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Deepseek AI News List | Blockchain.News
AI News List

List of AI News about Deepseek

Time Details
2025-09-08
23:00
Hangzhou AI Boom: How Subsidies and Top Talent Drive Growth for Six Little Dragons

According to DeepLearning.AI, Hangzhou is rapidly establishing itself as a major AI technology hub, driven by its 'six little dragons'—five leading AI companies (DeepSeek, BrainCo, Deep Robotics, ManyCore, Unitree Robotics) and game studio Game Science. The city leverages strategic subsidies, tax incentives, and partnerships with Zhejiang University for top-tier AI talent, while providing companies with access to Alibaba Cloud and high-performance GPUs. These initiatives have created a fertile environment for AI innovation, attracting startups and established tech firms alike and positioning Hangzhou as a competitive center for AI research, robotics, and commercial applications (source: DeepLearning.AI via The Batch, 2025).

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2025-08-21
06:33
DeepSeek AI Tools & Agents Upgrades: Enhanced Results on SWE and Terminal-Bench, Improved Multi-Step Reasoning

According to DeepSeek (@deepseek_ai), the latest upgrades to their AI tools and agents have delivered significantly better results on SWE and Terminal-Bench benchmarks, highlighting stronger multi-step reasoning for complex search tasks and substantial gains in thinking efficiency. These technical improvements are particularly relevant for AI-powered developer tools, coding assistants, and enterprise search solutions, where robust reasoning and efficient task execution drive productivity and business value. (Source: DeepSeek Twitter, August 21, 2025)

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2025-06-05
00:00
DeepSeek Reveals Cost-Effective Training Techniques for Mixture-of-Experts AI Models Using Nvidia H800 GPUs

According to @deepseek_ai, DeepSeek has disclosed detailed strategies for training its advanced mixture-of-experts models, DeepSeek-R1 and DeepSeek-V3, by leveraging 2,048 Nvidia H800 GPUs and innovative memory-efficient methods such as FP8 precision. These approaches enabled DeepSeek to achieve significant computational savings, drastically reducing training expenses compared to standard large language model training costs (source: @deepseek_ai, 2024-06-21). This development demonstrates practical opportunities for AI startups and enterprises to scale state-of-the-art models with lower infrastructure investments, accelerating AI adoption in cost-sensitive markets and enhancing accessibility for AI-driven business applications.

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