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Comment by toxik | Hacker News Reader

This text was your classic “I’m writing a tutorial to see how much I know”, being only intelligible to the exact set of people who already know what it’s about. A lot of important ideas glossed over, eg “we don’t know log pt” then immediately using it in the loss function.

I probably sound very negative but I feel that you have some responsibility to your readers if you publish your text.

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I wouldn't be so harsh. The author of the blog post is also the author of the paper. If I had to guess, the author decided (or was advised) that writing a blog post about the paper would increase its visibility (which turned out to be true, considering it's on HN). So they wrote a blog post summarizing the paper. But because they are really deep in the weeds with the technical content of the paper, might not have done a great job at making it more accessible to a wider audience. Doing that well takes some skill, and is not usually something graduate students get much, if any, training on. (Edit: on reading the blog post, I do have to agree that the presentation is pretty abstruse to the point that the paper is a better source. And yes, I am a "Phd level AI researcher").

they didn’t submit it here - I’m not sure they bear a responsibility to make sure their work is accessible to HN or any other target audience. If you don’t find an otherwise respectful and substantive blog post useful, perhaps best to leave it be.

I find these posts to be great. It lets you absorb the main idea of the paper without even reading it. I wish there were more like it. And btw, “responsibility to your readers”? It’s a blog post on the internet. Nobody owes anybody anything.

Are you a PHD level AI researcher? If not, I don't think you are the intended audience.

I'm not, so as a reader here, I'm just happy that I didn't have to pay to get exposed to new challenging ideas. A lot of other science is sadly paywalled inside of journals.

Give it a few weeks for the fast followers to dumb it down for us.

I am, yes. Most of our field’s research is publicly available on arxiv by the way.

Indeed, the paper associated with the blog post is also on arXiv. But how does that relate to your complaint?

toxik was responding to the apparent position that it's OK for the material to be presented in a confusing way, because the research has been provided freely rather than locked behind a paywall. It's apropos to point out that this isn't a great excuse.

Okay, but are you a diffusion model/generative AI researcher?

As a researcher in this field, I found the article fairly understandable and accessible. I suspect that was the target audience.

The domain of diffusion models have a lot of terminology and concepts that are not explained here, so maybe that's why it's harder to understand?

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