Jensen Huang on the smartest person he's ever met;

Does the world really need another smart young investor with a generalist tech fund? Having a strong network isn’t enough anymore. Everyone has access to one. You need a genuinely unique perspective and an edge you truly believe in. Otherwise, why venture into venture capital at all? This business is hard and soul-crushing. Unless you’re passionate about it and truly believe you can be great, don’t enter venture capital. What’s your unique edge? #NewYork

Why do candidates like Abdul El-Sayed and Zohran Mamdani have to keep proving they’re not a collection of stereotypes? @realmoamer dives into the Islamophobia running rampant in American politics

Illuminating exchange with a 19 year old voter in Detroit, worried about @AbdulElSayed’s name. @AJEnglish #Midterms2026 #Michigan

The wait is over. Introducing PIA’s new uniform, designed by Hassan Sheheryar Yasin (HSY). A fresh expression of style, a distinctive new look, and a vision that moves us forward. #PIA #HSY #PIANewLook #NewJourneyNewLook #PIANewUniform #FlyingForward #everyone #followers #trending #cabincrewlook #PakistanInternationalAirlines #NewEra

Microsoft’s boardroom at a defining moment for AI. The stewardship of Harvard. The work of rebuilding Ukraine’s economy. Few people have held a vantage point like Penny Pritzker’s, and I have the privilege of calling her my friend and every day partner at @InspiredCap. We finally sat down together on my podcast: Youtube: ordnl.link/NGkzCPW Apple: ordnl.link/alDjGw7 Spotify: ordnl.link/5dPA0jt

Excited to be bringing the @CommureOS platform to our partners @MediclinicME, one of the largest care providers in UAE. AI transformation of the healthcare experience requires purpose-built Ambient, RCM, and CDI workflows.

Applied Intuition has been awarded a contract by the @USNavy to scale Data Engine across its fleet. As the infrastructure layer for maritime autonomy, Data Engine collects operational data through passive navigation capabilities installed on Navy ships. Here’s how the loop works: 🔵 Collect data from ships at sea 🔵 Curate it and train perception models 🔵 Evaluate the models 🔵 Deploy them back to vessels over the air The result is what one ship learns now reaches the entire fleet – a threat encountered by a single vessel is turned into an improved model and deployed over the air, so every ship can detect it. Our goal is for each military service to run its own Data Engine, tailored to its domain and feeding into a common infrastructure. Learn more about our work with the U.S. Navy here: appliedintuition.com/blog/na…

Is JD Vance seriously suggesting that immigrants have brought a culture of revolution and protest TO FRANCE? @jonstewart

When software shapes the car, what’s left for design? The human. See how we’re building the next generation of intelligent cars with @Stellantis. appliedintuition.com/blog/st…

Today, our team is excited to announce @InspiredCap Fund IV, $450M in new capital to back early-stage founders. There has never been a moment in history when more is possible. It takes just one person with exceptional ambition to take on the world’s problems and change billions of lives for the better.

Dreamlabs is better, faster, cheaper.

Today, Dream Labs is out of stealth. In February 2026, while we were at NVIDIA, @scott_e_reed @youliangtan @GeYunhao and I started calling a new kind of robot model a World Action Model, one that learns how the world moves from video, then uses that to decide how to act. Today, we’re releasing Dream-1 through a public API that anyone in the world can use. When you rent a car you’ve never driven, you don’t learn to drive all over again. Within a few minutes, it feels like your own. Robots should work the same way: a good model should be able to pick up a new robot and get to work, without being retrained from scratch. That’s what we’ve spent the last 6 months on, and it’s what Dream-1 does. It controls bimanual robots with parallel grippers it has never seen before, zero-shot, on unseen tasks in unseen environments. Generalization has been my North Star in robotics: first by distilling video world models into VLAs (DreamGen), then by learning to act from those world models directly (DreamZero), and now by transferring across robots (Dream-1). I’ve come to believe it’s the only direction that lasts. Pushing a narrow model from 90% to 99.9% on a single task is real work, but much of it gets washed away once a model that generalizes arrives. Since the start of the company we’ve stayed single-minded about generalization.That focus is what got Dream-1 to control robots it was never trained on, on tasks it was never shown. And it’s what makes a public API possible at all: a model that only does one task on one robot can’t serve millions of users. Dream-1 is still a research beta, and it will make mistakes. But it already drives cars it’s never seen. Generalization wins in the end, and the end is closer than it looks. Here’s Dream-1 taking the wheel of a robot it’s never seen. Full details 👇