Then the question becomes, can it incorporate targeted feedback, or is it a oneshot-or-bust affair?
My experience is that ChatGPT is very good at iterating on text (prose, code) but fairly bad at iterating on images. It struggles to integrate small changes, choosing instead to start over from scratch, with wildly different results. Thinking especially here of architectural stuff, where it does a great job laying out furniture in a room, but when I ask it to keep everything the same but change the colour of one piece, it goes completely off the rails.
I would assume it depends on how it generates the images.
I've used Claude to generate fairly simple icons and launch images for an iOS game and I make sure to have it start with SVG files since those can be defined as code first. This way it's easier to iterate on specific elements of the image (certain shapes need to be moved to a different position, color needs to be changed, text needs an update, etc.).
You can use targeted feedback - but it's on the user to verify whether the edits were completely localized. In my experience NB mostly tends to make relatively surgical edits but if you're not careful it'll introduce other minute changes.
And that point you can either start over or just feather/mask with the original in any Photoshop type application.
Almost entirely. I called out the one discrepancy in my post:
> “Data Ingestion (Read-Only)” is a bit off.
[flagged]
It’s subtly incorrect. R/w permissions for example are described incorrectly on some nodes.
Then the question becomes, can it incorporate targeted feedback, or is it a oneshot-or-bust affair?
My experience is that ChatGPT is very good at iterating on text (prose, code) but fairly bad at iterating on images. It struggles to integrate small changes, choosing instead to start over from scratch, with wildly different results. Thinking especially here of architectural stuff, where it does a great job laying out furniture in a room, but when I ask it to keep everything the same but change the colour of one piece, it goes completely off the rails.
Nano Banana is really good at iterating on images, as shown by the pancake skull example I borrowed from Max Woolf: https://simonwillison.net/2025/Nov/20/nano-banana-pro/#tryin...
I've tried iterating on slides with test on them a bit and it seems to be competent at that too.
I would assume it depends on how it generates the images.
I've used Claude to generate fairly simple icons and launch images for an iOS game and I make sure to have it start with SVG files since those can be defined as code first. This way it's easier to iterate on specific elements of the image (certain shapes need to be moved to a different position, color needs to be changed, text needs an update, etc.).
FWIW not sure how Nano Banana Pro works though.
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You can use targeted feedback - but it's on the user to verify whether the edits were completely localized. In my experience NB mostly tends to make relatively surgical edits but if you're not careful it'll introduce other minute changes.
And that point you can either start over or just feather/mask with the original in any Photoshop type application.
None of it was accurate.
But boy was it beautiful.
Funny thing to say considering the author of Datasette himself says it's accurate.