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Comment by petesergeant | Hacker News Reader

> largely 2-horse

The absolute frontier is largely 2-horse, but the rest of the pack is very close behind, which I'm grateful for. Grok, Facebook, and the Chinese vendors are producing excellent models.

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Gemini is also pretty nice for researching things. It turns out that having the whole internet indexed and having unlimited access to YouTube is a force multiplier of some kind.

Since Google has their own TPUs, TPS is also pretty high w.r.t. Claude, for example.

I think Gemini could be a great product, if they didn't decide to stuff it down my throat at every possible occasion.

Recent example: on my e-reader, tapping a word I don't now and clicking "Translate" pulls up the possible translations from a dictionary, a local file just a couple of megabytes big, near instantly, on this tiny processor.

Doing the same on my Android phone starts a Gemini-chat with the prompt "Translate the word x into y". Takes forever, internet access needed, results vary, burns who knows who much energy.

Why? Just why?

So that some team at Google can hit their OKRs for user adoption and get promoted.

That kind of shoving down is pretty bad, I agree.

I neither use Android devices nor Google Search, so the only Gemini thing I see is the Gemini chat interface.

I understand the pain, though.

Gemini is unbelievably bad for research in my experience. It hallucinates like it's 2023, doesn't use its own search, makes up fake rationalizations for why it didn't need to etc.

It's baffling that OpenAI managed to get better at web search than the company literally synonymous with web search.

Pretty interesting. It one-shots correct information with references and a further reading list 99% of the time for me.

I don't want it to fill in the gaps though, but make it reference anything and everything it brings, hence it doesn't hallucinate much.

If something feels off, I ask it to back it with concrete data, and if it can't, I don't consider that information correct. That happened once, though, and web doesn't have any information on that thing either. So in that case, not only it had no information on the web, the training data had no information on that thing either short of feeding confidential design documents if they were ever present in the first place.

The question was about an instrument preamp though, so nothing crucial.

I guess there's one thing that could really bias my anecdata: I almost exclusively use Gemini in a professional/niche domain and ChatGPT in a personal/idle curiosity context, so there's a chance that ChatGPT is just as wrong but I am not knowledgeable in a given domain to tell.

Since Gemini 3.8 Flash release, I've been largely using that for my coding ttasks. The architectureand design work I use Opus, but the rest of the work is done via Gemini 3.8. I don'thave to worry aboutweekly limits even on the $20 plan. I get down to about 10% weekly limit before it resets. Currently I'm building my own generative modeling + aero sim package largely using Gemini and it's been working great.

... and, must be said a plethora of largely unsung, small, unknown "labs", outfits, "researchers" and the like. There is a long tail of smart people having at this. I guess, sheer compute aside, I think much progress - or, at least, important pieces thereof, will come from there.-

There are some niche research areas where bog standard machine learning algorithms make miracles. LLM is just the poster child. AI/ML is a much larger and wider research area.

Your "bog" if unrelated, put me in mind of a "Cambrian explosion" (of intelligence) in this case, ongoing.-

In a way: We have had "recursive self improvement" (RSI) - that's what genetics is, giving rise to ... us.-

This time around, I think the truly worrying thing is that, having transcended the biological substrate, the pace is unlike anything previously seen.-

Just a thought.-

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