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NCSOFT’s LIMIT ZERO BREAKERS is opening its gates ⚔️ ◾ Test runs from June 10 - 15 ◾ Recruitment open until June 2 ◾ Available on PC, iOS & Android ◾ Anime-style action RPG with real-time combat ◾ Limited slots via closed test sign-ups LIMIT ZERO BREAKERS is developed...

20,205 Aufrufe • vor 3 Monaten •via X (Twitter)

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Yesterday, I appeared on the Mario Nawfal show, discussing the situation in #Iran and the #Gulf. Among the main points I made, in a nutshell, were the following: ◾ Iran has gained the upper hand in the Strait of #Hormuz, true, but it is not a card it can use at any time or recklessly. It is indeed a “nuclear bomb” as some prefer to call it, but every weapon of mass destruction, in this case an economic one, comes with inevitable collateral damage to friends and trade partners. Here and this time, that means the entire global economy. ◾ Every bargaining chip Iran may have from this point forward is strictly contingent on the survivability of the regime in Tehran, while its two main foes, the #US and #Israel, face no such existential threat. Nor would a change of government in those countries affect their military capabilities. This risk applies only to the Ayatollahs. ◾ We virtually have no reliable data from inside Iran to assess the real political and military damage. That will become visible only after a period of calm. We are likely looking at widespread damage to regime infrastructure, but not a complete dismantling of its military capabilities. This is particularly true for the regime’s internal security forces, which remain largely unharmed and ready to act in the face of renewed anti-regime protests. ◾ Gulf monarchies will likely make their final decision on possible normalization with Iran or further hostilities only after the scale of damage inside Iran is properly assessed. That moment will be a game-changing turning point for everyone. ◾ It is correct that the world will remain on edge because of Iran’s leverage over the Strait of Hormuz if the Trump administration fails to establish a Middle Eastern NATO to protect it. At the same time, Iran will remain on edge as the threat of abrupt regime change continues to loom. The genie is out of the bottle. It will be a series of nonstop global nightmares until the Tehran regime is gone. There is no return from here.

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Announcing DreamDojo: our open-source, interactive world model that takes robot motor controls and generates the future in pixels. No engine, no meshes, no hand-authored dynamics. It's Simulation 2.0. Time for robotics to take the bitter lesson pill. Real-world robot learning is bottlenecked by time, wear, safety, and resets. If we want Physical AI to move at pretraining speed, we need a simulator that adapts to pretraining scale with as little human engineering as possible. Our key insights: (1) human egocentric videos are a scalable source of first-person physics; (2) latent actions make them "robot-readable" across different hardware; (3) real-time inference unlocks live teleop, policy eval, and test-time planning *inside* a dream. We pre-train on 44K hours of human videos: cheap, abundant, and collected with zero robot-in-the-loop. Humans have already explored the combinatorics: we grasp, pour, fold, assemble, fail, retry—across cluttered scenes, shifting viewpoints, changing light, and hour-long task chains—at a scale no robot fleet could match. The missing piece: these videos have no action labels. So we introduce latent actions: a unified representation inferred directly from videos that captures "what changed between world states" without knowing the underlying hardware. This lets us train on any first-person video as if it came with motor commands attached. As a result, DreamDojo generalizes zero-shot to objects and environments never seen in any robot training set, because humans saw them first. Next, we post-train onto each robot to fit its specific hardware. Think of it as separating "how the world looks and behaves" from "how this particular robot actuates." The base model follows the general physical rules, then "snaps onto" the robot's unique mechanics. It's kind of like loading a new character and scene assets into Unreal Engine, but done through gradient descent and generalizes far beyond the post-training dataset. A world simulator is only useful if it runs fast enough to close the loop. We train a real-time version of DreamDojo that runs at 10 FPS, stable for over a minute of continuous rollout. This unlocks exciting possibilities: - Live teleoperation *inside* a dream. Connect a VR controller, stream actions into DreamDojo, and teleop a virtual robot in real time. We demo this on Unitree G1 with a PICO headset and one RTX 5090. - Policy evaluation. You can benchmark a policy checkpoint in DreamDojo instead of the real world. The simulated success rates strongly correlate with real-world results - accurate enough to rank checkpoints without burning a single motor. - Model-based planning. Sample multiple action proposals → simulate them all in parallel → pick the best future. Gains +17% real-world success out of the box on a fruit packing task. We open-source everything!! Weights, code, post-training dataset, eval set, and whitepaper with tons of details to reproduce. DreamDojo is based on NVIDIA Cosmos, which is open-weight too. 2026 is the year of World Models for physical AI. We want you to build with us. Happy scaling! Links in thread:

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Our new game infrastructure is being designed to be compatible with PC, Android, and IOS operating systems, featuring TPP-FPP game modes. 🎮 While conducting multiplayer tests on PC, we are effectively testing for mobile as well. The operation we are running simultaneously for PC and mobile saves us a great deal of time, allowing us to use a single game engine and generate two different outputs for cross-platform compatibility. 💻📱 We have created a code sequence that enables AI bots to attack you based on your KDA ratio, giving a real-player sensation. 🤖 We've considered all details, from hearing, seeing, to hiding behind objects and flanking you. 🎧👀 In single-player mode, you will experience a unique gameplay experience where you must defeat challenging enemies a good fight for a good reward is necessary! ⚔️🏆 While we continue to work on enabling Android and iOS users to join the same game, we will first launch multiplayer on PC and Android within their respective platforms. 🤝 We will refine the TDM and FFA modes, where you can challenge online opponents and earn COF, with player feedback to make them the best they can be. 🛠️👥 Following the release of COF, we will host tournaments with substantial prizes. 🏅 These matches, which can be played individually or in teams, will be live-streamed on Twitch and YouTube. 📺 You can form a team with your friends or collaborate with community members to create your new team. 👫🎮 To facilitate this, we will create a special area on our website to display data for teams and players. 🌐 We are nearing the completion of all our setups; our test server has successfully run our game and provided a stable service for 2 days. ✅ We will publish the minimum system requirements for PC. 🖥️ The texture sizes for the mobile game have been adjusted for phone optimization, ensuring smooth gameplay without lag. 📲 Now we will take a short break and continue from where we left off tomorrow. 🛌🔜 #cof #ecas #eth #p2e #BlockchainGaming #multiplayer #online #onlinegames #DEX

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HoraceVT 🪄🎬

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Autonomous driving through very dense dynamic traffic, with extremely tight-complex-stochastic traffic-dynamics on sub-urban roads, connecting to an open ground, with absolutely zero traffic-rules. This is the most heavily cluttered environment where we have tested our #autonomousdriving technology, presenting many of the adversarial negotiation scenarios as well, throughout the autonomous navigation task. This demo was done at the Mata Baglamukhi Madir campus in the city of Nalkheda, in MP, India, and was done in the presence of heavy police forces deployed that day on the ground, as can be seen in our demo. Our autonomous vehicle starts from the temple with a generic open environment, with zero traffic rules, with very narrow corridors created out of barricades for vehicles movement by the security forces. In the corridor no two vehicles can pass through at the same time, and our vehicle was tasked with driving through this corridor, while negotiating its way from any traffic, two-wheelers, or pedestrians it faces, with dense presence of bikes and cars on either side, presenting a very challenging environment for #autonomousvehicles. The vehicle exits the open area, and then assumes generic dual lane navigation, avoiding both static and dynamic obstacles, before encountering a police check-post, where the vehicle is supposed to wait if the barricade is closed, and proceed if open. Upon exiting the checkpost, the vehicle negotiates a traffic-intersection with stochastic and adversarial driving behaviour of other vehicles on the road. Our vehicle continuously faced heavily cluttered traffic scene, where entities on the road can execute a random driving pattern, making the decision making task very challenging. We did the demo over a period of two days, successfully executing multiple (30+) trials in this setting. This demo was again a culmination of our prior works and demos: Kankali Kali Mata demo, on-roads, bidirectional negotiation capability on single lane roads, and open environment Level-5 negotiation capability as showcased in our Toll-Plaza demo. We again scaled up classical decision making and motion planning algorithmic framework, to adapt to such a level of density of obstacles on the road. This framework is further being scaled up with #reinforcementlearning and unsupervised #deeplearning at Swaayatt Robots. We will again do a demo in the month of June here, showcasing autonomously acquired skills to pave the way for Level-5 autonomous driving, and to solve the Level-4 autonomy problem by the end of 2024. #MachineLearning

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