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Author: pIXELsHAM.com
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SPAR3D – Stable Point-Aware Reconstruction of 3D Objects from Single Images
SPAR3D is a fast single-image 3D reconstructor with intermediate point cloud generation, which allows for interactive user edits and achieves state-of-the-art performance.
https://github.com/Stability-AI/stable-point-aware-3d
https://stability.ai/news/stable-point-aware-3d?utm_source=x&utm_medium=social&utm_campaign=SPAR3D
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MiniMax-01 goes open source
MiniMax is thrilled to announce the release of the MiniMax-01 series, featuring two groundbreaking models:
MiniMax-Text-01: A foundational language model.
MiniMax-VL-01: A visual multi-modal model.Both models are now open-source, paving the way for innovation and accessibility in AI development!
🔑 Key Innovations
1. Lightning Attention Architecture: Combines 7/8 Lightning Attention with 1/8 Softmax Attention, delivering unparalleled performance.
2. Massive Scale with MoE (Mixture of Experts): 456B parameters with 32 experts and 45.9B activated parameters.
3. 4M-Token Context Window: Processes up to 4 million tokens, 20–32x the capacity of leading models, redefining what’s possible in long-context AI applications.💡 Why MiniMax-01 Matters
1. Innovative Architecture for Top-Tier Performance
The MiniMax-01 series introduces the Lightning Attention mechanism, a bold alternative to traditional Transformer architectures, delivering unmatched efficiency and scalability.2. 4M Ultra-Long Context: Ushering in the AI Agent Era
With the ability to handle 4 million tokens, MiniMax-01 is designed to lead the next wave of agent-based applications, where extended context handling and sustained memory are critical.3. Unbeatable Cost-Effectiveness
Through proprietary architectural innovations and infrastructure optimization, we’re offering the most competitive pricing in the industry:
$0.2 per million input tokens
$1.1 per million output tokens🌟 Experience the Future of AI Today
We believe MiniMax-01 is poised to transform AI applications across industries. Whether you’re building next-gen AI agents, tackling ultra-long context tasks, or exploring new frontiers in AI, MiniMax-01 is here to empower your vision.✅ Try it now for free: hailuo.ai
📄 Read the technical paper: filecdn.minimax.chat/_Arxiv_MiniMax_01_Report.pdf
🌐 Learn more: minimaxi.com/en/news/minimax-01-series-2
💡API Platform: intl.minimaxi.com/
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László Gaál – Google Veo2 tests
https://www.linkedin.com/posts/laszloga_veo2-activity-7278344748464029696-z18_
https://www.linkedin.com/posts/laszloga_veo2-veo2-activity-7279424228779507712-zDgC
https://www.linkedin.com/posts/laszloga_veo2-activity-7280530104722583552-tGgJ
https://www.linkedin.com/posts/laszloga_veo2-activity-7280881794663510016-e8i8
https://www.linkedin.com/posts/laszloga_veo2-activity-7277947758932606976–7i9
https://www.linkedin.com/posts/laszloga_veo2-activity-7283050136446935041-EJGs
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Bloomberg – Sam Altman on ChatGPT’s First Two Years, Elon Musk and AI Under Trump
https://www.bloomberg.com/features/2025-sam-altman-interview
One of the strengths of that original OpenAI group was recruiting. Somehow you managed to corner the market on a ton of the top AI research talent, often with much less money to offer than your competitors. What was the pitch?
The pitch was just come build AGI. And the reason it worked—I cannot overstate how heretical it was at the time to say we’re gonna build AGI. So you filter out 99% of the world, and you only get the really talented, original thinkers. And that’s really powerful. If you’re doing the same thing everybody else is doing, if you’re building, like, the 10,000th photo-sharing app? Really hard to recruit talent.
OpenAI senior executives at the company’s headquarters in San Francisco on March 13, 2023, from left: Sam Altman, chief executive officer; Mira Murati, chief technology officer; Greg Brockman, president; and Ilya Sutskever, chief scientist. Photographer: Jim Wilson/The New York Times
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LG 45GX990A – The world’s first bendable gaming monitor
The monitor resembles a typical thin flat screen when in its home position, but it can flex its 45-inch body to 900R curvature in the blink of an eye.
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DMesh++ – An Efficient Differentiable Mesh for Complex Shapes
https://sonsang.github.io/dmesh2-project
An efficient differentiable mesh-based method that can effectively handle complex 2D and 3D shapes. For instance, it can be used for reconstructing complex shapes from point clouds and multi-view images.
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