Anthropic

Sparkle’s ‘Thundermage’ concept pitches Thunderbolt as a GPU port

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Anthropic

Apple’s iPad Mini model is now at its lowest price in...

AI Observer
Anthropic

This OnePlus tablet is better for movies and entertainment than iPads....

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Anthropic

Samsung Unveils Galaxy A56 5G, Galaxy A36 5G, and Galaxy A26...

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Anthropic

Absa expands in Dubai to join the Middle East-African investment push

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Anthropic

Bootstrapped OneKitty tackles transparency in crowdfunding with WhatsApp chatbots.

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Anthropic

Why data centre investors are flocking the coastline of Lagos

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Anthropic

Hyundai Insteroid, a rare model we want but may not receive

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Anthropic

Google announces new security requirements for HTTPS providers

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Anthropic

Gartner predicts $644 billion in AI spending by 2025, but few...

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Anthropic

The first 3D-printed train stations in Japan were assembled in record...

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News

Researchers at UT Austin Introduce Panda: A Foundation Model for Nonlinear...

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Education

Qwen Researchers Proposes QwenLong-L1: A Reinforcement Learning Framework for Long-Context Reasoning...

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Natural Language Processing

Meta AI Introduces Multi-SpatialMLLM: A Multi-Frame Spatial Understanding with Multi-modal Large...

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News

A Step-by-Step Coding Implementation of an Agent2Agent Framework for Collaborative and...

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AI Observer

Researchers at UT Austin Introduce Panda: A Foundation Model for Nonlinear...

Chaotic systems, such as fluid dynamics or brain activity, are highly sensitive to initial conditions, making long-term predictions difficult. Even minor errors in modeling these systems can rapidly grow, which limits the effectiveness of many scientific machine learning (SciML) approaches. Traditional forecasting methods rely on models trained on specific...