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This article clarifies the distinct roles of Agent Skills and Model Context Protocol (MCP), compares their core features, shows how they complement each other through design philosophy and real‑world scenarios, and provides a decision framework for choosing the right tool in AI agent architectures.
OxygenREC presents a fast‑slow thinking, instruction‑following generative framework that overcomes latency, reasoning, and multi‑scene scalability challenges in e‑commerce recommendation, delivering unified training, low‑latency inference, and significant business impact across JD.com scenarios.
This article details JD Retail's 9N‑LLM unified training framework that tackles the massive data, hardware heterogeneity, and algorithmic challenges of generative recommendation by integrating TensorFlow and PyTorch, supporting GPU/NPU, and delivering high‑throughput sample processing, sparse/dense optimization, and flexible reinforcement‑learning capabilities.
Uni-Layout introduces a unified framework that integrates a universal layout generator, a human‑feedback‑simulating evaluator, and a dynamic margin preference optimization technique to align generation and evaluation across diverse e‑commerce design tasks, backed by a new 100k human‑annotated dataset.
This article explains why a unified MCP‑Server framework supporting stdio, streamableHttp, and SSE is needed for AI‑driven services, describes its modular design, directory layout, startup commands, environment‑based configuration, key code components, deployment scripts, and showcases successful integration with JoyCode and JoyAgent.
The article examines JD’s AI‑powered shopping app, detailing its chatbot‑style interface, intent‑driven workflow, AI‑enhanced product recommendation, multi‑scenario integration such as travel and dining, and the underlying research on Fast‑Slow thinking and the SA‑GCPO algorithm that powers the experience.
This extensive guide walks readers through the fundamentals of large language models, covering transformer architecture, pre‑training and fine‑tuning techniques, scaling laws, emergent abilities, mixture‑of‑experts designs, and practical comparisons, providing clear explanations, code snippets, and visual illustrations for deep learning practitioners.
The paper introduces a semi‑supervised, extensible unified framework (SSUF) that integrates knowledge, label, and structural enhancements to overcome data sparsity, label bias, and fragmented sub‑tasks in e‑commerce query intent prediction, achieving superior offline and online performance.
This article analyzes why a warehouse management system’s master‑slave database replication lagged up to 30 minutes during nightly inventory snapshot generation, evaluates several mitigation strategies, and details the chosen big‑data‑driven solution that moved snapshots to Elasticsearch, reducing lag and disk usage.
The OxyGent 1.0.8 release introduces multimodal messaging, fine‑grained control, MCP reconnection, and front‑end streaming, while detailing its stateless AOP architecture, execution lifecycle, four data scopes, real‑world use cases, community feedback, and a step‑by‑step tutorial for rapid adoption.
