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  • GLM-5.1: Self-Improving Agentic Model with Long-Horizon Problem-Solving
    GLM-5.1: Self-Improving Agentic Model with Long-Horizon Problem-Solving

    This is a very big deal: GLM-5.1 model can autonomously evaluate and improve its own work over long periods without explicit metrics, shifting from one-shot outputs to sustained, self-directed problem solving. Lets go Chubby♨️ (@kimmonismus) Another big release: GLM-5.1! China is on fire! significant increase in evals compared to GLM-5.0 tl;dr GLM-5.1 is the new open-source agentic coding model that significantly outperforms its predecessor by sustaining long-horizon problem-solving over hundreds of iterations, continuously improving results instead of plateauing, achieving state-of-the-art performance on complex software engineering benchmarks. — https://nitter.net/kimmonismus/status/2041553412878180691#m

    → View original post on X — @kimmonismus, 2026-04-07 17:30 UTC

  • Artemis II Free-Return Trajectory: Gravity Powers Spacecraft Home
    Artemis II Free-Return Trajectory: Gravity Powers Spacecraft Home

    Artemis II and the Apollo 13 trick: How gravity can bring astronauts home without fuel or an engine? NASA’s Artemis II: How a Free-Return Trajectory Lets Gravity Do the Work How math, timing, and the Moon can help bring astronauts back to Earth Most people think of spaceflight as a contest of thrust between the fuel and the engine. Big rockets lift off. Fuel disappears by the ton, but Artemis II depends on something quieter and, in some ways, more elegant: geometry. Seen through an ML vantage point, the free-return trajectory behaves like a machine learning precision-tuned control problem, where the machine learning model is celestial mechanics and the loss function is wasted fuel and mission risk, and the winning solution is something that lets gravity do as much of the work as possible. NASA’s Orion spacecraft is flying a free-return trajectory, a path that uses the combined gravity of Earth and the Moon to carry the crew out to the Moon and then bring them back toward Earth. That is the key idea. When Orion is placed on the right outbound track, gravity and geometry do much of the rest. Engines matter; they matter most at the beginning. The circles shown around Earth are not Orion wandering around or trying to escape gravity. They are planned Earth orbits that let NASA check the spacecraft, build the right departure geometry, and set up the main push outward. Then came the translunar injection burn: nearly six minutes of engine firing that used roughly 1000 pounds of propellant—not gasoline, but a spacecraft fuel-and-oxidizer mix stored in Orion’s service module—to place the capsule onto the figure-eight free-return path from Earth to the Moon and back again. From there, the mission does not rely on a major engine burn behind the Moon to get home. Instead, lunar gravity deflects the spacecraft's trajectory, sending it back toward Earth. This is why the trajectory matters so much. It builds a return into the mission from the start. The idea is familiar to anyone who remembers the Apollo 13 trick. After the oxygen cylinder explosion aboard that spacecraft in 1970, NASA needed a way to get the crew back safely. A free-return path became central to that effort. Artemis II uses that same basic logic by design rather than in an emergency. The physics behind it is not mysterious, even if orbital mechanics can look intimidating. A spacecraft in flight is constantly trading speed against gravity. Engineers shape that trade with extraordinary precision. In simplified form, the orbital energy depends on speed and distance: Trajectory energy = v²/2 − mμ/r Here, v is the spacecraft’s speed, r is its distance from the body it is moving around, and mu represents the strength of gravity for that body. A translunar injection burn changes that balance. Instead of staying in a closed orbit around Earth, Orion is pushed onto a much longer arc that reaches the Moon’s neighborhood. An aerospace-friendly way to picture the problem is as a landscape of gravity wells. Earth sits in well. The Moon moves in another. Put the spacecraft on the right ridge line, with the right energy, and it can slide from Earth’s domain into the Moon’s and then back again. As one aerospace engineer explained to Scientific American, once the spacecraft reaches the right “height” on that topographic map, it can follow that path essentially for free. That is the real meaning of free return. It is not that the spacecraft needs no propulsion at all. Artemis II still has built-in correction burns, and NASA has already adjusted its plan by skipping two of three smaller corrective maneuvers after the main burn performed so well, but the core return path does not depend on a large engine firing at the far side of the Moon, when Orion is out of radio contact with Earth. That lowers risk. It also helps explain why the mission could set a new human distance record from Earth. Orion reached 252,756 miles from Earth as it arced around the Moon before beginning the trip back. The spacecraft was not simply going far for the sake of going far. The long loop is part of the geometry that allows lunar gravity to redirect it toward Earth without an engine burn. Then comes the final stage left: reentry. At that point, Orion is no longer being flown home, burning fuel in the ordinary sense. It is falling back into Earth’s gravity well at tremendous speed. Reentry is a controlled descent through the atmosphere like Apollo 13, with the heat shield absorbing the thrust and the capsule arriving at the right angle for splashdown. That final return is less like powered flight and more like a carefully managed plunge. The larger lesson of Artemis II is that deep-space travel is not only a matter of force. It is also a matter of timing, angle, and restraint. Burn too little and you stay trapped on Earth. Burn too much or in the wrong direction and you waste fuel or miss the path you need, but if the numbers are right, the Moon itself becomes part of the navigation system. That was true in the Apollo era. It is still true now. Artemis II is a reminder that in space, the best engineering is often the kind that lets physics do the heavy lifting. #BigData #Analytics #AI #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode References Artemis II Mission Tracker. (2026). Artemis II Mission Tracker | Live Orion 3D Timeline. artemistracker.com/ European Space Agency. (n.d.). European Service Module: Propulsion. ESA. esa.int/Science_Exploration/… NASA Communications. (2026a, April 2). Artemis II flight update: Perigee raise burn complete. NASA. nasa.gov/blogs/missions/2026… NASA Communications. (2026b, April 2). Artemis II Flight Day 2: Orion completes TLI burn, crew begins journey to the Moon. NASA. nasa.gov/blogs/missions/2026… Vergano, D. (2026, April 7). NASA’s Artemis II “free return” trajectory lets gravity do the work. Scientific American. scientificamerican.com/artic…

    → View original post on X — @gp_pulipaka, 2026-04-07 17:26 UTC

  • Industrial vibration monitoring evolution patterns IIoT

    Industrial vibration monitoring is evolving fast. Seeing similar patterns? @IIoT_World @CRudinschi @agentic_factory @KirkDBorne @EvanKirstel

    → View original post on X — @fogoros

  • Converting 30k arXiv papers to Markdown using SOTA OCR

    New blog post: converting 30k @arxiv papers to Markdown using SOTA OCR models to enable chat with paper functionality Includes: > leveraging an open OCR model (Chandra 2 by @datalabto) > running on GPU infra – @huggingface Jobs > using Codex with a SKILL.md

    → View original post on X — @huggingface

  • Hugging Face Storage: Feedback on New Cloud Storage Solution

    did you try https://
    huggingface.co/storage? would love to hear your feedback

    → View original post on X — @clementdelangue

  • Kevin Ellis on DreamCoder: Neurosymbolic AI and Program Synthesis

    On the pod: our most-requested guest! @ellisk_kellis from @Cornell shares the origins of his influential neurosymbolic paper "DreamCoder". Plus: program synthesis, wake-sleep library learning, world models, running an AI research lab, and more.

    → View original post on X — @fchollet, 2026-04-07 16:33 UTC

  • GLM-5.1: Revolutionary Open-Source Agentic Coding Model Released
    GLM-5.1: Revolutionary Open-Source Agentic Coding Model Released

    Another big release: GLM-5.1! China is on fire! significant increase in evals compared to GLM-5.0 tl;dr GLM-5.1 is the new open-source agentic coding model that significantly outperforms its predecessor by sustaining long-horizon problem-solving over hundreds of iterations, continuously improving results instead of plateauing, achieving state-of-the-art performance on complex software engineering benchmarks. Z.ai (@Zai_org) Introducing GLM-5.1: The Next Level of Open Source – Top-Tier Performance: #1 in open source and #3 globally across SWE-Bench Pro, Terminal-Bench, and NL2Repo. – Built for Long-Horizon Tasks: Runs autonomously for 8 hours, refining strategies through thousands of iterations. Blog: z.ai/blog/glm-5.1 Weights: huggingface.co/zai-org/GLM-5… API: docs.z.ai/guides/llm/glm-5.1 Coding Plan: z.ai/subscribe Coming to chat.z.ai in the next few days. — https://nitter.net/Zai_org/status/2041550153354519022#m

    → View original post on X — @kimmonismus, 2026-04-07 16:27 UTC

  • Gravity-Defying Robotic Warehouse System with Ceiling and Wall Climbing

    Gravity-Defying #Robotic #Warehouse System: Ceiling- and Wall-Climbing #Automation via @ZappyZappy7 #Robot #MachineLearning #ArtificialIntelligence #ML

    → View original post on X — @ronald_vanloon, 2026-04-07 16:25 UTC

  • Embeddings: The Unsung Hero Driving Model Accuracy Forward

    Embedding is an unsung hero in model accuracy and today is a big leap forward. It's at the heart of grounding; it's the layer that does the hard work of searching, retrieving, organizing, and connecting information across sources for a holistic response.

    → View original post on X — @mustafasuleyman

  • Bing releases Harrier, new state-of-the-art embedding model
    Bing releases Harrier, new state-of-the-art embedding model

    Another SOTA model drop! This time from the @Bing team: meet Harrier, a new open-source embedding model with state-of-the-art performance and the #1 spot on the industry standard multilingual MTEB-v2 benchmark. Jordi Ribas (@JordiRib1) I’m pleased to share that our search team has open sourced an embedding model called Harrier that is currently ranking #1 on the multilingual MTEB-v2 benchmark leaderboard. Harrier delivers SOTA performance on retrieval quality, semantic matching, and contextual analysis across workloads, supporting more than 100 languages and handles long inputs up to 32K. It is built for the next generation semantic search for Bing and our web grounding (RAG) service for AI agents, which already powers nearly every major AI chatbot today. As you can see in the leadership board, our Harrier model is currently ahead of other excellent models based on Gemini, Gemma, Llama, Qwen, and more. I’m grateful for the hard work of our team to get to this top ranking, and I’m excited to see all the healthy competition in the space, which should ultimately lead to more innovations that will benefit everyone. Learn more: msft.it/6019QNB0b — https://nitter.net/JordiRib1/status/2041550352739164404#m

    → View original post on X — @clementdelangue, 2026-04-07 16:22 UTC