Goal: Develop an AI-powered news aggregator to identify and categorize real-life horror stories.
Task 1: Develop an AI-powered news aggregator
Research: Identify suitable Python libraries for web scraping (BeautifulSoup, Scrapy, Requests), RSS feed parsing (feedparser), and NLP for categorization (NLTK, spaCy, Transformers).
Source Identification: Create a list of reputable news sources, public domain archives, and horror-centric websites that can be scraped or parsed via RSS.
Data Ingestion: Implement web scraping and RSS parsing to collect articles. Prioritize sources that provide structured data (e.g., JSON-LD schema).
Categorization (NLP): Develop a classification model (or use pre-trained models) to categorize articles into relevant horror sub-genres (e.g., true crime, unexplained phenomena, historical atrocities, paranormal).
Database: Store processed articles with their categories, source, and relevant metadata in a local database (e.g., SQLite, MongoDB).
Task 2: Implement a content summarization engine
Research: Explore text summarization techniques and libraries (e.g., Hugging Face Transformers for abstractive summarization, NLTK for extractive).
Implementation: Integrate a summarization model to create concise and engaging snippets for each identified article.
Optimization: Fine-tune the summarization model for horror-specific content, ensuring it captures the unsettling tone.
Task 3: Automate social media content generation
Templates: Design templates for 'Did You Know?' facts, 'Unexplained Files' posts, and 'Historical Horrors' threads for Twitter, Instagram, and TikTok.
Content Generation: Use the summarized articles and categorized data to populate these templates with relevant text and hashtags.
Image/Video Generation: For visual platforms, use AI tools (e.g., generate_image, generate_video from project memory) to create compelling visuals that match the horror theme.
Scheduling & Posting: Implement a scheduling mechanism to automatically post to social media platforms with direct links to the blog posts.
Task 4: Generate 4 massive 2000-word SEO articles
Keyword Research: Conduct in-depth keyword research for 'real horror', 'true crime', 'unexplained events', 'historical mysteries', and related long-tail keywords.
Outline Generation: Create detailed outlines for each of the four articles based on keyword research and identified horror sub-genres.
Content Writing: Use an AI writing agent (e.g., Igris or Kaisel, or a specialized deep_research call) to generate 2000-word articles, ensuring SEO optimization, internal links, and external citations.
Review & Editing: Manually review and edit generated articles for quality, accuracy, and terrifying tone.
Publishing: Integrate with the blog's publishing system (if available) or create markdown files for manual upload.
Task 5: Automate content syndication
Platform Integration: Research APIs/methods for posting to Reddit, Facebook groups, and Pinterest boards.
Syndication Logic: Develop a system to automatically syndicate new articles and social media posts, ensuring proper attribution and backlinks.
Rate Limiting & Compliance: Implement rate limiting and adhere to platform-specific guidelines to avoid being flagged as spam.