Agentic Pelican on a Bicycle

I remember I even had one case where there was a stealth model running in preview via Open Router and I asked it for an SVG of a pelican riding a bicycle and correctly guessed the model vendor based on the response!

If Sonnet doesn't solve my problem, sometimes Codex actually does.

So it isn't like Codex is always worse. I just prefer to try Sonnet 4.5 first.

And as you say, they cheerfully assert that they've done the job, for real this time, every time.

The naive approach that gets you results like ChatGPT is to produce output tokens based on the prompt and generate a new image from the output. It is really difficult to maintain details from the input image with this approach.

A more advanced approach is to generate a stream of "edits" to the input image instead. You see this with Gemini, which sometimes maintains original image details to a fault; e.g. it will preserve human faces at all cost, probably as a result of training.

I think the round-trip through SVG is an extreme challenge to train through and essentially forces the LLM to progressively edit the SVG source, which can result in something like the Gemini approach above.

[0]: https://www.groundlight.ai/blog/how-vlm-works-tokens

I think that the problem here is that svg is structured information and an image is unstructured blob, and the translation between them requires planning and understanding. Maybe if instead of treating an svg like a raster image in the prompt is wrong. I think that prompting the image like code (which svg basically is) would result in better outputs.

This is just my uninformed opinion.

Nano Banana is rather terrible at multi-turn chats, just like any other model, despite the claim it's been trained for it. Scattered context and irrelevant distractors are always bad, compressing the conversation into a single turn fixes this.

I suspect this is either a training data issue, or an issue with the people building these things not recognizing the problem, but it's weird how persistent and cross-model the issue is, even in model releases that specifically call out better/more steerable composition behavior.

Ask for multiple solutions?

For the svg generation, it would be an interesting experiment to seed it with increasingly poor initial images and see at what point if any the models don’t anchor on the initial image and just try something else

I’d be curious if the approach would be improved by having the model generate a full pelican from scratch each time and having it judge which variation is an improvement. Or if something should be altered in each loop, perhaps it should be the prompt instead

"creating an svg is surprisingly revealing" No it is not. They all do the same thing, they add suns and movement lines, and some more details. Like they were all trained on the same thing.

he makes up his own definition of "agent" there are at least 6 different definitions of this word now in this space. And his is again new for no reason.

The core idea here is "being vague and letting the models make weird random choice" This is the exact opposite of ALL direct instructions from the major model and coding agent programs at this time.

Actual interesting methodology would have been to create all combinations of the variables: let them use different svg to image tools and compare them, try many many different prompts with more specific instructions like "try to be more mechanically accurate"

Analysis is baseless assumptions: It is not "adding realism" all the models just had more pictures of roads trees suns and clouds... so it kept going back to the training data to add more like you keep telling it to do. It certainly wasn't understanding "more mechanically coherent" If it started focusing on the bike, it had more detailed bike pictures in the training data with chains.

This is why all the ai stuff is infuriating, people are mistaking so much for "good" or "useful" . At best this is a laugh once joke about how bad it is.

I admit the first time I saw that big george carlin generated video/stand up comedy, there was a special new feeling about "what on earth did they prompt to get this combination of visual and audio?" But that was such a fleeting thing I never need again

It was a fun little post that felt accurate (ie confirmed my own biases ;)) about the current state of LLM models in a silly, but real, use-case.

The continual drive to out "llm written" articles feels a bit silly to me at this point. They are now part of the tools and tech we use, for better or worse. And to be clear, I think in a lot of cases it leans towards 'worse'.

But do you question if a video or photo was made with digital editing or filters or 'ai' tools (many of which we've had for years, just under different names) ? Do you worry about what tech was used in making your favorite album or song?

I get it, LLMs make it easy to produce trash content, but this is not a new problem. If you see trash, call it out as trash on its flaws, not on a presumption of how it was made.