MASTERMIND

MASTERMIND

MASTERMIND documentation on the blockchain

Related articles

RAGE for LLM as a Tool to Create Reasoning Agents as MASTERMIND

Introduction: article created as first test of GPT-RESEARCHER as a research tool The integration of Retrieval-Augmented Generative Engine (RAGE) with Large Language Models (LLMs) represents a significant advancement in the field of artificial intelligence, particularly in enhancing the reasoning capabilities of these models. This report delves into the application of RAGE in transforming LLMs into sophisticated reasoning agents, akin to a “MASTERMIND,” capable of strategic reasoning and intelligent decision-making. The focus is on how RAG […]

Learn More
SHAMBA LUV — 1 trillion LUV = $0.2064 — LUV/ETH on Uniswap

Giving Uniswap the LUV: One Pool at Maximum — and the Road to Many

The liquidity reference of record for SHAMBA LUV: exact launch times read from the chain, the pool seeded at 2^112-1 wei, live reserves to 18 decimals, verified contracts at github.com/SHAMBA-LUV, and the Uniswap buy/sell rails.

Learn More
Symbolic Logic

LogicTables Class: Managing Logic and Beliefs

The LogicTables class in logic.py is designed to handle logical expressions, evaluate their truth values, and manage beliefs as valid truths. It integrates with the SimpleMInd or similar neural network system to process and use truths effectively. Key Features: Initialization and Logging The LogicTables class initializes with logging configuration to capture debug information: Adding Variables and Expressions Truth tables are generated to evaluate logical expressions: Expressions are evaluated using logical operators: def evaluate_expression(self, expr, values):allowed_operators […]

Learn More