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11 articles
This article explains the advanced concepts behind Alibaba's Qwen3.8-Max, a 2.4 trillion-parameter multimodal model, including multimodal capabilities, mixture-of-experts architecture, and parameter scaling effects.
Learn to build and train world model architectures using PyTorch, understanding the technology behind AMI Labs' approach to AI development.
This article explains how Anthropic's Claude Fable 5 uses a multi-agent delegation system to reduce computational costs by having it act as a planner that delegates tasks to smaller, more efficient models like Sonnet 5.
Vercel CEO Guillermo Rauch advocates for separating AI models from agents to optimize production environments and improve cost-effectiveness. His perspective highlights the growing need for modular AI architectures that can adapt to enterprise requirements.
This explainer explores the concept of 'looping AI' - a revolutionary approach where multiple AI agents continuously interact in feedback cycles, enabling autonomous self-improvement and adaptation in dynamic environments.
Perplexity's new 'Search as Code' architecture allows AI models to write their own search pipelines, outperforming competitors while cutting token costs by up to 85%.
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.
A new tutorial explores the implementation of OpenMythos, a theoretical reconstruction of the Claude Mythos architecture, focusing on recurrent-depth transformers and adaptive computation techniques.
This article explains the advanced AI concepts behind Qwen 3.6-35B-A3B, a multimodal model that combines MoE routing, RAG, and session persistence for intelligent, context-aware AI applications.
This article explains the advanced AI concepts behind Meta's Muse Spark, including thought compression and parallel agent orchestration, and how they enable more sophisticated multimodal reasoning.
This explainer article dives into NVIDIA's Nemotron-Cascade 2, an advanced Mixture-of-Experts (MoE) model that demonstrates how strategic parameter allocation can enhance reasoning capabilities while maintaining computational efficiency.
