MCP vs RAG vs AI Agents Discussions around Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and AI Agents are prevalent. Often, these concepts are mistakenly conflated. However, they do not represent competing ideas; instead, they address distinct challenges at various levels of the technical stack. The Model Context Protocol (MCP) defines the mechanism through which
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Richard Socher and Bryan McCann, recognized as highly cited AI researchers globally, have published 35 predictions for 2026 (URL: https://bit.ly/You_013126Predictions). Among these, three notable predictions include: The large language model (LLM) revolution will experience a period of resource reallocation, with investment shifting back towards foundational research. “Reward engineering” is anticipated to emerge as a distinct
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OpenAI recently introduced ChatGPT Atlas, a web browser where a Large Language Model (LLM) functions as a co-pilot across the internet. Users can pose inquiries about any web page, delegate tasks to ChatGPT, or allow it to browse autonomously in Agent mode while other work is performed. Achieving this sophisticated user experience presented significant engineering
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LinkedIn provides rapid user experiences to hundreds of millions of global members, whether they are accessing their feeds or sending messages. This seamless operation relies on the collaborative functioning of thousands of software services. Service Discovery, as an essential infrastructure system, facilitates this intricate coordination. In contemporary large-scale applications, LinkedIn employs tens of thousands of
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