Pre-Mortem Social Sentinel

Imagine a social media manager facing constant pressure to create engaging content while avoiding PR disasters. What if they could see the future impact of their posts -before- they went live? Inspired by Isaac Asimov's robotic laws guiding behavior and Christopher Nolan's 'Tenet' concept of manipulating causality, the 'Pre-Mortem Social Sentinel' (PSS) is an AI designed to prevent social media mishaps and optimize outcomes.

Concept: The PSS acts as an intelligent 'pre-flight check' for social media content. It leverages advanced natural language processing (NLP) to simulate how a post will perform in the social sphere, identifying potential risks and opportunities before it's published. This allows users to 'invert' their content choices—editing or refining a post -now- to alter its predicted future trajectory.

How it works:
1. Voice-to-Content & Trend Scraper (Inspired by 'Voice Commands' Scraper): Users can either paste text or -speak- their social media draft (post, comment, tweet idea) into the PSS. The system transcribes spoken input, performs immediate sentiment and keyword analysis, and can also scrape real-time trending topics and public sentiment from various social platforms to identify timely content opportunities or potential pitfalls related to current conversations.
2. Asimov's Social Laws Engine (Inspired by 'I, Robot'): The PSS operates under three core 'Social Laws' for content management:
- Law 1 (Reputation Protection): Analyze drafted content for potential negative sentiment, controversial keywords, cultural insensitivity, or misinterpretations that could harm the user's brand or reputation. It flags these issues with high confidence and suggests alternative phrasing or warnings.
- Law 2 (Engagement Optimization): Based on historical data from the user's social accounts and general social media trends, predict potential positive engagement (likes, shares, comments, saves) and audience reach. It suggests improvements for clarity, SEO, emotional resonance, and calls-to-action.
- Law 3 (Adaptive Timing & Content): Recommend optimal posting times and content adjustments based on the audience's past interaction patterns, current platform algorithms, and identified peak engagement periods.
3. Temporal Trajectory Predictor (Inspired by 'Tenet'): This is the core 'pre-mortem' feature. For a given draft, the PSS generates a simulated 'future timeline' of its potential performance. It visualizes:
- Sentiment Trajectory: A dynamic graph predicting how public sentiment towards the post might evolve over 24-48 hours, highlighting potential spikes in negativity or positivity.
- Engagement Curve: A predicted graph of likes, comments, and shares over time, allowing users to anticipate peak performance.
- 'Disaster Zones': Specific phrases, topics, or hashtags within the draft that are highly likely to attract negative attention, backlash, or unintended interpretations. These are highlighted with immediate suggestions for 'inversion' (editing) to mitigate risk.

Users can then 'invert' their draft—edit it based on these real-time, future-oriented predictions—and re-run the analysis to see how the predicted future changes, ensuring they publish content with the highest chance of positive impact and the lowest risk. The project could be implemented as a web-based tool (e.g., Python Flask/Streamlit) utilizing open-source NLP libraries and speech-to-text APIs, making it low-cost and easy for individuals to use. Monetization would be via subscription tiers tailored for influencers, small businesses, and marketing freelancers, offering high earning potential.