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...
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.
Not sure how anyone trusts their output without going through it line by line to make sure they don't pull that crap.
In what ways is a human brain's "intent" distinct from the "intent" shown by a goal-directed AI system?
We are in a situation where a technology was developed with malicious intent to produce results that pleases us at the cost of cutting corners. And "we" hope that we will get away with it.
Yeah, I know, just more slop. But I do think the second agent’s eagerness to please is aligned more in your favor in that instance, so it’s likely to find most issues.
The bigger problem I’ve found is that it’ll also find all kinds of very minor edge cases that you have to pick through.
Asking slightly tongue in cheek but at what point does this stop making sense if we can't trust the output, the people creating the models are already getting surprised in bad ways (if we take their words at face value) with how the models are behaving already etc.
We have the folks over here saying "AI is amazing" and the other other folks over there saying "AI is terrible".
I've largely sat it out so far and I listen to both camps (and people in the middle as well) and I keep half an eye on what they are up to (including periodically evaluating them) but my overarching impression is still "Why would we trust this when it hasn't shown it's trustworthy?"
But I wouldn’t say I “trust” these agents. The degree to which I double check their work depends heavily on the consequences if it gets something wrong. Not too dissimilar from another human dev in that sense.
So for the SaaS that supports my family, there are some things I have it build where I glance at the PR for a minute or two, but if it broke something on this admin page that only I see, there’s no real downside and I’ll find out pretty quickly next time I use it. And it’s fine 95% of the time, so it doesn’t feel like the best use of my time to double-check it carefully.
But for some of the complex internal flows where a bug could be both catastrophic and difficult to even discover for awhile, I still check it very carefully.
For a little one-off vibe coded demo thing like OP shared, I wouldn’t look at the code at all, I’d just have another agent check it and fix anything it finds. Very low stakes.
Obviously maybe it’s not composable like that exactly in real world but that’s the intent of agents checking agents
I once made a counter judge, and a loop to make corrections deemed true positives. The loop cost me a lot and still left the results to be desirable.
They argue the net is positive but clearly the “100x productivity multiplier” claims have been dashed on the shoals of reality for these groups.
This is anecdotal, but it’s across the board in my vicinity. I’m curious how common this is and if it’s just “the new normal” to adopt the nauseating Covid phrase.
These types of high-level tests are frustrating beyond belief to humans due to their lack of specificity, but with the agents, they don't get annoyed investigating possible regressions from non-specific signals.
They also aren't as painful to maintain as one would think, because a regression flagging test can be traced by the agent and represented as the business rule that was violated. I've found recent models to be really excellent at discerning a true regression from an outdated test assertion, especially if they are able to trace the failing test back to the PR and work ticket that built it.
Thought processs: "Oh, simonw is asking me to make something cool, I think I know what he really wants..."
Gemini 3.7 flash outputs so many tokens per answer it doesn't matter how fast its TPS is, sol will end up being both cheaper and faster than Gemini. So ppl are paying more for a given task, waiting longer and using a dumber intelligence because "TPS number shiny".
Gemini 3.8 outputs 11k more tokens PER TASK on average in AAII than 3.7 putting it dead last in output tokens per task in the leaderboard.
https://artificialanalysis.ai/#cost-tabs
That said, Luna is the undisputed king here at the moment and is what I use as my workhorse model.
Not sure if you read your own link but Sol 56 high ranks smack between Gemini 3.8 flash medium and high. Gemini 3.8 flash comes in as more expensive per task than Sol 56 high according to artificial analysis.
Luna high is literally 30X cheaper than Gemini 3.8 flash high.
You can limit the model viewer and they're getting better at testing multiple effort levels now: https://artificialanalysis.ai/?models=gpt-5-6-sol-medium%2Cg...
One reason is clear: Sol uses dramatically fewer output tokens than Gemini 38 flash https://artificialanalysis.ai/?models=gemini-3-8-flash%2Cgem...
Ps. For the last week I diverged to Luna too, still need to check 3.8 flash.
But 3.6 flash was my go-to model 3 weeks ago and before it was deepseek flash/pro for a while.
None of the claude models seemed cost effective though.
> Aside from reading identically forwards and backwards down to the letter
No it doesn't.
When I typed "Are we not pure noon, ergo, we play life; yet, we hate bad fear" into Google, I got more weird results from Gemini: it claimed, incorrectly, that it is an anagram of the "well-known philosophical statement" (?), "We are not pure nature, we are history".
https://share.google/aimode/wJosKnHig6oVYaG18
(?): the reference seems to be to Jose Ortega y Gasset's line, "El hombre no tiene naturaleza, lo que tiene es historia" -- "Man[kind] has no nature, what it has is history."
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)
Datasets contains lots of people sharing particles simulations in various ways, with a bunch of people replying "that's so cool" and similar, so 10 years later someone asks an LLM for "cool thing" and "particle simulations" rank pretty far up when it thinks about what others have called cool.
Also the reason LLMs are positive, enchanting, pleasant, glorifying, demagogues.
Not because it's skewed tone in the data. They are acute politicians.
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 ;)
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
and probably a barely modified knock-off of some github project that it trained on
User: use them both
Made me giggle.
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.