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109 items
This article is a hands-on field guide covering three critical failure surfaces for large language models: prompt injection, unsafe output handling, and model poisoning. It presents practical attack and defense perspectives tailored for practitioners dealing with LLM security risks.
The article provides a practical introduction to artificial intelligence and machine learning fundamentals, then explains the inner workings of large language models (LLMs), and finally examines the security risks that come with these technologies.
CData Software posted a brief article noting that many teams rolling out LLMs find that while models are fast, the data sources feeding them often introduce latency. The full content is available on their website under the title “The Definitive Guide to Live Data Access for LLM Applications,” but the public post only provides this introductory statement.
In this opinion piece, the author argues that 'intelligence per sample' and 'intelligence per watt' are two of the most important unsolved problems in artificial intelligence, framing them as missing metrics for measuring progress. The available snippet contains no further elaboration, data, or concrete examples.
The article's body is limited to a single promotional sentence stating 'Lessons from My Development Experience as an AI Engineer' and a 'Continue reading on Medium' link. No technical specifics about the multi-agent system, LangGraph, or LangSmith are present. The content offers no tutorial, implementation details, code examples, or benchmarks. It is essentially a placeholder with no actionable information.
The article, available only as a teaser, claims that multi-agent orchestration is transforming software engineering. The full text is behind a paywall, and the provided snippet mentions a system in profitable companies that uses multiple AI models. No concrete facts, examples, or data are included.