@dylanmcguir3 and I just raised a $7.5M pre-seed led by @a16z to build a robotic surgeon @alephsurgery Much of the early investment in Physical AI has focused on industries where labor is relatively abundant and low-cost. We believe the biggest opportunities to change the world lie at the opposite end of the spectrum. Surgery is among the most valuable forms of labor on the planet, but it's far too scarce and expensive - due to an exceptionally long training pipeline and highly concentrated expertise. We’re building the first surgeon that scales: one system that can acquire, refine, and transfer surgical skills across millions of procedures. v1 is already learning to perform surgical tasks autonomously from demonstrations, and improve from its own experience. Lots of great folks behind us! @BoxGroup, Reveille VC, Constellation, @forward_deploy, @Valkyrie_vc, @DiscipulusVent, @maniacvc + more

We have raised $200M at a $5B valuation to scale self-improving software development in the enterprise. @FactoryAI has grown to serve hundreds of thousands of developers at companies including RBC, Adobe, Nvidia, T-Mobile, and Palo Alto Networks. We will use this capital to accelerate our investments in research, product, and global go-to-market.

I knew Kareem all the way back in 2011 when... @clay

@Clay just raised a $115M Series D at a $7.1B valuation. Now, more than 17,000 teams build on Clay, including Anthropic, SpaceXAI, Google, OpenAI, Stripe, Visa, UPS — and 80% of the Forbes AI50. @nytimes just broke the news, but the story goes far beyond the raise. AI is unleashing the biggest wave of company creation in history. Clay’s goal is to be the engine they use to grow. Clay is a self-learning revenue engine that figures out the best GTM play for your company, then runs it. We started as the company with the best data about other companies. Then we became the infrastructure to run campaigns across email, ads, and landing pages. Now we’re building agents that learn from every campaign, predict the next best action, and get better each time they run. Software engineers use coding agents to write code. GTM engineers now use growth agents to autonomously grow their companies. In 2023, we introduced GTME as a new role for people creating revenue systems with data, automation, and AI. Two years later, thousands of GTMEs are growing the world's best companies. They are gathering in nearly 100 Clay Clubs around the world, from Bangalore to Boston and Lahore to Lisbon. That’s why we’re proud to launch a $1M scholarship fund, helping more people enter the profession. @Wellington_Mgmt led our Series D, with participation from @sequoia, @stepstonegroup, @a16z Perennial, @MeritechCapital, DST Global, @CapitalG, @BoxGroup, @boldstartvc, @BloombergBeta, and @EvolutionEquity. A gracious thank you goes out to our customers, community, partners, team, and every GTM engineer shaping this with us. We are going to unveil the future of Agentic GTM at SCULPT on October 8. Stay tuned to see how to use AI to grow every company. 🖤🩵❤️💛

Today, we’re announcing that Forus has raised a $150M Series C at a $3B valuation to build the AI network for medicine. The round was led by @BainCapVC with participation from @ThriveCapital, @generalcatalyst, @Accel, @Redpoint @BoxGroup, @pearvc, @AvraCap, @humancapital, @neo, @vast_ventures, and @svangel Forus supports millions of people across all 50 states and is already used by providers to treat patients in 85% of U.S. residential ZIP codes. 9 of the top 15 global biopharma companies work with us, alongside many fast-growing biotechs. Our goal is to put Forus in every doctor’s office in the country and accelerate the medicine pipeline from discovery to treatment. The opportunity ahead of us is much larger than what we can take on today. We aren’t constrained by capital or customer demand. We are constrained by the talent and capacity of our team. We’re hiring exceptional engineers and operators in New York to help us take it on. Come build with us.

We’re announcing that Forus has raised a $150M Series C at a $3B valuation to build the AI network for medicine. Medicine will remain one of the world’s most important industries until humanity achieves immortality, and we are only at the beginning of a new era in what it can do. GLP-1s are changing obesity and cardiometabolic disease. Gene therapies can treat diseases at their genetic source. New cancer treatments are turning diagnoses that were once fatal into diseases people can live with. AI is enabling us to discover of new drugs even faster. But discovery is only the beginning. It still takes more than a decade and billions of dollars to turn a new molecule into an approved medicine, and once it reaches market, more than a third of patients prescribed specialty treatments never receive their first dose. Forus is creating the AI network to accelerate the entire medicine pipeline, from development and launch through prescription and treatment. We connect the companies creating medicines with the doctors who prescribe them and the patients who need them. Today, Forus supports millions of people across all 50 states and is already used by providers to treat patients in 85% of U.S. residential ZIP codes. 9 of the top 15 global biopharma companies work with us, alongside many fast-growing biotechs. Our goal is to put Forus in every doctor’s office in the country and unlock an order of magnitude more medicine for society. There is a generational opportunity to rethink how new medicine reaches people. Forus is becoming how medicine moves from discovery to treatment in America, and as we scale, we’ll become the most important company in life sciences. Forus is not constrained by capital or customer demand; we are constrained by the talent and capacity of our team. To take on the opportunity in front of us, we need exceptional engineers and operators in New York who want to move fast and help us make something that matters. Come build with us. Our Series C was led by Bain Capital Ventures, with Thrive Capital, General Catalyst, Accel, Redpoint, BoxGroup, Pear VC, Vast Ventures, and SV Angel investing alongside them.

meet @mostik_ai! what happens when you put 12 PhDs in one room for four months? first place on the ARC-AGI leaderboard, which I can't say much about while the competition is still running. and this, which I can. everyone's arguing about whether open models will catch up to frontier models. we think it's the wrong question. here's the one we pose: why does a frontier model have to generate your answer at all, when the only thing you need from it is the reasoning? we do this by enabling models to communicate in latent space. through our protocol, hidden states pass straight from a frontier model into a small one running on your infrastructure -- no text between them, and neither model is fine-tuned. two models from different families, sharing reasoning, both left untouched. how do we know it works? we tested it on a setup where a 753B model reads the problem, and a 4B edge-class model writes the answer. with this approach, we get results 80% as accurate as the frontier model, but at 20x faster performance. we're committed to preventing frontier model lock-in and are already partnering with inference providers to accelerate open-weight adoption. we've done this between 15 of us, in four months, 12 PhDs and a Fields medalist, backed by @generalcatalyst WIRED has the first external account of the company and the work: wired.com/story/russian-star… full writeup, the setup, and all the numbers: mostik.ai/read-more

10 years ago I wouldn't shut up about how important distributed GPU infrastructure would become. 7 years ago, I joined Crusoe as their third engineer. A year ago, I co-founded @AranyaInc. Today we operate $500M+ of compute, and we've raised $11M to date to go much further. $9M seed led by @firstround, $2M pre-seed led by @asylumventures. Blog post link: aranya.tech/blog/the-missing…

Today we’re announcing r-1, our new document parsing model. It’s more accurate than our most powerful agentic OCR models, faster, and up to 6x cheaper. At @reductoai, we spent two years building specialized models for complex visual layouts, tables spanning multiple pages, and key formatting like strikethroughs. We then used everything we learned to build r-1, the first in a new generation of models designed to handle the hardest documents without multiplying cost. This early preview delivers a 20% lower error rate than our most accurate legacy agentic models and will keep improving with new checkpoints over the next few weeks. Accuracy is only the beginning. r-1 preview is available at 1¢ per page all-in, with additional volume discounts as you scale. In the near term we’re also going to release r-1 mini, and an auto mode that intelligently selects the right approach for each page. If you’re currently using another parser, we’re offering up to $5,000 in credits to evaluate and migrate. You can claim the migration offer and learn more about r-1 using the links in the comments. Happy parsing!

Today, we’re introducing Light. ⚡ We’re building the API platform for electricity—enabling companies to launch branded electricity plans embedded directly into their products. And we’ve raised a $46M Series A led by Matrix. We’re just getting started.

Some of the earliest money in Cursor (the largest vc-backed acquisition ever) turned a $750,000 investment into a ~$1 billion return. @davidtisch and his midas touch at Box Group strike again! Great profile here of one of the best early stage investors who wins while going against the grain @agarfinks fortune.com/2026/08/26/david…

Congrats to @mntruell @amanrsanger and the entire @cursor_ai team… We @BoxGroup have been investors since the very beginning and never sold a share… what an honor to be part of such a magical journey “Lots ahead”

Introducing Assert (assert.dev): the command center for reviewing engineering work Watch me review a new feature with Assert's product diff: a UI generated by our agent using mock data. It has completely changed how we work

Out of the box, long-horizon agents struggle to accurately perform end to end work in the real economy (outside of coding) because those tasks are not easily verifiable, the data is hard to scale, and going from inputs to real outcomes can actually take many days. Even if you had a reliable way to verify outcomes at scale (and weren’t bothered by the multi-hour iteration loops), the sheer volume of decisions by the agent that occur in a multi-hour job makes it hard to know whether performing well will generalize to production. Over the last two years at @trybasis, we've been solving this problem by supervising the process our agents take to get to outcomes, rather than just looking at whether the outcome itself is correct. We think this is the key to building production agents at scale. It's what has allowed us to run agents in production that operate for hours, sometimes days, and reliably perform tasks like entire complex tax returns end to end. Today, alongside @braintrust, we're open sourcing a standard for defining, evaluating, and eventually rewarding agent behaviors. Thread below with all the details on how we’re scaling behaviors to close the loop for long-horizon agents.

AI is extremely good at spending your money very quietly. our own token spend went from a rounding error to more than 10% of payroll in a year. one week in May we burned through $1.5 m. our CFO didn't love telling me that number, and he really didn't love telling the internet. but every finance leader we talk to is living the same story. the bill keeps going up and teams can't answer basic questions about it. which team is driving it? which models they're using? what changed this month? whether a cheaper model would do the same job? so finance gets two bad options: keep paying and hope, or cut broadly and slow down the work that's actually compounding. we built a third one. Ramp now connects to OpenAI, Anthropic, Gemini, Cursor and pulls it all into one place. see it, understand it, control it - down to a single API key. built with 1,000+ companies managing 100T+ tokens a month, and now spending less than they expected too! try it today at ramp.com/ai-cost-monitoring

Introducing Aidan, your newest AI employee Aidan is the first computer-using AI built for realtime conversation We’ve raised $45M from @sequoia and @8VC to bring Aidan to the world @NotionHQ and @DecagonAI already use Aidan to run customer interactions. This is how @withsableai works:

It is officially a White Circle Summer ⚪ At least it is in Sweden! We're excited to announce our technical partnership with @Lovable to help make AI safer, more secure, and more reliable at scale. Read more below ↓

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.

Many companies are #1 in a benchmark they crafted. We worked with @micro1 to create an independently audited benchmark to measure document extraction performance with long documents. The results of LongExtractBench show the nuances companies are likely to find in the real world. micro1 tested frontier models with max reasoning and document processing platforms with their strongest configurations, and found notable precision/recall and completion tradeoffs across most. Reducto’s Deep Extract leads the industry by a wide margin. 🧵

While everyone’s displacing jobs, we’re creating them. A Hero for every family.  @hellotohera’s favorite friend 83 year old Stanley Zareff dropped by our office to learn what being a Hero means and announce our 27M Series A led by @BainCapVC, and continued participation by @Accel and @iaventures 🧵