Talk:Folding@home#rfc 49ABF1D

I'm somewhat surprised to see [https://en.wikipedia.org/w/index.php?title=Folding@home&diff=893426766&oldid=891242091 this cleanup tag] here, since this is a featured article. Does any part of this article need to be revised or rewritten? Jarble (talk) 16:46, 30 April 2019 (UTC)

:I would say it should be cleaned up and rewritten. For one thing the article claims that F@H pioneered volunteer distributed computing, when Seti@Home was out long before F@H. Nabeel_co (talk) 19:48, 25 March 2020 (UTC)

:I just removed the clean up tag. I think it is very well-written. Fangfufu (talk) 11:51, 4 March 2023 (UTC)

"The project has pioneered" It didn't pioneer those things... It was notable, but projects like Seti@home, Electric Sheep, Distributed.net, and GIMPS all pre-dated Folding@home. It's true that the F@H client has probably taken advantage of more varied processor architectures than most projects, but they were far from the first for CPU or GPU distributed volunteer computing. This is so blatantly inaccurate, I'm surprised that it's on the second paragraph of a fairly popular Wikipedia article. Definitely needs correcting. Nabeel_co (talk) 20:03, 25 March 2020 (UTC)

:{{done}} I also think the sentence after it is questionable since it uses "paradigm shift" in a broader context than the source. Tonystewart14 (talk) 03:30, 20 May 2020 (UTC)

Please edit the main page, dropping the PLAYSTATION 3 from the list of supported devices. — Preceding unsigned comment added by 68.109.194.172 (talk) 03:16, 5 July 2020 (UTC)

This reads like a highly promotional article, written to advertise Folding@home. Readers should be alerted that the prediction of protein structure, the hard-nose test for Folding@home has already been solved by a Google group. The problem has been solved using artificial intelligence by DeepMind, a company owned by Google. DeepMind developed the predictive tool AlphaFold that incorporates Rosetta input among many others to make their most accurate predictions of protein structure. AlphaFold has successfully predicted the structures of more than 200 million proteins from approximately 1 million species, covering virtually the entire protein universe.{{cite journal | author = Callaway E | title = The entire protein universe: AI predicts shape of nearly every known protein | journal = Nature | volume = 608 | pages = 15-16 | date = July 2022 | doi = 10.1038/d41586-022-02083-2 | doi-access = free }} TruchaForelle (talk) 22:44, 5 October 2022 (UTC)

:AlphaFold and Folding@Home solve different kind of question. While AlphaFold returns the picture of a folded protein in its most energetically stable conformation, Folding@home returns a video of the protein undergoing folding, traversing its energy landscape.

:An example article explaining the difference is here:

:https://foldingathome.org/2020/12/08/protein-folding-and-related-problems-remain-unsolved-despite-alphafolds-advance/?lng=en Fangfufu (talk) 11:53, 4 March 2023 (UTC)

{{reflist-talk}}

This sentence is wrong. 768 petaFLOPS are 0.768 exaFLOPS. Could we fix it? Pier4r (talk) 11:52, 30 December 2022 (UTC)