YouTube Engagement Automation Desktop App
Publicada el 2026-07-14
Descripción de la oferta
Role: Senior Python Developer & Automation Engineer Task: Design and implement a robust Linux-based desktop application for YouTube video engagement automation. The application must manage multiple Gmail accounts, utilize geographically persistent proxies, and simulate human-like viewing behavior with AI-generated comments. The core logic must rely on Hashtag-Based Categorization to isolate accounts and proxies. Core Features & Requirements: Account & Proxy Management with Geographic Persistence: Support for importing multiple Gmail accounts with passwords. One Proxy Per Account: Each Gmail account is permanently or semi-permanently assigned to a single proxy IP. Geographic Fallback Logic: When a proxy for an account fails (or is first assigned), the system must select a new proxy based on geographic proximity. Priority 1: Same City/Town as the previous/assigned proxy. Priority 2: If no proxies are available in the same City, select one from the same State/Region. Priority 3: If no proxies are available in the same State, select one from the same Country. A GUI/CLI to manage proxy pools (e.g., JSON file or API connection to a proxy provider). Hashtag-Based Category Isolation (The "Tag Bucket" System): Hashtag Extraction: For any target video, the app must scrape the video description or DOM to extract all hashtags (e.g., #GTA5, #OpenWorld, #RockstarGames). Primary Category Key: Use the first or most frequent hashtag as the primary "Category Bucket" identifier (e.g., if a video has #GTA5 and #Gaming, the bucket is GTA5). Isolation Logic: Create a logical "Bucket" for each unique Hashtag (e.g., Bucket_GTA5, Bucket_Anime, Bucket_Tech). Account Assignment: If the target video’s primary hashtag is new (e.g., switching from a #GTA5 video to an #Anime video): Identify Gmail accounts that have not been used in this specific Hashtag Bucket before. Assign these fresh accounts (and their associated geographic proxies) to this new bucket. Persistence: Once an account is used for a specific hashtag (e.g., #GTA5), it remains in that bucket for future videos with the same primary hashtag to build niche authority. Storage: Use a SQLite database to track: Account_ID -> Assigned_Hashtag_Bucket -> Assigned_Proxy_IP -> Last_Activity. Automated Viewing Sequence (The "Watch Flow"): Pre-Watch: Randomly select and watch 1–5 other videos that share the same primary hashtag to warm up the viewer profile. Main Watch: Watch the target video for a configurable duration (e.g., 75%–100%) to count as a "view." Post-Watch: Randomly select and watch 1–5 more videos with the same primary hashtag. Note: "Watch" implies keeping the video tab active, handling ads, and simulating mouse movements/inactivity intervals. Engagement Actions (Like, Subscribe, Comment): Like: Click the "Like" button on the target video. Subscribe: Click the "Subscribe" button. AI-Generated Comments: Extract key phrases, subtitles, or transcript snippets from the target video. Send this text context to an LLM API. Generate a unique, context-aware comment for each account. Post the comment on the target video. Configuration Dashboard: Allow users to define: Number of views/subscribers/likes/comments per video. Proxy provider settings. AI Provider settings (API key, model selection). Speed and randomization intervals. Hashtag Strategy: Option to use the first hashtag or the most frequent hashtag as the primary bucket key. Tech Stack Preferences: Language: Python 3.10+ Browser Automation: Playwright (preferred for speed and modern API) or Selenium. GUI Framework: PyQt6 or Streamlit. Storage: SQLite for tracking Account-Hashtag-Proxy relationships. AI Integration: requests or openai library. Deliverables: Database Schema: Design the SQLite schema for tracking Accounts, Proxies, Hashtag Buckets, and Assignment History. Hashtag Extraction Logic: Python function using Playwright/Selenium to scrape hashtags from a YouTube video description. Proxy Selection Algorithm: Pseudo-code or Python function implementing the City -> State -> Country fallback logic. Bucket Assignment Logic: Python class or function that determines which accounts are "fresh" for a new hashtag and assigns them. Core Automation Engine: Python code structure for the viewing flow, including the AI comment generation. Anti-Bot Strategy: Recommendations for handling YouTube's detection (cookie management, viewport resizing, idle timers) specifically for the Hashtag Bucket system.
Skills
Fuente original: freelancer