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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.
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.
This Medium tutorial by Armin Rahimi describes a simple back-of-the-envelope method commonly used across the field to estimate the number of GPUs needed to train a large language model. It focuses on providing the practical intuition behind the calculation. The preview does not disclose specific quantitative examples.
A Medium article asserts that Microsoft has solved a major bottleneck faced by AI engineers developing coding agents. The preview text, however, does not elaborate on the nature of the bottleneck, the solution, or any technical specifics. The full article is behind a paywall, leaving no concrete information available.