/explore
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ankane.org | ||
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lancecarlson.com
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| | | | | Imagine a system that not only stores data but grasps its very essence. A system that can swiftly navigate through myriad data points to pinpoint the most relevant. That's the power of vector databases. They're tailor-made for QA systems, acting as memory boosters for large language models. Their ability to seamlessly link questions to exact answers showcases their pivotal role in modern AI. I'm going to show you how you can setup your very own vector database using Ruby on Rails so you can ask your data questions! | |
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pboyd.io
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| | | | | Here's a fun list to look through: Dumb Password Rules. Most of the rules seem arbitrary, like only allowing digits, but some hint at deeper problems. For instance, preventing single-quotes. They aren't inserting passwords into a database without a SQL placeholder, right? Nearly every site on that list has a needlessly short maximum password size. If they're storing passwords correctly, there's no need for this. This post will go through a few bad ways to store a password and you can see what I mean.... | |
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greg.molnar.io
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| | | | | A pull request on Rails and Devise triggered me to write this blogpost. | |
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www.daniellowengrub.com
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| | | [AI summary] The text discusses the implementation of homomorphic operations in the context of RLWE (Ring Learning With Errors) and GSW (Gentry-Sahai-Waters) encryption schemes. Key concepts include the use of encryptions of zero to facilitate homomorphic multiplication, the structure of GSW ciphertexts as matrices of RLWE ciphertexts, and the role of scaling factors to manage error growth during multiplication. The main goal is to enable secure computation of polynomial products without revealing the underlying plaintexts. | ||
