[fabian2k]

The secondary structure graphic is entirely wrong. It's full of bad chemical formulas, and I would assume is AI-generated.

I'm quite impressed by the amino acid overview graphic. I'm sure all images are AI-generated, and this one is something I didn't expect AI to be able to do yet. There are mistakes in there (e.g. Threenine instead of Threonine, charged amino groups for some amino acids), but it doesn't look immediately wrong. Though I haven't needed to know the chemical formular for all the amino acids in a long time, so there are probably more errors in there I didn't immediately notice. The angles and lengths of the bonds are not entirely consistent, but that also happens without AI sometimes if someone doesn't know the drawing tools well. The labels are probably the clearest indicator, because they are partly wrong and they are not consistent as they also include the non-side-chain parts sometimes, which doesn't make sense.

The biology part of the text looks somehwat reasonable overall, I didn't notice any completely outrageous statements at a quick glance. Though I don't like the "folding is reproducible" statement as that is a huge oversimplification. Proteins do misfold, and there is an entire apparatus in the cells to handle those cases and clean them up.

[D-Machine]

This article is garbage and makes many incorrect claims, and it is clearly AI-generated. E.g. the claim that "AlphaFold doesn't simulate physics. It recognizes patterns learned from 170,000+ known protein structures" couldn't be farther from the truth. Physical models are baked right into AlphaFold models and development at multiple steps, it is a highly unique architecture and approach.

AlphaFold models also used TPUs: https://github.com/google-deepmind/alphafold/issues/31#issue...

EDIT: Also annoying is the usual bullshit about "attention" being some kind of magic. It isn't even clear AlphaFold uses the same kind of attention as typical LLM transformers, because it uses custom "Evoformer" layers instead: https://www.nature.com/articles/s41586-021-03819-2_reference...

[lukah]

I interpreted that section as alphafold not learning physics, but rather correlations within a constrained setting that a-priori correspond to physically sound inferences. It has a specific architecture that allows the model to make inferences that are more physically plausible than not, but not that it’s discovering actual, causally verifiable laws of nature (like what I’d assume are encoded into another non-ML approach to the folding problem for example).

[Agingcoder]

It’s also not a solved problem unlike what the article claims, unless ‘solved’ doesn’t mean ‘works all the time ‘.

[augment_me]

The text structure screams GPT5 sadly, so I would not be surprised if not only the text but the images were wrong.

[coolness]

Yeah, I don't really understand why someone would make a blog and use AI to write the articles. Isn't having a blog more about the joy of writing and the learning you do while writing it?

[lm28469]

Because it's what cool people do, so if you want to be cool you do it. They didn't realise the cool part was actually having the knowledge and actually writing the text.

There are many similar things where people just take shortcuts because they don't understand the interesting part is the process/skill not the final result. It probably has to do with external validation, reddit is full of "art" subs being polluted by these people, generative ai is even leaking into leather work, wood carving, lino cut, it's a cancer

[seec]

Well, the world has become very superficial. People rarely question how they end up with a specific result, which makes cheating/outsourcing quite a good deal and even profitable for many.

[IAmBroom]

Also, resume padding.

[robbie-c]

I think it's just an AI-generated simplification, sucks that it made it to the front page. The subject matter is interesting, I would have loved to have read something written by an expert!

[fabian2k]

I would assume so, but I didn't see any smoking guns in the text itself. But I'm also not familiar with the newest models here and their quirks.

[D-Machine]

See my point above (https://news.ycombinator.com/item?id=46271980) for smoking guns. There are some pretty basic and grievous factual errors re: GPUs being used when in fact TPUs are used, and completely false claims about physical models not being huge parts of AlphaFold development and even architecture.

[fabian2k]

Those errors don't seem AI-specific to me, they could easily be made by humans.

[D-Machine]

True, it is the style of the post that reveals obvious overuse of AI. The errors could well be made by a human, especially since a trivial visit to Wikipedia or one of the original papers will show most of what is being said here re: the actual deep models to be wrong. This is more likely the error of a human than an AI.

EDIT: Ugh, it is late. I mean, if you used e.g. ChatGPT-5.X with extended thinking and search, it would not make these grievous errors. However, ChatGPT without search and the default style, produces junk basically indistinguishable from this kind of post. So, for me, the smoking gun is that not even the most basic due diligence (reading Wikipedia or looking at the actual papers) has been done, and, given the length and style of the post, this is effectively a smoking gun for (cheap, free-version) AI use.

But, more importantly, it is indistinguishable in quality from AI slop, and so garbage regardless.