Hey d,

Since last year, I've been dabbling with Machine Learning (ML) on a macro photography project. I'm trying to accurately identify dangerous spiders.

Users take 3+ photos
My spider detector finds the spiders and crops the photos
It classifies the spiders (17 buckets of species)

This year, I added a second opinion. A new layer of intelligence sits on the classifiers and helps reason about the spider observation. It looks at the evidence and can downgrade a classification if there's disagreement.

I'm using that same intelligence to help clean up my data set. In my own test of 1,530 photos, GPT-5.6 Luna (no thinking) found a spider in ~97% of my sample photos.

Luna actually found spiders that my own spider detector missed, and it showed me that a few random hat photos had snuck into the data set I trained on. I need to review 4-5% of the training data to improve the accuracy.

Watch my latest video, sponsored by OpenAI.

One other neat thing is that the star rating is powered by my own image-quality classifier, which I trained on 12,000 spider photos.

I marked each photo as good or bad (based on the lighting, blur, focus, etc). You can see how higher stars are linked to better accuracy across 20,000 photos I trained on.

5 stars = 82.4% accuracy
1 star = 66.5% accuracy

What are you building?

Talk soon,
Paul Solt
Paul@SuperEasyApps.com

P.S. Need help with your app? Book a 1:1 App Strategy session.

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