agents for human scientists @TuvaAI & theory of RNA @NYU_courant // fmrly founder @soraidinc (acq. by @clear), physics/ee @stanford
New York, NY
Joined December 2012
- Tweets134
- Following509
- Followers272
- Likes565
Pinned Tweet
Today, we’re launching @TuvaAI to build a human-centric future for agentic science.
Humans collaborating with AI can learn more than either working alone: humans guiding the research, equipped with agents that can test ideas quickly and autonomously, will deliver the next wave of scientific discovery. But science is all about the details. Neither Einstein nor AGI can drive your project forward without intimately understanding your growing, interrelated web of hypotheses, intermediate results, working threads, failed experiments, datasets, notes, and water cooler conversations.
Our first product, Rao, was born out of my frustration having to reteach LLMs these constantly changing details for every single task in my own research. Rao maintains a persistent, dynamic research memory that learns and remembers project context, history, and threads. This research memory then links into our scientific agents as well as other AI software you may already be using, such as Claude or Codex. With Rao, every scientist turns into a PI managing a proactive, agentic research team that automatically stays on the same page.
Rao is live in VS Code and by CLI, and can help with any scientific task that can be done in a computer. Our early users include computational biologists, theoretical neuroscientists, chip designers, and polymer physicists who use Rao daily to ideate, do math, analyze data, and write papers and grants.
Now, we’re excited to announce Rao in closed beta. If your science mostly happens in a computer—theory, computational modeling, experimental data analysis, scientific writing—Rao can help you discover more, faster. Sign up for our waitlist, and if you’re a fit, we’ll work with you to tailor new, contextual agents that augment the way you do science. Link in the comments.
More personally: I’ve been doing scientific research since I was a kid. Scientists do so much for the world, choosing a painstaking career for the joy of discovery, developing technologies we use every day along the way. And yet, the tools scientists use are often cumbersome and woefully out of date. For me, Tuva is much about empowering the people of science with products they love as it is about the discoveries we’ll help them make. And I’m lucky to work with some wonderful humans along the way—@SamsaraDurvasu1 @anuvellore @KimchiOfer among many others. If our mission resonates, please reach out.
Vamsi Varanasi retweeted
My biggest tell in poker is hitting the six seven when being dealt a six / seven
Vamsi Varanasi retweeted
An AI researcher at Princeton uses Rao to explore new research directions! He prefers Claude as a sounding board for ideas, but wants Codex to do preliminary analysis on his topics of interest. Rao lets him orchestrate agents across both.
Reach out if Rao could be useful for you! DM us or check out our website linked below.
Vamsi Varanasi retweeted
He was onto something
Vamsi Varanasi retweeted
“You become the PI of your research group of agents.”
Vamsi Varanasi (@feynmanpoint ), cofounder of @TuvaAI, is building that vision.
Tuva builds shared research memory across all of your agents, meaning they can be proactive and contextual about the sittings of your project. Think swarms of agents chasing down open threads, constantly scanning the literature, surfacing ideas, and bulletproofing your results, all while you direct and validate.
We’re happy to welcome Vamsi to Reactorfield for a session on building a company brain for research.
Proactive literature search means Rao makes connections to my project while I sleep! And we have more research memory-aware agents in the pipeline. Reach out to try it out!
Excited to announce the the new Rao desktop app! Here, you can coordinate agents that can work across the Rao interface, Claude Code, and Codex and act based on where your project actually is.
One new agent runs proactive literature reviews, referencing your project context to find papers most relevant to your recent progress and ideas. It’s hard to know when inspiration will be most helpful and it’s often tucked away in years-old papers, so we’ve designed this Rao agent to inspiration-hunt for you.
Visit our website or DM us to try Rao! Link in the comments.
Super excited for the launch of @ReactorfieldAI! If you're a scientist looking to become more AI native, please check them out
AI transformed coding, science is next
@ReactorfieldAI makes scientists and deep tech startups AI-native
Built by me and @berkbuilds to accelerate science with frontier AI
Vamsi Varanasi retweeted
AI transformed coding, science is next
@ReactorfieldAI makes scientists and deep tech startups AI-native
Built by me and @berkbuilds to accelerate science with frontier AI
Vamsi Varanasi retweeted
Before tech - I was a scientist myself (got a PhD in semiconductor physics)
I can tell you first hand that research and research processes need tools like this to enable better experiment design and speed up learnings and iteration to enable new discoveries
I am personally very excited about this work, and believe it can be extraordinarily high impact
Let's go @feynmanpoint and team @TuvaAI !
Today, we’re launching @TuvaAI to build a human-centric future for agentic science.
Humans collaborating with AI can learn more than either working alone: humans guiding the research, equipped with agents that can test ideas quickly and autonomously, will deliver the next wave of scientific discovery. But science is all about the details. Neither Einstein nor AGI can drive your project forward without intimately understanding your growing, interrelated web of hypotheses, intermediate results, working threads, failed experiments, datasets, notes, and water cooler conversations.
Our first product, Rao, was born out of my frustration having to reteach LLMs these constantly changing details for every single task in my own research. Rao maintains a persistent, dynamic research memory that learns and remembers project context, history, and threads. This research memory then links into our scientific agents as well as other AI software you may already be using, such as Claude or Codex. With Rao, every scientist turns into a PI managing a proactive, agentic research team that automatically stays on the same page.
Rao is live in VS Code and by CLI, and can help with any scientific task that can be done in a computer. Our early users include computational biologists, theoretical neuroscientists, chip designers, and polymer physicists who use Rao daily to ideate, do math, analyze data, and write papers and grants.
Now, we’re excited to announce Rao in closed beta. If your science mostly happens in a computer—theory, computational modeling, experimental data analysis, scientific writing—Rao can help you discover more, faster. Sign up for our waitlist, and if you’re a fit, we’ll work with you to tailor new, contextual agents that augment the way you do science. Link in the comments.
More personally: I’ve been doing scientific research since I was a kid. Scientists do so much for the world, choosing a painstaking career for the joy of discovery, developing technologies we use every day along the way. And yet, the tools scientists use are often cumbersome and woefully out of date. For me, Tuva is much about empowering the people of science with products they love as it is about the discoveries we’ll help them make. And I’m lucky to work with some wonderful humans along the way—@SamsaraDurvasu1 @anuvellore @KimchiOfer among many others. If our mission resonates, please reach out.
Join us to build the future of scientific research: r.tuva.ai/vTdHQiX
We’re letting people off of our waitlist quickly: r.tuva.ai/qRLSXAz
Vamsi Varanasi retweeted
Introducing Adaline 2.0 - The Agent Self-Improvement Layer
Adaline turns Traces into Behaviors,
Behaviors surface Issues,
Issues become auto-generated Evals + Data,
Adaline then generates new agent candidates and tests them.
You review the winners and ship!
Vamsi Varanasi retweeted
I have spent my entire life working on this and thinking about this for the past 4 years. I don't know what will happen in 20 years, but I can promise you that on the 5-10 year timescale, scientists are not out of their jobs. AI is going to massively accelerate the pace of science, increase productivity, let individual scientists make way more discoveries way faster, and is going to make science overall more fun. But the model is going to be collaboration between humans and AI, not replacement.
The key difference here between science and e.g. software engineering is that science is not verifiable in any rapid/convenient way (unlike software), unlike programming. We still need humans for their scientific taste.
Today we all lost our jobs.....
Three Nature papers showing that scientists in the conventional sense are obsolete
At least read the first one.... the AI replaced all things that the scientist does ....
nature.com/articles/s41586-0…
Vamsi Varanasi retweeted
rough shape of progress based on current data
'22 - chatgpt - consumer s/w
'25 - claude/coding - enterprise s/w
'26 - '29 - [redacted] - advanced mfg, iphone killer, materials 2.0
'29 - ??
enabled by cambrian explosion of r&d to clear progress bottlenecks
Vamsi Varanasi retweeted
While I generally agree with the point that agents being good at code means the first application in bio will be agents writing code (to paraphrase), this misses the key point about how to enable this effectively: context.
Have learned a lot building and deploying frontier agents into pharma over the past few months. Believe progress in biotech will be faster than many anticipate, and we can learn a lot from how software is unfolding.
It’s unlikely biology will jump straight to fully autonomous AI scientists. Like software, agents first get useful where work is executable, feedback-rich, and economically bottlenecked. In software, that substrate is code. In biology, it is measurement-grounded data analysis.
As agents reliably turn raw molecular data into trusted scientific outputs, they become the interface through which AI starts to understand biology. Essay below:
