So I’ve mentioned this in the past on the usual debates about IOTD that there should now be enough data out there (general images, TPNs, TPs, IOTDs) to be able to build a ML-based tool that can look at an image and return a score.
I had no idea how to build this, but with Claude having advanced so much, it suddenly dawned on me that probably Claude could build something.
So that’s what I’ve tested & done.
As it uses credits to call, I’m not putting it into the public domain yet (as I pay for my credits!).
But the results have been fascinating and amusing.
I set up a composite score out of 10 by assigning weights to compare a given target image to IOTD, TPN and all images etc (putting more emphasis on the putatively better images of IOTD, TP etc).
And just let Claude loose on analysing images, comparing a given target image, and returning a score. Not just a score but also feedback on 8 specific domains (signal-to-noise, processing/contrast etc). It also returns a summary of strengths, areas for improvement, and IOTD assessment on what it would take for an image to get to IOTD.
Some initial takeaways (non-comprehensive):
The systematic parameter by parameter feedback it gives is actually pretty interesting - consistent and well structured.
This feedback also flushes out what it analyses IOTD preferences as being - it reveals what humans would describe as the biases that are implicit in IOTD selections (eg: phrases like to reach what IOTD assessments prefer, there needs to be…)
Putting TPs, TPNs and IOTDs into the tool is also interesting - very amusing when you see an IOTD score lower than other images that score higher. To my mind this really draws out what we all know - that the IOTD anointment the way it is currently done is not consistent. We’ve all seen images and wondered “how did that get IOTD”, and others where we’ve wondered “how on earth did that not get IOTD/TP” etc. Well Claude is showing that arbitrariness is v much there.
I’ve found it very interesting and useful - having a systematic rubric to analyse images actually gives v good constructive feedback. And reading a dispassionate data-driven critique on IOTD assessment is also illuminating, and sometimes amusing.
Anyway, I’m going to use it for systematic, consistent feedback on my own images, as well as occasional amusement.
I’m also going to eventually tweak it to include a comparative score and feedback based on APOD. (The overall generalised comparison it came back with on APOD vs IOTD was also v interesting).
Sorry a long post!
TLDR? You’re going to get better, more consistent, more systematic, more data-driven, more constructive feedback from an AI tool than arbitrary human experts. And boy oh boy is IOTD arbitrary and inconsistent when you run it through the overall data. (And remember Claude really is blind when it comes to the provenance of an image).

(Once its analysed, you may need to just scroll downwards as the analysis is presented further below the image, rather than as a fresh screen)