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Gemini 3.1 Pro

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Re: Gemini 3.1 Pro

#371

Post by petesergeant »

simonw wrote: post #332
xrd wrote: post #306

These models are so powerful. It's totally possible to build entire software products in the fraction of the time it took before. But, reading the comments here, the behaviors from one version to another point version (not major version mind you) seem very divergent. It feels like we are now able to manage incredibly smart engineers for a month at the price of a good sushi dinner. But it also feels like you have to b…

I had an interesting experience recently where I ran Opus 4.6 against a problem that o4-mini had previously convinced me wasn't tractable... and Opus 4.6 found me a great solution. https://github.com/simonw/sqlite-chronicle/issues/20 This inspired me to point the latest models at a bunch of my older projects, resulting in a flurry of fixes and unblocks.

I continue to get great value out of having claude and codex bound together in a loop: https://github.com/pjlsergeant/moarcode

Re: Gemini 3.1 Pro

#372

Post by arcatech »

Legend2440 wrote: post #237
stri8ted wrote: post #160

Earlier quoted context omitted.

Exactly. As far as I'm concerned, the benchmark is useless. It's way too easy and rewarding to train on it.

Y'all are way too skeptical, no matter what cool thing AI does you'll make up an excuse for how they must somehow be cheating.

Or maybe you’re too trusting of companies who have already proven to not be trustworthy?

Re: Gemini 3.1 Pro

#373

Post by jcims »

pqdbr wrote: post #222
jcims wrote: post #118

Pelican on a bicycle in drawio - https://imgur.com/a/tNgITTR (FWIW I'm finding a lot of utility in LLMs doing diagrams in tools like drawio)

How are you prompting it to draw diagrams in drawio

Sometimes it helps to also provide a drawio file that has the elements you wan't (eg. cloud service icons or whatever), but you just feed it the content you want diagrammed and let it eat.

Even if it's not completely correct, it usually creates something that's much closer to complete than a blank page.

Re: Gemini 3.1 Pro

#375

Post by ggregoire »

quacky_batak wrote: post #54

I’m keen to know how and where are you using Gemini. Anthropic is clearly targeted to developers and OpenAI is general go to AI model. Who are the target demographic for Gemini models? ik that they are good and Flash is super impressive. but i’m curious

I use it in Google Search. For example yesterday I typed in Google "postgres generate series 24 hour" and this morning "ffmpeg convert mp4 to wav". Previously I would have clicked on the first StackOverflow result (RIP), now I just take it from the Gemini summary (I'd say 95% of the time it's correct for basic programming language questions. I remember some hallucinations about psycopg3 and date-fns tho. As usual with AI, you need to already know the answer, at least partially, to detect the bs).

Also what's great about Gemini in Google Search is that the answer comes with several links, I use them sometimes to validate the correctness of the solution, or check how old the solution is (I've never used chatGPT so I don't know if chatGPT does it).

Re: Gemini 3.1 Pro

#377

Post by patrickmcnamara »

1024core wrote: post #260

It got the car wash question perfectly: You are definitely going to have to drive it there—unless you want to put it in neutral and push! While 200 feet is a very short and easy walk, if you walk over there without your car, you won't have anything to wash once you arrive. The car needs to make the trip with you so it can get the soap and water. Since it's basically right next door, it'll be the shortest drive of you…

Truly we entering the era of AGI.

Re: Gemini 3.1 Pro

#378

Post by jasonjmcghee »

simonw wrote: post #52

Pretty great pelican: https://simonwillison.net/2026/Feb/19/gemini-31-pro/ - took over 5 minutes though, but I think that's because they're having performance teething problems on launch day.

What's crazy is you've influenced them to spend real effort ensuring their model is good at generating animated svgs of animals operating vehicles.

The most absurd benchmaxxing.

https://x.com/jeffdean/status/2024525132266688757?s=46&t=ZjF...

Re: Gemini 3.1 Pro

#379

Post by dekhn »

aberoham wrote: post #301
dekhn wrote: post #145

Earlier quoted context omitted.

I am a professional software developer who has been programming for 40 years (C, C++, Python, assembly, any number of other languages). I work in ML (infrastructure, not research) and spent a decade working at Google. In short, I consider Gemini to be a highly capable intern (grad student level) who is smarter and more tenacious than me, but also needs significant guidance to reach a useful goal. I used Gemini to com…

Wow, you have to try claude code with Opus-4.6..

I agree, but I don't have a subscription.

The remaining technical challenge I have is related to stage positioning- in my system, it's important that all the image frames we collect are tagged with the correct positions. Due to some technical challenges, right now the stage positions are slightly out of sync with the frames, which will be a fairly tricky problem to solve. It's certainly worth trying all the major systems to see what they propose.

Re: Gemini 3.1 Pro

#380

Post by steve_adams_86 »

emp17344 wrote: post #176
steve_adams_86 wrote: post #106

Earlier quoted context omitted.

Ugh, the gears and chain don't mesh and there's no sprocket on the rear hub But seriously, I can't believe LLMs are able to one-shot a pelican on a bicycle this well. I wouldn't have guessed this was going to emerge as a capability from LLMs 6 years ago. I see why it does now, but... It still amazes me that they're so good at some things.

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

I asked myself the same thing as I typed that comment, and I'm not sure what the answer is. I don't think models are specifically trained on this (though of course they're trained on how to generate SVGs in general), but I'm prepared to be wrong.

I have a feeling the most 'emergent' aspect was that LLMs have generally been able to produce coherent SVG for quite a while, likely without specific training at first. Since then I suspect there has been more tailored training because improvements have been so dramatic. Of course it makes sense that text-based images using very distinct structure and properties could be manipulated reasonably well by a text-based language model, but it's still fascinating to me just how well it can work.

Perhaps what's most incredible about it is how versatile human language is, even when it lacks so many dimensions as bits on a machine. Yet it's still cool that we can resurrect those bits at rest and transmogrify them back into coherent projections of photons from a screen.

I don't think LLMs are AGI or about to completely flip the world upside down or whatever, but it seems undeniably magical when you break it down.

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