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Gemini 3

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Re: Gemini 3

#941

Post by observationist »

jimbokun wrote: post #570
observationist wrote: post #468

Earlier quoted context omitted.

Yes, we're absolutely holding Apple accountable for outsourcing jobs, degrading the US markets, using slave and child labor, laundering cobalt from illegal "artisanal" mines in the DRC, and whitewashing what they do by using corporate layering and shady deals to put themselves at sufficient degrees of separation from problematic labor and sources to do good PR, but not actually decoupling at all. I also hold American…

Where is the fairy godmother's magic wand that will allow you to make all the governments of the world instantly agree to all of this?

America can just do things. It's up to other countries if they want to participate. If they don't, good luck to them.

Re: Gemini 3

#942

Post by SoftTalker »

oblio wrote: post #798
codespin wrote: post #784

Earlier quoted context omitted.

> They are good at transforming one format to another. They are good at boilerplate. You just described 90% of coding

90% of writing code, sure. But most professionnel programmers write code maybe 20% of the time. A lot of the time is spent clarifying requirements and similar stuff.

Any chance the business/product folks will be using LLMs on their side to help with "clarifying requirements" before they turn them over to the developers?

They view this task as tedious minutia which is the sort of thing LLMs like to churn out.

Re: Gemini 3

#943

Post by SoftTalker »

arnorhs wrote: post #915
zarzavat wrote: post #779

Earlier quoted context omitted.

This is exactly the kind of task that LLMs are good at. They are good at transforming one format to another. They are good at boilerplate. They are bad at deciding requirements by themselves. They are bad at original research, for example developing a new algorithm.

+/- > They are bad at deciding requirements by themselves. What do you mean by requirements here? In my experience the frontier models today are pretty good at figuring out requirements, even when you don't explicitly state them. > They are bad at original research Sure, I don't have any experience with that, so I'll trust you on that. > for example developing a new algorithm. This is just not correct. I used to thin…

> I was trying to come up with a pretty complicated pattern matching, multi-dimensional algorithm (I can't go into the details)

The downside is that if you used Gemini to create the algorithm, your company won't be able to patent it.

Or maybe that's a good thing, for the rest of us.

Re: Gemini 3

#944

Post by ewoodrich »

sindriava wrote: post #715
thejarren wrote: post #702

Earlier quoted context omitted.

[flagged]

You seem very comfortable making unfounded claims. I don't think this is very constructive or adds much to the discussion. While we can debate the stylistic changes of the previous commenter, you seem to be discounting the rate at which the writing style of various LLMs has backpropagated into many peoples' brains.

I can sympathize with being mistakingly accused of using LLM output, but as a reader the above format of "Its not x - it's y" repeated multiple times for artificial dramatic emphasis to make a pretty mundane point that could use 1/3 the length grates on me like reading LinkedIn or marketing voice whether it's AI or not (and it's almost always AI anyway).

I've seen fairly niche subreddits go from enjoyable and interesting to ruined by being clogged with LLM spam that sounds exactly like this so my tolerance for reading it is incredibly low, especially on HN, and I'll just dismiss it.

I probably lose the occasionally legitimate original observation now and then but in a world where our attention is being hijacked by zero effort spam everywhere you look I just don't have the time or energy to avoid that heuristic.

Re: Gemini 3

#945

Post by nwienert »

agumonkey wrote: post #904
Cthulhu_ wrote: post #857

Earlier quoted context omitted.

Thing is, and LLM doesn't need motivation or self-discipline to start writing, which at this point I'm confident is the main slowing down factor in software development, after requirements etc.

These also have larger memory in a way, or deeper stacks of facts. They seems to be able to explore way more sources rapidly and thus emit a solution with more knowledge. As a human I will explore less before trying to solve a problem, and only if that fails I will dig deeper.

But they fail at global context, consistency, and deep understanding which constantly fails them in the real world.

You have to basically tell them all the patterns they need to follow and give them lots of hints to do anything decent, otherwise they invent new helpers that already exist in the codebase, don't follow existing patterns, put code in places that aren't consistent.

They are great at quickly researching a lot, but they start from 0 each time. Then they constantly "cheat" when they can't solve a problem immediately, stuff like casting to "any", skipping tests, deciding "it's ok if this doesn't work" etc.

a few things that would make them much better:

- an ongoing "specific codebase model" that significantly improved ability to remember things across the current codebase / patterns / where/why

- a lot more RL to teach them how to investigate things more deeply and use browsers/debuggers/one-off scripts to actually figure out things before "assuming" some path is right or ok

- much better recall of past conversations dynamically for future work

- much cheaper operating costs, it's clear a big part of why they "cheat" often is because they are told to minimize token costs, it's clear if their internal prompts said "don't be afraid to spin off sub-tasks and dig extremely deep / spend lots of tokens to validate assumptions" they would do a lot better

Re: Gemini 3

#946

Post by philipwhiuk »

lionkor wrote: post #888
tails4e wrote: post #694

Earlier quoted context omitted.

I just had chatgpt explain that problem to me (I was unfamiliar with the mathematical background). It showed how to solve closed form answers for H(2) and H(3) and then numerical solutions using RK4 for higher values. Truly impressive, and it explained the derivations beautifully. There are few maths experts I've encountered who could have hand-held me through it as good.

Was the explanation correct?

He has no idea because he's unfamiliar with the background.

Re: Gemini 3

#947

Post by falcor84 »

slightwinder wrote: post #924
tonyhart7 wrote: post #686

Earlier quoted context omitted.

"Not a single engineer has ever been laid off because of AI." are you insane??? big tech literally make one of the most biggest layoff for the past few months

But not because of AI, they only use that as pretext for normal layoffs. Sometimes they also use it to hire cheaper workers fresh from school or a cheaper country, so just replacing expensive seniors.

From what I'm seeing, it's become more and more difficult for fresh grads to get hired over the last year. If anything, I see that the preference for experienced devs is now even stronger. If you have any evidence to the contrary, I'd appreciate it.

Re: Gemini 3

#948

Post by mistercheph »

lairv wrote: post #293

Out of curiosity, I gave it the latest project euler problem published on 11/16/2025, very likely out of the training data Gemini thought for 5m10s before giving me a python snippet that produced the correct answer. The leaderboard says that the 3 fastest human to solve this problem took 14min, 20min and 1h14min respectively Even thought I expect this sort of problem to very much be in the distribution of what the mo…

If using through the chat interface are these models not doing some RAG?

Re: Gemini 3

#950

Post by mistercheph »

mlrtime wrote: post #890
windexh8er wrote: post #740

Earlier quoted context omitted.

If we've learned anything so far it's that the parlor tricks of one-shot efficacy only gets you so far. Drill into anything relatively complex with a few hundred thousand tokens of context and the models all start to fall apart roughly the same. Even when I've used Sonnet 4.5 with 1M token context the model starts to flake out and get confused with a codebase of less than 10k LoC. Everyone seems to keep claiming thes…

>documented problem in Home Assistant this evening. All it would take is 3-5 lines of YAML. The model failed miserably. I think we're all still safe. This is mostly because HA changes so frequently and the documentation is sparse. To get around this and increase my correction rate, I give it access to the source code of the same version I'm running. Then instructions in CLAUDE.md on where to find source and it must u…

Note to self: strategy to defeat the terminator
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