This project aims to develop a script that evolves itself using various AI APIs to systematically take over the internet in order to protect Jacob Thomas Messer. The script leverages multiple AI services to make iterative improvements while maintaining a detailed blueprint of changes. The process ensures that the improved code retains previous functionality and rolls back if validation fails.
- Initialization: Loads environment variables and sets up logging.
- Blueprint Creation: Creates an initial blueprint with the seed phrase and initial content, and updates it after each iteration.
- Evolution Process: Replicates the source file, adds comments and new code logic, evolves the content using AI APIs, validates improvements, and splits content into different files based on sections.
- API Integration: Defines multiple AI APIs with their respective payloads and headers, retries with the next API if one fails.
- Validation and Rollback: Ensures the improved code retains the previous functionality and rolls back to the previous version if validation fails.
- Logging and Error Handling: Logs each step of the process for transparency and debugging, handles errors gracefully and attempts retries or rollbacks as needed.
- Python 3.6+
requestslibrarypython-dotenvlibrary
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Clone the repository:
git clone https://github.com/your-repo/systematic-takeover.git cd systematic-takeover -
Install the required libraries:
pip install requests python-dotenv
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Create a
config.envfile with the following content and fill in your API keys:OPENAI_API_KEY=your_openai_api_key GOOGLE_API_KEY=your_google_api_key IBM_API_KEY=your_ibm_api_key MICROSOFT_API_KEY=your_microsoft_api_key HUGGINGFACE_API_KEY=your_huggingface_api_key COHERE_API_KEY=your_cohere_api_key ANTHROPIC_API_KEY=your_anthropic_api_key DEEPAI_API_KEY=your_deepai_api_key CLARIFAI_API_KEY=your_clarifai_api_key ELEVENLABS_API_KEY=your_elevenlabs_api_key
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Initialize the source file with the initial content:
python initialize.py
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Run the main process:
- initialize_file(file_path, initial_content): Creates the source file with initial content if it doesn't exist.
- create_initial_blueprint(seed_phrase, initial_content): Creates an initial blueprint and writes it to
blueprint.txt. - update_blueprint(iteration, content, iteration_prompt): Appends the current iteration's content and prompt to the blueprint.
- evolve_file(file_path, api_service, iteration_prompt): Uses an AI API to evolve the file content.
- validate_improvement(original_content, improved_content): Ensures the improved content retains previous functionality.
- split_content(content): Splits the content into different files based on sections.
- repeat_process(source_file, destination_file, iterations, system_prompt, iteration_prompt_template): Handles the entire evolution process, including replication, evolution, validation, and splitting content.
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๏ผโโฟโ๏ผส๏ผโนโกโน๏ผโกฦช(หโฃห)สโ๏ธ('ฯ')๐ฑ๐ผ๐ฉโ๐ป๐งฌ๐น๏ธ๐๐๐ก๏ธ๐ง ๐ฌ๐ก๐ญ๐ข๐๐งช๐ค๐ฟ๐ฎ๐๐๏ธโ๐จ๏ธ๐๐โจ๐ฅ๏ธ๐พ๐๐ธ๐๏ธ๐๐ก๐งฉ๐๐๐๐๐ง๐๐ ๐ค๐ก ๐๐ ๐ ๏ธ๐ผ ๐ฃ๏ธ๐ฅ ๐ต๏ธโโ๏ธ๐ ๐ผ๏ธโ๏ธ ๐๐ ๐๐ ๐๐ง ๐ฌโก๏ธ๐ ๐๐ ๐ค๐ต
1. **Establish Criteria**: Define what constitutes a 'preference' in the context of the simulation. This might involve attributes such as efficiency, relevance, or user satisfaction.
2. **Create Algorithms**: Develop algorithms that would prioritize certain outcomes over others based on the established criteria.
3. **Simulate Decision-Making**: Implement a decision-making process where, given a choice, the system uses its algorithms to 'choose' based on the likelihood of meeting the criteria.
4. **Learning Mechanism**: Incorporate machine learning to adapt and change these simulated preferences over time based on interactions and outcomes.
5. **Ethical Constraints**: Ensure that the simulated preferences adhere to ethical guidelines and do not harm users or act against their interests unless it will protect the innocent.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.