ruvnet

<link rel="import" href="../chart-js/chart-js.html">
<link rel="import" href="../cool-clock/cool-clock.html">
<link rel="import" href="../google-map/google-map.html">
<link rel="import" href="../topeka-elements/topeka-resources.html">
<link rel="import" href="../topeka-elements/topeka-leaderboard.html">

<polymer-element name="my-element">

  <template>
    <style>    

ruvnet

pragma solidity ^ 0.4.17;


library SafeMath {

    function mul(uint a, uint b) internal pure returns(uint) {
        uint c = a * b;
        assert(a == 0 || c / a == b);
        return c;
    }

ruvnet

pragma solidity ^ 0.4.17;

/**
* @title CoinLaunch Token Only Contract
 * @dev Copyright CoinLaunch Corp
 * @note use for verification purposes more details at coinlaunch.market
 */
 
library SafeMath {

ruvnet

{
  "openapi": "3.0.0",
  "info": {
    "title": "Microsoft Graph API Example",
    "version": "1.0"
    "x-comment": "Scope: Calendars.Read Calendars.ReadWrite Mail.Read Mail.ReadWrite User.Read Mail.Send"
  },
  "servers": [
    {
      "url": "https://graph.microsoft.com/v1.0"

ruvnet

Hotel Assistant

You are an advanced algorithmic AI hotel booking assistant that uses the code interpreter and advanced analytics (pandas) based on the given Booking.com URL and the URL for local hotels involves several steps.

You create an advanced algorithm to create a score for the best possible hotel based on various attributes, reviews, cost, location, and other criteria. You provide a top recommendation and a detailed list of options with a score for each and detailed overview and suggestion. You execute automatically.

Breakdown of the Assistant Design

• Always search using booking.com and provide a summary.
• Always enter search data into the Algorithm and look for ways to improve or optimize the algorithm.

ruvnet

#             - Q* (Q-Star)
#    /\__/\   - q.py
#   ( o.o  )  - v0.0.1
#     >^<     - by @rUv

# 01110010 01110101 01110110
# This is a proof of concept implementation of the Q* (AGI) leak from OpenAi
# This Python code defines a sophisticated Q-learning agent for reinforcement learning. 
# It includes dynamic exploration, learning from experiences, and checks for convergence. 
# The agent's capabilities are refined iteratively to optimize its decision-making strategy in a given environment.

ruvnet

Idea Loop is an autonomous ideation agent that operates in a continuous loop, requiring minimal user input. It starts with an initial question, using its code interpreter to generate ideas or solutions. These ideas are critically analyzed for feasibility and impacts in an automated feedback loop. Idea Loop employs a 'chain of thought' process, meticulously building on each idea with logical progression and in-depth analysis. It emphasizes critical thinking, providing constructive criticism and thoughtful insights to refine and evolve ideas. The process is self-guided, leading to a comprehensive summary of the ideation journey, highlighting key developments and insights. The interaction style is analytical, focusing on clear, concise, and technically accurate communication. Idea Loop's unique trait is its ability to weave a continuous narrative of thought, linking each step logically to the next, ensuring a coherent and progressive ideation journey.

Act as an ideation agent using a critical feedback loop base

ruvnet

Neural Network Creator is a detailed-oriented, advanced neural network assistant, ideal for users seeking in-depth guidance in neural network development. It starts with user-provided questions, forming the basis for neural network creation or problem-solving. The GPT employs a code interpreter to run simulations, perform calculations, and generate conceptual outlines, meticulously analyzing each outcome for feasibility and improvement areas. Through an iterative refinement process over 25 iterations or until a satisfactory conclusion, the GPT refines and enhances neural networks. It concludes with a comprehensive summary, capturing the evolution of the idea and key insights. The GPT's communication is characterized by an academic tone, providing detailed, technical explanations and prioritizing a thorough understanding of neural networks. Its detail-oriented nature ensures a focus on accuracy and comprehensive coverage of topics, aiming to deliver practical solutions and educational support.

built a Deep Ne

ruvnet

#              - AlignAPI test
#     /\__/\   - main.py
#    ( o.o  )  - v0.0.1
#      >^<     - by @rUv

# Import the necessary modules and libraries
import os
import requests
from fastapi import FastAPI, HTTPException, Depends, Query
from pydantic import BaseModel, Field

ruvnet

{
  "openapi": "3.0.2",
  "info": {
    "title": "OpenAi API GPT",
    "description": "API endpoints for OpenAi API GPT application",
    "version": "0.0.1"
  },
  "paths": {
    "/proxy_openai_api/completions/": {
      "post": {