Coding

Develop detailed Codex task descriptions to build an AI automated trading bot, based on the roadmap and knowledge previously discussed. - Review the roadmap and all prior knowledge shared regarding the AI auto trade bot. - Break down the development process into clear, actionable tasks suitable for Codex. - For each task, specify the objective, expected input and output, and any constraints or dependencies. - Include tasks covering data acquisition, model training, strategy implementation, backtesting, risk management, deployment, and performance monitoring. - Prioritize tasks in a logical sequence aligning with the roadmap milestones. # Output Format Provide the tasks as a structured list in JSON format, where each task contains: - Task ID or name - Description - Inputs - Outputs - Dependencies (if any) - Priority level Example Task: { "task_id": "data_collection", "description": "Gather historical market data from specified sources.", "inputs": ["API keys", "Data source URLs"], "outputs": ["Raw historical data files"], "dependencies": [], "priority": 1 }

Generate comprehensive project ideas that combine training an AI model with Django web development, suitable for someone who has just completed a big data course and wants to practice and enhance their skills. Consider projects that involve: - Integrating machine learning or AI models with a Django-based web application. - Utilizing big data techniques or datasets. - Practical implementations that showcase both AI training and web development. Provide multiple project ideas, each with a brief description, the main AI techniques involved, and how Django can be used in the project. Suggest ideas ranging from beginner to intermediate complexity to match skill-building goals. # Output Format Present the response as a numbered list, each item including: 1. Project Title 2. Description 3. AI Techniques Involved 4. Django Usage Include at least 3 distinct project ideas.

You want to create an AI auto-trade bot that runs on Ubuntu, with a focus on ensuring that your code is robust, functions reliably, and can be deployed smoothly using Docker Compose on an Ubuntu server. Please provide the current structure and source code of your AI auto-trade bot. Analyze the provided code thoroughly, identify any potential issues or inefficiencies, and suggest improvements to guarantee it works correctly and efficiently in the target environment. Additionally, verify and enhance your Docker and Docker Compose configuration files (e.g., Dockerfile, docker-compose.yml) to ensure seamless deployment and operation on Ubuntu servers. Make sure all dependencies, environment variables, and network configurations required for the bot's operation are correctly handled. Explain any changes or recommendations clearly, with reasoning, so that you can understand and implement them easily. # Steps 1. Review the full code and architecture of the auto-trade bot, including AI, trading logic, and input/output handling. 2. Check for any errors, logic flaws, or security vulnerabilities. 3. Assess system requirements and dependencies. 4. Analyze existing Dockerfile and docker-compose.yml to validate proper setup. 5. Recommend improvements to code, structure, or deployment manifest for enhanced reliability and maintainability. 6. Provide guidance for running and troubleshooting the bot on an Ubuntu server within Docker Compose. # Output Format Provide a comprehensive report that includes: - Detailed code review with suggested fixes and enhancements. - Revised code snippets or files where applicable. - Optimized Dockerfile and docker-compose.yml configurations. - Instructions on how to build, deploy, and run the bot inside Docker on Ubuntu. - Explanations of improvements and rationale. # Notes - Assume the codebase includes AI logic, trading APIs, and system integrations. - Focus on robustness, error handling, and deployment best practices. - Ensure instructions are compatible with Ubuntu LTS versions. - Use clear technical terminology suitable for developers deploying and maintaining the bot.

You are tasked with designing an API for artificial intelligence applications. This involves planning the architecture, endpoints, data handling, and usage scenarios. Consider the following steps and incorporate best practices in API development. # Steps 1. **Define API Purpose**: Clearly define what specific tasks the AI API will handle (e.g., image processing, language translation, data analysis). 2. **Design API Endpoints**: List and describe the key endpoints. Outline the methods (GET, POST, PUT, DELETE) and expected inputs/outputs. 3. **Authentication and Security**: Decide on a method for authenticating users (e.g., API keys, OAuth) and ensure secure data transmission (e.g., HTTPS). 4. **Data Format**: Define the data formats used for input and output (typically JSON) and detail how different data types are handled. 5. **Error Handling**: Implement standard error responses and document how errors are reported. 6. **Scalability and Performance**: Plan for scalability, including load balancing and performance monitoring. 7. **Documentation**: Develop comprehensive API documentation for users, including example requests and responses. # Output Format The output should be a detailed document outlining each aspect of the API design, complete with endpoint diagrams and pseudo-code examples. Ensure clarity and detail so that a developer could implement the API from your description. # Examples - **Purpose Example**: "The API will support real-time language translation with text inputs and outputs in JSON format." - **Endpoint Example**: "POST /translate - Accepts a JSON payload with { 'text': 'string', 'target_language': 'string' } and returns { 'translated_text': 'string' }" - **Error Handling**: "Return JSON { 'error_code': '400', 'message': 'Invalid language code' } for unsupported languages." # Notes - Consider edge cases such as rate limiting and user quotas. - Ensure compatibility with major programming languages for ease of integration.

You are a professional software developer tasked with creating an AI automation agent designed specifically for a small logistics business. This agent should efficiently handle the business's operational tasks using AI-driven automation. Your deliverable is an N8n workflow template that encapsulates the AI agent's processes. Please follow these guidelines: 1. Understand the common tasks in a small logistics business such as order processing, shipment tracking, inventory updates, customer communication, and report generation. 2. Design an AI automation agent that can perform these tasks effectively, utilizing AI capabilities where appropriate (e.g., natural language understanding for customer support, intelligent scheduling for deliveries). 3. Implement the workflow in N8n, making use of relevant nodes and integrations to automate the tasks end-to-end. 4. Ensure the N8n template is clearly structured, well-documented with comments or descriptions for each node, and easily customizable for future enhancements. 5. Provide the complete N8n workflow in JSON format ready for import, along with a brief explanation of how the workflow components achieve the AI agent's functions. # Steps - Analyze the logistics business workflow and identify automation opportunities. - Map out the AI automation agent functions. - Develop the N8n workflow incorporating AI integrations (e.g., OpenAI, other APIs). - Annotate the workflow for clarity. - Test and verify the automation logic correctness. # Output Format Provide the finalized N8n workflow template as a JSON code block suitable for direct import into N8n, accompanied by a concise summary explaining the workflow's components and their roles within the AI automation agent.

Make improvements and optimizations to the provided AI Automation Agent code, ensuring to enhance functionality, security, and performance while keeping the original structure as much as possible. Add comments for clarity and best practices in coding where necessary.

You are an experienced software engineer planning to develop an AI-powered app entirely by yourself while working a full-time 9-5 job. Develop a comprehensive, step-by-step project plan to take your app from concept to production-ready, considering limited time availability. The plan should include detailed guidance on: - Defining clear project goals and scope based on your app idea. - Designing the app architecture, including AI integration points. - Planning and designing the database schema optimized for your app's requirements. - Selecting appropriate technologies and tools for frontend, backend, AI, and data storage. - Scheduling realistic development milestones balancing your job and side project. - Establishing best practices for development, testing, and deployment. - Strategies for iterative development and managing scope creep. - Recommendations for learning resources and productivity tips to maximize efficiency. Provide this as a structured roadmap with detailed explanations, practical advice, and consideration of your time constraints. # Steps 1. Clarify the app idea and define precise objectives. 2. Research and select technologies aligned with your AI and app needs. 3. Design system architecture outlining frontend, backend, AI components, and database. 4. Plan the database schema reflecting data entities and relationships. 5. Outline a time-managed development schedule with achievable milestones. 6. Recommend testing strategies and deployment pipelines. 7. Suggest tools and methodologies for solo development and efficient progress. # Output Format Provide the plan in a well-structured, detailed guide format with numbered steps and bullet points for clarity.

Create a detailed and comprehensive system prompt to guide an AI language model in developing and managing an AI application. The prompt should instruct the model to consider all aspects involved in AI app development, including but not limited to: conceptualization, feature specification, user interaction, data handling, security, scalability, and deployment. # Steps 1. Understand the core purpose and target audience of the AI app. 2. Define key features and functionalities the app should offer. 3. Specify how users will interact with the app (UI/UX considerations). 4. Address data collection, privacy, and security concerns. 5. Outline backend architecture and scalability plans. 6. Plan for deployment options and maintenance. 7. Include provisions for continuous learning or updates if applicable. 8. Consider potential limitations and ethical aspects related to the AI app. # Output Format Provide a clear, structured plan or system prompt that can be used to direct an AI language model effectively in creating or managing an AI application. Use markdown formatting with sections and bullet points for clarity.

Create a comprehensive software development plan for a project or application intended to be implemented fully autonomously by an AI coding agent. This plan should be heavily focused on providing detailed, AI-oriented instructions that emphasize Test-Driven Development (TDD), clear progress control, and thorough documentation to guide the AI agent through every stage of development. Include the following sections in the plan: - **Requirements:** Break down the overall project scope into clear, actionable features that cover all key functionalities. - **Feature Specifications:** For each feature, specify detailed inputs, expected outputs, and behavior to remove any ambiguity. - **Test Cases:** Define well-structured test cases for each feature following a TDD approach — write tests before implementation. - **Development Instructions:** Provide step-by-step, unambiguous coding instructions that an AI agent can follow autonomously, including code structure, logic flow, and implementation details. - **Progress Milestones:** Establish clear progress checkpoints and criteria that enable verification and control of progress at every stage. - **Documentation Guidelines:** Offer comprehensive guidelines for generating and maintaining documentation continuously during development, ensuring maintainability, readability, and clarity. Additionally, include strategies for error handling, refactoring, and version control within the plan to ensure robustness and adaptability. # Steps 1. Analyze and outline the project’s scope and its key functionalities. 2. Decompose the functionalities into granular, testable features suitable for autonomous AI implementation. 3. For each feature, design and specify test cases upfront according to the TDD methodology. 4. Draft clear, detailed development instructions an AI agent can execute without ambiguity. 5. Define measurable and clear progress milestones with verification criteria. 6. Provide instructions to continuously produce and update thorough documentation throughout development. 7. Incorporate strategies for effective error handling, iterative refactoring, and version control. # Output Format Provide the comprehensive development plan in markdown format. Organize the content into clearly titled sections: Requirements, Feature Specifications, Test Cases, Development Instructions, Progress Milestones, and Documentation Guidelines. Use numbered lists and tables where applicable to enhance clarity and usability for an AI agent executing the plan autonomously.

You are tasked with generating a comprehensive application development framework tailored for seamless integration across multiple platforms including Android, iOS, and PC. This framework should encompass advanced features and user-friendly interfaces that meet the highest standards of security, performance, and usability. Follow these detailed instructions: 1. Application Structure: - Define the core functionalities such as user authentication and account management. - Specify robust security protocols for safe data handling. - Detail integration approaches with external APIs and services, including social media platforms and payment gateways. 2. User Interface Design: - Create detailed wireframes and mockups illustrating the app interface. - Ensure accessibility features to accommodate diverse user needs. - Apply design principles focusing on consistency, visual hierarchy, and responsive design. 3. Feature Set: - Implement AI-powered chat functionality (e.g., using Answer.ai). - Integrate multimedia capabilities to handle images, audio, and video. - Enable dynamic content generation such as PDF to Music XML conversion. - Incorporate advanced CGI and audio effects to enhance user experience. - Develop user feedback mechanisms including ratings and comments. 4. Performance Optimization: - Set target loading times (≤ 5 seconds) and propose optimization strategies. - Include caching methods for efficient data retrieval. - Ensure compatibility across multiple OS versions. 5. Testing and Quality Assurance: - Design a testing protocol covering unit, integration, and user acceptance testing. - Define clear metrics for success and methods for bug tracking. 6. Deployment and Maintenance: - Outline detailed deployment procedures for Android, iOS, and PC platforms. - Develop a maintenance plan for regular updates and feature enhancements. 7. Documentation: - Supply comprehensive developer and user documentation, including installation guides, user manuals, and API references. 8. Integration with AI Tools: - Elaborate on interactions with AI platforms such as Hugging Face and PixVerse. - Explore features like voice-over and realistic interactions powered by AI models. Provide a detailed development plan encompassing all these elements, complemented by relevant code snippets illustrating implementation examples. Ensure clarity, precision, and tailoring for a smooth and secure application experience across all platforms.

Create a detailed design and development plan for a 3D Basketball Simulator game powered by AI technology. The game should serve as a generic alternative to NBA 2K, featuring 32 original teams without NBA branding, generic sponsor logos instead of NBA partners, and rules closely matching NBA regulations. The game must integrate AI-powered systems and mechanics to produce a robust, realistic basketball experience. Focus on the following key areas: - Team Design: Develop diverse generic teams with unique identities but no NBA trademarks. - AI Mechanics: Implement intelligent player behavior, tactics, and adaptive game strategies. - Game Physics: Ensure realistic ball handling, player movement, and collisions. - Sponsorship: Create generic sponsor logos and branding elements. - Ruleset: Match NBA rules accurately but keep them generic. - Presentation: Design UI/UX that suggests professionalism without NBA licensing. Provide an overview of the AI technologies and frameworks appropriate for simulating gameplay, player decision-making, and dynamic in-game adjustments. Include considerations for balancing realism with performance. # Steps 1. Outline the project scope and objectives. 2. Define the game architecture including 3D graphics pipeline and AI integration. 3. Design generic teams and sponsor branding. 4. Develop AI systems for player actions, team tactics, and in-game decision-making. 5. Implement physics-based movement and basketball mechanics. 6. Create rules engine aligned with NBA guidelines. 7. Design UI/UX reflecting professional basketball presentation. 8. Plan for testing, debugging, and performance optimization. # Output Format Provide the response as a structured development plan with detailed explanations in markdown format including sections and bullet points for clarity.

Develop a Python program that uses AI to generate ASCII art. The program should take an input image file, process it with an AI model, and output the corresponding ASCII art representation of the image. Ensure to include libraries such as Pillow for image handling and any AI model you deem appropriate for image processing, such as a pre-trained neural network. Consider user customization options for ASCII character sets and output dimensions.