Published on: 2025-06-05 11:31:37
VectorSmuggle "The smuggle is real!" A comprehensive proof-of-concept demonstrating sophisticated vector-based data exfiltration techniques in AI/ML environments. This educational security research project illustrates potential risks in RAG systems and provides tools for defensive analysis. đź“‹ Overview VectorSmuggle demonstrates advanced techniques for covert data exfiltration through vector embeddings, showcasing how sensitive information can be hidden within seemingly legitimate RAG operati
Keywords: analysis data research security vectorsmuggle
Find related items on AmazonPublished on: 2025-08-25 16:01:41
Let's remove Quaternions from every 3D Engine (An Interactive Introduction to Rotors from Geometric Algebra) The clearest explanation of 3D geometric algebra within 15 minutes that I've seen so far —BrokenSymmetry I am sold. While I can understand quaternions to an extent, this way of thinking is a much more intuitive and elegant approach. —Jack Rasksilver This sets a high standard for educational material, and is a shining example of how we can improve education with today's technologies. —Se
Keywords: mathbf product vector vectors wedge
Find related items on AmazonPublished on: 2025-10-14 01:32:53
Robert Vanderbei has written a beautiful series of articles and talks about a method for finding the radius of the earth based on a single photograph of a sunset over a large, calm lake. Vanderbei’s analysis is an elegant and subtle exercise in classical trigonometry. In this post, I would like to present an alternative analysis in a different language: Geometric Algebra. I believe that geometric algebra is a more powerful system for formulating and solving trigonometry problems than the classi
Keywords: algebra geometric left product vectors
Find related items on AmazonPublished on: 2025-10-22 16:20:20
Published: Mar 8, 2025 at To understand how an AI can understand that the word “cat” is similar to “kitten,” you must realize cosine similarity. In short, with the help of embeddings, we can represent words as vectors in a high-dimensional space. If the word “cat” is represented as a vector [1, 0, 0], the word “kitten” would be represented as [1, 0, 1]. Now, we can use cosine similarity to measure the similarity between the two vectors. In this blog post, we will break down the concept of cosin
Keywords: cosine similarity veca vecb vectors
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