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fundamental augmented general intelligence

funAGI workflow fundamental autonomous general intelligence framework

The funAGI system is designed as a modular framework for developing an autonomous general intelligence. The workflow integrates several components and libraries to achieve adaptability, dynamic interaction, continuous optimization, and secure data management. Below is a detailed explanation of the funAGI workflow based on the provided files and documentation. 1. Component Initialization 2. Core AGI Logic 3. User Interaction 4. Reasoning and Logic 5. API and Integration 6. Communication and Interaction 7. Installation and Requirements […]

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Hackathon Challenge:

OpenAI Assistants API Llama-Index/MongoDB In this hackathon, you will build and iterate on an LLM-based application using AI observability to validate the performance of your app. You can choose between two sets of tools for building your app: Tool set 1: The OpenAI Assistants API Tool set 2: Llama-Index, MongoDB and GPT-4. With either choice, you will use TruLens to validate and improve the performance of your application. By bringing together TruEra, OpenAI, Llama-Index, and […]

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MASTERMIND aGLM with RAGE

Building a rational Autonomous General Learning Model with Retrieval Augmented Generative Engine to create a dynamic learning loop with machine.dreaming for machine.learning as a self-healing architecture. MASTERMIND uses the Autonomous General Learning Model (aGLM) enhanced by the Retrieval Augmented Generative Engine (RAGE) to create a sophisticated AI system capable of intelligent decision-making and dynamic adaptation to real-time data. This combination leverages the strengths of both components to ensure that responses are not only based on […]

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