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Agentic pelican on a bicycle
- davesque 1y agoI 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.
- simonw 1y agoOddly 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!
- williamstein 1y ago> Some models (looking at you, GPT-5-Codex) seemed to mistake “more complex” for “better.” That's what working with GPT-5-Codex on actual code also feels like.
- gh0stcat 1y agoYES, and the sad truth is that the only person who can write good, simple code is likely the one who doesn't need an AI helper. ;(
- aurareturn 1y agoFunny because I've felt that way and have switched back to Claude Sonnet 4.5 for agentic coding. 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.
- pinbender 1y agoSo it's an accurate simulation of a programmer then
- lubujackson 1y agoWhat 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.
- halflife 1y agoI’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. This is just my uninformed opinion.
- pinko 1y agoI 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.
- conception 1y agoAsk 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.
- tadfisher 1y agoTools 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. [0]: https://www.groundlight.ai/blog/how-vlm-works-tokens https://www.groundlight.ai/blog/how-vlm-works-tokens
- sorenjan 1y agoIt 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.
- ripped_britches 1y agoI have tried to do agentic figma in this way but same results: attempt 1 becomes frozen and no forward progress can be made.
- andy99 1y agoThis 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?
- HPMOR 1y agoSomething about the cadence, structure, and staccato nature of the bottom paragraphs also felt very LLMed.
- Retr0id 1y agoI assume this was written by a human and then "improved" by an LLM.
- basilgohar 1y agoIt'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.
- dlivingston 1y agoIn some cases, the artifacts of the medium can be desirable -- especially when consuming media that was created with those artifacts in mind. Take a look at pixel art on CRTs vs LCDs [0] and Toy Story on film vs. digital [1]. [0]: https://wackoid.com/game/10-pictures-that-show-why-crt-tvs-are-better-for-gaming/ https://wackoid.com/game/10-pictures-that-show-why-crt-tvs-a... [1]: https://animationobsessive.substack.com/p/the-toy-story-you-remember https://animationobsessive.substack.com/p/the-toy-story-you-...
- simonw 1y agoI tried an experiment like this a while back (for the GPT-5 launch) and was surprised at how ineffective it was. This is a better version of what I tried but suffers from the same problem - the models seem to stick close to their original shapes and add new details rather than creating an image from scratch that's a significantly better variant of what they tried originally.
- andy99 1y agoI feel like I’ve seen this with code too, where it’s unlikely to scrap something and try a new approach a more likely to double down iterating on a bad approach. 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
- simonw 1y agoYeah, for code I'll often start an entirely new chat and paste in just the bits I liked from the previous attempt.
- consumer451 1y ago> Yeah, for code I'll often start an entirely new chat and paste in just the bits I liked from the previous attempt. Hi Simon, it is very likely that I am misunderstanding your comment, however: Do you use chatbot UIs like chatgtp.com, claudi.ai, LibreChat for coding, instead of something Cursor, Windsurf, Kiro, etc? If that is the case, I am really curious about this. Or, did you just mean for benchmarking various models via simple chat UIs?
- simonw 1y agoI'm usually in either the ChatGPT or Claude web interfaces or working directly in Claude Code or Codex CLI. In either case case I'll often reset the context by starting a new session.
- smusamashah 1y agoA single run (irrespective of number of iterations) on any model is not a good data point. If first output is crappy, the next 3 iterations will improve the same crap. This was not a good test.
- measurablefunc 1y agoIterating a Markov chain does not make it any more or less "agentic". This is yet another instance of corporate marketing departments redefining words b/c they are confused about what exactly they're trying to build & sell.
- tptacek 1y agoIt's agentic because it's an iterated loop that relies on tool calls. The conversion of prompt to SVG is (presumably) a pure product of inference. But the rasterized SVG that the loop evaluates isn't; it's the product of hardcoded svg->jpg translation code (the model isn't inferring the raster). The loop is thus, in some small way, "grounded" (though not as firmly as a coding agent is grounded in a type-checking compiler's refusal to admit a hallucinated API).
- measurablefunc 1y agoHow does your argument work if I move the rasterization into the Markov chain? Or is your assumption that (SVG, JPG) pairs can never be encoded w/ a neural network?
- tptacek 1y agoIf that was what was actually happening you'd have a point, but it isn't.
- measurablefunc 1y agoThe definitions are not coherent. It's obvious enough to anyone who understands the technical details.
- tptacek 1y agoThe technical definition of an agent is an LLM being called in a loop, some of which calls include tool definitions. That's exactly what this is.
- bogdanoff_2 1y agoCould this be improved if the evaluation was done by an independent sub-agent?
- tptacek 1y agoIs it running out of space in its context window?
- bogdanoff_2 1y agoMy rational is that perhaps it's being biased towards continuing doing what it's doing, or biased towards telling that it has done a good job and not being self-critical.
- NiloCK 1y agoThis is a lot better than my attempt exactly one year ago: https://paritybits.me/llm-drawing-with-eyes-open/ https://paritybits.me/llm-drawing-with-eyes-open/ Poor feet.
- arresin 1y agoA G E N T I C it uses AGENTS ITS AGENTIC AGENTIC.
- Glyptodon 1y agoI don't have all the most recent models but I've found image generation to be terribly disappointing in most that I've tried in that it doesn't seem capable of understanding directions or fixing mistakes. "Create a drawing of a cliff in the desert." Get something passing. "Add a waterfall." Get a waterfall that has no connection or outlet. "Make the waterfall connect to a creek that runs off to the left." Get a waterway that goes by the waterfall without connecting and goes straight through the center of the image. Give up on that and notice that the shadows go to the left but the sun is straight behind. "Move the sun to the right so that it matches the shadows more accurately." Sun stays in the same spot, but grows while exaggerated and inaccurate shadows show up that seem to imply the backside of the cliff doesn't block light. ...
- huevosabio 1y agoI 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. ```
- kfarr 1y agoI 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.
- bcoates 1y agoThat 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.
- awaikour 1y agoEvaluating 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.
- boxed 1y agoIf you give it credit for the chain, you need to also notice that that bike has a fixed front wheel. It literally can not be turned.
- tm11zz 1y agoVery nice results! I recently created this SVG CLI agent with a similar idea: https://github.com/svgnew/Saul https://github.com/svgnew/Saul
- rossant 1y agoWhat prevents LLM designers from cheating and including a human handcrafted SVG into the model for this specific request (allowing for variations between calls)?
- sally_glance 1y agoNothing, but looking at the current results either no one tried yet or it didn't work very well. And the pelican benchmark has been around for a while so the opportunity was there.
- HanClinto 1y agoWhat'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?