Automating Social Media Marketing with AI-Generated Content

The Hook and Demo Workflow

The author generates social media content by identifying high-performing viral videos and remixing them for specific product use cases. The strategy relies on a two-part video structure: a high-retention AI-generated hook followed by a product demo. The author notes that AI-generated talking-head videos often trigger negative user sentiment, so the current strategy favors non-talking formats or videos where the AI generation is clearly stylized.

Technical Implementation

The end-to-end pipeline uses a combination of specialized models for image and video generation:

• Image Generation: The author uses NanoBanana to generate a static first frame of the avatar. This model is chosen specifically because it allows for reference image uploads, enabling a face-swap effect using a user-provided photo.
• Video Generation: The hook video is generated using Kling 3.0. By providing the static frame from NanoBanana as the initial input, the model generates the subsequent movement for the hook.
• Script Remixing: Claude Sonnet 3.5 is used to rewrite existing viral hook scripts to align with the specific features of the target product.
• Stitching: The final assembly of the hook, demo footage, and text overlays is performed programmatically using FFmpeg.

Account Strategy

Before posting, the author emphasizes the importance of 'warming up' new accounts for 3 to 5 days. This involves standard user behavior, such as scrolling, liking, and commenting, to bypass platform spam and bot detection filters. The author suggests that while this is an imperfect science, it provides a baseline for account credibility.