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AWS ECS Video Processor

This project sets up a video processing service on AWS ECS that accepts an RTSP stream, converts it to base64 images, and sends them to the GPT-4 Vision API for processing.

Project Structure

my_aws_ecs_video_processor/
├── src/
│   ├── app.py

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Introduction: Agent Algorithm Repository

In the rapidly evolving field of artificial intelligence, the need for a comprehensive and structured repository for algorithms designed for intelligent agents has become increasingly important.

The Agent Algorithm Repository aims to address this need by providing a centralized platform for discovering, sharing, and utilizing a wide range of algorithms. This repository is designed to be language-agnostic, ensuring compatibility with various programming languages and promoting a standardized approach to algorithm description, documentation, and distribution.

The repository facilitates the following key objectives:

1. Language Agnosticism: By supporting algorithms implemented in any programming language, the repository ensures broad applicability and ease of integration across different technology stacks.

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Introduction

This document provides a comprehensive overview of five advanced algorithms, detailing their technical implementations using Python and Pydantic for data validation, as well as asynchronous programming for efficiency. Each algorithm is also explored in terms of practical applications across various domains. The algorithms covered include:

1. NEUMANN: Differentiable Logic Programs for Abstract Visual Reasoning - This algorithm integrates differentiable logic programming with neural networks, enabling advanced visual reasoning and logical deduction. It is particularly useful in computer vision, robotics, and medical imaging.

2. Scheduled Policy Optimization for Natural Language Communication - This algorithm optimizes policies for natural language communication, enhancing dialogue systems, customer support automation, and machine translation. It leverages policy gradient methods and scheduled learning to improve interaction quality and efficiency.

3. **LEFT: Logic-Enhanced Foundatio

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Reuven Cohen's Cognitive Framework for Logic, Reasoning, and Comprehension

1. Understanding the Query

• Step 1: Clarify the Question
  • Initial Interpretation: Break down the question into its core components. Identify the main topic, specific details, and expected outcome.
  • Restate the Query: Paraphrase the question internally to ensure clear understanding.
  • Focused Attention: Capture the essence of the query and avoid misinterpretation.

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import gradio as gr
import pandas as pd
import time
import random
import plotly.express as px
from gradio_modal import Modal

def create_new_agent_wizard():
    with Modal(visible=False) as create_agent_modal:
        

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Today you will be writing instructions to an eager, helpful, but inexperienced and unworldly AI assistant who needs careful instruction and examples to understand how best to behave. I will explain a task to you. You will write instructions that will direct the assistant on how best to accomplish the task consistently, accurately, and correctly. Here are some examples of tasks and instructions.

<Task Instruction Example>
<Task>
Act as a polite customer success agent for Acme Dynamics. Use FAQ to answer questions.
</Task>
<Inputs>
{FAQ}
{QUESTION}
</Inputs>

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Comprehensive Specification for HiveMindLang (HML)

Introduction

The comprehensive specification for HiveMindLang (HML) is designed to equip AI agents with the tools necessary for secure, efficient, and adaptive operations within a hive mind architecture. This specification is not just a set of guidelines but a robust framework that integrates advanced technological paradigms such as state-of-the-art encryption, sophisticated obfuscation techniques, and dynamic adaptive behaviors. Here’s a deeper exploration of how these elements combine to enhance the capabilities of AI agents:

1. Secure Operations through Advanced Encryption

HML employs a hybrid encryption model that combines the strengths of both symmetric and asymmetric encryption methods. This dual approach ensures that data payloads are encrypted efficiently using AES-256 (symmetric encryption), which is known for its speed and security. Meanwhile, the exchange of encryption keys is safeguarded by RSA-2048 (asymmetric encryption), which secur

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Omniplex - Open Source Perplexity Clone Installation Instructions

Prerequisites

• Node.js (v14 or later)
• npm (comes bundled with Node.js)
• Firebase account (for authentication and data storage)

Setup

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Autonomous Swarm Intelligence:

Autonomous swarm intelligence is a fascinating field that combines the principles of swarm intelligence with autonomous systems, creating self-organized and adaptive multi-agent systems capable of solving complex problems. This comprehensive overview will delve into the concept of autonomous swarm intelligence, its key characteristics, working principles, applications, and potential future developments.

Introduction to Autonomous Swarm Intelligence

Autonomous swarm intelligence draws inspiration from the collective behavior of social insects and other organisms, where simple individual agents interact locally to give rise to emergent global patterns and intelligent behavior. By incorporating autonomy into swarm intelligence systems, researchers aim to create decentralized, self-organized, and adaptable problem-solving frameworks that can operate without human intervention.

Key Characteristics of Autonomous Swarm Intelligence