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37 articles
Xiaomi's MiMo team, with TileRT, has achieved over 1000 tokens per second on a 1-trillion-parameter model using a single 8-GPU commodity node, marking a significant leap in LLM inference performance.
This article explains the technical concepts behind Large Language Models (LLMs) and why major companies like Airbnb are investing in proprietary AI research labs rather than relying on external partnerships.
This explainer explores the advanced AI concept of planning agents, demonstrating how large language models combine with tool integration and reasoning capabilities to autonomously execute complex tasks like trip planning.
OpenAI has released its Frontier Governance Framework to help enterprises scale safe and compliant AI deployments globally. The framework provides a structured approach to managing systemic risks in large language models and supports sustainable, commercial-grade AI infrastructure.
This article explains the concept of agentic AI, how it works, and the key technical challenges that currently limit its autonomy and effectiveness.
This article explores how Microsoft's adoption of Claude Code illustrates the growing trend of enterprise AI integration, focusing on the mechanisms, implications, and cost structures of deploying AI tools within large organizations.
This explainer article explains the concept of pre-training in AI, its technical mechanisms, and why it's crucial for developing large language models like Claude.
This article explains the advanced technical concepts behind Google's Gemini AI, including its multimodal architecture, attention mechanisms, and implications for AI development and deployment.
This article explores advanced prompting techniques for large language models, including negative constraints, structured JSON outputs, and multi-hypothesis verbalized sampling, essential for reliable production deployment.
Harvard study shows AI models outperform human doctors in emergency room diagnostics, marking a significant advancement in AI healthcare applications.
This article explains the concept of AI benchmarking, how it's used to evaluate AI models, and why recent claims that China is falling behind the US in the AI race are not fully supported by independent data.
This explainer explores Anthropic's BioMysteryBench, a new AI evaluation framework designed to test large language models in bioinformatics. It examines how the benchmark works, why it matters for AI development, and what it reveals about AI capabilities in specialized scientific domains.
