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Meaning is created in the semantic layer of the model.
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Let’s be honest—most people use ChatGPT like they’re ordering chai at a railway station. “Bhai ek answer dena…” —vague, rushed, and mildly disappointing. And then they complain: “AI overrated hai yaar.”
Meta Used 50+ AI Agents to Turn Tribal Knowledge Into a Searchable Map
Every large engineering organization has a shadow system that never shows up in the architecture diagram. It is not the code itself. It is the tribal knowledge around the code: the naming quirk that breaks builds, the “deprecated” enum value you still cannot remove, and the hidden dependency only one senior engineer remembers. It is also the file path everyone uses but nobody…
Artificial intelligence is transforming rapidly, and the success of AI systems depends not only on model size but also on how effectively co
Why Gartner’s Context Engineering is like fixing the windshield wipers… on a car with a failing engine.
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Des chercheurs de l'Université Jiaotong de Shanghai (SJTU), de l'Institut de recherche sur l'intelligence artificielle (SII) et du Centre d'analyse de l'intelligence artificielle (GAIR) retracent l'évolution de l'ingénierie du contexte sur plus de vingt ans, la redéfinissant comme un enjeu fondamental de la communication homme-machine, depuis l'informatique primitive (ère 1.0) jusqu'aux agents intelligents actuels (ère 2.0) et au-delà. Leur article propose une définition systématique, une analyse historique et un cadre de conception pour le développement de systèmes d'IA sensibles au contexte.
Context Engineering Evolution Timeline: A comprehensive visualization of the development trajectory of Context Engineering implementations from 2020 to 2025, showing the evolution from foundational RAG systems to sophisticated multi-agent architectures and tool-integrated reasoning systems.
