THE AI WATCHMAN - OVERVIEW
What We're Building
Remember our Stable Diffusion session? We had a blast generating AI art, but here's the thing: sometimes these models produce unexpected results. Extra fingers, weird artifacts, or content that makes you go "whoa, that's not what I asked for."
What if AI could watch AI?
That's what we're exploring tonight. We'll use a "vision model" (an AI that can see and understand images) to automatically check the output of an image generator. The whole pipeline runs on your own machine. No cloud services. No Big Tech looking at your pictures.
The Problem
When you generate hundreds of AI images, you can't manually review them all. You need automation. But how do you automate "does this look okay?"
The answer: use another AI to look at the images and tell you.
+----------------+ +----------------+ +----------------+
| AI Generated | ----> | Vision Model | ----> | Pass/Reject |
| Images | | Analysis | | Decision |
+----------------+ +----------------+ +----------------+What You'll Learn
By the end of this tutorial, you'll understand:
- What a vision model is and how it "sees" images
- How to send images to a local AI for analysis
- How to get structured, parseable responses
- How to use pattern matching to make pass/fail decisions
We'll do everything the "raw" way using basic command-line tools and bash scripting. No special scripts to install. Everything here you can type yourself and adapt to your own projects.
What We're NOT Doing
We're not giving you a finished tool to download and run. The goal is to teach you the fundamentals so you can build your own automation.
By the end, you'll have:
- Working curl commands you can copy/paste
- Bash scripts for batch processing
- The knowledge to adapt this to your own needs
Prerequisites
To follow along, you'll need:
- A computer (Mac, Linux, or Windows with WSL)
- Terminal/command line access
- Willingness to type commands
That's it. We'll install everything else as we go.