Where do AI systems lose confidence in your content? Discovery, selection, crawling, rendering, and indexing hold the answer.
Hidden instructions in content can subtly bias AI, and our scenario shows how prompt injection works, highlighting the need for oversight and a structured response playbook.
Unlock Google Gemini AI with these 7 prompts demonstrating research, coding, music, and travel capabilities efficiently.
First of four parts Before we can understand how attackers exploit large language models, we need to understand how these models work. This first article in our four-part series on prompt injections ...
Abstract: A flexible prescribed performance control (FPPC) approach for input saturated nonlinear systems (ISNSs) with unmeasurable states is first presented in this article. Compared to the standard ...
Abstract: This paper presents a novel output-feedback direct adaptive controller with a decoupling design to completely attenuate periodic disturbances in the linear multi-input multi-output (MIMO) ...
"Prompt Engineering in Practice" teaches you how to write, refine, organize, and optimize AI prompts that generate relevant and useful text and images. The book covers essential techniques for working ...
mcp-agent's vision is that MCP is all you need to build agents, and that simple patterns are more robust than complex architectures for shipping high-quality agents.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results