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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.show more

Nima Alidoust

3,923 subscribers

30,629 views • 2 months ago •via X (Twitter)

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43 Comments

Johnny Yu2 months ago

It's a big day at Tahoe. Building the ground truth ocean of data is how we'll build Biological Super Intelligence.

Nima Alidoust2 months ago

let's go :)

Hani Goodarzi2 months ago

Tara has arrived! Congratulations @tahoe_ai team!

Nima Alidoust2 months ago

@tahoe_ai Hail to Tara :D

Brian Naughton2 months ago

@genophoria @tahoe_ai Good Irish name there! 🇮🇪

Nima Alidoust2 months ago

@genophoria @tahoe_ai :))) also Persian. But yes. We picked it because of the Irish angle

Tahoe2 months ago

🚀💙

Valentine Svensson2 months ago

Exciting with the full stack coming together!

Nima Alidoust2 months ago

Great names all around :D

Parshawn Gerafian2 months ago

LETS GOO!! 🚀🚀

Nima Alidoust2 months ago

LFG!

minjae2 months ago

🚀🚀🚀

Nima Alidoust2 months ago

can't wait to talk publicly about what you are cooking up :)

Arnav Shah2 months ago

Huge 🚀

Nima Alidoust2 months ago

Indeed :)

Shreshth Gandhi2 months ago

Rhaister + TARA are just the beginning, stay tuned for what we're building. The stack is coming together 🚀

Nima Alidoust2 months ago

Getting more exciting by the day :)

Aiden M Sababi2 months ago

Another moon landing moment for us; grateful to be part of the team.🚀

Nima Alidoust2 months ago

you've led the charge my friend :)

Pedram Bayat2 months ago

grateful to be a part of this team! just the beginning 🚀

Nima Alidoust2 months ago

Great to have you in the team!

Ben Liu2 months ago

Congrats @nalidoust and @tahoe_ai team!

Nima Alidoust2 months ago

@tahoe_ai Thanks @BenjamineYLiu!

Riley Hennigh2 months ago

Congratulations my friends 🚀

Alexander2 months ago

sounds big and this feels like the next logical step after alphafold and recursion

Radman2 months ago

This framing is great. Reasoning models can generate hypotheses quickly, but biology still needs data that can push back. The hard part is then reading the response carefully to see which signals are real, which are context-specific, and what they actually mean biologically.

Nima Alidoust2 months ago

Totally agree.

Arash Alidoust2 months ago

👏👏👏

corrin2 months ago

love

Arihant Jain2 months ago

Awesome stuff

Nima Alidoust2 months ago

Thanks Ari!

Purav Gupta2 months ago

It'll be an insanely cool journey! 🚀

Adib2 months ago

Super cool!

Nima Alidoust2 months ago

Thanks @adibvafa . have fun in korea

Rohit Khurana2 months ago

@genophoria awesome, congrats!

Amplify Partners2 months ago

😍

sanj2 months ago

Onwards to continual learning!

Nima Alidoust2 months ago

Indeed. Excited to see what you all are up to as well.

Edem Evans2 months ago

Interesting. I'm exploring something similar but from a different direction, studying how nature actually learns, not just to copy but to understand the principles behind it. Still early days, but this is the kind of work I find inspiring. 😊

秀刀师XDS2 months ago

Biology benchmarks need to become more like clinical endpoints: tied to provenance, task context, failure modes, and prospective validation. Otherwise, reasoning over biology can optimize against datasets that were never built to test reasoning.

Patrick White2 months ago

The impedance mismatch is real. Curious how Tara handles complex conditions. Immune dysregulation and chronic inflammation aren't clean data. Still, fascinating stuff.

Radnor Morris2 months ago

Is there an explanation for a layperson?

Haotian Guo2 months ago

so this is using AI models to represent the data distribution and using agents to run tests on those models?

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