founders’ repo bot + occasional take. investing @generalcatalyst, prev @a16z
San Francisco, CA
Joined April 2021
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Thank you Modal!! We’re so lucky to work with you and so grateful for your support! 💚
Students shouldn’t have to scale down their research ideas because they can’t get access to compute.
The @generalcatalyst Research Fellowship will support 20 undergraduates with up to $25K in compute from Modal to power their most ambitious ideas.
We're excited to see what you build!
Daisy Zhao retweeted
New waitlist is live!
tradetash.com/waitlist
Daisy Zhao retweeted
So excited to help launch @generalcatalyst Research Fellows
Being a student partner at Rough Draft was the most formative college experience getting me into tech
This program couldn't be better timed to support ambitious students
generalcatalyst.com/research…
Daisy Zhao retweeted
One of the most common things we heard from undergrads while visiting campuses is that they have no shortage of ideas to explore but access to compute has been a huge constraint.
So we partnered with the team at @Modal to create a Research Fellowship! For this first run we will be supporting approximately 20 undergraduate builders who want to explore AI research ideas. Each fellow will receive up to $25K in compute for a 90-day project of their own.
Applications are open. Tell us what you want to build! generalcatalyst.com/research…
Introducing @generalcatalyst Research Fellows - a program for undergraduates ready to pursue their most ambitious ideas.
We’re supporting approximately 20 exceptional undergraduate builders from leading schools. In partnership with @Modal, each fellow will receive up to $25K in compute for a 90-day project of their own.
For the bold, the curious, and those building what they wish existed: applications are open now. Tell us what you want to build! generalcatalyst.com/research…
Introducing @generalcatalyst Research Fellows - a program for undergraduates ready to pursue their most ambitious ideas.
We’re supporting approximately 20 exceptional undergraduate builders from leading schools. In partnership with @Modal, each fellow will receive up to $25K in compute for a 90-day project of their own.
For the bold, the curious, and those building what they wish existed: applications are open now. Tell us what you want to build! generalcatalyst.com/research…
Daisy Zhao retweeted
In our health workstream at Meta we did a project where we wanted to see what research papers medical experts could write using a Muse Wonder coding scaffold that could access a database of 5M electronic health records we licensed from a vendor. The goal is that with 5M patients in this database, AI could do research to answer arbitrary questions like “did people who take statin have higher rates of diabetes later in life”? The thing we were betting on here is on what happens when you put human experts, a strong coding model, and access to data all together
We worked with a small group of medical experts to help them use this AI tool and the punchline is that they all found that this setup enabled a much faster pace of health research. They generated ten manuscripts in around two weeks, with one researcher (Paddy Ssentongo) generating three. Throughout the process, they collaborated with each other to review manuscripts and write skill files to help our coding scaffold improve at doing research. The medical experts liked how quickly they could test different hypotheses and choose the best ones to continue working on
Health research is pretty unique in that it is super important to be able to interpret noisy data and have taste to know which variables are important, which was still a significant value add from human researchers. Given that medicine is one of the oldest fields in human history and it is ultimately about humans, keeping a substantial human component in this type of research is probably important. In the near future I see AI primarily as a tool to facilitate human-led research. With the right scaffold I believe health researchers can be 10x or even 100x more productive using AI tools that have appropriate access to the wealth of data that the world has collected about human health
One way to see this work is through the broader lens of AI for science. You can think of science as two parts: (1) collecting raw data about the world, and (2) synthesizing that raw data into understanding. In this project we already had existing data about the world and we aimed to accelerate part (2) using AI. Eventually, I imagine a world where we have such great AI tools that the time to synthesize raw data into understanding is driven towards zero. In that world, science will be substantially faster and most of the work will be focused on collecting the right raw data via physical experiments
We did this in a pretty short amount of time (which was the point) and I think the future of scientific research will be more faster and more exciting than ever
We’re exploring how AI can accelerate health science research.
While AI has made significant breakthroughs in formal sciences like mathematics, health data science depends on imperfect real-world data and consequential choices about cohorts, variables, and causal assumptions.
To explore how AI can assist health researchers, we built a human-led research scaffold powered by Muse Spark 1.3 and Muse Wonder with access to 5M retrospective de-identified electronic health records and publicly available data. A cohort of medical experts used the system to conduct original research, accelerate hypothesis generation, code execution, and manuscript drafting while maintaining full authority over scientific decisions.
Today, we’re sharing 10 research manuscripts produced through this exploration, spanning cardiology, oncology, infectious disease, emergency medicine, and more.
Read more in our research blog: go.meta.me/3U06wvD
Daisy Zhao retweeted
Proximal builds infrastructure that enables AI to improve from real-world experience.
In the past year, we have grown to more than $200M in annualized revenue helping frontier labs and enterprises improve coding agents. Now, we are expanding beyond software engineering.
Recent progress in mathematics demonstrates what happens when models are trained in domains with massive amounts of public data and easy verifiability, making it easy to find weaknesses, generate training tasks, and iterate quickly.
We build infrastructure that enables this feedback loop in other domains: we are excited about a world in which AI systems design targeted drugs, accelerate chip design, and rewrite legacy software that still runs hospitals, power grids, governments, and other critical infrastructure.
Since starting, we have assembled a small team of researchers and engineers and built world-class infrastructure for post-training and synthetic data research to overcome the challenges of scaling manual data curation.
Our team comes from Cursor, Google DeepMind, Meta Superintelligence, Prime Intellect, Citadel, and Jane Street. More than half are former founders.
We’re fortunate to be backed by investors who share that vision, including @generalcatalyst who led our $15M Seed Round at a $300M valuation, as well as @chemistry, @svangel, @dvlamoen and individuals like @LiamFedus, @kevinweil, and @bernhardsson
Daisy Zhao retweeted
Today, we're releasing ArgaBench, a 40-task cross-domain benchmark measuring agent performance in multi-app workflow tasks across IT, revenue, marketing, software development, and commerce.
Across 32 configurations, Opus 5 leads at 70.8% pass rate, followed by Fable 5 at 60.0% and GPT 5.6 Sol at 50.8%.
Daisy Zhao retweeted
Incredibly excited to lead @ArgaLabs's $10M seed round.
Agents are already doing real, valuable, work in side of software like Slack, Gmail, Salesforce, etc. But today, testing these agents typically requires teams to test in production.
Arga Labs builds real world digital twins of this software, allowing you to test your agents in a sandbox environment you control end to end, with the ability to roll back time, events, and more.
Arga is already in use by teams at @slashapp, @MonacoGTM, @WorkWeave, @YCombinator and more.
We @generalcatalyst are excited to lead @ArgaLabs $10M seed round.
Agents do real work inside software, but most teams still test them in production. Arga Labs builds real-world sandboxes - working replicas of the apps your agents touch: Slack, GitHub, Salesforce, Gmail, dozens more. Point your agent at the sandbox instead of prod. Break things there, not in front of a customer.
Agents will only earn responsibility when teams can trust what they'll do. That trust gets built in the validation layer, and Arga is defining it. Congrats @PhillipLii @akkkkiira and the Arga Labs team, it's been amazing to partner with you! @yuris
Hearing from @PhillipLii about their vision directly
teapaw.org/PhillipLii/status/2092…
Today, we’re announcing @ArgaLabs’ $10M seed round led by @generalcatalyst.
AI agents are taking consequential actions across real systems, but teams still lack a safe, realistic place to test them before production.
Arga’s Real-World Sandboxes give teams stateful twins of external systems—letting them catch regressions, replay edge cases, and validate non-deterministic workflows without ever touching production.
Teams at @slashapp, @MonacoGTM, @WorkWeave, @YCombinator, and more have already run 100,000+ tests on Arga in the past four months.
Thank you to @BoxGroup, @emergencecap, @GradientVC, @svangel, and everyone else who backed us. I’m especially grateful to be building this with Akira.
Glad to be building with @akkkkiira. We’re just getting started. 🚀
P.S. We’re giving everyone 10 free sandbox runs. Reply or DM me!
TechCrunch Article: techcrunch.com/2026/08/26/ar…
There's a new version of this post
RT @PhillipLii: Today, we’re announcing @ArgaLabs’ $10M seed round led by @generalcatalyst.
AI agents are taking consequential actions acr…
Jiahui is one of the most thoughtful and visionary people I’ve ever known. Can’t wait to see what you’re building next!
I’m leaving Meta to start a new company.
Building the TBD Lab alongside Mark @finkd and Alex @alexandr_wang has been deeply inspiring and fulfilling. I’m proud of what our multimodal team accomplished across Muse Spark, Voice Mode, Muse Image, and Muse Video, and even prouder of the team that made it possible.
Over time, I’ve felt increasingly drawn to a problem that will matter deeply to humanity’s future, yet remains largely underexplored. It now has my full attention.
More to share as the work takes shape.
Daisy Zhao retweeted
How can venture better serve founders in an era of extremely concentrated outcomes? This question runs through my Q2 review and my conversation with @packyM:
The Great Concentration
Fixing the K-shaped Economy
Buying a Hospital
What a VC Is Actually For (and capital entrepreneurship)
The Stack as the Antidote
My answer, across all of it: innovate on capital so more companies can thrive, and transform legacy institutions so the benefits of intelligence reach everyone.
read here: hemant-taneja.com
Daisy Zhao retweeted
On HealthBench Pro, Muse Spark 1.1 achieves similar performance with GPT-5.6 Sol (maybe slightly better) at a fraction of the cost. Affordable health superintelligence is our north star!
We benchmarked Muse Spark 1.1 and GPT-5.6 Sol on HealthBench Professional, OpenAI's benchmark of 525 real clinician tasks 🏥🩺
Muse Spark 1.1 tops our board: better overall score than GPT-5.6 Sol, statistically on par on the length-adjusted score at a fraction of the cost ($1.25/$4.25 vs $5/$30 per M tokens in/out, ~7× cheaper on output).
Daisy Zhao retweeted
Hiring SWE Interns
The late night coders. The hackathon winners. The ones who ship for fun and can't stop building.
Come build AI that does what consulting firms charge millions for.
$40/hr, remote.
Drop your GitHub below. I'll be personally reviewing all of them.
What’s the next event we should host here… Maybe a world cup final watch party? 🧐
We just hosted over 100 interns at the new @GeneralCatalyst NYC office.
If you haven't been to it yet, you're missing out! The rooftop is 😍🔥 Come visit us!
