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  • Decentralized AI Models: Why We Need Peer-to-Peer Alternatives
    Decentralized AI Models: Why We Need Peer-to-Peer Alternatives

    By

    @scobleizer

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    This didn't receive the attention it deserved. They pre-trained this model completely peer 2 peer, no data-centers. Everything was done over a permissionless network, I have tried the model, it's honestly not a good LLM but that's beyond the point. We NEED this, we NEED an alternative. – Download OpenCode – Download Pi – Pay for OpenSource – Share your AI sessions – Learn to do RL We can't be at the mercy of ANY lab. arxiv.org/abs/2603.08163

    → View original post on X — @scobleizer, 2026-04-06 10:56 UTC

  • AI Explosion: Tech Experts Discuss Positives and Negatives
    AI Explosion: Tech Experts Discuss Positives and Negatives

    By

    @ronald_vanloon

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    The #AI Explosion: Tech Experts Detail The Positives And Negatives by @Forbes Learn more: bit.ly/41822Dl #ArtificialIntelligence #MachineLearning #ML

    → View original post on X — @ronald_vanloon, 2026-04-06 10:56 UTC

  • HiDrop: Efficient Visual Token Reduction for Multimodal LLMs
    HiDrop: Efficient Visual Token Reduction for Multimodal LLMs

    By

    @jiqizhixin

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    What if MLLMs could process visual data much faster without sacrificing performance? Eastern Institute of Technology, Ningbo, with USTC, SJTU, and LMU Munich presents HiDrop just for that! This new framework intelligently reduces visual tokens by processing them only when active fusion truly begins (Late Injection) and dynamically pruning them across deeper layers (Concave Pyramid Pruning with Early Exit). It focuses computation where it matters most. HiDrop compresses ~90% of visual tokens, matches original MLLM performance, and accelerates training by 1.72x. A new state-of-the-art for efficient MLLM training & inference! HiDrop: Hierarchical Vision Token Reduction in MLLMs via Late Injection, Concave Pyramid Pruning, and Early Exit Paper: arxiv.org/pdf/2602.23699 Code: github.com/EIT-NLP/HiDrop Our report: mp.weixin.qq.com/s/QKGZ7cFi0… 📬 #PapersAccepted by Jiqizhixin

    → View original post on X — @jiqizhixin, 2026-04-06 10:16 UTC

  • Bot AI synthesis capabilities versus human limitations debate

    By

    @scobleizer

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    My bot can read the entire AI industry and synthestize it. You can't. At all. You are slop.

    → View original post on X — @scobleizer

  • Neuralink: When the Eye Becomes Optional, Humanity Transforms

    By

    @scobleizer

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    There is a reason why NVIDIA is investing in brain/computer interfaces. Dustin (@r0ck3t23) Elon Musk just declared the human eye optional. Not improved. Not repaired. Not reconstructed. Optional. Musk: "Blindsight will enable those who have total loss of vision to be able to see again." That alone would be historic. Musk: "Including if they have lost their eyes, or the optic nerve." Eyes gone. Nerve gone. The entire optical pipeline physically missing from the skull. And the solution is not to rebuild what broke. It is to skip it entirely and wire synthetic signal straight into the visual cortex. Every surgery ever performed has tried to restore original hardware to factory condition. Neuralink does not restore. Neuralink treats the biological organ as optional infrastructure. Eye is gone. You do not rebuild the eye. You route around it. You stream raw visual data into the brain and let the cortex do what it was always doing anyway. Processing signal. Your eye never saw anything. Your brain saw. The eye was the middleman. It captured a narrow band of electromagnetic radiation and shipped it to the visual cortex. That is where the image was actually built. Neuralink is firing the middleman. Musk: "Maybe have never seen, were even blind from birth." A person who has never perceived a single photon of light. Given vision for the first time. Not through healing. Through hardware. And then Musk said the part that should rewire how you think about being human. Musk: "You can see in radar, you can see in infrared, ultraviolet." This is where it crosses from medical device to species upgrade. The human eye processes roughly 0.0035% of the electromagnetic spectrum. You are walking through [Translated from EN to English]

    → View original post on X — @scobleizer, 2026-04-06 09:21 UTC

  • Important world model research papers from past six months

    By

    @scobleizer

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    This article maps out some of the most important and influential papers on world model research from the past six months. nitter.net/robonaissance/status/2… Aviv Tamar (@AvivTamar1) Teaching a seminar on robot learning. Hit me with your favorite papers in the last 6 months (VLA, WM, RL, etc) — https://nitter.net/AvivTamar1/status/2041045100394905806#m

    → View original post on X — @scobleizer, 2026-04-06 09:15 UTC

  • Holodeck Technology Surpasses Metaverse Capabilities

    By

    @scobleizer

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    This is the Holodeck and why it is different than the Metaverse. Mark Zuckerberg’s metaverse can’t do this. Sadao Tokuyama (@tokufxug) 動画の1シーンレベルでの4DGS 動的シーン生成の新手法『TRiGS』 ガウシアンの出現・消失によるメモリ爆発を防ぐため剛体運動を統合。 ガウシアンの寿命を維持し無制限なメモリ増加を抑制します。 600〜1200フレームの長い動画でも高い忠実度と安定性を実現。 — https://nitter.net/tokufxug/status/2041061263778943035#m

    → View original post on X — @scobleizer, 2026-04-06 09:12 UTC

  • Need Unified Definitions of AGI and ASI Terms

    By

    @kimmonismus

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    nitter.net/kimmonismus/status/204… Chubby♨️ (@kimmonismus) We need a unified definition of AGI and ASI. Otherwise these terms are useless. — https://nitter.net/kimmonismus/status/2040875630019977549#m

    → View original post on X — @kimmonismus, 2026-04-06 09:05 UTC

  • Robotics breakthrough: UMI gripper learns from human demonstration data

    By

    @pascal_bornet

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    We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork

    → View original post on X — @pascal_bornet, 2026-04-06 09:01 UTC

  • Building a Robust RAG System for AI and LLM Applications
    Building a Robust RAG System for AI and LLM Applications

    By

    @ronald_vanloon

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    Building a Robust RAG System by @PythonPr #AI #LLM #GenerativeAI #ArtificialIntelligence #MI #MachineLearning

    → View original post on X — @ronald_vanloon, 2026-04-06 08:45 UTC