Realtime AI News
从DeepSeek到Kimi K3：国产AI终逃不过“算力重工业化”法则 财新
从DeepSeek到Kimi K3：国产AI终逃不过“算力重工业化”法则 财新. 从DeepSeek到Kimi K3：国产AI终逃不过“算力重工业化”法则 财新
According to news.google.com, 从DeepSeek到Kimi K3：国产AI终逃不过“算力重工业化”法则 财新.
从DeepSeek到Kimi K3：国产AI终逃不过“算力重工业化”法则 财新
The signal matters because AI capabilities are moving into more specific product, infrastructure, or business workflows.
The next things to watch are availability, pricing or access limits, and whether the update creates a measurable workflow change for builders or enterprise users.
Why it matters
This update reflects the continued movement of AI capabilities into concrete product, platform, and industry contexts.
Nearby Updates
All07/28, 14:55
South Korean Startup Takeanap Launches 'D:bo' — A Design Decision Agent for the Post-Generative AI Era
South Korean startup Takeanap has unveiled D:bo, positioning it as the world's first 'Design Decision Agent' built specifically for design organizations. The agent automates the decision-making workflow after AI-generated assets are produced, targeting the bottleneck of revision cycles and alignment costs.
07/28, 12:44
Kimi K3 technical details revealed, commercial use requires license
Moonshot AI has publicly disclosed the technical details of its Kimi K3 large language model, but requires a commercial license for business use. The disclosure offers a rare inside look at one of China's leading AI models.
07/28, 12:30
Cursor launches its biggest India push with localized pricing ahead of SpaceX acquisition
Cursor says India has become its third-largest market globally and plans to expand local hiring and enterprise sales. The AI coding tool startup is rolling out localized pricing to capture more of India's vast developer population.
07/28, 12:18
Ant Group's inclusionAI Open-Sources LLaDA 2.2: World's First Large-Scale Agentic Diffusion Model with 128K Context
Ant Group's inclusionAI team has open-sourced LLaDA 2.2, the world's first large-scale Agentic diffusion language model with a 100-billion-parameter MoE architecture and native 128K context window. The model introduces Levenshtein editing, reinforcement learning from environmental feedback, and long-context engineering into a single diffusion-based agent system, narrowing the gap with top autoregressive models to under 2 points across seven agent benchmarks.