denizkavi

4 ms·

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by denizkavi 9mo ago
Oh yeah, I've seen this before! Cool stuff I would say primary concerns were: dependency issues, needing more than model weights to be able to consume models (Multiple Sequence Alignment needs to be split, has its own always on server,
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by denizkavi 9mo ago
That’s interesting. We’ve developed a kubernetes-based scheduler that we’ve found better takes into account our custom job priority needs, allows for more strict data isolation between tenants, and a production grade control plane, though t
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by denizkavi 9mo ago
It certainly was an investment for us to meet the security and enterprise-readiness criteria for our enterprise users. As an n of 1, we don't tend to do on-prem, and even much of the most skeptical companies will find a way to use clou
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by denizkavi 9mo ago
In this case the input to the model is the input structure of the protein target, i.e. you can define the whole search space for it to try to find a binder/drug against. We let you pick a preset recipe to follow at the top, which basic
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by denizkavi 9mo ago
Good amount of both! I would say proprietary models tend to be fine-tuned versions of the published ones, although many will be completely new architectures. We also let folks fine-tune models with their proprietary data on Tamarind directl
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by denizkavi 9mo ago
That's fair, I wish we were able to just add in a calculator for getting a price on a per hour basis, given your models of interest and intended volume. We actually did have this available early on, our rationale for why we structure i
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by denizkavi 9mo ago
Thanks! I think advantages we had over previous generations of companies is that demand and value for software has become much clearer for biopharma. The models are beginning to actually work for practical problems, most companies have AI,
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Launch HN: Tamarind Bio (YC W24) – AI Inference Provider for Drug Discovery
85 points by denizkavi 9mo ago|20 comments