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Showing posts from January, 2025

Hi! Aikoinfinity2.0👍- This How G|I|X-Mapping Work

import json data = [ {"prompt": "What is AikoInfinity?", "completion": "AikoInfinity is an AI-driven platform focused on adaptive learning and user-centered design.\n"}, {"prompt": "Explain the features of AikoInfinity 2.0.", "completion": "AikoInfinity 2.0 includes advanced AI functionalities, real-time notifications, event-driven architecture, and recommendation systems.\n"}, {"prompt": "How do I integrate Redis into AikoInfinity 2.0?", "completion": "To integrate Redis, configure caching for AI processes and use Redis for session management to optimize real-time interactions.\n"}, {"prompt": "What is the role of AI in AikoInfinity?", "completion": "AI drives personalized learning, intelligent chat interfaces, and advanced user behavior analytics within AikoInfinity.\n"}, {"prompt": "What’s ...

Welcome to the Official Blog of AikoInfinity: Where Innovation Meets Excellence

At AikoInfinity , we're driving the future of Artificial Intelligence with cutting-edge technology, transformative solutions, and a relentless commitment to innovation. Founded by Gazi Pollob , AikoInfinity is set to redefine the AI landscape by pushing the boundaries of what's possible in AI-driven personalization, scalability, and financial growth. 🌟 AikoInfinity's Vision: Revolutionizing the AI Landscape Our journey at AikoInfinity is inspired by a bold vision: to integrate OpenAI's state-of-the-art models with real-world applications, bringing unparalleled value to both individuals and businesses. With zero-shot learning and real-time personalization , we’re creating an adaptive AI ecosystem that evolves with the user, delivering insights and capabilities that were once only imagined. 💡 OpenAI: Empowering Us to Scale New Heights The partnership with OpenAI is the cornerstone of AikoInfinity's AI strategy. By leveraging OpenAI's System Base Model , we’r...

fetch_plinko_data()

import requests import openai import pandas as pd import numpy as np from bs4 import BeautifulSoup from dotenv import load_dotenv import os import time # Load environment variables from .env file load_dotenv() variables openai.api_key = os.getenv('OPENAI_API_KEY=sk-5k98H4I4kx3m234Lf1VfJ8wETc5RrTu5KlW') # # Function to fetch live Plinko data def fetch_plinko_data(): url = "https://1xlite-581089.top/en/allgamesentrance/plinko" response = requests.get(url) if response.status_code != 200: print("Failed to fetch data") return None soup = BeautifulSoup(response.text, 'html.parser') game_data = [] for row in soup.find_all("div", class_="game-row"): try: bet_amount = row.find("span", class_="bet-amount").text.strip() multiplier = row.find("span", class_="multiplier").text.strip() win_loss = row.find("span", class_="win-loss").text.strip() bet_amount = float(bet_amount.replace("BDT", "").strip()) multiplier = float(multiplier) win_loss = float(win_loss.replace("BDT", "").strip()) game_data.append({ "bet_amount": bet_amount, "multiplier": multiplier, "win_loss": win_loss }) except AttributeError: continue # Skip rows with missing data return game_data # Store round data in a log round_log = [] def log_round_data(bet_amount, multiplier, win_loss): round_log.append({ "bet_amount": bet_amount, "multiplier": multiplier, "win_loss": win_loss }) def get_round_log_df(): return pd.DataFrame(round_log) # Function to generate dynamic betting strategy using GPT-3 def smart_betting_strategy(current_rounds): history = "\n".join([f"Round {i+1}: Bet = {r['bet_amount']} BDT, Multiplier = {r['multiplier']}, Outcome = {r['win_loss']} BDT" for i, r in enumerate(current_rounds)] ) prompt = f"Given the following round outcomes, suggest a smart betting strategy for the next round:\n\n{history}\n\nSuggested Strategy:" response = openai.Completion.create( engine="gpt-3.5-turbo", # Change to your preferred GPT model prompt=prompt, max_tokens=150, n=1, stop=None, temperature=0.7 ) strategy = response.choices[0].text.strip() return strategy # Function to calculate volatility def calculate_volatility(df): return np.std(df["multiplier"]) # Adjust bet size based on volatility def adjust_bet_size_based_on_volatility(volatility, base_bet=20): if volatility > 0.5: return base_bet * 0.8 elif volatility < 0.2: return base_bet * 1.2 else: return base_bet # Automated loop to run the betting system def run_auto_betting_system(): bet_amount = 20 # Base bet round_number = 1 while round_number <= 100: # Running for 100 rounds print(f"\nRound {round_number}:") # Fetch live Plinko data plinko_data = fetch_plinko_data() if plinko_data is None: print("Failed to fetch data, skipping round.") continue # Log the fetched data for data in plinko_data: log_round_data(data['bet_amount'], data['multiplier'], data['win_loss']) # Get the current round log and calculate volatility df = get_round_log_df() volatility = calculate_volatility(df) adjusted_bet = adjust_bet_size_based_on_volatility(volatility) print(f"Adjusted Bet Size: {adjusted_bet} BDT") # Get strategy from OpenAI strategy = smart_betting_strategy(df.to_dict(orient='records')) print("Suggested Strategy:", strategy) # Simulate next round (or interact with actual game API if available) round_number += 1 time.sleep(2) # Simulate a delay between rounds # Run the betting system if __name__ == "__main__": run_auto_betting_system()