May 10, 2025 Comments0 FacebookTwitterPinterestWhatsApp Education Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks By AI Observer More from this stream MCP and the Innovation Paradox: Why open standards can save AI... AI Observer - 3 hours ago Huawei Introduces Pangu Ultra MoE: A 718B-Parameter Sparse Language Model Trained... AI Observer - 3 hours ago A Coding Guide to Unlock mem0 Memory for Anthropic Claude Bot:... AI Observer - 3 hours ago Tencent Released PrimitiveAnything: A New AI Framework That Reconstructs 3D Shapes... AI Observer - 3 hours ago Recomended MCP and the Innovation Paradox: Why open standards can save AI from itself. May 10,... Huawei Introduces Pangu Ultra MoE: A 718B-Parameter Sparse Language Model Trained Efficiently on Ascend NPUs Using Simulation-Driven Architecture and System-Level Optimization Sparse large language... A Coding Guide to Unlock mem0 Memory for Anthropic Claude Bot: Enabling Context-Rich Conversations In this tutorial,... Tencent Released PrimitiveAnything: A New AI Framework That Reconstructs 3D Shapes Using Auto-Regressive Primitive Generation Shape primitive abstraction,... A Coding Implementation of Accelerating Active Learning Annotation with Adala and Google Gemini In this tutorial,... LightOn AI Released GTE-ModernColBERT-v1: A Scalable Token-Level Semantic Search Model for Long-Document Retrieval and Benchmark-Leading Performance Semantic retrieval focuses...