We are releasing Dyna-2.1, the first Physical Agent that achieves reliable super long-horizon whole-body autonomy. It combines our brand-new semi-humanoid hardware with an agentic system built around Dyna-2 to handle ultra-long real-world workflows. Here is an uncut footage of Dyna-2.1 completing an entire hour-long laundry room workflow, just like a human does.

After 9 years. 16K+ family homes. 500M sqft inside real homes covered, we know home robots are being built in wrong order. So we built Matic the way nature grows a child. Finally, sharing our vision: 1.⁠ ⁠Why robots get stuck at the demo 2.⁠ ⁠Why labs can't ship robots 3.⁠ ⁠Why we started with eyes, not hands 4.⁠ ⁠The 99.999% rule in robotics 5.⁠ ⁠Where Matic is going next 1. Why most robots get stuck at demos Robot demos look brilliant in a room arranged around its limitations: familiar lighting, predictable furniture, nothing troublesome on the floor. A family’s home makes no such accommodations. There are cables, toys, shifting rugs and, occasionally, dog poop. Yes, humanoids will need to avoid that too. Repeating a task under familiar conditions of uncluttered floors is not the same as handling an unfamiliar home with all its chaos. 2. Why labs can't ship robots SLAM (Simultaneous Localization and Mapping) is how a robot maps its surroundings and locates itself within them. Ask most academics and they'll tell you it's a solved problem. But real homes are the most chaotic spaces that exist. There are glass doors that look like open doorways, mirrors, stairs, rugs, charging cables, and Legos placed all around. So ask yourself: if indoor mapping and navigation is "solved," where are the robots? Why aren't airports, hotels and grocery stores full of them? Calling it “solved” misses the question families actually care about: can I leave this thing alone and trust it? 3. Why we started with eyes, not hands Nature doesn't give birth to a fully working human. We took our cue from how nature develops humans: capabilities built on one another. Children learn to see before they learn to grab, and to grab before they learn to plan and handle the unexpected. Perception comes first. Manipulation follows. More complex responsibilities come after that. At every stage, the robot must earn the next job by doing its current one well enough. Before asking a robot to pick up a sock, we wanted it to understand where it was, where the sock was, and how to reach it. Most of the industry started at the top, with humanoids. We started at the bottom, with a robot whose job is to see and move through a home precisely with 1cm accuracy in any lighting condition. Floor cleaning gave that foundation an immediately useful job. It forced us to confront navigation, clutter, privacy and everyday reliability before reaching for more complex chores. The floor cleaner isn’t a detour from our larger ambition. It is how we’re building toward it. 4. The 99.999% rule in robotics At 90%, one in ten decisions is wrong, and a robot makes thousands in a single clean. That's the robot that bumps, gets lost, and falls down the stairs. Every extra nine is a new mountain. The failures get rarer, harder to find, and exponentially harder to fix. They barely even happen until they happen in your home. Matic's visual SLAM runs at 99.999% in 16K+ homes. We’re the ONLY unsupervised home robot at scale with pure vision-only full autonomy. If you ask us, the gap between 80% and 99.999% is where our 9 years of engineering went. We close that gap inside real homes. When Matic handles something imperfectly, it saves a short clip and keeps it on the device. It only leaves if the family chooses to share it. If they do, it gets labeled, fed back into the model, and every Matic gets smarter, including theirs. Families control what leaves their homes. We do the work of making the product better. Better robots earn trust > trust brings more homes > and more homes teach the robot more. 5. Where Matic is going next Phase one was perception and it's nearly done. Matic has covered 500 million square feet, 400K miles in thousands of real lived-in homes. Now phase two is going to be about manipulation. And our idea is to build a robot that doesn't just move through your home, but acts in it to eliminate even more chores. Each step must be useful today, not justified by something we promise tomorrow. The evolution of Matic is the revolution. The goal isn’t to put the most impressive humanoid in your living room. It’s giving your family time and energy back through robots that earn your trust, protect your privacy, and help without becoming another responsibility. More time for each other. Less work getting in the way. That’s Matic. Get yours today at

.@itsayaanmomin has never met a problem he didn’t want to investigate. He’s worked on antibiotic-resistant bacteria and surgical robots. When GPT-3 landed and everyone else was asking it to write poems, he was experimenting with an early version of agentic coding. That led to another question: if there were going to be many models, how would you know which one to use? So he co-founded Neutrino to route requests across them. Then the problem moved down another layer: how do you actually serve these models efficiently at scale? That took him to @FireworksAI_HQ, where he has done a little bit of everything over the past two years. Along the way, he developed the kind of familiarity with chips, infrastructure, and compute that means he can probably tell you more about AMD GPUs than you were planning to ask. Ayaan is now joining @chemistry as an investor. What ties all of this together is an instinct to keep going deeper. Ayaan doesn’t tend to stop at how something works; he wants to understand what makes it possible in the first place. That perspective feels particularly relevant right now. As AI becomes more interactive, multimodal, and embedded in the physical world, some of the most important questions are moving below the application layer: how compute is priced, which silicon is available, where the power comes from, and how all of those constraints shape what gets built next. His interests extend well beyond technology. He has strong opinions on Goya’s Black Paintings and disappears into national parks for days at a time despite being very afraid of bears (but not mountain lions). Welcome to Chemistry, Ayaan! @Mark_Goldberg_, @kshenster, @loubohan, and I can't wait to work with you!

Congrats to @loubohan on his partner promotion at @chemistry! Curiosity is his superpower, so we decided to make him a Wikipedia page to honor the milestone. cc @kshenster @ethankurz

Atoms Finance is hiring. We’re expanding the team to support our growth. Controller - Senior Director, Financial Operations - Senior Director, Technical Accounting - Vice President of Finance Technology - DM me if you or someone in your network is a good fit. Happy to connect you directly with the team.

I’m proud to announce that Antioch has raised $32 million in Series A funding, led by @GreylockVC with participation from @A_StarVC, @Category_VC, @BoxGroup, @IcehouseVenture, and angels. Physical AI is the defining technological and economic opportunity of our lifetime. Rapid advancements in intelligence and compute have brought us right to the precipice of this new era. What’s holding the industry back is development speed — iteration is hardware-bound, testing is slow and costly, and data is scarce. We founded Antioch with the belief that scaled, high-fidelity simulation is the solution, and will push us into the physical AI age. Autonomy teams use Antioch to accelerate development, test and validate their systems at scale, and generate massive quantities of synthetic training data. We’ve seen the development velocity that Antioch brings to advanced manufacturing, humanoid robotics, smart security, model training, and more. Now, we’re scaling rapidly to put Antioch in the hands of every autonomy team. Thank you to the wonderful customers, investors, partners, friends, and family who are on this journey with us. Read about what we’re building in @Forbes (link in first reply) and tune into @tbpn today at 3:35pm ET to hear more. We’re hiring across simulation, infrastructure, machine learning, 3D graphics, go-to-market, and operations.

The venture market is increasingly bifurcating into two camps: massive consensus bets and contrarian companies as far from the heat as the dark side of the moon. There will be gems - and false gold - in both.

Excited to share that we're expanding our Supply Chain and Manufacturing teams at Atoms. Head of Supply Chain, Transport Head of Quality Senior Buyer If you or someone you know is a fit, my DMs are open. I’d be happy to connect you with the team. Links below in comments.

It's been a hot robot summer 🤖 Robots can now drive cars, fold laundry, and cable data centers. But the gap between 95% and 99.9% task success is the gap between a demo and a business. Our take on physical AI, and where the $40T market gets won

It's been a hot robot summer 🤖 Robots can now drive cars, fold laundry, and cable data centers. But the gap between 95% and 99.9% task success is the gap between a demo and a business. Our take on physical AI, and where the $40T market gets won

It's been a hot robot summer 🤖 Robots can now drive cars, fold laundry, and cable data centers. But the gap between 95% and 99.9% task success is the gap between a demo and a business. Our take on physical AI, and where the $40T market gets won

It's been a hot robot summer 🤖 Robots can now drive cars, fold laundry, and cable data centers. But the gap between 95% and 99.9% task success is the gap between a demo and a business. Our take on physical AI, and where the $40T market gets won