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Germany’s new 5-axis CNC machine delivers ultra precise, high efficiency metal removal for aerospace aluminum parts.

146,913 views • 7 months ago •via X (Twitter)

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🚨 AMERICA JUST BUILT THE WORLD’S MOST POWERFUL METAL 3D PRINTER AND IT’S ABOUT TO MASS-PRODUCE ROCKETS AND MISSILES. Divergent Technologies has unveiled the Monolith One, a giant industrial metal printer standing over 8 meters tall and armed with 12 high-powered lasers delivering a combined 24 kilowatts of energy. Unlike typical 3D printers used for prototypes, this machine is built for serious, high-volume production. It can print large, complex aerospace and defense parts in aluminum, titanium, steel, and nickel alloys and it roughly doubles the output of current systems. Why this matters: • Divergent plans to install 64 more of these machines in a massive new 430,000 sq ft factory in Long Beach, California • Once running, the facility aims to produce tens of thousands of munition airframes per year plus hundreds of thousands of critical metal components • It slashes manufacturing time from months down to weeks or even days • The company already supplies major players like Lockheed Martin and RTX The deeper implication: This isn’t just another 3D printer. It represents a shift toward software-defined, on-demand manufacturing at industrial scale for mission-critical hardware. As defense and aerospace demand skyrockets, traditional supply chains are too slow. Systems like Monolith One could become a cornerstone of faster, more resilient domestic production especially for complex structures that are difficult or impossible to make conventionally. We’re watching the industrialization of additive manufacturing in real time. How do you think large-scale 3D printing will change aerospace and defense manufacturing over the next decade? Follow for more frontier manufacturing and defense technology.

TheNewPhysics

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NEW: After swearing in new recruits at the Los Angeles Military Entrance Processing Station (MEPS), Secretary of War Pete Hegseth departed for Divergent as part of his Arsenal Freedom Tour. Divergent Technologies, Inc. (Divergent) is a Torrance, California-based advanced manufacturing company specializing in defense and aerospace production. Its Divergent Adaptive Production System (DAPS) integrates AI-driven generative design, metal 3D printing, and automated robotic assembly to produce lightweight, high-performance structures rapidly and cost-effectively, reducing weight, part counts, and environmental impact compared to traditional methods. Since pivoting heavily into defense in 2022, Divergent has secured major contracts with prime contractors including General Atomics, Lockheed Martin, Raytheon, and Triumph Group. These cover everything from sustainment parts to full airframe systems and hypersonic components. One thing that makes Divergent unique is they only hire US persons, and they are 100% independent from Chinese supply chains. The company raised $290 million in Series E funding in September 2025, achieving a $2.3 billion valuation to scale production for U.S. military needs. Hegseth’s Pete Hegseth Arsenal Freedom Tour highlights innovative defense manufacturing, AI integration, and technologies to strengthen the U.S. Defense Industrial Base under President Trump's peace-through-strength agenda. Hegseth is traveling with press, myself included, to showcase companies like Divergent that enable faster, more agile production for warfighters. This visit aligns with the Trump administration's push to revitalize American defense manufacturing and rapidly field emerging technologies.

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Depth Any Video with Scalable Synthetic Data AI physicists and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.

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