I am seeking to develop a custom program that simulates realistic user profiles on YouTube and Google services. The goal is to emulate authentic human behavior as closely as possible to avoid detection, using residential mobile proxies from Spain for IP rotation, APIs for virtual phone numbers (for SMS verification during account creation), and captcha solvers to handle any automated challenges. The program will automate the creation of Gmail accounts, logins, and interactions on YouTube and Google, with a focus on two distinct phases: a "warming up" phase to establish natural user patterns, and a targeted "promotion" mode to boost visibility for specific videos by searching and watching them organically.Key Requirements and Features:Proxy and IP Management: Use residential mobile proxies exclusively from Spain to mimic local users. I'll provide the list of proxies (e.g., in a format like IP:port:username:password). Integrate an API for IP rotation (details to be provided, such as endpoint URL, authentication key, and rotation interval). The program should support up to 6 concurrent threads, each representing a simulated "device" with its own proxy/IP. Rotate IPs dynamically every 10-30 minutes per thread (randomized) or after a set number of actions (e.g., 5-10 interactions) to prevent pattern detection. Ensure that each thread uses a unique IP at startup and handles failures by switching to a new proxy from the pool. User Agents and Device Simulation: Rotate user agents to imitate real mobile devices (primarily Android or iOS from Spain, e.g., Samsung Galaxy, iPhone models) or desktop browsers (e.g., Chrome on Windows/Mac) if better suited for certain actions. Mobile user agents are preferred for realism in video watching, but allow configuration for PC agents if needed. Include randomization of screen resolutions, time zones (set to Spain's CET/CEST), languages (Spanish as default, with occasional English mixes), and browser fingerprints (using libraries like Selenium with undetected-chromedriver or Puppeteer-extra to evade bot detection). APIs and Accesses:Virtual Phone Numbers API: Integrate an API for renting temporary Spanish phone numbers for SMS verification during Gmail account creation (e.g., services like SMS-Activate or similar; I'll provide API endpoint, key, and parameters). The program should request a number, wait for SMS code, verify it, and release the number after use. Captcha Solver API: Use a solver like 2Captcha or Anti-Captcha (details provided: API key, endpoint). Automatically detect and solve reCAPTCHAs, hCAPTCHAs, or FunCAPTCHAs during account creation, login, or interactions. Ensure all API calls are rate-limited and error-handled to avoid bans. Account Creation and Management:Automate Gmail account creation: Generate random but realistic Spanish names (e.g., first/last names from a predefined list or generator), birthdates (18-45 years old), genders, and passwords (strong, randomized with mixes of letters/numbers/symbols). Handle the full signup flow: Navigate to accounts.google.com, fill forms, solve captchas, verify via SMS, and confirm recovery email if prompted (use a disposable email for recovery). After creation, store account credentials (email, password) and session cookies (extracted post-login) in a secure database (e.g., SQLite or JSON file, encrypted). This allows reusing sessions without re-logging in future runs—load cookies directly into the browser session to maintain authenticated state. Support creating batches of accounts (e.g., 10-50 at a time), with delays between creations (5-15 minutes randomized) to mimic human pacing. Phase 1: Warming Up (Natural User Simulation):For each new account, run a 3-7 day (configurable) warming period to build a "normal" profile. Actions to emulate human behavior:Google Searches: Perform 5-20 random searches per session (e.g., "recetas españolas fáciles", "noticias España hoy", "mejores playas en Barcelona") using the Google search bar. Click on 2-5 results, scroll pages, spend 30-120 seconds reading (simulated via delays and mouse movements). YouTube Interactions:Watch 10-30 random videos per day: Start from YouTube homepage, browse recommended or trending sections (Spain-specific), select videos randomly (e.g., music, news, tutorials, vlogs in Spanish). Watch videos partially or fully (30-100% duration, randomized), with pauses, seeks (forward/back 10-30 seconds), and speed changes (e.g., 1.25x occasionally). View related videos: After a video, click 1-3 suggestions and repeat. Subscribe to 2-5 channels randomly (e.g., popular Spanish creators in categories like entertainment, sports, cooking). Like 20-50% of watched videos (randomized). Comment on 10-30% of videos: Generate natural, short Spanish comments (e.g., "¡Genial video!", "Me encanta esto", with emojis) using a template system or AI-generated text for variety. Avoid spam patterns by varying length, timing, and content. Sessions: 2-5 sessions per day, each 20-60 minutes, with random breaks (e.g., 1-5 minutes idle). Include human-like mouse movements, scrolls, and keystrokes using automation libraries. Randomization: Vary daily routines (e.g., more searches one day, more videos the next) to avoid predictability. Phase 2: Targeted Promotion Mode:After warming, switch to searching and watching specific videos. Input: Provide keywords (e.g., via config file or command-line: "mi video tutorial cocina", "review producto España"). Actions:Search YouTube for the keyword, scroll results, and click on matching videos (prioritize exact matches to my content). Watch the full video (100% duration), with realistic behaviors: Pause 2-4 times, rewind sections, watch at normal speed, interact mid-video (e.g., like after 50%). Optionally: Comment positively (e.g., "Excelente explicación, gracias!"), like, and subscribe if not already. Repeat for 1-5 related searches per session to blend in. Limit to 1-3 videos per session to stay under radar, with sessions spaced 4-12 hours apart. Technical Implementation:Framework: Use Python with Selenium (for browser automation) or Playwright (for better stealth). Include anti-detection measures: Randomized delays (e.g., via time.sleep with Gaussian distribution), human-like typing (slow key presses), and proxy integration via webdriver options. Threading: Use multiprocessing or threading for up to 6 parallel devices, each in its own browser instance with isolated cookies/proxies. Logging and Monitoring: Log all actions, errors, and successes to a file. Include a dashboard or console output for real-time status (e.g., "Account X: Watching video Y"). Error Handling: Retry failed actions (e.g., login fails due to captcha) up to 3 times, with exponential backoff. Flag and pause accounts if suspicious activity is detected (e.g., via Google warnings). Storage: Save sessions as cookies.json per account. Allow reloading for persistent use without re-auth. Configurability: Use a YAML/JSON config file for proxies, APIs, keywords, phase durations, and randomization ranges. Realism Enhancements: Incorporate variability in all actions (e.g., Weibull distribution for timings). Simulate mobile gestures if using mobile agents (e.g., swipe scrolls). Avoid exact repetitions; use seed-based randomness for reproducibility if needed. This program should run on a server or local machine, with options for headless mode (but include visible mode for testing). Please provide the full source code or a detailed implementation plan based on this spec, and let me know if you need any additional API details or proxy lists to proceed. The emphasis is on stealth and human emulation to ensure longevity of accounts.