I love this experiment and am surprised that the Claude models performed that much better than the competition. Opus was particularly impressive both in the quality itself and the ability to iterate meaningfully.
Now... Was this article LLM written?
This part triggered all my LLM flags:
```
Adding a bicycle chain isn’t just decoration—it shows understanding of mechanical relationships. The wheel spokes, the adjusted proportions—these are signs of vision-driven refinement working as intended.
```
Did not feel LLM written to me - at least not overtly so. LLM editing/assisted perhaps?
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.
No, I don't have anything against using LLMs to write. My problem is that I enjoy reading people in part for diversity of style.
I already spend too much time reading LLM outputs on my own interactions. And I get sick of their style because of it. So when I read it during leisure time, it just triggers a gut rejection.
Especially because they are so formulaic / template-y.
What an insightful comment! (sorry, couldn’t help it)
I agree about the silliness. God forbid i am a non-native English speaker and I have a bit of an of odd writing style in a real Brits eye. Or that I use ‘—‘ instead of ‘-‘ because usually typing two dashes converts to the long one on Mac (try even four, technology is crazy these days), and it just feels a bit nicer. OR that I adopt occasional use of ‘;’ because I feel like it (Yes. English is supposed to have short sentences. Unlike other languages. Beautiful. Sue me.)
I don’t care if they helped themselves with AI to improve writing or turn a bullet point into a sentence. It’s when the volume of text doesn’t justify the lack of content or value that I call bs and go to the next one. At this point it might as well be human generated content, but I don’t care, outcome’s the same.
Regarding the post — it’s a cute little article and the pelicans do seem be making a point with their funky shapes
I mean at some point you have to evaluate the content on its merit and they have a point — a chain is functional not just decorative in its precise placement.
Evaluating the content on its merit I'd question whether the author has seen a bicycle before. Yes, in the final iteration with Opus it added a chain, but it's missing a triangle which clearly shows a lack of understanding of mechanical relationships.
Ignoring the wording, em-dashes, etc. I'd assume an LLM not only wrote the article but also judged the pictures. That or the author has a much more relaxed opinion on what a pelican on a bicycle should actually look like. I don't think I would call Sonnet's arms and handlebars improved, nor would I call Haiku's legs and feet "proper." And if you overlay GPT-5 Medium's two photos the shapes proportions are nearly identical.
That phrase template isn’t just overdone—it's something some text models are obsessed with. The em-dashes, the contrastive language—these are signs of LLMs being asked to summarize or expand a compelling blog post.
What I take from this is that LLMs are somewhat miraculous in generation but terrible at revision. Especially with images, they are very resistant to adjusting initial approaches.
I wonder if there is a consistent way to force structural revisions. I have found Nano Banana particularly terrible at revisions, even something like "change the image dimensions to..." it will confidently claim success but do nothing.
A thing I've been noticing across the board is that current generative AI systems are horrible at composition. It’s most obvious in image generation models where the composition and blocking tend to be jarringly simple and on point (hyper-symmetry, all-middleground, or one of like three canned "artistic" compositions) no matter how you prompt them, but you see it in things like text output as well once you notice it.
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.
I almost always get better results from LLMs by going back and editing my prompt and starting again, rather than trying to correct/guide it interactively. Almost as if having mistakes in your context window is an instruction to generate more mistakes! (I'm sure it's not quite that simple)
I see this all the time when asking Claude or ChapGPT to produce a single-page two-column PDF summarizing the conclusions of our chat. Literally 99% of the time I get a multi-page unpredictably-formatted mess, even after gently asking over and over for specific fixes to the formatting mistake/s.
And as you say, they cheerfully assert that they've done the job, for real this time, every time.
Ask for the asciidoc and asciidoctor command to make a PDF instead. Chat bots aren’t designed to make PDFs. They are just trying to use tools in the background, probably starting with markdown.
Tools are still evolving out of the VLM/LLM split [0]. The reason image-to-image tasks are so variable in quality and vastly inferior to text-to-image tasks is because there is an entirely separate model that is trained on transforming an input image into tokens in the LLM's vector space.
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.
Revision should be much easier than generation, e.g. reflection style CoT (draft-critique-revision) is typically the simplest way to get things done with these models. It's always possible to overthink, though.
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’m not quite sure.
I think that adversarial network works pretty well at image generation.
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.
The prompt just said to iterate until they were satisfied. Adding something like "don't be afraid to change your approach or make significant revisions" would probably give different results.
I feels like it's a bit hard to take much from this without running this trial many times for each model. Then it would be possible to see if there are consistent themes among each model's solutions. Otherwise, it feels like the specific style of each result could be somewhat random. I didn't see any mention of running multiple trials for each model.
Oddly enough, I've found models are actually quite consistent in their drawings of pelicans riding bicycles.
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!
This wasn’t just “add more details”—it was “make this mechanically coherent.”
The overall text doesn’t appear to be AI written, making this all the more confusing. Is AI making people write this way now on their own? Or is it actually written by an LLM and just doesn’t look like it?
It's going to become the "MP3 sizzle" that young people at the time started to prefer once compressed audio became the norm on iPods and other portable music players, along with film grain and the judder of 24fps video. Artifacts imposed by the medium themselves become desirable once they become normal an associated and in fact signs of "quality", when, in fact, they are introduced noise and distortion to an otherwise more pristine or clean signal.
See also the "warmth" that certain vinyl enthusiasts sought after from their analog recordings which most certainly was mainly dust and defects in the groves rather than any actual tangible quality of the audio itself.
What's troubling to me is that it doesn't seem to have much account for "drift" -- it sort-of just goes down a single path and tries to improve as it goes.
What about structuring the agentic loop to do a simple genetic algorithm -- generate N children (probably 2 or 3), choose the best of the N+1 options (original vs. child A vs. child B vs. child C, and so-on) and then iterate again?
It would be interesting to see if they would get better results if they didn't grade their own work. Feed the output to a different model and let that suggest improvements, almost like a GAN.
Readit News
Now... Was this article LLM written?
This part triggered all my LLM flags: ``` Adding a bicycle chain isn’t just decoration—it shows understanding of mechanical relationships. The wheel spokes, the adjusted proportions—these are signs of vision-driven refinement working as intended. ```
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.
I already spend too much time reading LLM outputs on my own interactions. And I get sick of their style because of it. So when I read it during leisure time, it just triggers a gut rejection.
Especially because they are so formulaic / template-y.
I agree about the silliness. God forbid i am a non-native English speaker and I have a bit of an of odd writing style in a real Brits eye. Or that I use ‘—‘ instead of ‘-‘ because usually typing two dashes converts to the long one on Mac (try even four, technology is crazy these days), and it just feels a bit nicer. OR that I adopt occasional use of ‘;’ because I feel like it (Yes. English is supposed to have short sentences. Unlike other languages. Beautiful. Sue me.)
I don’t care if they helped themselves with AI to improve writing or turn a bullet point into a sentence. It’s when the volume of text doesn’t justify the lack of content or value that I call bs and go to the next one. At this point it might as well be human generated content, but I don’t care, outcome’s the same.
Regarding the post — it’s a cute little article and the pelicans do seem be making a point with their funky shapes
Ignoring the wording, em-dashes, etc. I'd assume an LLM not only wrote the article but also judged the pictures. That or the author has a much more relaxed opinion on what a pelican on a bicycle should actually look like. I don't think I would call Sonnet's arms and handlebars improved, nor would I call Haiku's legs and feet "proper." And if you overlay GPT-5 Medium's two photos the shapes proportions are nearly identical.
I wonder if there is a consistent way to force structural revisions. I have found Nano Banana particularly terrible at revisions, even something like "change the image dimensions to..." it will confidently claim success but do nothing.
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.
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
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 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.
Ask for multiple solutions?
That's what working with GPT-5-Codex on actual code also feels like.
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.
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!
See also the "warmth" that certain vinyl enthusiasts sought after from their analog recordings which most certainly was mainly dust and defects in the groves rather than any actual tangible quality of the audio itself.
Deleted Comment
What about structuring the agentic loop to do a simple genetic algorithm -- generate N children (probably 2 or 3), choose the best of the N+1 options (original vs. child A vs. child B vs. child C, and so-on) and then iterate again?
Poor feet.