Building an AI Social Media Autopilot — The Technical Decisions That Actually Matter

I’ve spent the past year building Do Not Eat, an AI platform that generates, publishes, and manages social media content across Instagram, TikTok, YouTube, LinkedIn, and Threads. Along the way, I’ve run into a lot of technical decisions that don’t have obvious answers. This isn’t a product pitch. This is a breakdown of the engineering and product choices behind AI social media automation — what works, what we got wrong, and what I’d tell another developer building in this space. The Core Problem: Voice Matching at Scale The #1 complaint about AI-generated social media content is that it all sounds the same. Generic. Robotic. Interchangeable. The naive approach is prompt engineering: "Write an Instagram caption in a casual, friendly tone about [topic]." This produces acceptable-ish content, but it sounds like every other AI caption on the internet. The better approach is brand voice profiling. Here’s how it works: 1. Ingest existing content. Pull the user’s last 50–100 posts across all