686113d9ffb347b081b17ff5a","ref":"refs/heads/main","pushedAt":"2024-04-13T18:04:47.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\n## Chapter 6: Comparative Analysis and Advanced Integration Strategy\r\n\r\nThis chapter provides a comparative analysis of the RAGE (Retrieval Augmented Generative Engine) framework, highlighting its advanced integration strategies and unique positioning within the AI landscape. By leveraging state-of-the-art technologies and innovative techniques like machine dreaming, RAGE demonstrates a significant advancement over traditional AI systems.\r\n\r\n### Machine Dreaming: Enhancing AI with Autonomous Fine-Tuning\r\n\r\nMachine dreaming within RAGE represents a revolutionary approach to enhancing AI capabilities. This technique allows for real-time, autonomous fine-tuning of models using insights parsed from accumulated knowledge stored in Vectara databases. This process ensures that RAGE can adapt to new information and complex scenarios seamlessly, without requiring manual intervention.\r\n\r\n- **Dynamic Learning**: Machine dreaming enables dynamic learning capabilities within RAGE, allowing it to autonomously adjust its operations based on continuous data analysis and feedback.\r\n- **Efficiency and Adaptability**: The autotune feature of machine dreaming significantly increases the efficiency of the learning process and ensures that RAGE remains at the cutting edge of AI performance.\r\n\r\n### Integration with Together.ai: Facilitating Collaborative AI Development\r\n\r\nThe strategic integration of RAGE with Together.ai amplifies its collaborative capabilities, enhancing the overall model training and AI system management. Together.ai provides a robust platform where AI models, like those within RAGE, can interact and learn from each other, leading to faster and more effective AI solutions.\r\n\r\n- **Collaborative Ecosystem**: Together.ai fosters a dynamic environment that promotes the sharing of insights and strategies among different AI systems, enhancing collective intelligence.\r\n- **Optimized Model Training**: Utilizing Together.ai's advanced tools and environments, RAGE can refine its algorithms and customize its responses more effectively, ensuring tailored solutions to operational needs.\r\n\r\n### Self-Healing Capabilities through aGLM’s Feedback Loop\r\n\r\nIncorporating self-healing capabilities into RAGE, facilitated by the Autonomous General Learning Model's (aGLM) feedback loop, significantly enhances its reliability and operational integrity. This autonomous feature enables RAGE to detect, diagnose, and correct its own inefficiencies and errors in real-time.\r\n\r\n- **System Longevity and Reliability**: The self-healing process proactively addresses potential failures and optimizes performance, substantially reducing downtime and extending the system’s operational life.\r\n- **Autonomous Optimization**: By continuously monitoring its own performance and adjusting parameters accordingly, RAGE maintains optimal functionality without external input, leading to sustained high performance.\r\n\r\n### Conclusion\r\n\r\nThe integration of advanced technologies and strategies in RAGE not only surpasses traditional AI capabilities but also establishes new standards for adaptability, efficiency, and autonomy in artificial intelligence systems. The use of machine dreaming and collaborative platforms like Together.ai, combined with the self-healing mechanisms provided by aGLM, uniquely positions RAGE as a leader in the next generation of AI development. These features ensure that RAGE is not only a tool for today but also a foundation for future AI innovations.","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"d40663027bbcf830af3a1011884fb26cdf5ebbe7","after":"f64971b8ddc03943b5c9e945185434eb9d9f0e9d","ref":"refs/heads/main","pushedAt":"2024-04-13T17:59:30.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\n## Machine Dreaming: Enabling Autonomous Intelligence to Create Knowledge from Memory\r\n\r\nMachine dreaming within the RAGE framework (Retrieval Augmented Generative Engine) represents a transformative approach to how autonomous intelligence systems like aGLM (Autonomous General Learning Model) utilize and create knowledge from stored memory. This capability enhances RAGE’s adaptability and intelligence, allowing it to autonomously refine and extend its own knowledge base.\r\n\r\n### Concept Overview\r\n\r\nMachine dreaming is an advanced machine learning process where aGLM autonomously analyzes stored memory to generate new insights and knowledge. This memory includes historical data, user interactions, system metrics, and external information that the system has accumulated over time.\r\n\r\n### Functionality and Implementation\r\n\r\n- **Memory Parsing**: aGLM actively parses through stored data within RAGE’s memory databases, identifying useful patterns, trends, and anomalies. This process involves deep analysis of the content, context, and correlations within the data.\r\n\r\n- **Knowledge Creation**: From the parsed data, aGLM synthesizes new knowledge. This might involve deducing new rules, creating predictive models, or forming new hypotheses that can guide future interactions and decisions.\r\n\r\n- **Autonomous Learning and Adaptation**: The new knowledge is not merely stored; it is integrated into RAGE's operational framework. This integration allows the system to adapt its behavior and responses based on newly created knowledge, essentially learning from its own generated insights.\r\n\r\n### Benefits and Impact\r\n\r\n- **Continuous Improvement**: Machine dreaming ensures that RAGE continually evolves by learning from its past experiences and any newly acquired information, thereby improving its performance and decision-making capabilities over time.\r\n\r\n- **Proactive Adaptation**: This process enables RAGE to proactively adapt to new scenarios or changes in its environment, maintaining relevance and effectiveness without needing frequent manual updates or retraining.\r\n\r\n- **Increased Operational Efficiency**: By autonomously generating and integrating new knowledge, RAGE reduces the dependency on external data sources and human intervention, streamlining operations and reducing response times.\r\n\r\n### Use Cases\r\n\r\n- **Healthcare Decision Support**: In healthcare, machine dreaming allows RAGE to analyze patient data and past case studies to autonomously generate and suggest novel treatment plans or flag potential risks, thereby supporting medical professionals in making better-informed decisions.\r\n\r\n- **Financial Modeling**: In finance, RAGE can autonomously create and refine financial models based on continuously updated market data and past transaction records, improving predictive accuracy for investments and market movements.\r\n\r\n- **Customer Experience Personalization**: In retail and e-commerce, machine dreaming enables RAGE to analyze customer behavior and preferences over time, autonomously generating insights that help tailor marketing strategies and product recommendations at an individual level.\r\n\r\n### Conclusion\r\n\r\nMachine dreaming fundamentally enhances the way autonomous systems like RAGE create and utilize knowledge, making them not just reactive to but predictive and proactive in various operational contexts. This capability sets a new benchmark in the development of intelligent systems, paving the way for more sophisticated, autonomous, and adaptive applications across industries.","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"af49381ca8a34c97468f53985e42ec13dfb2248c","after":"d40663027bbcf830af3a1011884fb26cdf5ebbe7","ref":"refs/heads/main","pushedAt":"2024-04-13T17:52:27.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\n## Chapter 6: Comparative Analysis and Advanced Integration Strategy\r\n\r\n### Machine Dreaming: Real-time Dynamic Learning Autotune Feature\r\n\r\nMachine dreaming within RAGE serves as an advanced autotune feature that significantly enhances its capabilities as a real-time dynamic learning agent. This mechanism allows RAGE to fine-tune its models automatically by analyzing and integrating parsed events from its accumulated knowledge stored in Vectara databases. This process facilitates continuous adaptation to new information and complex scenarios, greatly reducing the need for manual adjustments and intervention.\r\n\r\n- **Autotune in Action**: Machine dreaming autonomously adjusts and refines the AI's behavior, ensuring that learning and response strategies are optimized in real-time based on the latest data insights.\r\n- **Benefits**: This capability not only increases the efficiency of the learning process but also ensures that RAGE remains at the cutting edge of AI performance, adapting quickly to changes in its operating environment.\r\n\r\n### Integration with Together.ai: Enhancing Collaborative Capabilities\r\n\r\nThe integration of RAGE with Together.ai further enhances its collaborative capabilities, enabling a more cohesive and synchronized approach to AI training and model management. Together.ai provides a platform where multiple AI agents, like RAGE, can interact, learn from one another, and improve collectively.\r\n\r\n- **Dynamic Environments**: Together.ai's environment supports dynamic interaction among AI systems, which promotes faster learning and more robust AI solutions through shared experiences and data.\r\n- **Optimized Model Training**: By utilizing Together.ai's sophisticated training tools, RAGE can enhance its learning algorithms and strategies, ensuring that the models are not only precise but also tailored to specific operational needs.\r\n\r\n### Self-Healing: Autonomous Function from aGLM’s Dynamic Learning Feedback Loop\r\n\r\nIncorporating self-healing capabilities into RAGE through aGLM’s dynamic learning feedback loop marks a significant advancement in its operational integrity and reliability. This autonomous function allows RAGE to identify, diagnose, and rectify issues in real-time, maintaining system performance without external intervention.\r\n\r\n- **Continuous System Optimization**: The self-healing process continuously monitors RAGE’s performance, automatically initiating corrective measures whenever potential issues are detected.\r\n- **Enhanced System Longevity and Reliability**: By resolving operational issues proactively, this feature significantly reduces downtime and extends the system’s operational life.\r\n\r\n### Conclusion\r\n\r\nThe comparative analysis of RAGE’s integration with advanced technologies like machine dreaming and Together.ai illustrates its superior adaptability and learning capabilities compared to traditional AI systems. These integrations not only enhance RAGE’s functionality but also position it as a leader in the evolution of intelligent systems. The addition of self-healing through aGLM’s feedback loop further underscores its advanced capability to maintain and optimize itself autonomously, showcasing the potential of AI systems to operate independently in dynamic and complex environments.","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"971247b5fbd2666e789a9170e044dfca09de2856","after":"af49381ca8a34c97468f53985e42ec13dfb2248c","ref":"refs/heads/main","pushedAt":"2024-04-13T17:51:38.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\nAI augmented intelligence","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"4f1ed67075ddf24c808504376efa6fb1515e349c","after":"971247b5fbd2666e789a9170e044dfca09de2856","ref":"refs/heads/main","pushedAt":"2024-04-13T17:50:01.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\n## Chapter 6: Comparative Analysis and Advanced Integration Strategy\r\n\r\n### Machine Dreaming: Real-time Dynamic Learning Autotune Feature\r\n\r\nMachine dreaming within RAGE serves as an advanced autotune feature that significantly enhances its capabilities as a real-time dynamic learning agent. This mechanism allows RAGE to fine-tune its models automatically by analyzing and integrating parsed events from its accumulated knowledge stored in Vectara databases. This process facilitates continuous adaptation to new information and complex scenarios, greatly reducing the need for manual adjustments and intervention.\r\n\r\n- **Autotune in Action**: Machine dreaming autonomously adjusts and refines the AI's behavior, ensuring that learning and response strategies are optimized in real-time based on the latest data insights.\r\n- **Benefits**: This capability not only increases the efficiency of the learning process but also ensures that RAGE remains at the cutting edge of AI performance, adapting quickly to changes in its operating environment.\r\n\r\n### Integration with Together.ai: Enhancing Collaborative Capabilities\r\n\r\nThe integration of RAGE with Together.ai further enhances its collaborative capabilities, enabling a more cohesive and synchronized approach to AI training and model management. Together.ai provides a platform where multiple AI agents, like RAGE, can interact, learn from one another, and improve collectively.\r\n\r\n- **Dynamic Environments**: Together.ai's environment supports dynamic interaction among AI systems, which promotes faster learning and more robust AI solutions through shared experiences and data.\r\n- **Optimized Model Training**: By utilizing Together.ai's sophisticated training tools, RAGE can enhance its learning algorithms and strategies, ensuring that the models are not only precise but also tailored to specific operational needs.\r\n\r\n### Self-Healing: Autonomous Function from aGLM’s Dynamic Learning Feedback Loop\r\n\r\nIncorporating self-healing capabilities into RAGE through aGLM’s dynamic learning feedback loop marks a significant advancement in its operational integrity and reliability. This autonomous function allows RAGE to identify, diagnose, and rectify issues in real-time, maintaining system performance without external intervention.\r\n\r\n- **Continuous System Optimization**: The self-healing process continuously monitors RAGE’s performance, automatically initiating corrective measures whenever potential issues are detected.\r\n- **Enhanced System Longevity and Reliability**: By resolving operational issues proactively, this feature significantly reduces downtime and extends the system’s operational life.\r\n\r\n### Conclusion\r\n\r\nThe comparative analysis of RAGE’s integration with advanced technologies like machine dreaming and Together.ai illustrates its superior adaptability and learning capabilities compared to traditional AI systems. These integrations not only enhance RAGE’s functionality but also position it as a leader in the evolution of intelligent systems. The addition of self-healing through aGLM’s feedback loop further underscores its advanced capability to maintain and optimize itself autonomously, showcasing the potential of AI systems to operate independently in dynamic and complex environments.","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"062515d2b0f4ce983959114ffe75b66a0317cf85","after":"4f1ed67075ddf24c808504376efa6fb1515e349c","ref":"refs/heads/main","pushedAt":"2024-04-13T17:42:52.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\ngy, facilitating more intuitive, responsive, and efficient AI systems. The planned enhancements and integrations discussed will not only maintain RAGE's relevance but also solidify its position as a leader in the AI industry, driving significant innovation and transformation across various sectors.\r\n\r\n\r\n## Chapter 7: Future Prospects\r\n\r\n### Scalability\r\n\r\nAs organizations increasingly rely on AI-driven solutions to navigate complex and evolving business landscapes, the ability to scale efficiently becomes crucial. RAGE, with its robust architecture and integration capabilities, is uniquely positioned to support the growth of enterprises and the expansion of their data requirements. Here's how RAGE addresses scalability:\r\n\r\n- **Modular Architecture**: RAGE's modular design allows for seamless scalability. Components such as the RAGE Retrieval System, MASTERMIND, and aGLM can be independently scaled to meet increasing demands without disrupting overall system performance.\r\n- **Cloud-Based Deployment**: Leveraging cloud infrastructure ensures that RAGE can dynamically adjust computing resources based on real-time needs, providing scalability and flexibility without the high costs associated with traditional hardware expansions.\r\n- **Data Management Efficiency**: With advanced data indexing and retrieval mechanisms, RAGE can manage vast volumes of data efficiently, ensuring that performance remains optimal even as data loads increase.\r\n\r\n### Innovations on the Horizon\r\n\r\nThe field of artificial intelligence is rapidly evolving, and RAGE is at the forefront of this evolution. Continuous research and development efforts are focused on enhancing RAGE’s capabilities and introducing new functionalities. Future innovations may include:\r\n\r\n- **Enhanced Machine Learning Models**: Ongoing research aims to integrate newer and more powerful machine learning models into RAGE, enhancing its predictive capabilities and making the system more intuitive and responsive to user needs.\r\n- **Improved Natural Language Understanding**: Advancements in natural language processing techniques will enable RAGE to understand and interpret human language with greater nuance and accuracy, facilitating more effective and human-like interactions.\r\n- **Autonomous Decision-Making Features**: Future versions of RAGE may include more advanced decision-making algorithms that allow for greater autonomy in complex scenarios, reducing the need for human intervention and increasing operational efficiency.\r\n\r\n### Integration with Emerging Technologies\r\n\r\nRAGE’s future development also involves integration with other cutting-edge technologies:\r\n\r\n- **Blockchain for Enhanced Security**: Integrating blockchain technology to manage data transactions within RAGE could enhance security and transparency, especially in sensitive applications such as financial services or healthcare.\r\n- **Internet of Things (IoT) Integration**: By connecting RAGE with IoT devices, data collection and processing can be further automated and enriched, enabling more dynamic interactions and smarter environments.\r\n- **Augmented and Virtual Reality**: Merging AR and VR with RAGE could revolutionize user interfaces, particularly in fields such as education, training, and remote work, by providing more immersive and interactive experiences.","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"8fb82c630ffc0930b3834b9303d04b6a94288339","after":"062515d2b0f4ce983959114ffe75b66a0317cf85","ref":"refs/heads/main","pushedAt":"2024-04-13T17:38:44.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\n### Conclusion\r\n\r\nThe future of RAGE looks promising, with plans to expand its capabilities and adapt to the ever-changing landscape of technology. As RAGE continues to evolve, it will set new benchmarks in AI technology, further enabling organizations to harness the full potential of artificial intelligence. These advancements will ensure that RAGE remains not only relevant but also a leader in the AI industry, driving innovation and transformation across various sectors.","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"060e3079c2aac0ad2572e26140b85a16e01ee47a","after":"8fb82c630ffc0930b3834b9303d04b6a94288339","ref":"refs/heads/main","pushedAt":"2024-04-13T17:32:27.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\nhttps://github.com/pythaiml/automindx/blob/main/algm.md","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"1a85d6710bcb80f3b9934b76d87231de3ef1b2ff","after":"060e3079c2aac0ad2572e26140b85a16e01ee47a","ref":"refs/heads/main","pushedAt":"2024-04-13T17:27:46.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\n**MASTERMIND**\r\n\r\n- **Overview**: MASTERMIND is a crucial component of the RAGE framework, serving as the orchestration and reasoning hub. It manages workflows and oversees the logical integration of various system processes.\r\n- **Functionality**: It coordinates the flow of data and decisions across the subsystems, ensuring that each component of RAGE operates cohesively and efficiently. By handling complex reasoning and decision-making processes, MASTERMIND ensures that responses generated by RAGE are both logically consistent and contextually apt.\r\n- **Reference**: For more details on MASTERMIND and its capabilities, visit the GitHub repository at [MASTERMIND](https://github.com/mastermindml/mastermind).","shortMessageHtmlLink":"Update ragepaper.md"}},{"before":"6c87da5c22573ddbc0abc07ab33c5858cdf18270","after":"1a85d6710bcb80f3b9934b76d87231de3ef1b2ff","ref":"refs/heads/main","pushedAt":"2024-04-13T17:26:48.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"Professor-Codephreak","name":"codephreak","path":"/Professor-Codephreak","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/140855987?s=80&v=4"},"commit":{"message":"Update ragepaper.md\n\nhttps://bankon.gitbook.io/aglm-investor/aglm","shortMessageHtmlLink":"Update ragepaper.md"}}],"hasNextPage":true,"hasPreviousPage":false,"activityType":"all","actor":null,"timePeriod":"all","sort":"DESC","perPage":30,"cursor":"djE6ks8AAAAEXVmXLQA","startCursor":null,"endCursor":null}},"title":"Activity · GATERAGE/RAGE"}