Here's quite an impressive follow-up. I have a tool which knows how to render Markdown documents with embedded SVG content - I use it for the pelican test.

Since this transcript has HTML in it, I decided to upgrade that tool to also render HTML.

I set Gemini 3.8 Flash the task, using my own VERY shonky coding agent tool (llm-coding-agent) - and it did a solid job.

So now you can see the "cool thing in html" rendered within the Markdown document using code that Gemini 3.8 Flash also wrote: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...

Transcript where it built that is here: https://gist.github.com/simonw/3e36b98292dfdc1b3baff158faa74...

Definitely cool.

I noticed it felt a little janky on my PC despite being "60 FPS"...then I noticed the "60 FPS" is hard-coded into the HTML.

https://gist.github.com/simonw/b6149a49d327164d67d62c3d12992...

> Aside from reading identically forwards and backwards down to the letter

No it doesn't.

Why do all LLMs do a particle simulation when you ask them this prompt ? Qwen3.6, Qwen3.8 and Ling 3.0 Tiny all did the same thing !

I find Ling 3.0 tiny particularly interesting as it looks really nice for a tiny model with 7.9B total parameters, with only 1.3B parameters activated per token. Here is the result https://coolthing-ling-3-tiny.tiiny.site (sorry for the weird hosting, first I found that worked)

(it cost me almost 0 cents and done in 49 seconds)

Focus on speed and being OK with temporarily being #3/4 in intelligence might be the counterintuitive approach which makes Google win long term (whether accidentally or strategically). Can't wait to try Gemini Pro later this year!

I saw your username, clicked the link without reading, and was very confused to see a cosmic vortex and not a pelican.

It's been great even since gemini-3.1-flash-lite, which I heavily use in both complex vertical domain tools calling, plus JS code writing for eval-style dynamic tools. At least in my applications, cost x quality x speed there are simply no alternatives.

Pretty typical "cool HTML toy" LLM output, tbh. The only thing impressive about this is how fast it generated it (13 seconds is wild!), but that's more of testament to Google's infrastructural advantage than to the quality of the model.

For comparison's sake, I tried something similar with a couple other cheap models I've used lately, with the prompt "Impress me. Make something cool in HTML. Ensure that it is mobile friendly." (Added the mobile condition as I was on my phone when I did it).

Mimo-2.5 created something similar, only a bit less complex than Gemini's (though, at least the FPS counter is real!), in a minute or two for about 1/3 of a cent: https://gisthost.github.io/?740c325c21e9bfbee59c4f94d9aab0af

GLM-5.3-Flash, currently my workhorse model, spent 12 minutes (ouch) thinking about the prompt. Didn't cost me anything directly because I have a GLM sub, but I did the math and it would have cost about 1.1 cents through the API. Turned out nicely in my opinion (though in reality, it still isn't really anything special): https://gisthost.github.io/?9ef050e16cec2561e6504e725a3f0bcc

Side note: thanks for setting up that Gist Host tool, it's very convenient!

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Editing to add this bonus from Mercury-2.5-Preview, which I just learned released a couple days ago. It's much less impressive-looking than any of the above, but it cost less than 1/20th of a cent, and the response was generated effectively instantly: https://gisthost.github.io/?02f40b50aa891bf396bfaaa3a7998203

> this thing is really good at HTML JavaScript is pretty exciting.

I would hope the people who make one of the most used JS engines in the world are capable of making a model good at JavaScript ;)

LLM: produces a toolbox of an id and a clock

User: use them both

Made me giggle.

OK, but what about a pelican in a bycicle.

Mission accomplished. That's both cool and fast.

it's such a weird split how most AI companies are trying to be the best, but Google really has a different mission statement. they already have users. lots of users. they need to be working on building models they can deploy and use with the most number of people, as they already have the users.

i don't know if Gemini models per se are fully is in line with that purpose, but the results we see keep seeming to be in-line with that split-of-focus.

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