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The Download: Caiwei’s Three Things and Reasons to be Optimistic about...

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The latest AMD Radeon RX9060 XT information reveals high boost clocks...

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NVIDIA to launch a cut-down H20 Chip for China as soon...

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Apple’s silicon roadmap is sweeping, with the M6, M7 and smart...

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Fake AI video creators drop new Noodlophile information stealer malware

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A Deep Technical Dive into Next-Generation Interoperability Protocols: Model Context Protocol...

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Enterprise AI Without GPU Burn: Salesforce’s xGen-small Optimizes for Context, Cost,...

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ByteDance Open-Sources DeerFlow: A Modular Multi-Agent Framework for Deep Research Automation

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Microsoft Researchers Introduce ARTIST: A Reinforcement Learning Framework That Equips LLMs...

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ZeroSearch from Alibaba Uses Reinforcement Learning and Simulated Documents to Teach...

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Understanding the Dual Nature of OpenAI

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OpenBMB Releases MiniCPM4: Ultra-Efficient Language Models for Edge Devices with Sparse...

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The concerted effort of maintaining application resilience

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Ericsson and AWS bet on AI to create self-healing networks

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Meta buys stake in Scale AI, raising antitrust concerns

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OpenBMB Releases MiniCPM4: Ultra-Efficient Language Models for Edge Devices with Sparse...

The Need for Efficient On-Device Language Models Large language models have become integral to AI systems, enabling tasks like multilingual translation, virtual assistance, and automated reasoning through transformer-based architectures. While highly capable, these models are typically large, requiring powerful cloud infrastructure for training and inference. This reliance leads to latency,...