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An experiment running the same research prompt about LENR and superconductivity through six AI systems in five languages reveals significant linguistic bias, with non-English queries surfacing information about real industrial commitments that English-only searches miss.
The article argues that context engineering, which involves structuring the information and memory available to an AI, is more critical for performance than prompt engineering alone. It provides a structured overview of a course designed to teach how to build reliable AI systems by managing context layers like session history and persistent memory.
This article analyzes the arXiv paper "Dive into Claude Code," discussing the key engineering implementation aspects of coding Agent systems like Claude Code in real-world environments, including capabilities such as shell execution, file modification, and external service invocation.
The article argues that there is a high likelihood (60%+) of fully automated AI R&D—where AI systems can build their own successors without human involvement—by the end of 2028, citing evidence from coding benchmarks like SWE-Bench and trends in AI autonomy.