I don’t dare question the HR gods about their designs :)

I should add: structure prediction inference is INCREDIBLY efficient for the form factor and power profile.

It's an inference monster.

Structure prediction on it works.

Design to come.

This will be updated later today... research.nvidia.com/labs/dbr/ass...

I still feel criminal for the handle but the manuscript embodies it well.

Moore’s law applied to speed not accuracy. I don’t think fundamentally the discoveries we are after are entirely dependent on speed.

I think the better law here is garbage in garbage out.

In that sense, you can wait for better data/curation, but it’s also fun to take destiny in your own hands :)

Thanks to @pkoo562.bsky.social , @kevinkaichuang.bsky.social and Ananthan, and all of CSHL' staff for the help bringing this volume out. Hopefully, we could inspire all those fantastic biologists to reach out to their computational friends to take the next leap in their scientific discoveries.

If only there was something like try/catch in every programming/scripting language ever :P

The maddening bit is UniProt offers a handy URL that resolves whatever query you pass to it to a list of hits www.uniprot.org/help/api_que...

Whenever I used to implement search bars I’d use my dbs as autocomplete but one hop to that API endpoint to catch whatever the user inputs 🤷‍♂️

🔸Proteina is a fantastic collaboration with wonderful colleagues at NVIDIA:

🔥 Tomas Geffner*, @kdidi.bsky.social*, Zuobai Zhang*, Danny Reidenbach, Zhonglin Cao, @jyim.bsky.social , Mario Geiger, @machine.learning.bio, Emine Kucukbenli, @arashv.bsky.social, @karstenkreis.bsky.social* 🔥

(10/n)