Interestingly, clocks are also an easy tell for when you're dreaming, if you're a lucid dreamer; they never work normally in dreams.

An amusing pattern that dates back to "1kg of steel is heavier of course" in GPT-3.5.

Grok 4 and Kimi nailed it the first time for me, then only Kimi on the second pass.

"Create HTML/CSS of an analog clock showing ${time}. Include numbers (or numerals) if you wish, and have a CSS animated second hand. Make it responsive and use a white background. Return ONLY the HTML/CSS code with no markdown formatting."

Lol, are you using ai to create fan translations of エロ漫画 ?

we should call them "prompt witch doctors" or maybe "prompt alchemists".

This experiment, however, clearly states the goal with this prompt: `Create HTML/CSS of an analog clock showing ${time}. Include numbers (or numerals) if you wish, and have a CSS animated second hand. Make it responsive and use a white background. Return ONLY the HTML/CSS code with no markdown formatting.`

An LLM should be able to interpret that, and should be able to perform a wide range of tasks in that same style - countdown timers, clocks, calendars, floating quote bubble cycling through list of 100 pithy quotations, etc. Individual, clearly defined elements should have complex representations in latent space that correspond to the human understanding of those elements. Tasks and operations and goals should likewise align with our understanding. Qwen 2.5 and some others clearly aren't modeling clocks very well, or maybe the html/css rendering latents are broken. If you pick a semantic axis(like analog clocks), you can run a suite of tests to demonstrate their understanding by using limited one-shot interactions.

Reasoning models can adapt on the fly, and are capable of cheating - one shots might have crappy representations for some contexts, but after a lot of repetition and refinement, as long as there's a stable, well represented proxy for quality somewhere in the semantics it understands, it can deconstruct a task to fundamentals and eventually reach high quality output.

These type of tests also allow us to identify mode collapses - you can use complex sophisticated prompting to get most image models to produce accurate analog clocks displaying any time, but in the simple one shot tests, the models tend to only be able to produce the time 10:10, and you'll get wild artifacts and distortions if you try to force any other configuration of hands.

Image models are so bad at hands that they couldn't even get clock hands right, until recently anyway. Nano banana and some other models are much better at avoiding mode collapses, and can traverse complex and sophisticated compositions smoothly. You want that same sort of semantic generalization in text generating models, so hopefully some of the techniques cross over to other modalities.

I keep hoping they'll be able to use SAE or some form of analysis on static weight distributions in order to uncover some sort of structural feature of mode collapse, with a taxonomy of different failure modes and causes, like limited data, or corrupt/poisoned data, and so on. Seems like if you had that, you could deliberately iterate on, correct issues, or generate supporting training material to offset big distortions in a model.

For example, try asking Nano Banana to do something simpler, like "draw a picture of 13 circles." It likely will not work.

We have yet to design a language to cover that, and it might be just a donquijotism we're all diving into.

https://www.reddit.com/r/singularity/comments/1fqjaxy/contex...

Maybe older models?

"You're absolutely right! I made a mistake. I have now comprehensively solved this problem. Here is the corrected output: [totally incorrect output]."

None of them ever seem to have the ability to say "I cannot seem to do this" or "I am uncertain if this is correct, confidence level 25%" The only time they will give up or refuse to do something is when they are deliberately programmed to censor for often dubious "AI safety" reasons. All other times, they come back again and again with extreme confidence as they totally produce garbage output.

i'm curious if the clock image it was giving you was the same one it was giving me

https://gemini.google.com/share/780db71cfb73

Put another way, it was hoped that once the dataset got rich enough, developing this understanding is actually more efficient for the neural network than memorizing the training data.

The useful question to ask, if you believe the hope is not bearing fruit, is why. Point specifically to the absent data or the flawed assumption being made.

Or more realistically, put in the creative and difficult research work required to discover the answer to that question.

gpt-image-1 and Imagen are wickedly smart.

The new Nano Banana 2 that has been briefly teased around the internet can solve incredibly complicated differential equations on chalk boards with full proof of work.

https://minimaxir.com/2025/11/nano-banana-prompts/

"Here's the line-by-line specification of the program I need you to write. Write that program."

Can grok generate images? What would the result be?

I will try your prompt on chatgpt and gemini

It's annoying but I prefer it to how Gemini gets depressed if it takes a few tries to make progress. Like, thanks for not gaslighing me, but now I'm feeling sorry for a big pile of numbers, which was not a stated goal in my prompt.

My intuition is at the start when I was like "choose one of these 10 or unknown", that unknown left a big gray area, so as I added more classes the model could say "I know it's not X, because it's more similar to Y"

I feel like in this case though, the broken clocks are broken because they don't serve the purpose of visually transmitting information, they do look like clocks tho. I'm sure if you fed the output back into the LLM and ask what time it is it would say IDK, or more likely make something up and be wrong. (at least the egregious ones where the hands are flying everywhere)

It's why non-coders think it's doing an amazing job at software.

But it's worryingly why using it for research, where you necessarily don't know what you don't know, is going to trip up even smarter people.

I disagree, those tasks are perfect for LLMs, since a bug you can't verify isn't a problem when vibecoding.

I could not get to the store because of the cookie banner that does not work (at left on mobile chrome and ff). The Internet Archive page: https://archive.ph/qz4ep

I wonder how this test could be modified for people that have neurological problems - my father's hands shake a lot but I would like to try the test on him (I do not have suspicions, just curious).

I passed it :)

Hmm, ambiguity. I would be the smart ass that drew a digital clock for them, or a shaku-dokei.

What changes to the prompt are you referring to?

According the comment on the site, the prompt is the following:

Create HTML/CSS of an analog clock showing ${time}. Include numbers (or numerals) if you wish, and have a CSS animated second hand. Make it responsive and use a white background. Return ONLY the HTML/CSS code with no markdown formatting.

The prompt doesn't seem to change.

In a world where Javascript and Electron are still getting (again, rightfully) skewered for inefficiency despite often exceeding the performance of many compiled languages, we should not dismiss the discussion around efficiency so easily.

I hate prompt discovery (not engineering this thing!), but it actually matters.