We are making Tara available to the public. Tara is a plug-in we developed internally to connect reasoning models with our datasets and the tools we build on top of them. Going forward, Tara will be the channel through which @tahoe_ai releases some of our other frontier datasets and models. Stay tuned. You can connect your agent to Tara with a single prompt. It currently includes our Tahoe-100M dataset and its derivatives. It is particularly good at making connections between Tahoe’s data and other public datasets and models. Our team, from AI scientists to drug hunters, has been using Tara extensively for a wide range of tasks. It has become a foundational tool for building and conducting research on large-scale biological datasets. We have been constantly surprised by how frontier AI, coupled with Tara, can uncover non-obvious connections across vast sources of biological data and enable discoveries that were difficult to imagine before. We are excited to see what other creative minds do with it.

Today we are introducing Tara. Biological datasets are a source of insights and a means to train biological AI models. As the ability to reason at scale emerges, they take on a new role: the ground truth for testing what reasoning models produce, and the environment in which those models operate, get feedback, and improve. Tara, our autonomous research agent, is embedded in our ever-expanding datasets, lab-generated and synthetic, and built to test and evolve the hypotheses frontier models generate, matching the pace at which they produce new ideas. By keeping those models grounded in a vast space of high-precision biological data, we believe we can compound biological reasoning and close the impedance mismatch between hypothesis generation and validation.

Today we release Rhaister, an elegant statistical model that predicts drug phenotypes in new contexts w/ accuracies comparable to experimental assays. And dropping Emerald Bay, a 2M cell dataset measuring long time-course phenotypes across 1000s of drug-cell line interactions.

In our new blog post, we team up with @NVIDIAHealth to make it possible to map how drug effects cascades through gene regulatory networks.

We build datasets, train AI models, and use them for the ultimate goal of discovering and developing new medicines, starting from oncology. This joint venture represents another major step towards that and a preview of the business models enabled by our platform.