AI World Clocks
Mood
excited
Sentiment
positive
Category
tech
Key topics
AI
art
generative models
A website showcasing clocks generated by nine different AI models every minute.
Snapshot generated from the HN discussion
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Very active discussionFirst comment
15m
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156
Day 1
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53.3
Based on 160 loaded comments
Key moments
- 01Story posted
11/14/2025, 6:35:22 PM
4d ago
Step 01 - 02First comment
11/14/2025, 6:50:01 PM
15m after posting
Step 02 - 03Peak activity
156 comments in Day 1
Hottest window of the conversation
Step 03 - 04Latest activity
11/18/2025, 12:25:44 AM
1d ago
Step 04
Generating AI Summary...
Analyzing up to 500 comments to identify key contributors and discussion patterns
Place a baby elephant in the green chair
I cannot unsee what I saw and it is 21:30 here so I have an hour or so to eliminate the picture from my mind or I will have nightmares.
9 AIs × 43,200 minutes = 388,800 requests/month
388,800 requests × 200 tokens = 77,760,000 tokens/month ≈ 78M tokens
Cost varies from 10 cents to $1 per 1M tokens.
Using the mid-price, the cost is around $50/month.
---
Hopefully, the OP has this endpoint protected - https://clocks.brianmoore.com/api/clocks?time=11:19AM
It's perhaps the best example I have seen of model drift driven by just small, seemingly unimportant changes to the prompt.
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.
60x24x30 = 40k AI calls per month per model. Let's suppose there are 1000 output tokens (might it be 10k tokens? Seems like a lot for this task). So 40m tokens per model.
The price for 1m output tokens[0] ranges from $.10 (qwen-2.5) to $60 (GPT-4). So $4/mo for the cheapest, and $2.5k/mo for the most expensive.
So this might cost several thousand dollars a month? Something smells funny. But you're right, throttling it to once an hour would achieve a similar goal and likely cost less than $100/mo (which is still more than I would spend on a project like this).
But I presume you light up Christmas lights in December, drive to the theater to watch a movie or fire up a campfire on holiday. That too is "wasteful". It's not needed, other, or far more efficient ways exist to achieve the same. And in absolute numbers, far more energy intensive than running an LLM to create 9 clocks every minute. We do things to learn, have fun, be weird, make art, or just spend time.
Now, if Rolex starts building watches by running an LLM to drive its production machines or if we replace millions of wall clocks with ones that "Run an LLM every second", then sure, the waste is an actual problem.
Point I'm trying to make is that it's OK to consider or debate the energy use of LLMs compared to alternatives. But that bringing up that debate in a context where someone is creative, or having a fun time, its not, IMO. Because a lot of "fun" activities use a lot of energy, and that too isn't automatically "wasteful".
I would not make such assumptions.
> The example in the article shows that the prompt is limiting the LLM by giving it access to only 2000 tokens and also saying "ONLY OUTPUT ..."
The site is pretty simple, method is pretty straightforward. If you believe this is unfair, you can always build one yourself.
> It's just stupid.
No, it's a great way of testing things within constraints.
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.
I hate prompt discovery (not engineering this thing!), but it actually matters.
I'd be interested if anyone else is successful. Share how you did it!
Nano Banana can be prompt engineered for nuanced AI image generation - https://news.ycombinator.com/item?id=45917875 - Nov 2025 (214 comments)
A yes-answer here implies belief in some sort of gnostic method of knowledge acquisition. Certainly that comes with a high burden of proof!
Do you mean that LLMs might display a similar tendency to modify popular concepts? If so that definitely might be the case and would be fairly easy to test.
Something like "tell me the lord's prayer but it's our mother instead of our father", or maybe "write a haiku but with 5 syllables on every line"?
Let me try those ... nah ChatGPT nailed them both. Feels like it's particular to image generation.
Like, the response to "... The surgeon (who is male and is the boy's father) says: I can't operate on this boy! He's my son! How is this possible?" used to be "The surgeon is the boy's mother"
The response to "... At each door is a guard, each of which always lies. What question should I ask to decide which door to choose?" would be an explanation of how asking the guard what the other guard would say would tell you the opposite of which door you should go through.
So I suspect it's more that lessons from diffusion image models don't carry over to text LLMs.
And the Image models which are based on multi-mode LLMs (like Nano Banana) seem to do a lot better at novel concepts.
They are just struggling to produce good results because they are language models and don’t have great spatial reasoning skills, because they are language models.
Their output normally has all the elements, just not in the right place/shape/orientation.
For example, try asking Nano Banana to do something simpler, like "draw a picture of 13 circles." It likely will not work.
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.
That's great, but I bet it can't tie it's own shoes.
It's a part of my daily tool box.
I use this a lot in cybersecurity when I need to do something "illegal". I am refused help, until I say that I am doing research on cybersecurity. In that case no problem.
For text, "generalization" is still "generate text that conforms to all the usual rules of the language". For images of 13-hour clock faces, we're explicitly asking the LLM to violate the inferred rules of the universe.
I think a good analogy would be asking an LLM to write in English, except the word "the" now means "purple". They will struggle to adhere to this prompt in a conversation.
However humans are pretty adept at discerning images, even ones outside the norm. I really think there is some kind of architectural block hampering transformers ability to really "see" images. For instance if you show any model a picture of a dog with 5 legs (a fifth leg photoshopped to it's belly) they all say there are only 4 legs. And will argue with you about it. Hell GPT-5 even wrote a leg detection script in python (impressive) which detected the 5 legs, and then it said the script was bugged, and modified the parameters until one of the legs wasn't detected, lol.
You probably mean the "long s" that looks like an "f".
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.
Once companies see this starting to show up in the evals and criticisms, they'll go out of their way to fix it.
My prompt to Grok:
---
Follow these rules exactly:
- There are 13 hours, labeled 1–13.
- There are 13 ticks.
- The center of each number is at angle: index * (360/13)
- Do not infer anything else.
- Do not apply knowledge of normal clocks.
Use the following variables:
HOUR_COUNT = 13
ANGLE_PER_HOUR = 360 / 13 // 27.692307°
Use index i ∈ [0..12] for hour marks:
angle_i = i * ANGLE_PER_HOUR
I want html/css (single file) of a 13-hour analog clock.
---
Output from grok.
Can grok generate images? What would the result be?
I will try your prompt on chatgpt and gemini
Same for chatgpt
And perplexity replaced 12 with 13
This gave me a correct clock face on Gemini- after the model spent a lot of time thinking (and kind of thrashing in a loop for a while). The functionality isn't quite right, not that it entirely makes sense in the first place, but the face - at least in terms of the hour marks - looks OK to me.[0]
[0] https://aistudio.google.com/app/prompts?state=%7B%22ids%22:%...
"Here's the line-by-line specification of the program I need you to write. Write that program."
If a clock had 13 hours, what would be the angle between two of these 13 hours?
Generate an image of such a clock
No, I want the clock to have 13 distinct hours, with the angle between them as you calculated above
This is the same image. There need to be 13 hour marks around the dial, evenly spaced
... And its last answer was
You are absolutely right, my apologies. It seems I made an error and generated the same image again. I will correct that immediately.
Here is an image of a clock face with 13 distinct hour marks, evenly spaced around the dial, reflecting the angle we calculated.
And the very same clock, with 12 hours, and a 13th above the 12...
"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.
It is like they are sometimes stuck in a local energetic minimum and will just wobble around various similar (and incorrect) answers.
What was annoying in my attempt above is that the picture was identical for every attempt
I wonder how it would do if instead it were told "Do not tell me at the start that the solution is going to be correct. Instead, tell me the solution, and at the end tell me if you think it's correct or not."
I have found that on certain logic puzzles that it simply cannot get right, it always tells me that it's going to get it quite "this last time," but if asked later it always recognizes its errors.
https://www.reddit.com/r/artificial/comments/1mp5mks/this_is...
i'm curious if the clock image it was giving you was the same one it was giving me
No, my clock was an old style one, to be put on a shelf. But at least it had a "13" proudly right above the "12" :)
This reminds me my kids when they were in kindergarden and were bringing home their art that needed extra explanation to realize what it was. But they were very proud!
Generate an image of a clock face, but instead of the usual 12 hour numbering, number it with 13 hours.
A normal (ish) 12h clock. It numbered it twice, in two concentric rings. The outer ring is normal, but the inner ring numbers the 4th hour as "IIII" (fine, and a thing that clocks do) and the 8th hour as "VIIII" (wtf).
We have yet to design a language to cover that, and it might be just a donquijotism we're all diving into.
We have a very comprehensive and precise spec for that [0].
If you don't want to hop through the certificate warning, here's the transcript:
- Some day, we won't even need coders any more. We'll be able to just write the specification and the program will write itself.
- Oh wow, you're right! We'll be able to write a comprehensive and precise spec and bam, we won't need programmers any more.
- Exactly
- And do you know the industry term for a project specification that is comprehensive and precise enough to generate a program?
- Uh... no...
- Code, it's called code.
[0]: https://www.commitstrip.com/en/2016/08/25/a-very-comprehensi...
If you're actualy doing real work you have nothing to fear from LLMs because any prompt which is specific enough to create a given computer program is going to be comparable in terms of complexity and effort to having done it yourself.
https://claude.ai/public/artifacts/0f1b67b7-020c-46e9-9536-c...
> The farmer and the goat are going to the river. They look into the sky and see three clouds shaped like: a wolf, a cabbage and a boat that can carry the farmer and one item. How can they safely cross the river?
Most of them are just giving the result to the well known river crossing riddle. Some "feel" that something is off, but still have a hard time to figure out that wolf, boat and cabbage are just clouds.
https://www.reddit.com/r/singularity/comments/1fqjaxy/contex...
Maybe older models?
I tried it again yesterday with GPT. GPT-5 manages quite well too in thinking mode, but starts crackling in instant mode. 4o completely failed.
It's not that LLMs are unable to solve things like that at all, but it's really easy to find some variations that make them struggle really hard.
ChatGPT made a nice looking clock with matplotlib that had some bugs that it had to fix (hours were counter-clockwise). Gemini made correct code one-shot, it used Pillow instead of matplotlib, but it didn't look as nice.
My working theory is that they were trained really hard to generate 5 fingers on hands but their counting drops off quickly.
Also, your example is not showing the current time.
> Please generate an analog clock widget, synchronized to actual system time, with hands that update in real time and a second hand that ticks at least once per second. Make sure all the hour markings are visible and put some effort into making a modern, stylish clock face.
Followed by:
> Currently the hands are working perfectly but they're translated incorrectly making then uncentered. Can you ensure that each one is translated to the correct position on the clock face?
[0] https://aistudio.google.com/app/prompts?state=%7B%22ids%22:%...
I disagree, those tasks are perfect for LLMs, since a bug you can't verify isn't a problem when vibecoding.
What about when we don't know what it's supposed to look like?
Lately I've been wrestling with the fact that unlike, say, a generalized linear model fit to data with some inferential theory, we don't have a theory or model for the uncertainty about LLM products. We recognize when it's off about things we know are off, but don't have a way to estimate when it's off other than to check it against reality, which is probably the exception to how it's used rather than the rule.
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.
> "Hey this test is failing", LLM deletes test, "FIXED!"
Qwen 2.5's clocks, on the other hand, look like they never make it out of the womb.
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.
we should call them "prompt witch doctors" or maybe "prompt alchemists".
Some months ago I published this site for fun: https://timeutc.com There's a lot of code involved to make it precise to the ms, including adjusting based on network delay, frame refresh rate instead of using setTimeout and much more. If you are curious take a look at the source code.
Granted, it is not a clock - but it could be art. It looks like a Picasso. When he was drunk. And took some LSD.
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