I built one of the connected tools included in this launch (the Biomni HPC [1]), and I have spent an inordinate amount of my life working on this problem. (I also worked at Anthropic, but not on this product.)

As other comments have pointed out, this is for data science – but it's capable of more than making plots and writing papers [2]. It has integrations with many databases and computational tools, including a researcher's institutional cluster.

That alone is valuable. I founded a startup after struggling with this problem at a bio startup; integrating these tools and databases is hard and time consuming. If the only outcome of this product is that great APIs are built for LLMs, it will be a massive positive impact. Many databases used in computational genomics are still only accessible through FTP!

LLMs are particularly good at navigating these tools and databases. It's often very specialized, but straightforward, work that benefits from in-context skills. Seeing an early glimpse of my former customers – bioinformaticians – using LLMs to solve this problem is what led me to join Anthropic in 2024.

Also, this pattern isn't fundamentally constrained to data science: you can also integrate with a wet lab or a CRO for some kinds of science. This is what I'm spending my time on now.

This type of science doesn't solve everything, but it's useful in some niches. For example, progress on many rare diseases is bottlenecked by researcher attention rather than a fundamental breakthrough.

[1] https://x.com/phylo_bio/article/2029233694775624096

[2] In comparison, OpenAI's science product – Prism – was effectively a LaTeX editor they acquired with Crixet.

Connecting AI directly to the data sources (instead of just asking it to provide code that I run locally for myself) can get quite complicated in terms of meeting institutional policy, applicable law, data access-storage requirements (e.g. NIH data repositories), and can require legal agreements between institutions and the AI provider.

I cannot touch. At least not yet.

Can you speak to what makes this different from simply including or configuring various agent skills? Or is it simply the combination of lots of helpful defaults that makes this product useful?

Sounds like the perfect use case for some kind of framework where you have a local LLM (that can run on lower spec hardware) collaborating with the main LLM to optimise latency and all the other niche and legacy use cases ?