SOCIAL MEDIA CONTENT
In Development

AI video experiments. Learning in public.

Exploring AI text-to-video generation through automated short-form content. Every video is AI-generated, every process is documented — failures included.

4 Platforms
0 Videos
100% AI Generated
Mr. NUT — Video Creator

Watch the experiments.

AI-generated shorts published across all major platforms. Same content, different audiences, real analytics.

YouTube Shorts

@NUTsAImagination

TikTok

@NUTsAImagination

Instagram Reels

@NUTsAImagination

Facebook Reels

@NUTsAImagination

Accounts coming soon — building first content batch

Recent videos.

First AI-generated shorts launching soon. Check back for updates.

Video 1 Coming Soon
Video 2 Coming Soon
Video 3 Coming Soon

The tools powering this.

The stack behind automated AI video content — from generation to publishing.

Text-to-Video Generation

Runway Gen-4, Kling AI, Google Veo 3. Testing multiple providers to find what actually works.

Workflow Automation

Make.com + Google Sheets tracking. One trigger, full pipeline execution.

Multi-Platform Publishing

OnlySocial scheduler. Same content optimised per platform — aspect ratios, captions, hashtags.

Video Enhancement

Auto-captions, music integration, platform-specific optimisation.

Analytics

Native platform insights + manual A/B testing. No vanity metrics — just what helps make better content.

What is NUT's AImagination?

A side project exploring the intersection of AI text-to-video generation and automated content workflows. No polished production — just real experiments, learning outcomes, and transparency about what works (and what doesn't).

Every video is generated through text prompts, processed via AI tools, and published through automated pipelines. The goal isn't perfection — it's understanding the technology by building in public.

Why this exists

AI video generation has reached a point where individuals can produce content at volumes previously requiring entire production teams. But the real question isn't "can we?" — it's "should we?" and "what's actually worth automating?"

This project documents real use cases, cost analyses, quality assessments, and honest limitations. If you're considering AI video automation, follow along to see what's hype versus practical reality.