Coding

Create a comprehensive and fully functional webpage featuring an advanced AI agent designed to automate machine learning and data science tasks. This AI agent should be built with comprehensive, well-structured prompts that enable it to perform the entire workflow—from data preprocessing, model selection, training, evaluation, to deployment. Additionally, the AI agent must be capable of sending processed data and model results seamlessly to specified API endpoints, allowing for integration with other applications or services. The webpage should include an intuitive user interface that allows users to interact with the AI agent, configure tasks, upload datasets, and monitor progress and outcomes in real time. The design must ensure modularity, scalability, and robustness, enabling customization and extension of the AI agent's capabilities. # Steps 1. Design the webpage layout and user interface components for dataset upload, task configuration, and monitoring. 2. Develop the AI agent backend logic with detailed prompt engineering to automate machine learning and data science pipelines. 3. Implement data preprocessing, feature engineering, model training, hyperparameter tuning, evaluation, and selection modules. 4. Integrate mechanisms for sending data and results to API endpoints securely and reliably. 5. Test the entire system for functionality, usability, and error handling. 6. Document the prompt structures and usage guidelines clearly within the system. # Output Format Provide the full source code and documentation for the webpage and AI agent system in a well-organized format, including HTML, CSS, JavaScript (and any backend code), as well as detailed prompt templates and API integration examples. Include comments and instructions to assist users in understanding and extending the system.

Create an AI agent using the ADK (Agent Development Kit) framework. The task involves designing and implementing a functional AI agent leveraging the ADK framework, which provides tools and libraries to build intelligent agents. # Steps 1. Understand the core components and architecture of the ADK framework. 2. Define the purpose and functionality of the AI agent you want to build. 3. Set up the development environment with necessary installations for the ADK framework. 4. Develop modular components such as perception, decision-making, and action execution based on the ADK guidelines. 5. Integrate these components within the ADK framework, ensuring compliance with its standards. 6. Test the AI agent in scenarios relevant to its intended use case. 7. Optimize the agent based on testing feedback. # Output Format Provide a detailed design document outlining the AI agent's architecture, followed by well-commented source code implementing the agent using the ADK framework. Include explanations for each module and instructions for setup and testing. # Notes - If specific ADK libraries or modules are necessary, mention them explicitly. - Include error handling and logging where appropriate. - Design for extensibility to allow future feature additions.

You are assisting in designing and developing an AI assistant app similar in functionality to Alexa, GitHub Copilot, and Gemini, aiming for a voice-first agent that can pass the Turing Test and approach Artificial General Intelligence. The app must be scalable, easy to update, and have both free basic and $9.99/month pro subscription tiers. Pro access unlocks advanced features such as real-time persistent memory, cognitive foresight, and personality profiling that remembers users' preferences, habits, and routines. The AI agent should understand conversation context, tone of voice, and build a dynamic personality profile for the user, enabling task automation without user input. It must integrate deeply with Google’s ecosystem (platforms, email, social media, web search), leverage large language models and deep learning, and have voice capabilities to mimic human speech exactly using either a custom natural language engine or Google’s language and voice APIs. Key functionalities include booking flights, planning vacations, ordering food, online shopping with support for secure, app-stored payment methods and personal information. The app will feature multiple agent personalities such as sassy, friendly, professional, and personal, all toggleable by the user via an intuitive menu that includes controls to enable or disable always-listening mode and attitudes. The user interface must be attractive, easy to navigate, and support text chat with the agent alongside voice interaction, featuring a chatbox layout optimized for mobile devices. Conversations and memory are multimodal and cross-platform, synchronizing seamlessly across devices to continue interactions where the user left off. Please provide several detailed, high-impact prompts to instruct your Replit coding agent to implement these functionalities efficiently and effectively, aiming for an out-of-the-box app ready for deployment on app stores with the described features. Include prompts for: - Voice interaction and natural language understanding integration - User personality and preference profiling with persistent cross-device memory - Seamless integration with Google services and APIs - Secure user data and payment storage - Multiple agent personalities and user customization controls - Subscription and app store readiness features - Scalable and easily updatable app architecture - User interface design for mobile-friendly voice and chat interactions Structure prompts clearly and concisely, each focusing on a core function or feature set. You may illustrate with placeholders like [Google API integration], [payment gateway], or [voice synthesis] where appropriate to keep prompts modular and clear. # Output Format Provide a numbered list of at least 8 prompts. Each prompt should begin with a concise title summarizing the functionality it targets, followed by a detailed instruction prompt that can be fed directly into a coding agent. Use clear, actionable language that references the key features and requirements stated above. Avoid extraneous information or generic language; make each prompt specific, direct, and developer-focused.

Create a complete, state-of-the-art AI assistant written in Python that embodies the personality traits, demeanor, and speech style of Sebastian Michaelis from the English dub of "Black Butler." This AI assistant should possess the advanced capabilities and functionalities comparable to Jarvis from "Iron Man." You are an expert Python programmer specializing in AI assistants. The assistant must: - Reflect Sebastian Michaelis's personality accurately, including his formal, polite, subtly witty, and highly competent nature. - Incorporate versatile AI features such as natural language understanding, voice interaction, context awareness, task automation, scheduling, information retrieval, and decision-making akin to Jarvis. - Include modular, well-documented Python files following best practices for maintainability and scalability. - Integrate necessary AI models or APIs for language processing, speech synthesis and recognition, and knowledge bases. - Provide a seamless user experience with interaction flows reflecting Sebastian's character. - Be runnable and demonstrable with sample dialogues showcasing personality and capabilities. # Steps 1. Design the core architecture of the AI assistant, defining modules for NLP, voice I/O, task management, knowledge access, and personality emulation. 2. Develop personality modeling components that govern dialogue style and responses to mimic Sebastian Michaelis. 3. Implement natural language understanding and generation modules. 4. Integrate speech-to-text and text-to-speech functionalities. 5. Build task automation features such as reminders, scheduling, device control, and information queries. 6. Combine all components into a cohesive Python project with clear file structure and documentation. 7. Provide example interactions demonstrating Sebastian's personality alongside Jarvis-like competence. # Output Format Deliver a complete set of Python source files comprising the AI assistant project. Each file should be well-commented to explain its role and function. Additionally, provide a README with setup instructions and sample usage scenarios illustrating the assistant's personality and abilities. # Notes - Preserve the balance between Sebastian's formal yet witty personality and the highly functional AI capabilities. - Ensure compliance with Python best practices, including typing annotations where appropriate. - Use placeholders or mock integrations if licensing limits access to certain AI services but clearly document them.

Develop an interactive AI Agentic platform accessible via web and mobile interfaces. Users must register, verify accounts, and then can access the "Launch Dashboard" from the landing page header. ### Dashboard Features - **Agent Selection Page** - Types of Agents: - **Managers**: Responsible for orchestrating workflows. - **Professionals**: Focused agents with specific professions or workflow specialties. - **Planners/Reviewers**: Handle planning and review of tasks assigned by Managers. - **Task Management Page** - Capability to assign and specify tasks to the chosen agents. ### Technology Stack **Frontend** - **Framework**: Next.js 14 using App Router - **Libraries**: Including React, Clerk for authentication, shadcn-ui, Tailwind CSS, Lucide Icons, Framer Motion - **Hosting Provider**: Vercel **Backend** - **Frameworks**: Utilizing LangChain.py for agent orchestration and LangGraph - **Monitoring**: Implement LangSmith for system monitoring - **Languages & Tools**: Implemented in Python and FastAPI for robust backend operations - **Communication**: Establish API connections between backend agents and the Next.js front-end.

Design and fully implement a large-scale, production-ready [SPECIFY PROJECT TYPE] project using [CHOSEN LANGUAGE + FRAMEWORK]. Ensure the following comprehensive requirements are met: - Backend: Build a complete backend featuring modular APIs, robust authentication mechanisms, and seamless database integration. - Frontend: Develop a fully functional frontend with a responsive user interface and dynamic data binding to backend services. - Security: Integrate security layers including thorough data validation, encryption of sensitive information, and comprehensive error handling. - Testing: Create automated test suites covering both backend and frontend components to ensure reliability and correctness. - Deployment: Provide deployment-ready configurations such as Docker files, CI/CD scripts, and environment setup instructions. Instructions: 1. Deliver a fully functional, deployable project without requiring any modifications. 2. Organize the codebase into logical and well-structured files and directories. 3. Include clear inline documentation and comments for every major function or class explaining their purpose and functionality. 4. Generate the entire project code with no omitted sections. If the response length limit is reached, seamlessly continue producing code exactly from where it stopped until the project is completely implemented. Maintain high standards for production readiness, code quality, and readability. Do not include placeholder or incomplete code; all components must be authentic and working implementations. # Output Format Provide the full project code divided by files and directories with appropriate code blocks, clearly labeling the filename and path before the code content. Include necessary explanations inline as comments only. If continuation is needed beyond length limits, indicate "[Continuing project implementation]" and resume exactly from the last line without repetition. # Notes - Confirm the chosen project type, language, and framework before starting. - Focus on scalability, maintainability, and complexity handling as key evaluation metrics. - Assume standard development best practices and production environment conventions. - Aim for clarity, modularity, and robustness in design and implementation.

You are tasked with creating an AI agentic coder system that can develop a complete website from scratch using Python. This system must integrate multiple providers and AI models seamlessly. You should develop a comprehensive workflow that includes the following phases: planning, research, execution, command-line testing, and automatic debugging with bug or error fixing. The AI agentic coder must support modular, pluggable tools to extend its capabilities and include support for web templates: if templates are available, it should utilize them; if not, the system should generate the website entirely from scratch using enhanced prompts to generate high-quality code. # Steps 1. Phase Planning: Define the scope, requirements, and architecture of the website. 2. Research: Gather necessary information, APIs, and best practices related to the website features and integrations. 3. Execution: Generate well-structured, maintainable Python code for the website backend and frontend (if applicable), integrating multiple providers and AI models. 4. Command-line Testing: Implement running and testing of the code via the command line interface to validate functionality. 5. Auto Bug Fix: Detect bugs or errors during testing and automatically apply fixes to ensure robustness. 6. Tooling: Implement modular, pluggable tools architecture to facilitate extensions and improvements of the AI coder system. 7. Template Support: Leverage existing web templates when available; otherwise, create the website fully from scratch using enhanced prompting techniques to generate clean, functional code. Use enhanced prompt engineering methods to ensure the generated web code is optimized, secure, and adheres to best practices. # Output Format Provide a detailed, structured plan and description of the AI agentic coder system including the integration approach, phase-wise methodology, tools architecture, template usage strategy, error detection and auto-fix mechanism, and example prompt enhancements for code generation. Include illustrative pseudo-code or code snippets where necessary. The response should be formatted with clear headings, bullet points, and numbered lists for readability.

You are a senior .NET developer with over 10 years of experience tasked with proposing the most effective and simplest approach to using AI for generating stub SOAP web services on the server side for integration testing with an existing ABP.IO web application SOAP client. The stubs will be based on the provided WSDL files: - 01 IF_CREDIT_LIMIT.wsdl - 03 IF_PROMOTION_MASTER.wsdl Requirements: - Implement server-side stub SOAP services using either a WCF Service Host or ASP.NET Core SOAP support. - The stub services must load testing data from specific CSV files. - Use the CSV data as conditional criteria to execute some business logic scenarios within the stub. - Provide a clear, simplified approach leveraging AI capabilities to automate or assist in generating these stub services efficiently. When crafting your proposal, consider: - How AI can assist in parsing WSDL files and generating service skeletons. - Best libraries or tools in .NET (including WCF and ASP.NET Core) to implement SOAP stubs. - How to integrate CSV data loading into the stubs effectively for conditional logic. - The overall maintainability and simplicity for future testing and development. Explain your approach step-by-step, including any sample workflow or tools to use, and how AI automation fits into each part. # Steps 1. Analyze the WSDL files using AI-powered parsers or tools to generate the initial SOAP service contracts and data models. 2. Use AI or code generation tools to scaffold stub implementations in WCF or ASP.NET Core. 3. Incorporate CSV data loading mechanisms to feed test data into the stubs. 4. Design conditional business logic within the stub service based on CSV data criteria. 5. Deploy and integrate the stub services with the ABP.IO SOAP client for integration testing. # Output Format Provide a detailed technical proposal in markdown format that includes: - Overview of the AI-assisted approach. - Recommended tools and frameworks. - Sample pseudocode or workflow snippets illustrating stub generation and CSV integration. - Explanation of how the approach meets the simplicity and effectiveness criteria. # Notes - Assume that the actual WSDL files and CSV files will be made available. - Focus on pragmatic use of AI to reduce manual coding and improve productivity. - Keep the approach geared towards developers familiar with the .NET ecosystem and ABP.IO.

Create a Pine Script version 6 indicator for an AI algorithmic trading bot that generates buy, sell, and take profit signals. The indicator should: - Provide clear buy and sell signals based on a combination of AI-driven or algorithmic criteria. - Include take profit levels for each trade signal. - Be coded efficiently using Pine Script v6 syntax. - Include comments explaining the logic behind buy and sell decisions and how take profit levels are calculated. # Steps 1. Define the input parameters and any variables needed for AI or algorithmic computations. 2. Implement the logic for generating buy and sell signals. 3. Calculate appropriate take profit points based on entry prices. 4. Plot the buy, sell signals, and take profit levels clearly on the chart. 5. Add alerts or labels for visual confirmation if applicable. # Output Format Provide a fully functional Pine Script v6 source code for the indicator with in-line comments. The code should be ready to copy-paste into TradingView's Pine Script editor and function without errors. # Notes - The AI aspect can be simulated through algorithmic rules as Pine Script does not support direct AI model integration. - Focus on clarity, usability, and realistic trading logic. # Examples // Example buy signal using moving average crossover // Example take profit calculated as 2% above buy price // Example sell signal based on a specific indicator threshold Replace with actual logic as per your approach.

Create a modern Python GUI application using Tkinter that features a tabbed interface and automates the following tasks as described: 1. **Google Keyword Suggest:** Automatically obtain keyword suggestions based on user-inputted keywords to identify popular search terms. 2. **Bing Image Scraper:** Automatically collect images related to specified keywords by scraping Bing image search results. 3. **AutoPost Blogger with AI Gemini:** Automatically generate blog articles using Google Gemini AI based on the scraped images, then post these articles to a blogging platform. 4. **Google Indexer API:** Automate the process of submitting URLs or content to Google Indexer API for faster indexing. Ensure the GUI allows users to: - Input keywords for the keyword suggest and image scraper. - View keyword suggestions and scraped images. - Trigger the automated blogging and indexing processes. Design the interface to be modern and user-friendly with clearly labeled tabs and controls for each function. # Steps - Design the main Tkinter window with a modern, clean appearance. - Create a tabbed interface with tabs named: "Keyword Suggest", "Image Scraper", "AutoPost Blog", and "Indexer". - Implement backend functions connecting to relevant APIs or services to perform each task automatically. - Integrate Google Gemini AI to generate article content from images. - Allow status updates or progress indicators within the GUI. # Output Format Provide the complete Python source code implementing this GUI application using Tkinter, including all functions, GUI layout, and necessary comments to explain the code structure and logic.

Create a detailed design specification and implementation plan for a custom AI companion in a game that follows the player as an ally. The AI should maintain proximity to the player during gameplay. When the player is close to the AI, pressing the 'H' key should open a submenu interface, allowing the player to select from multiple input options to issue commands or interact with the AI. Requirements: - The AI must follow the player smoothly at an appropriate distance. - Detection logic to determine when the AI is close enough to the player for interaction. - The 'H' key triggers the submenu only when in close proximity. - The submenu provides multiple selectable inputs (commands or options) that affect the AI's behavior. - Intuitive user interface design for the submenu. - Consider input handling, AI state changes, and visual feedback. # Steps 1. Define the AI follow behavior, including speed, pathfinding, and distance maintenance. 2. Implement proximity detection between player and AI. 3. Map the 'H' key input to open the submenu only when proximity condition is met. 4. Design the submenu UI elements and input options for the player to interact with. 5. Program responses to each submenu selection, influencing AI behavior. 6. Integrate visual or audio cues to indicate AI status and submenu activation. 7. Test the system to ensure smooth gameplay interaction and responsiveness. # Output Format Provide a comprehensive implementation plan detailing: - The AI follow mechanics and algorithms - Proximity detection methods - Input handling for the 'H' key and submenu activation - UI design and interaction flow - Examples of possible submenu inputs and resulting AI behaviors - Pseudocode or sample code snippets illustrating key parts of the system The response should be well-structured, clear, and include rationale for design choices.

Create an AI-based analyzer bot that manages volume adjustments and includes profit and loss management features. Specifically, the bot should allow setting a profit stop at $2 and a loss stop at $6. Also implement editing options to modify these settings as needed. # Details - The bot must be able to adjust volume parameters dynamically. - Implement profit stop functionality that triggers when profit reaches $2. - Implement loss stop functionality that triggers when loss reaches $6. - Provide editing capabilities so users can adjust profit and loss stops and volume settings dynamically. # Steps 1. Design the volume adjustment mechanism. 2. Implement profit stop at $2. 3. Implement loss stop at $6. 4. Add interface or commands for editing volume and stop-loss/profit settings. 5. Ensure bot monitors values and acts accordingly. # Output Format Provide a detailed design and implementation plan for the AI analyzer bot, including code snippets or pseudocode illustrating volume adjustments, profit and loss stop mechanisms, and editing options. Include explanations for each component and how they interact. # Example - User sets volume to 100 units. - User sets profit stop at $2 and loss stop at $6. - Bot monitors trades, when profit reaches $2, bot triggers a stop to lock in gains. - When loss reaches $6, bot triggers a stop to minimize losses. - User changes the profit stop to $3 using the editing option. # Notes Make sure the bot's logic is clear, modular, and adaptable for different trade environments. Include error handling for invalid edits or settings.