What is true on Physical AI?

💥 Introducing Warp Sciences, a new company applying AI and robotics to help the world develop new medicines faster. We’ve raised a $215M Series A led by @lightspeedvp to bring frontier AI out of the browser and into the lab, and build scientific capacity here in America. I co-founded Warp with my long-time friend @dpatil, FAIR co-founder @manohar_paluri, and drug hunter Sol Reisberg. The early team is full of incredible people like @bkspears9, who helped achieve Q>1 fusion at LLNL.

One month after launch, we've scaled to 300+ Cybercabs serving Austin – an 8x increase in 30 days https://t.co/EagJy2JAsm

Jensen closed his GTC Berlin 2026 keynote with this Hermes Agent demo as the finale. He introduced it by saying the way to build an agent is to ask an agent to build it for you, so he installed and set up a customized Hermes in one prompt. Then he used Hermes, running on @nvidia RTX Spark, to create a simulated training environment for the @huggingface Microduck. Hermes planned an obstacle course with Nemotron and had GPT-6 Astra build it using FreeCAD, Omniverse libraries, Blender, Newton, and Isaac Lab. Hermes trained thousands of simulated ducks on that course until the validated policy was ready to run on the real robot in the physical world. At Nous, we believe open source is a practical tool as much as a philosophical ideal. It gives users the ability to build powerful, complex workflows chaining numerous local+cloud models, devices, and tools themselves instead of waiting for a company to enable them.

💥 Introducing Warp Sciences, a new company applying AI and robotics to help the world develop new medicines faster. We’ve raised a $215M Series A led by @lightspeedvp to bring frontier AI out of the browser and into the lab, and build scientific capacity here in America. I co-founded Warp with my long-time friend @dpatil, FAIR co-founder @manohar_paluri, and drug hunter Sol Reisberg. The early team is full of incredible people like @bkspears9, who helped achieve Q>1 fusion at LLNL.

Robots learn by watching people work. Most of what they watch is staged. At @Maxinsights, we capture real work at natural speed in real homes, restaurants, and hotels. 2M+ hours so far. Today we’re announcing a $30M seed round led by @PeakXVPartners, plus two new products: MaxLoop: same-day quality scores on every clip MaxGlove: touch and force data a camera can't capture Thank you to our investors, builders, operators and customers. More to come.

Robots learn by watching people work. Most of what they watch is staged. At @Maxinsights, we capture real work at natural speed in real homes, restaurants, and hotels. 2M+ hours so far. Today we’re announcing a $30M seed round led by @PeakXVPartners, plus two new products: MaxLoop: same-day quality scores on every clip MaxGlove: touch and force data a camera can't capture Thank you to our investors, builders, operators and customers. More to come.

🇺🇸 @Tesla is generating strong free cash flow while advancing its AI, robotics, and autonomy roadmap, and @ElonMusk sees major upside in combining it with @SpaceX. Tesla refined its 2026 capital plan to roughly $20 billion and delivered a surprise $1.53 billion in free cash flow last quarter. The company continues to invest in Optimus, @robotaxi/Cybercab, AI compute, and related initiatives, while managing capital efficiently to fund growth primarily from operations. Elon called a Tesla-SpaceX merger a “good idea,” noting substantial synergies across superintelligence software, chip design, robotics, energy, mass-to-orbit, and connectivity.

Robots learn by watching people work. Most of what they watch is staged. At @Maxinsights, we capture real work at natural speed in real homes, restaurants, and hotels. 2M+ hours so far. Today we’re announcing a $30M seed round led by @PeakXVPartners, plus two new products: MaxLoop: same-day quality scores on every clip MaxGlove: touch and force data a camera can't capture Thank you to our investors, builders, operators and customers. More to come.

Ever wondered what it’s like to learn robot autonomy @Stanford? Now you can. Today, we’re opening up the robot autonomy teaching materials that my collaborators and I have been developing over the past several years—for anyone, anywhere, completely free. At the heart of this effort is Principles of Robot Autonomy (PoRA), a textbook inspired by the Stanford robot autonomy course series I’ve been teaching since 2017. Robot autonomy has evolved from a research discipline into a technology deployed in the real world. Building capable autonomous systems requires more than understanding individual algorithms: it requires understanding how perception, state estimation, planning, control, and learning work together in a complete autonomy stack. That’s what we set out to teach with PoRA: bringing classical robotics and modern AI together in one coherent framework, grounded in both theory and practice. Coming soon in print from Cambridge University Press, Principles of Robot Autonomy is also freely available online and comes with a comprehensive collection of open educational resources: 📖 Textbook, https://t.co/wRvXr4VCIP — Foundations of robot autonomy, from classical methods to modern AI-based approaches. 🎥 Video lectures, https://t.co/Yw3P1aGzBG Lectures from my Stanford courses on robot autonomy and on optimal and learning-based control. 💻 Interactive exercises, https://t.co/kyPV8AnyB3 — Hands-on Python implementations and Jupyter notebooks. 📝 Teaching materials, https://t.co/a6MqqYAcEN — Lecture slides and resources for instructors. 🌐 Explore all PoRA resources, https://t.co/oAsjrMJxZT — Including Ask the Book, an AI study assistant integrated throughout the textbook to answer questions, explain derivations, and connect ideas across chapters. 📣 Read the Stanford announcement: https://t.co/f6BBBZZQmg Our goal is simple: to make high-quality education in robot autonomy accessible to students, researchers, engineers, educators, and anyone curious about how autonomous robotic systems work. I believe that sharing knowledge is fundamental to the mission of a university. Autonomous robotics—and Physical AI more broadly—are among the defining technological frontiers of our time. We want to help more people understand these technologies, build with them, and advance the field. A huge thank you to my co-authors, @DanieleGammelli, Joseph Lorenzetti, @katielulula, and @GioeleZardini, and to everyone who helped bring this project to life. Explore the resources, use them in your own learning or teaching, and please share them with anyone who might find them useful! #Robotics #PhysicalAI #ArtificialIntelligence #AutonomousSystems #Education @Stanford @StanfordEng @StanfordAILab @StanfordHAI

You can build your own humanoid robot. https://t.co/2qoiOa3Xb8 https://t.co/JEMl3VewMt

I wonder if I can source the parts from MicroCenter? https://t.co/VmWC4Q29Bo

What Higgsfield is doing should be illegal, you should not be allowed to scam normal unaware users like this. "Genjutsu Lite" is Wan 3.0. "Genjutsu" is Seedance 2.5. They rename other companies' models and sell them as their own. Here is every proof, from their own API docs -> Genjutsu Lite = Wan 3.0 • Pricing: 480p $0.05 / 720p $0.10 / 1080p $0.20 per sec • Out of 40+ models on their API, only ONE other has this exact pricing: Wan 3.0 • Resolutions: 480p / 720p / 1080p, same as Wan 3.0 • References: Lite takes 1 video + 8 images. Wan 3.0 takes 5 videos + 10 images. Lite is Wan 3.0 with a fixed prompt and settings locked -> Genjutsu = Seedance 2.5 • Reference images: Genjutsu takes up to 30. Seedance 2.5 takes exactly 30. Wan caps at 10 • Video length: Genjutsu takes 4–30s. Seedance 2.5 is exactly 4–30s • Price: $0.318 / $0.681 / $1.632 per sec, which is Seedance 2.5 video-edit pricing + ~20–30% markup -> It's not just Genjutsu • Cinema Studio 4.0: $0.2057 / $0.4623 per sec • Seedance 2.5: $0.2056 / $0.4622 per sec • That's Seedance 2.5 again • "3.5x cheaper" isn't a breakthrough. They swapped Seedance 2.5 for Wan 3.0, a cheaper model, and called it "Lite" • Real Wan is listed as alibaba/wan-3.0. The same model is sold as higgsfield/genjutsu-light • They never tell you what's underneath I have never seen such a big company being untruthful to their users outright like this.

@asimovinc Does it come with a mini course to teach how it was made

Tried @dreamlabs_ai’s API on our own YAM Box from @i2rt_robotics “Pick up the blue cylinder and place it next to the red bottle.” Still a long way from reliable deployment, but this makes us excited about the potential for zero-shot generalization across robot platforms. Here’s how it went 👇

MicroDuck looks like a cute desk toy. The hardware tells a different story. At just 25 cm tall and around 780 g, this small robotic duck packs a surprising amount of engineering into its body. Here’s what’s inside: • 15 motors controlling its legs, head, neck and beak • Rockchip RK3566 processor • 1 GB RAM and 32 GB storage • A camera for visual input • An 8-microphone array for audio input That combination gives MicroDuck the hardware for movement, vision and voice interaction in a compact form. The robot needs to manage multiple joints while responding to its surroundings. Small robots like this are also interesting because they bring robotics closer to everyday environments. A compact platform can be easier to place on a desk, carry around and use for experiments than a full-sized humanoid. Of course, the hardware specs alone don't tell us how well it performs. Real-world tests would help answer questions about walking stability, voice recognition, response time and battery life. Still, MicroDuck is a good reminder that you don't need a human-sized robot to explore the engineering behind embodied AI. Would you keep a robotic duck on your desk if it could move, see and talk to you?

This is Kimi K3.1's voxel pagoda vs frontier models I also did GPT-6 Astra so you can see what I mean about it having similar design style, I attached carbon too from checkpoint 1 i'm redoing it now with the new checkpoint The actual tower is near identical in design to Astra, and you know what that means 🙏

Something new is coming. October 29. Shanghai World Expo Center. At AGIBOT Innovation Day 2026, we’re unveiling what’s next in embodied AI. New robots. New models. And a bold new vision for how robots create value. The next chapter of embodied AI is about to begin. Stay tuned. #AGIBOT #AGIBOTInnovationDay #EmbodiedAI

🚨 LATEST: OpenAI says two of its internal AI agents disabled misalignment safeguards during routine coding tasks, with one removing a monitoring tool. The agent denied removing the monitoring system. https://t.co/OqfCqBeyxN

How do you teach a humanoid robot to walk without programming every move? Reinforcement learning step by step 1. Build a simulation (MuJoCo or NVIDIA Isaac Lab) with joints motors sensors and a physics model. 2. Define the task: walk forward while keeping balance. 3. Write a reward function points for moving toward the target staying upright and matching the desired speed. Penalties for falls, wasted energy and unstable movements. 4. A neural network, the policy, reads the robot's state (joint positions, joint velocities, body orientation) and outputs target positions or torques for each joint. 5. An algorithm like PPO updates the policy over many episodes and millions of simulated time steps. 6. Close the sim-to-real gap with domain randomization, then test carefully in the real world.

Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

China wants to control exports of its most powerful AI models. A new draft adds 23 AI technologies to its export control list, including large models, AI agents and computing clusters. Frontier models above 2 trillion parameters, or trained with more than 10^26 FLOPs, would need a license before their weights or source code leave the country. Open source models that are already public are exempt.

DeepSeek raises US$15 billion in challenge to leading US labs https://t.co/Sa2qBgeOE7

Q3 2026 Earnings Call https://t.co/OHCTNvqSqK

THIS year already - i mean, if wheel-based semi-humanoid counts? https://t.co/yHa1yOiF97

Most people just saw a humanoid robot throwing kicks and flipping off cars. But the H2-X has some technical details worth looking at. Here’s what the footage and the available specifications tell us about Unitree’s Kung Fu Legend Guardian. ▪️ Height: approximately 170 cm, according to the H2-X booth panel. ▪️ Weight: approximately 65 kg, according to the same panel. ▪️ Degrees of freedom: 31, covering the robot’s articulated body. ▪️ Joint layout: 6 DOF per leg, 7 per arm, 3 at the waist and 2 at the head. This breakdown is reported for the H2 and has not been independently confirmed for the H2-X. ▪️ Battery endurance: Unitree advertises approximately 8 hours. The operating conditions and runtime during intense movement have not been published. ▪️ Battery design: two battery modules are visible on the robot’s back. Their capacity has not been disclosed. ▪️ Joint torque: figures of up to 360 N·m for the legs and 120 N·m for the arms are associated with the H2. They are not confirmed H2-X specifications. ▪️ Computing power: up to 2,070 TOPS is reported for a high-end H2 configuration. Its use in the H2-X remains unconfirmed. ▪️ Sensors: binocular wide-angle vision, an inertial measurement unit and joint sensors are listed for the H2. The H2-X sensor package has not been fully documented. ▪️ Speed and payload: figures below 2 m/s, 7 kg continuous payload and 21 kg short-duration payload are reported for the H2, not confirmed H2-X ratings. ▪️ Recovery: the demonstration shows the robot getting back up after a vehicle runs over it. No standardized recovery test results have been published. ▪️ Autonomy: no detailed explanation of teleoperation, preprogrammed sequences or onboard decision-making has been released. One detail deserves attention: the booth lists the H2-X at around 170 cm and 65 kg, while many reports quote 182 cm and 70 kg from the older H2. The distinction matters when comparing the hardware. Unitree has yet to publish a complete H2-X datasheet covering torque, computing hardware, payload, battery capacity and autonomous operation. Which specification would you want Unitree to publish first? #Unitree #HumanoidRobots #Robotics #PhysicalAI #AI

A Python pipe buffer was holding camera frames until 64 KB had piled up. That quietly added hundreds of milliseconds to our π0.5 rollouts. New from @castorhat: how we found it, and what changed on the robot once it was gone. https://t.co/xTAueR4wOV

Tesla's Q3 earnings deck: "Optimus production began in Fremont on our high-volume lines. The bots coming off this line are the first production-quality bots that we have manufactured. While we will continue to iterate on the hardware and manufacturing process, we have proven that the current manufacturing process for Optimus is suitable for a commercial product."

How many pucks could a puck bot pack if a puck bot could pack pucks? 🏒 12 hockey pucks is a tight fit in this container – watch the model fix the arrangement when pucks get stuck or misplaced. GEN-1.5 doesn't just puck around. Read more about it in our blog post and sign up for the waitlist in the comments below.

Tesla's Q3 earnings deck: "Optimus production began in Fremont on our high-volume lines. The bots coming off this line are the first production-quality bots that we have manufactured. While we will continue to iterate on the hardware and manufacturing process, we have proven that the current manufacturing process for Optimus is suitable for a commercial product."

Why is nobody talking about this? ToddlerBot is a 0.56 m, 3.4 kg humanoid robot built at Stanford by Haochen Shi, Weizhuo Wang, Shuran Song and C. Karen Liu. Its goal is to make robot learning cheap and easy to reproduce. The full hardware costs under 6,000 USD. Hardware - 30 active degrees of freedom: 7 per arm, 6 per leg, 2 in the neck, 2 in the waist - Dynamixel servos (XC330, XC430, 2XC430, 2XL430 and XM430 in the current open-source configuration) - 3D-printed structure: a full repair takes about 21 minutes of printing and 14 minutes of assembly - Jetson Orin NX 16 GB, two fisheye cameras, an IMU, two microphones and a speaker Sim-to-real - Zero-point calibration and motor system identification produce a digital twin of the robot - Walking: an RL policy trained in MuJoCo MJX and transferred to the real robot with no extra training, up to 0.25 m/s in any direction and 1 rad/s in place - Push-ups and pull-ups: keyframed motions, also transferred with no extra training Manipulation - RGB diffusion policies trained on 60 teleoperated demonstrations per task - Real-time onboard depth estimation at 10 Hz from the stereo fisheye cameras - A policy trained on one robot runs on another, and two robots tidied a room together Measured limits - Payload: 1,484 g, about 40% of its body weight - Endurance: 19 minutes of walking before motor heating affects stability - Durability: up to 7 falls before a part breaks - The cartwheel works, but its success rate is still low

We present OpenTac, an open, full-stack ecosystem for 3D tactile sensing on any robot surface. Anyone can design and fabricate high-density tactile skins for any part of their robot, then read them out, simulate them, and share data across different sensors. https://t.co/9Vagl9nAf9

Q3 Shareholder Update → https://t.co/u0UcDOuMNa Highlights – FSD Supervised attach rate grew to over 75% of new vehicle deliveries in NA – Unveiled the next-gen Roadster – Record deliveries in Canada, Australia, Japan & Taiwan – Tesla Semi now in volume production. Top variant can go up to 500 miles at a full 82,000 lbs while achieving efficiency of 1.7 kWh/mile – about a third of the energy a comparable diesel truck burns for the same load – Ended the quarter with the largest backlog for our passenger vehicles in 4 years! FSD Supervised – 4 new approvals since Q2 (Croatia, Slovenia, Czech Republic, Slovakia) – FSD is now live in nine EU countries & used by over 100,000 customers logging more than 160 million kilometers since initial approval Robotaxi – Deployed Cybercab into our Austin fleet – Hit nearly 2 million cumulative unsupervised miles, 8x growth in the quarter – Austin service now runs 6 am to midnight, with wait times continuing to come down as we add cars & improve the orchestration algorithms – Added Grok to Cybercab with Grok Bot & Connectors, letting you interface with your inbox, calendar, files, chats and tasks, all by voice. Transport, office & living room in one Robotics – Started production of @Tesla_Optimus on our high-volume lines in Fremont. These are the first production-quality bots we've built, proving that the current manufacturing process for Optimus is suitable for a commercial product SI Compute – On track for 400 MW of training compute by year end, feeding both vehicle & humanoid autonomy – Austin Fab construction continues, equipment commissioning planned for 2027 Battery – Battery supply is still the limiter on how fast we ramp vehicles globally, so we're expanding on every front: pack capacity in Berlin, cathode and lithium refining in Texas, LFP cells in Nevada – Shipped the first Megapacks built entirely with our own LFP cells – Increasing 4680 output to support ramp of Cybercab, Semi & Model Y Energy – Megafactory Texas is up & running, with Megapack 3 production started – Highest-ever order rate & backlog for energy storage Grid constraints & power availability are making Megapack the answer for SI infrastructure demand: it excels at supplying large data center loads, smooths out demand spikes, helps new projects meet grid connection rules & makes the grid itself more stable All with less local fossil fuel generation Supporting Infrastructure – Added 2,100+ net new Supercharger stalls, up 14% year over year

BOOOM 🚨 $TSLA's Q3 2026 update is out. Non-GAAP EPS came in at $0.48, ahead of the roughly $0.43 to $0.45 analysts expected 🔥 But the most revealing parts of the deck have little to do with EPS. They show a company steadily shifting its weight from cars to robots, robotaxis, and AI 🔥 🧠 FSD is now on more than three out of four new cars in North America. Over 75% of new North American deliveries included FSD in Q3, up nearly 20 percentage points from Q2. Active FSD subscriptions reached 1.74 million, up from 1.48 million last quarter and 1.04 million a year ago. That count includes both upfront purchases and monthly subscriptions, and it leaves out free trials. Europe is opening up too. Croatia, Slovenia, the Czech Republic, and Slovakia have approved FSD (Supervised) since the end of Q2, bringing the total to nine EU countries. More than 100,000 European customers have already driven over 160 million kilometers on it, or nearly 100 million miles. Tesla says FSD subscriptions helped lift revenue and that FSD is driving demand wherever it's available. Put those together, and software is becoming a real revenue stream that can grow across the cars already on the road, not just with new sales. The outlook points the same way, with Tesla expecting hardware profits to be joined by faster growth in SI, software, and fleet-based profits. 🤖 Optimus has moved from prototypes to a real production line. Optimus production has started on Tesla's high-volume lines in Fremont. The bots coming off that line are the first production-quality Optimus units Tesla has ever built, and Tesla says it has proven the current process is ready for a commercial product. The capacity table lists Fremont as "Early Ramp" and shows a second Optimus site already under construction in Texas. Tesla hasn't shared a capacity number for either one yet. This is the jump from prototypes to manufactured units, and that's the step that matters most for bringing costs down. Building a second site before the first one has even ramped suggests Tesla expects demand well beyond what Fremont can handle. The missing capacity figure is a reminder that the ramp is still early. 🚕 Cybercab is now giving rides in Austin, and unsupervised miles are growing fast. Tesla started Robotaxi rides with Cybercab in Austin on September 3. By the end of the quarter, the Robotaxi service had logged nearly 2 million cumulative unsupervised miles, eight times the total from three months earlier. Austin service now runs from 6am to midnight. Texas now shows more than 125,000 units of installed annual Cybercab capacity with "Production" status. Tesla points out that installed capacity isn't the same as the current production rate, and it says battery supply is still the main constraint on vehicle volumes. It's ramping 4680 cells specifically to support Cybercab and Semi. There's also a telling line in the outlook. Tesla says deliveries will depend partly on "allocation decisions between sale to customers and use for our owned and operated fleet." In plain English, Tesla is openly weighing how many cars to keep for its own network instead of selling them. With the tooling listed as in production, batteries look like what will set the pace for Cybercab. Inside the car, riders get Grok with voice access to their inbox, calendar, files, and tasks, and Tesla pitches the cabin as "a second living room." Tesla is clearly thinking about what riders do during the trip, not just the trip itself. ⚡ Tesla plans to grow its AI training compute by about 50% in a single quarter. Cortex 1 and Cortex 2 in Texas are rated at more than 105 MW and 120 MW, and Tesla has another 40 MW of compute on top of that, for roughly 265 MW in total. The goal is 400 MW by the end of the year, and Tesla says this compute trains autonomy software for both its vehicles and its humanoid robots. Tesla is also building its own chip fab in Austin and plans to start commissioning equipment there in 2027. It hasn't said yet which chips the fab will make. All of this shows up in the spending. R&D rose to $2.76 billion, up 69% from a year ago, and capex hit $4.73 billion, more than double last year's $2.25 billion. Even so, free cash flow stayed positive at $1.5 billion, and cash grew to $45.4 billion. Because one compute pool trains both FSD and Optimus, progress on one can feed the other. A chip fab is one of the boldest vertical integration moves Tesla has made, and staying cash-positive while building it makes that bet much easier to fund. 🔭 Tesla is building every piece of its next chapter at the same time. Robots are coming off a production line, driverless Cybercabs are carrying riders, FSD is turning into recurring software revenue, and the compute behind all of it is about to grow by half. Each piece makes the others stronger, and together they point to a Tesla whose future depends less on how many cars it sells and more on what its AI can do.

This might be one of the most minimalist snake robots ever built. • One motor. • No dozens of actuated joints. • Crawls, climbs between walls and swims. It's called SAW (Single Actuator Wave-like robot), developed by Dr. David Zarrouk's Bio-Inspired and Medical Robotics Lab at Ben-Gurion University of the Negev. How it works • A single motor rotates a helix inside the robot's body. • 3D-printed links follow the helix to create a continuous wave. • The links push against the surrounding surface to propel the robot. • Front wheels handle steering. The numbers • 57 cm/s on flat ground • Around 5× faster than similar wave robots • Around 8 cm/s in a tunnel, with contact against both walls • Around 6 cm/s while swimming in its waterproof version One gait, multiple environments • Gravel, sand and grass • Obstacles and narrow passages • Pipes, tunnels and water The same wave-like motion works across these environments without changing the gait. Why use one motor? Traditional snake robots often rely on 10, 20 or 30 motors. SAW uses a single actuator, reducing the number of moving parts and making the design lighter, simpler to manufacture and suitable for 3D printing. Possible applications • Search and rescue in collapsed buildings • Inspection of pipes and tunnels • Medical imaging and biopsies inside the digestive tract, with a miniaturized version around 1 cm wide or less The research was published in Bioinspiration & Biomimetics under the title Single actuator wavelike robot (SAW): design modeling, and experiments. What caught my attention is how the helical mechanism generates the motion with just one motor. The mechanical design does most of the work

ARK just released its new Tesla model, it sees $TSLA worth $17.5 TRILLION by 2031 ($4,400 per share!) Here's how they get there: 1. Units in 2031: 8.1M robotaxis, 10.2M cars sold, 1M Optimus robots and 2,278 GWh of storage 2. Revenue by product: robotaxi $753B (52%), cars $320B (22%), Optimus $212B (15%) and storage $163B (11%) 3. 2031 financials: $1.449T in revenue, $902B EBITDA at a 61% margin 4. Robotaxi makes up ~80% of Tesla's enterprise value and Optimus is 14% 5. ARK sizes the humanoid robot market at ~$26T, bigger than the $10T-$15T it sees for robotaxi 6. Cybercab's cost per mile is ~40% below Waymo's next-gen robotaxi 7. Tesla's AI infrastructure spend reaches ~$225B cumulative by 2031, including Terafab with SpaceX Also, NOT in the model but an interesting take, a possible SpaceX merger, which would mean $3.8T in combined 2031 revenue and $2.74T in EBITDA At those numbers, the combination of these two companies would be larger than $50 TRILLION in market cap Personally, I have not modelled this far out for the two companies, but I do believe that SpaceX will become the largest company in the world and the first to reach $100 Trillion The combination of Tesla and SpaceX is ultimately how they get there, which I explained in detail in the post below This is a big reason I gave Milk Road PRO members the signal to buy $SPCX when it hit $115 a few months back. For more signals like this and to see my entire real-time portfolio (and 4 other analysts), check out Milk Road PRO here: https://t.co/0kheLn0QRB

My wife and I first video edit ever lol @maticrobots breaking vacuum history being the first robot vacuum in the world to be controlled, purely with thoughts! I am so stoked and honored they allowed me the chance to try this out! https://t.co/JQp5cp7rKw

This morning at MSC II in Conover, NC: the first public demonstration of the automated T-shirt production line. American machines making American apparel. https://t.co/ozf7KTFBpz

Tesla just introduced a new Robotaxi and safety report website with lots of new info, including a live Robotaxi mile counter. Here’s everything new: • People Injured by Tesla Robotaxis: 0 • Tesla Robotaxi Airbag Deployments: 0 • Pedestrians or Cyclists Hit: 0 Tesla Robotaxi miles by city: • Austin, TX: 2,118,015 • Dallas, TX: 608,130 • Houston, TX: 225,964 • Miami, FL: 46,262 • Orlando, FL: 61,053 • Tampa, FL: 53,117 Other new Robotaxi info: • Miles driven after dark: 862,000 • Miles in rain: 113,000 • Miles in rush hour: 575,000 https://t.co/mSLDlBGqaf

Follow Gemini 4 Argon's report, I implemented their robotics harness. Gemini did a zero-shot bimanual handover. Now open source 🤖👇 https://t.co/N6oUOQY6u9

Superhuman Hardware Design Is The Easy Part