Wishing you a blessed Dussehra & Sri Ramachandra Vijayotsava! 🏹 As Sri Rama vanquished Ravana, may the Names of Sri Hari conquer the inner demons of lust, anger & greed. True victory lies in pure bhakti. Jai Shri Rama! ✨🙏🏻

🚀 Introducing DeepInfer, our in-house inference framework, now fully open source! Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable. 🔗 github.com/deepseek-ai/DeepI… 1/4

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. 🧵

We're not going to keep pace with China's actuator development by doing process engineering with humans. China isn't going to reach actuators with precision that can perform surgery on cells with their current playbook of doing process engineering with humans. I believe everyone will move towards automated design and process improvement - the question is just how. Let me give you a real world example of how this works first I can design our Caldera-0-3115 motors to have an airgap of 0.08mm airgap instead of 0.16mm airgap but I can't manufacture it. If i could, this would translate to about 8% max torque at 2.5kW max throttle - which Counter UAS folks would absolutely love. This is more impactful than any design improvement I can make from optimizing EM design with Ansys and maaaybe set a new world record. But I just can't manufacture it - even if I did, it would die within x hours of flight time because at some point the stator and magnets would start colliding. Because my current manufacturing process creates error tolerance stackup from 1. flipping/refixturing of the multi-step lathing process of the rotor bell not perfectly coaxial 2. press fits 3. adhesives 4. tolerancing the grinding of magnets 5. at 22k rpm, the remaining 0.x miligrams of imbalances after dynamic balancing that sway the bell from one side to another as well which requires some margin. 6. warping from repeated thermal expansion I can't improve them without CNCing with different tools / cutting operations / fixtures; try different parameters for press fit, try different adhesives which responds differently to thermal expansion, etc. And that's just the airgap. Every feature has its own quirks and optimizations to be made. And this isn't a one time thing, this is a marathon that all actuator-makers are running, always, for every SKU. With the added complexity of a gearbox. So I understand Why @atlasmotion does custom motor design + US prototyping then does volume production in the Philippines. Their experienced manufacturing labor gives you the flexibility to handle different products AND process experimentation. Why @parthingle_x is building custom CNCs specialized for motors/actuators and a factory knowledge base with, telemetry and scheduling capabilities. This makes sense to me because it's a specialized tool to automatedly improve to cut better parts for motors. Why Almetra capture live factory floor video, immediately convert raw human movement into structured digital work steps, and orchestrate real-time floor feedback that systematically drives automated process improvements. We at Caldera are going the route of fully automating design and process iteration via factory intelligence, robotic assembly and software integration over all machines that participate in actuator manufacturing. All of us are building pieces of the same capability: running more experiments, learning from them faster, and getting the improvements into production. That's what US manufacturing needs to compete. The motor you can build today is the starting point. What matters is how quickly you can build a better one. Please tag people with a blurb of what they're doing if you think they are working in this field as well! It'd be super interesting to see what everyone is building and what are their takes.

Just another Sunday afternoon in SF: I paid $60 for 2 humanoid robots to clean for an hour. Here's how it went: (1) What happened: Sergey and Chelsea (the names of the two robots) finished about 80% of one floor in an hour: tidying, wiping down surfaces, vacuuming, mopping, cleaning the toilet. - They came with a staff engineer at the company, who dropped them off and picked them up after an hour. - They were controlled by 1 human operator each, in the Mission where the company office is at. - The engineer gave me a number to text them, I texted a request to wipe the mirror. They did it. (2) Performance and dexterity: Chelsea dropped a toy, struggled with a pillow, and had trouble balancing on the playmat. But overall, for all the tasks they did, grippers were enough. 5 fingers were not needed. - The engineer told me the hardest task for them was making bed. - They were quite loud, stomping around the house. Took a bit of time to get used to. They both wore gripper socks which protected the floors and helped them not slip. - Interaction: I didn't know they can respond via voice. In the end they were conversing with us in voice which was nice. (3) Unexpected observation: Their pants and shirts have holes in them. It's because they were dragged on and off the vans that transported them. (4) Other user feedback: I shared my experience with some friends afterwards. A user told me that unloading dishwasher was an "advanced task". They didn't attempt at mine since it was running at the time they were working. Another user said they scratched their hardwood floor. That's probably why they now wear gripper socks. (5) Did they do as well as human cleaners? In terms of the tasks they did, I think so. In terms of speed, they were ~80% speed of humans when it comes to the more delicate tasks, but I would say they are quite smooth. They didn't finish the whole place, so it wasn't a full service. (6) How does the math work out? Each booking was only for 1 hour, since their batteries need to be swapped after 1 hour. So they didn't finish the place. For a user: In SF, it's ~$200-220 to clean a full home around my neighborhood, with two human cleaners. Usually it takes around 2.5 hours. So around $40/manhour. This is $30/robot hour. Slightly cheaper, but again it's not fully finished with the 1 hour they were available. For the company: It took 2 robots, 2 human operators, 1 staff member to dropoff/pickup, and 1 driver of the van to transport the robot. Definitely labor intensive. But they also retain the data they collect for training purposes. (7) My biggest takeaway as a user: interaction / hospitality mattered. One of the humanoids was more friendly (Sergey), he waved at us when he came in. It'd have been great if they both introduced themselves in the beginning. Beyond that, being able to do 2 hour slots (instead of one) would be great to finish the cleaning. Overall, I appreciated their work and the fact that they got the robots out in people's homes. Deployment is not easy and so much learning from it each time

Next year, I will give one of my SpaceX shares to every kid in America. This will be the first of many.