Once I decide I want to "think a problem through with an LLM", I often start with just the voice mode. This forces me to say things out loud — which is remarkably effective (hear hear rubber duck debugging) — and it also gives me a fundamentally different way of consuming the information the LLM provides me. Instead of being delivered a massive amount of text, where some information could be wrong, I instead get a sequential system where I can stop/pause the LLM/redirect it as soon as something gets me curious or as I find problems with it said.

You would think that having this way of interacting would be limiting, as having a fast LLM output large chunks of information would let you skim through it and commit it to memory faster. Yet, for me, the combination of hearing things and, most of all, not having to consume so much potentially wrong info (what good is it to skim pointless stuff), ensures that ChatGPT's Advanced Voice mode is a great way to initially approach a problem.

After the first round with the voice mode is done, I often move to written-form brainstorming.

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[0] - Though I admit that almost all my Kagi searches end in "?" to trigger AI answer, and in ~50% of the cases, I don't click on any result.

[1] - Which AFAIK still exists on Plus plan, though I haven't hit it ~two months.

So far, most of the time, my impression was "I would have been so badly mislead and wouldn't even know it until too late". It would have saved me some negative time.

The only thing LLMs can consistently help me with so far is typing out mindless boilerplate, and yet it still sometimes requires manual fixing (but I do admit that it still does save effort). Anything else is hit or miss. The kind of stuff it does help researching with is usually the stuff that's easy to research without it anyway. It can sometimes shine with a gold nugget among all the mud it produces, but it's rare. The best thing is being able to describe something and ask what it's called, so you can then search for it in traditional ways.

That said, search engines have gotten significantly worse for research in the last decade or so, so the bar is lower for LLMs to be useful.

That was my impression with Perplexity too, which is why I mostly stopped using it, except for when I need a large search space covered fast and am willing to double-check anything that isn't obviously correct. Most of the time, it's o3. I guess this is the obligatory "are you using good enough models" part, but it really does make a difference. Even in ChatGPT, I don't use "web search" with default model (gpt-4o) because I find it hallucinate or misinterpret results too much.

> The kind of stuff it does help researching with is usually the stuff that's easy to research without it anyway.

I disagree, but then maybe it's also a matter of attitude. I've seen co-workers do exact same research as I did, in parallel, using the same tools (Perplexity and later o3); they tend to do it 5-10x faster than I do, but then they get bad results, and I don't.

Thing is, I have an unusually high need to own the understanding of any thing I'm learning. So where some co-workers are happy to vibe-check the output of o3 and then copy-paste it to team Notion and call their research done, I'll actually read it, and chase down anything that I feel confused about, and keep digging until things start to add up and I feel I have a consistent mental model of the topic (and know where the simplifications and unknowns are). Yes, sometimes I get lost in following tangents, and the whole thing takes much longer than I feel it should, then I don't get "misled by the LLM".

I do the same with people, and sometimes they hate it, because my digging makes them feel like I don't trust them. Well, I don't - most people hallucinate way more than SOTA LLMs.

Still, the research I'm talking about, would not be easy to do without LLMs, at least not for me. The models let me dig through things that would otherwise be overwhelming or too confusing to me, or not feasible in the time I have for it.

Own your understanding. That's my rule.

Same here. Don't get me wrong, LLMs can be helpful, but what I mean is that they can at best aid my research rather than perform it for me. In my experience, relying on them to do that would usually be disastrous - but they do sometimes help in cases where I feel stuck and would otherwise have to find some human to ask.

I guess it's the difference between "using LLMs while thinking" and "using LLM to do the thinking". The latter just does not work (unless all you're ever thinking about is trivial :P), the former can boost you up if you're smart about it. I don't think it's as big of a boost as many claim and it's still far from being reliable, but it's there and it's non-negligible. It's just that being smart about it is non-optional, as otherwise you end up with slop and don't even realize it.

This is called deprivation sensitivity. It’s different from intellectual curiosity, where the former is a need to understand vs. the latter, which is a need to know.

Deprivation sensitivity comes with anxiety and stress. Where intellectual curiosity is associated with joyous exploration.

I score very high with deprivation sensitivity. I have unbridled drive to acquire and retain important information.

It’s a blessing and curse. An exhausting way 2 live. I love it but sometimes wish I was not neurodivergent.

You can be completely aware of your experience and still feel anxiety. So your thinking is flawed.

Your response is telling. You are triggered by a benign comment and generalize harsh views towards all people.

You sound like a troubled young man who feels invisible.