Audience & Executive Summary
* Audience: Social media managers, marketers, and AI hobbyists (≤15 words)
* Executive summary: 60 words
Intro
* Relatable opener: How many of your followers crave fresh, unique visuals? (1)
* Topic: Building AI‑generated Instagram models (2)
* Why it matters: Automate creative workflows, reduce costs, and stay ahead of trends (3)
Outline
* Prerequisites: Python, API keys, basic ML knowledge
* Quick Definitions: AI model, diffusion, prompt engineering, moderation API
* Section 1: Choosing the right model (4)
* Section 2: Prompt crafting & moderation (5)
* Section 3: Integrating with Instagram Graph API (6)
* Section 4: Scaling & deployment (7)
Body – Building AI Instagram Models
* Selecting a model: Stable Diffusion vs. Midjourney vs. OpenAI’s DALL·E (4, 8)
* Fine‑tuning for brand voice and visual style (9)
* Prompt engineering best practices (10)
* Moderation & compliance using OpenAI’s safety filters (5, 11)
Body – Integration & Automation
* Using n8n to orchestrate image generation and posting (12, 18)
* Low‑code UI with Appsmith for non‑technical stakeholders (13, 15)
* Real‑time analytics with Supabase and LangChain (19)
* Handling Instagram API limits and best practices (6, 20)
Conclusion
* Key takeaways: Model choice, prompt precision, and moderation are critical (3, 5)
* Uncovered topics: Voice overlays, AR filters, and A/B testing (16, 21)
* Next steps: Prototype with a single model, iterate, and scale (7)
* Call‑to‑action: Start a pilot by generating a carousel post with Stable Diffusion and posting via n8n.