The seed fund designed for founders in AI.
San Francisco
Based in United States
- Tweets 1,034
- Following 137
- Followers 7,877
- Likes 2,229
👀 I wonder who led the pre-seed in this one? 😅
theinformation.com/articles/… @GradientVC
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At ASI, we're building AI systems for the critical industries, from military operations to logistics to energy to managing the airspace. Now the FAA is using this proven capability to roll it out on record timelines. @pbuckendorf
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We built a new kind of robot for the underdogs.
Today, robotics is stuck between ~$100k systems and low-cost robots not meant to survive deployment.
We think builders deserve better. Our mission is to develop the best hardware platform for others to deploy on.
Meet Feather, an affordable general-purpose robot designed for real work:
• 1m reach
• Human strength
• 10-hour battery
• Shipping for the past year + available today
Founded by @whoishoa and Parsa Bakhtiari, Feather is coming out of stealth with $7.6m in pre-seed funding led by @GradientVC, with participation from @BuilderVC, @geometryvc, @SEEDInnov, and Virgo VC.
We’re hiring across engineering, ops, and go-to-market. If you’re an absolute maverick, join our cause today – feather.dev
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1/ Congratulations to the @featherrobotics team on emerging from stealth and announcing their $7.6M seed round led by @GradientVC!
We're excited to partner with Hoa and Parsa on their mission to build the Android of Robotics.
techcrunch.com/2026/09/24/me…
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Congrats @featherrobotics! 🦾
Feather is betting on a customizable, $30,000 platform built for software developers.
spr.ly/6013BGiVl1
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We've reached a big milestone as one of the fastest growing wealth advisors in the country: Range has surpassed $1 billion in assets under management.
This is a huge checkpoint, but just the beginning of our journey as we scale to serve millions of households across the country.
A huge thanks to our members, our team of incredible Rangers, our custodial partner @Altruist, and our investors at @scalevp @gradientvc @cathayinnov @expa @redseaventures @53stations and @8bitcapital
We have more exciting announcements coming soon... Stay tuned.
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not sure if anyone else experienced this in sf but i went to a nice founder dinner today and I met someone new. One of the first questions he asked me was what school i went to.
“Stanford? MIT? Harvard?”
I told him UT Austin and it took him about 0 seconds to divert his attention and start texting on his phone. the conversation quickly ended after that.
apparently only people from t1 schools deserve basic respect in this city
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Linkup just released SPARSEUP, its first open-source model—and a meaningful contribution to open retrieval.
149M params. 56.4 nDCG@10 on BEIR-13. Apache 2.0. Sub-ms retrieval with Seismic.
It’s the strongest public vocabulary-based sparse retriever under 150M they know of. Now available on @huggingface! cc @GradientVC
Introducing SPARSEUP: the first model release from @Linkup_platform , and the missing sparse companion of DenseOn and LateOn.
Same backbone, same data, <150M, 56+ on BEIR-13. The model is open-source, use it!
Blog: linkup.so/blog/introducing-s…
Model: huggingface.co/Linkup-Platfo… (Apache 2.0)
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Today we’re announcing we’ve raised $20M in seed financing led by @QuietCapital , with participation from @GradientVC , @haystackvc and @CompoundVC and other amazing folks to build the verification layer for AI-driven biology.
We are also delighted to be welcoming @vqctran who is joining us from @GoogleDeepMind as a co-founder.
AI is not going to cure all diseases without a verification substrate - a scalable environment where models can test predictions and learn against living human biology.
AI is becoming increasingly capable of generating biological hypotheses, therapeutic candidates and experimental designs. But generating ideas is advancing much faster than our ability to test what those ideas will actually do in human biology. Existing systems force a tradeoff: the most biologically relevant approaches are slow and expensive, while faster in vitro and computational systems often lack the fidelity needed to capture human response.
Polyphron’s bet is that tissue is the fastest, cheapest, most parallelizable verification substrate that still contains the biology that matters and we believe the path to biological superintelligence runs through closed-loop interaction with these living and simulated human systems.
Thank you to @axios for covering the announcement.
Link to our new website in the thread.
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🚀
After 10 incredible years at Google, I've left @GoogleDeepMind to join @thematthewosman and @FabioZB_I at Polyphron as a co-founder and explore the next frontier in my research passions.
Together, we're training frontier models that autonomously explore, understand, and physically reproduce human tissue biology. It's our grand vision to grow tissues realistic enough to match human response to all drugs, and realistic enough to recover organ function when transplanted into living organisms.
Other than the bitter lesson, I think another lesson remains true in the history of ML: the more directly you can optimize your end goal, the better your outcome will be. IMO this is one lesson largely missed in the drug discovery and computational biology space today, and one that Matt and Fabio understood very clearly (and why I joined them on this journey!).
At the heart of our company is one giant RL problem: propose a recipe to grow tissue, sample life itself, reward directly against the functionality of the tissue. If this sounds exciting to you as either an AI or Life scientist, feel free to reach out! We're building the coolest lab in NYC :)
I'll never forget all of the {pretraining, RL, self-improvement} projects I worked on with my brilliant friends at GDM over the years, but I'm even more excited to share the things we discover next!
(p.s. this isn’t the first time i RL’d something based off of similarity, iykyk)
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Polyphron announced a $20M seed from @GradientVC @QuietCapital @CompoundVC in @axios today!
They’ve built human tissue models and generation to validate new AI-created drugs. It’s an incredible mission that is unique and has the capacity to revolutionize how we get new drugs to market quickly and safely.
axios.com/pro/biotech-deals/…
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BackOps just raised a $42M Series B led by @insightpartners, six months after its $26M Series A led by @ttunguz @Theoryvc and only eighteen months after @GradientVC led the pre-seed!
The bigger story: Backops is moving beyond automating individual tasks in fulfillment centers to becoming a “resolution layer” across the messy systems that run the physical economy—understanding context, making decisions, taking action, and learning from every outcome.
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Great Saturday hosting the @GradientVC x @GoogleDeepMind x @tokensandai Open Models Hackathon 🤖
So much fun seeing everyone build with @LambdaAPI , @NangoHQ & @RespanAI & @googlegemma and some really cool 23 demos by the end of the day!!
Thanks @DynamicWebPaige, @matthewridenour, @JaidevShah4, @bastienbeurier, @francchen, @TheZachMueller, @simontiu for all the supports and for judging!
More to come 🚀
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🔻Grateful to @GradientVC for hosting the open models hackathon this afternoon, sponsored by @LambdaAPI, @GoogleDeepMind, Nango, @tokensandai, & @RespanAI.
So many projects built with @GoogleGemma, including an open version of Claude Design & open meeting transcripts/synopses!
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So fun hosting an AI hackathon at @GradientVC today! Can’t wait to see what everyone builds! Thanks @GoogleDeepMind @RespanAI @LambdaAPI for the support 🚀
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Excited to be a Startup Battlefield judge at TechCrunch Disrupt this year!
techcrunch.com/2026/09/10/fi…
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Photo of when I visited Oura in 2019 to make sure it was a real thing
It was real and now it’s $1.4B in revenue
Amazing. Congrats to all 🚀
(And to Teemu Kurppa for all his hard work, pictured)
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"I'm not worried about discriminatory pricing. I'm worried about discriminatory access."
Zach Bratun-Glennon, General Partner at Gradient, warns discriminatory pricing and data access could concentrate AI power, yet resists market regulation.
"Only if you have very large budgets to spend and, you know, you promise to share your data back with the model company and you, you know, happen to be providing scarce data, then you get to use their frontier model."
"I don't want to regulate our way into solutions here."
"I'm betting on the ecosystem. Some something will emerge."
@thezbg @GradientVC
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