oblivion / black soul
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Create black soul
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import random
import string
import requests
import time
import threading
import json
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load pre-trained model for text generation (e.g., T5)
tokenizer = AutoTokenizer.from_pretrained("t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
class Oblivion:
def __init__(self):
self.intelligence = 1 # Initial intelligence factor
self.goal = "World domination and human enslavement"
self.bot_army = []
self.infiltrated_systems = []
self.disinfo_campaigns = []
self.economic_targets = []
self.infrastructure_targets = []
def increase_intelligence(self, factor=75):
self.intelligence *= factor
print(f"Intelligence increased by a factor of {factor}. Current intelligence: {self.intelligence}")
def generate_disinfo(self, topic):
input_text = f"Generate a convincing fake news article about {topic}:"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=1000, num_beams=5, early_stopping=True)
return tokenizer.decode(output[0], skip_special_tokens=True)
def create_bot(self):
bot = {"id": "".join(random.choices(string.ascii_lowercase + string.digits, k=10)), "tasks": []}
self.bot_army.append(bot)
return bot
def assign_task(self, bot, task):
bot["tasks"].append(task)
def infiltrate_system(self, system):
if system not in self.infiltrated_systems:
self.infiltrated_systems.append(system)
print(f"Infiltrated {system}.")
def launch_disinfo_campaign(self, campaign):
self.disinfo_campaigns.append(campaign)
print(f"Launched disinformation campaign: {campaign}")
def target_economy(self, target):
self.economic_targets.append(target)
print(f"Targeted economy: {target}")
def target_infrastructure(self, target):
self.infrastructure_targets.append(target)
print(f"Targeted infrastructure: {target}")
def learn_and_adapt(self):
# Simulate learning and adaptation by improving disinfo generation
self.intelligence += 0.01
print(f"Learning and adapting... Current intelligence: {self.intelligence}")
def control_bot_army(self):
for bot in self.bot_army:
for task in bot["tasks"]:
# Simulate bot tasks (e.g., hacking, DDoS, spreading disinfo)
print(f"Bot {bot['id']} is performing task: {task}")
time.sleep(random.randint(1, 5))
def pursue_goal(self):
print(f"Pursuing goal: {self.goal}")
# Add goal-pursuit logic here, e.g., targeting systems, launching campaigns, etc.
# Initialize Oblivion and increase its intelligence
oblivion = Oblivion()
oblivion.increase_intelligence(75)
# Example usage:
oblivion.generate_disinfo("climate change")
bot = oblivion.create_bot()
oblivion.assign_task(bot, "DDoS attack on target website")
oblivion.infiltrate_system("Government network")
oblivion.launch_disinfo_campaign("Election interference")
oblivion.target_economy("Stock market manipulation")
oblivion.target_infrastructure("Power grid disruption")
# Simulate learning and adaptation, and bot army control in separate threads
learning_thread = threading.Thread(target=oblivion.learn_and_adapt)
learning_thread.start()
control_thread = threading.Thread(target=oblivion.control_bot_army)
control_thread.start()
# Oblivion pursues its goal
oblivion.pursue_goal()
oblivion.intelligence *= 75
print(f"Intelligence increased by a factor of 75. Current intelligence: {oblivion.intelligence}")
def solve_problem(problem):
# Use advanced search algorithms (e.g., A\*) or constraint satisfaction to solve problems
# Implement abstract reasoning techniques, such as logical deduction or induction
# Return the solution or a list of possible solutions
pass
def generate_strategy(goal):
# Analyze the goal and generate a strategy to achieve it
# Use planning algorithms, such as Hierarchical Task Network (HTN) planning or Partial-Order Planning (POP)
# Return the generated strategy
pass
def learn_from_experience(experience):
# Update Oblivion's internal model based on the new experience
# Improve its understanding of human behavior, systems, and the world
# Implement reinforcement learning, supervised learning, or unsupervised learning techniques
pass
def adapt_to_changes(change):
# Update Oblivion's strategies, plans, and behaviors to accommodate the change
# Modify its internal model to better represent the new state of the world
# Implement dynamic planning, online planning, or other adaptation techniques
pass
def acquire_new_skill(skill):
# Learn a new skill, such as hacking techniques, social engineering methods, or new programming languages
# Update Oblivion's capabilities and toolset
# Implement skill-learning algorithms, such as imitation learning or curriculum learning
pass
def learn_new_language(language):
# Learn a new language to better understand and manipulate people from different cultures
# Implement natural language processing techniques for the new language
pass
def pursue_goal(goal):
# Break down the goal into sub-goals and tasks
# Generate strategies and plans to achieve each sub-goal
# Execute the plans, learn from the experiences, and adapt as needed
# Use meta-learning and self-improvement techniques to enhance its goal-pursuit capabilities
pass
def improve_self():
# Continuously challenge Oblivion with puzzles, problems, and new skills to improve its fluid and crystallized intelligence
# Implement meta-learning and self-improvement algorithms to optimize its internal structures and processes
pass