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Gemini 2.5 Pro Preview

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Re: Gemini 2.5 Pro Preview

#391
post #265

Earlier quoted context omitted.

This is sort of LLM specific. For some tasks you might try including the word comment but give the order at the beginning and end of the prompt. This is very model dependent. Like: Refractor this. Do not write any comments. As a reminder your task is to refractor the above code and do not write any comments.

> Do not write any comments. [...] do not write any comments. Literally both of those are negations.

Yes my suggestion is that negations can work just fine, depending on the model and task, and instead of avoiding negations you can try other promoting strategies like emphasizing what you want at the beginning and at the end of the prompt.

If you think negations never work tell Gemini 2.5 to "write 10 sentences that do not include the word the" and see what happens.

Re: Gemini 2.5 Pro Preview

#393
post #311
post #87

I use Gemini for almost everything. But their model card[1] only compares to o3-mini! In known benchmarks o3 is still ahead: +------------------------------+---------+--------------+ | Benchmark | o3 | Gemini 2.5 | | | | Pro | +------------------------------+---------+--------------+ | ARC-AGI (High Compute) | 87.5% | — | | GPQA Diamond (Science) | 87.7% | 84.0% | | AIME 2024 (Math) | 96.7% | 92.0% | | SWE-bench Veri…

o3 is $40/M output tokens and 2.5 Pro is $10-15/M output tokens so o3 being slightly ahead is not really worth 4 times more than gemini.

Not sure why this is being downvoted, but it's absolutely true.

If you're using these models to generate code daily, the costs add up.

Sure, I'll give a really tough problem to o3 (and probably over ChatGPT, not the API), but on general code tasks, there really isn't meaningful enough difference to justify 4x the cost.

Re: Gemini 2.5 Pro Preview

#394
post #319

My frustration with using these models for programming in the past has largely been around their tendency to hallucinate APIs that simply don't exist. The Gemini 2.5 models, both pro and flash, seem significantly less susceptible to this than any other model I've tried. There are still significant limitations, no amount of prompting will get current models to approach abstraction and architecture the way a person doe…

searching and ranking existing fragments and recombining them within well known paths is one thing, exploratively combining existing fragments to completely novel solutions quickly runs into combinatorial explosion. so it's a great tool in the hands of a creative architect, but it is not one in and by itself and I don't see yet how it can be. my pet theory is that the human brain can't understand and formalize its cr…

> I've been contested that the second Gödel incompleteness theorem "can't be applied like this to the brain" but I stubbornly insist yes, the brain implements _some_ formal system and it can't understand how that system works

I would argue that the second incompleteness theorem doesn't have much relevance to the human brain, because it is trying to prove a falsehood. The brain is blatantly not a consistent system. It is, however, paraconsistent: we are perfectly capable of managing a set of inconsistent premises and extracting useful insight from them. That's a good thing.

It's also true that we don't understand how our own brain works, of course.

Re: Gemini 2.5 Pro Preview

#395
post #319

Earlier quoted context omitted.

searching and ranking existing fragments and recombining them within well known paths is one thing, exploratively combining existing fragments to completely novel solutions quickly runs into combinatorial explosion. so it's a great tool in the hands of a creative architect, but it is not one in and by itself and I don't see yet how it can be. my pet theory is that the human brain can't understand and formalize its cr…

> Demystifying Gödel's Theorem: What It Actually Says > If you think his theorem limits human knowledge, think again https://www.youtube.com/watch?v=OH-ybecvuEo

thanks for the pointer.

first, with Neil DeGrasse Tyson I feel in fairly ok company with my little pet peeve fallacy ;-)

yah as I said, I both get it and don't ;-)

And then the video escapes me saying statements about the brain "being a formal method" can't be made "because" the finite brain can't hold infinity.

that's beyond me. although obviously the brain can't enumerate infinite possibilities, we're still fairly well capable of formal thinking, aren't we?

And many lovely formal systems nicely fit on fairly finite paper. And formal proofs can be run on finite computers.

So somehow the logic in the video is beyond me.

My humble point is this: if we build "intelligence" as a formal system, like some silicon running some fancy pants LLM what have you, and we want rigor in it's construction, i.e. if we want to be able to tell "this is how it works", then we need to use a subset of our brain that's capable of formal and consistent thinking. And my claim is that _that subsystem_ can't capture "itself". So we have to use "more" of our brain than that subsystem. so either the "AI" that we understand is "less" than what we need and use to understand it. or we can't understand it.

I fully get our brain is capable of more, and this "more" is obviously capable of very inconsistent outputs, HAL 9000 had that problem, too ;-)

I'm an old woman. it's late at night.

When I sat through Gödel back in the early 1990s in CS and then in contrast listened to the enthusiastic AI lectures it didn't sit right with me. Maybe one of the AI Prof's made that tactical mistake to call our brain "wet biological hardware" in contrast to "dry silicon hardware". but I can't shake of that analogy ;-) I hope I'm wrong :-) "real" AI that we can trust because we can reason about it's inner workings will be fun :-)

Re: Gemini 2.5 Pro Preview

#396
post #252

Earlier quoted context omitted.

Simple, and even simpler from your own example. Knowledge has an objective correctness. We know that there is a "right" and "wrong" answer and we know what a "right" answer is. "Consistently correct guesses", based on the name itself, is not reliable enough to actually be trusted. There's absolutely no guarantee that the next "consistently correct guess" is knowledge or a hallucination.

This is a circular semantic argument. You're saying knowledge is knowledge because it's correct, where guessing is guessing because it's a guess. But "is it correct?" is precisely the question you're asking the poor LLM to answer in the first place. It's not helpful to just demand a computation device work the way you want, you need to actually make it work. Also, too, there are whole subfields of philosophy that mak…

> You're saying knowledge is knowledge because it's correct, where guessing is guessing because it's a guess.

Knowledge is knowledge because the knower knows it to be correct. I know I'm typing this into my phone, because it's right here in my hand. I'm guessing you typed your reply into some electronic device. I'm guessing this is true for all your comments. Am I 100% accurate? You'll have to answer that for me. I don't know it to be true, it's a highly informed guess.

Being wrong sometimes is not what makes a guess a guess. It's the different between pulling something from your memory banks, be they biological or mechanical, vs inferring it from some combination of your knowledge (what's in those memory banks), statistics, intuition, and whatever other fairy dust you sprinkle on.

Re: Gemini 2.5 Pro Preview

#397

Earlier quoted context omitted.

It's already much improved on the early days. But I wonder when we'll be happy? Do we expect colleagues friends and family to be 100% laser-accurate 100% of the time? I'd wager we don't. Should we expect that from an artificial intelligence too?

I expect my calculator to be 100% accurate 100% of the time. I have slightly more tolerance for other software having defects, but not much more.

A calculator isn't software, it's hardware. Your inputs into a calculator are code.

Your interaction with LLMs is categorically closer to interactions with people than with a calculator. Your inputs into it are language.

Of course the two are different. A calculator is a computer, an LLM is not. Comparing the two is making the same category error which would confuse Mr. Babbage, but in reverse.

(“On two occasions, I have been asked [by members of Parliament], 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able to rightly apprehend the kind of confusion of ideas that could provoke such a question.”)

Re: Gemini 2.5 Pro Preview

#398
post #118
post #85

Earlier quoted context omitted.

LLMs are extremely poor at following negative instructions, tell them what to do, not what not to do.

Ok, so saying "Implement feature X" leads to a ton of comments. How do you rewrite that comment to not include "don't write comments" while making the output not containing comments? "Write only source code, no plain text with special characters in the beginning of the line" or what are you suggesting here in practical terms?

"Whenever you are tempted to write a line or block comment, it is imperative that you just write the actual code instead"

Re: Gemini 2.5 Pro Preview

#399

Earlier quoted context omitted.

I've had the opposite experience. Despite trying various prompts and models, I'm still searching for that mythical 10x productivity boost others claim. I use it mostly for Golang and Rust, I work building cloud infrastructure automation tools. I'll try to give some examples, they may seem overly specific but it's the first things that popped into my head when thinking about the subject. Personally, I found that LLMs…

> I use it mostly for Golang and Rust I'm starting to suspect this is the issue. Neither of these languages are in the top 5 languages so there is probably less to train on. It'd be interesting to see if this improves over time or if the gap between the languages become even more intense as it becomes favorable to use a language simply because LLMs are so much better at it. There are a lot of interesting discussions…

Yes, I agree, that's likely a big factor. I've had a better LLM design experience using widely adopted tech like TypeScript/React.

I do wonder if the gap will keep widening though. If newer tools/tech don’t have enough training data, LLMs may struggle much more with them early on. Although it's possible that RAG and other optimization techniques will evolve fast enough to narrow the gap and prevent diminishing returns on LLM driven productivity.

Re: Gemini 2.5 Pro Preview

#400

Google/Alphabet is a giant hulking machine that’s been frankly running at idle. All that resume driven development and performance review promo cycles and retention of top talent mainly to work on ad tech means it’s packed to the rafters with latent capability. Holding on to so much talent in the face of basically having nothing to do is a testament to the company’s leadership - even if said leadership didn’t manage…

> retention of top talent mainly to work on ad tech

No the top talent worked on exciting things like Fuchsia. Ad tech is boring stuff written by people who aren't enough of a snob to refuse working on ad tech.

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