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Comment by gavinray | Hacker News Reader

Gemini, for whatever reason, has consistently been the best for explaining scientific concepts for me.

Especially with the recent addition of graphic visualization widgets

It manages to strike the perfect balance between depth and style of prose that other Frontier models just can't quite seem to match

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I personally find it much better than Claude in day to day tasks. Easier to understand and to the point without getting overly verbose like Claude always does. I solved some pretty difficult engineering problems with it, and most of the time I run Claude in parallel, and more often than not I found Claude to be less precise actually.

Gemini is the best at convincing you that you understand scientific concepts. Unless you have applied that knowledge in other contexts and been given feedback that your understanding is correct you have no idea whether it is good at actually explaining them.

That's generally true for all LLMs, but as with any tool - you can actually use it in a way that produces actual, verifiable results. Obviously it requires a lot of work that 99% of people won't put into what is essentially seen as a 'magic black box', but I do agree with GP that Gemini is quite useful in actually breaking things down in an understandable way, compared to most other models.

My experience with it is mostly using it against an actual, academic source, where I can easily tell if it starts making things up. I wouldn't rely on it for anything that I can't confirm myself.

I had a research question in my area of expertise. Tried it with ChatGPT, Claude, and Gemini. I think all three could have solved the question, but ChatGPT was an unreadable mess with too many sections and headings, Claude got stuck thinking and never produced an output. Gemini gave succinct explanation that I think was similar to ChatGPT conceptually but that I was able to follow and verify. The answer was actually fairly trivial to understand; I just hadn't gone at the problem from that perspective.

I know it is common for people to try AI in domains they aren't experts in, but it is also common for people to use AI in domains they are experts in.

This is the only use case I've found it superior to ChatGPT or Claude for - helping me understand higher level math. It's slightly easier to understand than the other models

GPT has gone downhill in terms of prose readability.

On Antrhopic side Claude 5.5 improved over 5, but honestly in this dimension the bar was extremely low.

This is also what keeps me using it. And whether this is rational or not, I feel that if it has a better / more concise understanding of core science/math, it also has a better shot at writing concise and correct code for those topics (a lot of what I do).

Do you have some prompt examples I can run. Thanks

I'll publish a few of my convos

1. Sleep debts effect on muscle size and strength gain

https://share.gemini.google/Gvdb5Q02INrl

2. Fish oil and extra cellular vesicles

https://share.gemini.google/fem9EBCe4mfy

3. Pharmacology question about human-equivalent dose

https://share.gemini.google/RWNYJ2eiNjcU

Yea, but you are just accepting AI slop.

Here is your first example, "corrected": https://share.gemini.google/DCSLT4PPap5q

Having an AI do a quick check of research studies is about as effective as just trusting a Vogue magazine article. You are just fooling yourself into thinking there is something useful there, there is not.

Well OP is asking about a specific paper and you are asking for it to be disproven, different requests and the LLM is just doing what you want.

Classic "you're holding it wrong" (maybe enhanced by exercise/nutrition science being what it is with a study and expert representing every side of an argument)

I'd personally probably do it like

> Someone told me about a recent paper which showed that sleep deprivation had surprisingly little impact on muscle hypertrophy, and that mechanical stimulus seemed to be the dominant factor.

> Can you comprehensively fact-check this, and let me know what the general scientific consensus on the topic is?

Even then you can't be certain of course. Even reading the cited studies you can't be certain unless you know enough to interpret them. But you'll probably be a bit more informed than you would be if you were just going off news article titles you see on social media like the average person.

Not OP, but I asked Gemini 3.8 Flash this question (via self-hosted LibreChat) and got an excellent answer:

    I'm trying to figure out how heat dissipation works. Specifically, I have a block of aluminum with extruded fins (as a heat sink) and I'm trying to understand how many 30-watt LEDs I can place on it. I have a fan as well, and I'd like to understand how fast I need to run it at different power levels (the LEDs are dimmable). I can gather measurements empirically (which I'll do), but I also want to understand the basic mathematical basis I should use for estimating these values.

How can you determine if it's explanation is good if you don't already know the answer? You are not qualified to know if it's answer is correct, by hypothesis.

I normally use it as a foothold to query other sources, like Wikipedia. Or for software, I can just try running it.

Just talk to 3.8 flash about whatever (caveats: ancient knowledge cutoff of jan 2025, small parameter model so if niche '90s korean animated series is your jam, it's gonna be rough). The model is a real joy to talk to, I even will feed it SOTA model output to clean up and clarify for me.

Its the first model ever that I have been worried about a successor losing the "feel" that it has.

https://www.youtube.com/watch?v=NO2I3ueVQrk&lc=UgxRXtipA3QbA...

Comparison with other models.

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