> it's almost impossible to get Gemini to not do "helpful" drive-by-refactors

Just asking "Explain what this service does?" turns into

[No response for three minutes...]

+729 -522

Would be really interesting to see an "Eager McBeaver" bench around this concept. When doing real work, a model's ability to stay within the bounds of a given task has almost become more important than its raw capabilities now that every frontier model is so dang good.

Every one of these models is so great at propelling the ship forward, that I increasingly care more and more about which models are the easiest to steer in the direction I actually want to go.

I have the same issue. Even when I ask it to do code-reviews and very explicitly tell it not to change files, it will occasionally just start "fixing" things.

> it's almost impossible to get Gemini to not do "helpful" drive-by-refactors

This has not been my experience. I do Elixir primarily and Gemini has helped build some really cool products and massive refactors along the way. And it would even pick up security issues and potential optimizations along the way

What HAS been an issue constantly though was randomly the model will absolutely not respond at all and some random error would occur which is embarrassing for a company like Google with the infrastructure they own.

> it's almost impossible to get Gemini to not do "helpful" drive-by-refactors

Not like human programmers. I would never do this and have never struggled with it in the past, no...

This matches my experience using Gemini CLI to code. It would also frequently get stuck in loops. It was so bad compared to Codex that I feel like I must have been doing something fundamentally wrong.

I was using gemini antigravity in opencode a few weeks ago before they started banning everyone for that and I got into the habit of writing "do x, then wait for instructions".

That helped quite a bit but it would still go off on it's own from time to time.

Every time I have tried using `gemini-cli` it just thinks endlessly and never actually gives a response.

Do you have Personalization Instructions set up for your LLM models?

You can make their responses fairly dry/brief.

true, whenever I ask Gemini to help me with a prompt for generating an image of XYZ, it generates the image.

I like how they also did a frog on a penny-farthing and a giraffe driving a tiny car and an ostrich on roller skates and a turtle kickflipping a skateboard and a dachshund driving a stretch limousine.

Animated SVG is huge. People in different professions are worrying to different degrees in terms of being replaced by ML, but this one is huge with regards to digital art.

Can't wait until they finally get to real world CAD

He's svg-mogging

So let's put things we're interested in in the benchmarks.

I'm not against pelicans!

You don't have to benchmax everything, just the benchmarks in the right social circles

It if funny to think that Jeff Dean personally worked to optimize the pelican riding a bike benchmark.

A few thoughts:

- One thing to be aware of is that LLMs can be much smarter than their ability to articulate that intelligence in words. For example, GPT-3.5 Turbo was beastly at chess (1800 elo?) when prompted to complete PGN transcripts, but if you asked it questions in chat, its knowledge was abysmal. LLMs don't generalize as well as humans, and sometimes they can have the ability to do tasks without the ability to articulate things that feel essential to the tasks (like answering whether the bicycle is facing left or right).

- Secondly, what has made AI labs so bullish on future progress over the past few years is that they see how little work it takes to get their results. Often, if an LLM sucks at something that's because no one worked on it (not always, of course). If you directly train a skill, you can see giant leaps in ability with fairly small effort. Big leaps in SVG creation could be coming from relatively small targeted efforts, where none existed before.

My best guess is that the labs put a lot of work into HTML and CSS spatial stuff because web frontend is such an important application of the models, and those improvements leaked through to SVG as well.

All models have improved, but from my understanding, Gemini is the main one that was specifically trained on photos/video/etc in addition to text. Other models like earlier chatgpt builds would use plugins to handle anything beyond text, such as using a plugin to convert an image into text so that chatgpt could "see" it.

Gemini was multimodal from the start, and is naturally better at doing tasks that involve pictures/videos/3d spatial logic/etc.

The newer chatgpt models are also now multimodal, which has probably helped with their svg art as well, but I think Gemini still has an edge here

> Does anyone understand why LLMs have gotten so good at this?

Added more IF/THEN/ELSE conditions.

That’s Simon’s goal. “All I’ve ever wanted from life is a genuinely great SVG vector illustration of a pelican riding a bicycle. My dastardly multi-year plan is to trick multiple AI labs into investing vast resources to cheat at my benchmark until I get one.”

https://simonwillison.net/2025/Nov/13/training-for-pelicans-...

Soon? I'd be willing to bet it's been included in the training set at least 6 months by now. Not so obvious so it generates always perfect pelicans on bikes, but sufficiently for the "minibench" to be less useful today than in the past.

Simons been doing this exact test for nearly 18 months now, if vendors want to benchmaxx it then they've had more than enough time to do so already.

Forget the paperclip maximizer - AGI will turn the whole world into pelicans on bikes.

It seems to be having capacity problems right now but I'll run that as soon as I can get it to work.

Loads of examples here https://x.com/jeffdean/status/2024525132266688757

Or, even more challenging, an okapi on a recumbent ?!

I don't know what of this is the prompt and what was the output, but that's a pretty bad schematic (for both aesthetic and circuit-design reasons).

that's pretty amazing for an LLM but as an EE, if my intern did this i would sigh inwardly and pull up some existing schematics for some brief guidance on symbol layout.

Is this capability “emergent”, or do AI firms specifically target SVG generation in order to improve it? How would we be able to tell?

next time you host a party, have people try to draw a bicycle on your whiteboard (you have a whiteboard in your house right? you should, anyway...)

human adults are generally quite bad at drawing them, unless they spend a lot of time actually thinking about bicycles as objects

And the left leg is straight while the right leg is bent.

EDIT: And the chain should pass behind the seat stay.

"integrating a bicycle basket, complete with a fish for the pelican... also ensuring the basket is on top of the bike, and that the fish is correctly positioned with its head up... basket is orange, with a fish inside for fun."

how thoughtful of the ai to include a snack. truly a "thanks for all the fish"

A fish for the road

Sea https://simonwillison.net/2025/Nov/13/training-for-pelicans-...

That would be a especially challenging for vector output. I tried just now on ChatGPT 5.2 to jump straight to an image, with this prompt:

"make me a cartoon image of a pelican riding a bicycle, but make it from a front 3/4 view, that is riding toward the viewer."

The result was basically a head-on view, but I expect if you then put that back in and said, "take this image and vectorize it as an SVG" you'd have a much better time than trying to one-shot the SVG directly from a description.

... but of course, if that's so, then what's preventing the model from being smart enough to identify this workflow and follow it on its own to get the task completed?

It's a pelican. What do you expect a pelican to have in his bike's basket?

It's a pretty funny and coherent touch!

Yeah, why only _one_ fish?

It's obvious that pelican is riding long distance, no way a single fish is sufficiently energy dense for more than a few miles.

Can't the model do basic math???

Where else are cycling Pelican's meant to keep their fish?

At this point, i think maybe they're training on all of the previous pelicans, and one of them decided to put a hat on it?

Disclaimer: This is an unsubstantiated claim that i made up

Jeff Dean just posted an animated version: https://x.com/JeffDean/status/2024525132266688757

Harder: the bike must work

Hardest: the pelican must work