lover, not-fighter. building and funding startups: @kindredventures, @heartbeat
on my 📲 too much
Joined October 2008
- Tweets35.6K
- Following799
- Followers40K
- Likes75.5K
Pinned Tweet
so grateful to get to live in this incredible time, and do this interesting work.
thank you all for the friendship and support.
News! We are proud to share our latest $355M set of funds, combining early stage and early growth.
Kanyi Maqubela retweeted
The new Holon-S1 model from @SophontAI is a strong example of how specialized domain-specific models can compete with or beat pure-scaling approaches in many fields - in this case, medical AI. Kudos to @iScienceLuvr @humanscotti and the Sophont team! Proud of you at @KindredVentures 👏👏👏
Cancer patients with the same diagnosis often have very different tumor biology.
Molecular testing can reveal underlying differences, but that's slow and expensive.
Introducing Holon-S1, our platform to predict genes and biomarkers from routine pathology slides.
• beats two other models at predicting spatial gene activity in a controlled comparison
• has identified potentially novel biomarkers in breast cancer and colon cancer
• evaluated across 16 organ types and more than 15 scanners, using slides from both formalin-fixed, paraffin-embedded and fresh-frozen tissue.
We believe Holon-S1 has the potential to help researchers find new biomarkers in slides they already have, discover treatment targets, and bring more personalized cancer care within reach!
Kanyi Maqubela retweeted
AI x bio has had an insane week.
> Radical Numerics: Omnii-1 designed phages given to a human patient
> Claude: 17 scorpion-toxin binders validated in the lab
> OpenAI: acquired Orchestra Bio
> OpenAI + PerturbAI: agents found new neurodegeneration leads in 32 minutes
> RFD4-Proteina: open programmable protein design model
> BioEmu2: new biomolecular simulation model
> Chaperone: agent framework connecting 50+ protein design tools
> Xaira: launched X-Scientist for agentic drug discovery
> Sophont: Holon-S1 predicts gene activity from pathology slides
> Boltz + BMS: bringing biomolecular models into pharma workflows
> Phylo + Bayer: deploying Biomni Lab across research teams
> Kevin Weil, ex-OpenAI, launches Warp Sciences: AI + robotic labs for drug discovery
> Carbon-X: open DNA model its team says matches GPT-6.1 Sol at ~1,000x lower cost
This is happening across the whole stack: models, protein design, simulation, agents, robotic labs, pathology, pharma workflows, and actual patient use. Bio is becoming computer.
Kanyi Maqubela retweeted
Sophont CEO @iScienceLuvr explains how Holon-S1 can extract insights that usually require expensive molecular tests from an ordinary cancer biopsy slide:
"Why is it so hard to cure cancer? Because cancer’s a very diverse disease. Even though two people may have the same exact diagnosis, the underlying biology can be quite different."
"There are various tools for doing this, molecular tests and so on, but a lot of these tools can be quite expensive and time-consuming. Can we extract a lot of those similar insights directly from a tissue slide?"
"Whenever a cancer patient has a solid tumor, the gold standard is you take a biopsy, put it on a slide, under the microscope, and a doctor is analyzing that."
"We can take that same exact slide and with AI extract a lot more information about what’s happening at the molecular level, what genes are being expressed, and what makes that tumor unique for that patient."
@SophontAI
Cancer patients with the same diagnosis often have very different tumor biology.
Molecular testing can reveal underlying differences, but that's slow and expensive.
Introducing Holon-S1, our platform to predict genes and biomarkers from routine pathology slides.
• beats two other models at predicting spatial gene activity in a controlled comparison
• has identified potentially novel biomarkers in breast cancer and colon cancer
• evaluated across 16 organ types and more than 15 scanners, using slides from both formalin-fixed, paraffin-embedded and fresh-frozen tissue.
We believe Holon-S1 has the potential to help researchers find new biomarkers in slides they already have, discover treatment targets, and bring more personalized cancer care within reach!
Congrats @iScienceLuvr and @humanscotti on this insane launch!
Every cancer is, by definition, unique: to understand intratumoral heterogeneity requires genetic expression, biomarkers (that will correlate to therapeutic response), and a patient's unique phenotype.
Holon-S1 from @SophontAI can predict these characteristics, at the per patient level: exposing novel biomarkers for their initial targets -- breast cancer and colon cancer -- too.
Read below.
xitter.cf/iScienceLuvr/status/21…
Cancer patients with the same diagnosis often have very different tumor biology.
Molecular testing can reveal underlying differences, but that's slow and expensive.
Introducing Holon-S1, our platform to predict genes and biomarkers from routine pathology slides.
• beats two other models at predicting spatial gene activity in a controlled comparison
• has identified potentially novel biomarkers in breast cancer and colon cancer
• evaluated across 16 organ types and more than 15 scanners, using slides from both formalin-fixed, paraffin-embedded and fresh-frozen tissue.
We believe Holon-S1 has the potential to help researchers find new biomarkers in slides they already have, discover treatment targets, and bring more personalized cancer care within reach!
You're going to hear more quotes like this at the frontier. It's my favorite kind of quote:
"we ran Holon-S1 on an osteosarcoma patient's public slides. It was never trained on osteosarcoma."
xitter.cf/iScienceLuvr/status/21…
In a retrospective N-of-1 case study, we ran Holon-S1 on an osteosarcoma patient's public slides. It was never trained on osteosarcoma.
We observed that fibroblast-associated genes were overexpressed, which was previously observed by this patient's medical team using sophisticated molecular tests and was targeted for treatment.
Kanyi Maqubela retweeted
Super excited to back the pioneering founders of @dreamlabs_ai on their world action model vision! Congrats to @jang_yoel, @scott_e_reed, @GeYunhao @youliangtan on their:
🔋$90M seed raise with @BCapitalGroup @ACapitalLP @svangel @Unusual_VC @Wing_VC @bznotes @Thom_Wolf @jhong @gharik @adrianaoun and us at @KindredVentures ❤️
🤖 shipping the Dream-1 model via API for general availability. The first World Action Model for public use by developers.
📈 achievement of frontier performance of top scores across the board by @robocurve on difficulty performance, latency, and cost against the SOTA LLMs from @anthropic @OpenAI @GoogleDeepMind 👏🏽👏🏽👏🏽
Today, Dream Labs is coming out of stealth with Dream-1, a robot foundation model that controls bimanual robots it has never seen, on tasks it was never shown, in places it has never been, from $3/hour.
Dream API is live for everyone, today. 🧵
new pareto frontier 👀
Ahead of launch, @robocurve evaluated Dream-1 independently. Result: a new Pareto frontier for robot control. 16x cheaper, 1.7x faster, and better performance (59 vs. 48 on RoboDojo-RC Tier 1) than the previous best alternative.
LLMs can plan. Robots still need a cheap, fast, and general System 1 to act.
going to have to sit with this one for a bit: seems to suggest we can toggle between reality states. psychedelic experts know something about this.
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness.
New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
Man, what is it with the West Coast downtowns? DTLA, DTSF, Sac, Portland, (increasingly) Seattle just underwhelm so thoroughly.
Kanyi Maqubela retweeted
we've seen driverless cars, industrious arms and humanoids, and even athletic robots.
now, welcome to the world of fun character robots - bringing animatronics into the AI era!
go Hugo and the @dynamicreatures team 🧸
Introducing Hugo: the first human companion robot.
At Boston Dynamics, I was Chief Strategy Officer, bringing robots into the real world.
My co-founder, Farbod Farshidian, led research at the RAI Institute.
Together, 15+ years teaching robots to run, jump, and work.
So we asked: what if robots could connect with people?
Hugo runs on the first Vision-Language-Expression (VLE) model.
He links what he sees and hears to movement, expression, and gesture.
He notices you, reads the room, and reacts in real time.
And he's only the first.
Dynamic Creatures is @BostonDynamics' official entertainment and hospitality partner.
We're building a cast of characters with film studios, resorts, and theme parks.
dynamiccreatures.com
When your consumer agent creates a virtual card on their own BIN to transact on your behalf, consumer won't get the credit card rewards.
Gotta imagine there will be some blowback there.
Kanyi Maqubela retweeted
interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had
with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works
already-clean.goodfire.com/
