together.ai provides a cloud environment playground for a number of LLM including Mixtral8x7Bv1. This model was chosen for the 32k ++ context window and suitable point of departure dataset for deployment of aGLM Autonomous General Learning Model. aGLM design goals include RAGE with MASTERMIND controller for logic and reasoning. The following three screenshots show the first use of aGLM recognising aGLM and MASTERMIND RAGE components to include machine.dreaming and knowledge as THOT from aGLM parse. […]

The Sharpest Voice in AI Knowledge Delivery Welcome to the Kuntai: DEEPDIVE Podcast, a no-nonsense, intellectually fierce exploration into the ever-evolving world of AI, data, and innovation. Hosted at rage.pythai.net, Kuntai’s mission is simple: challenge the boundaries of knowledge, provoke deeper thought, and leave no stone unturned in the pursuit of intellectual mastery. What to Expect from Kuntai: DeepDive In this exclusive podcast series, we bring you the brilliant insights crafted by Kuntai—18 meticulously written […]

understandin the ezAGI framework requires a fundamental comprehension of reasoning with SocraticReasoning.py disclaimer: ezAGI fundamental Augmented Generative Intelligence may or not be be fun. use at own risk. breaking changes version 1 To fully audit the behavior of how the premise field is populated in the SocraticReasoning class, we will: SocraticReasoning.py Audit Initialization and setup of SocraticReasoning class Adding Premises Programmatically Adding Premises Interactively Now, let’s look at the interactive part of the interact method: […]