<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>vr on Security Addict</title><link>https://blog.medarkus.net/tags/vr/</link><description>Recent content in vr on Security Addict</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><managingEditor>me@darkus.dev (DeMarcus Williams)</managingEditor><webMaster>me@darkus.dev (DeMarcus Williams)</webMaster><lastBuildDate>Wed, 03 May 2023 21:35:44 -0400</lastBuildDate><atom:link href="https://blog.medarkus.net/tags/vr/index.xml" rel="self" type="application/rss+xml"/><item><title>Code Analysis With Langchain</title><link>https://blog.medarkus.net/posts/code-analysis-with-langchain/</link><pubDate>Wed, 03 May 2023 21:35:44 -0400</pubDate><author>me@darkus.dev (DeMarcus Williams)</author><guid>https://blog.medarkus.net/posts/code-analysis-with-langchain/</guid><description>Introduction There&amp;rsquo;s been a lot of buzz lately about Generative AI, LLMs and their applications. It&amp;rsquo;s becoming increasingly clear that machine learning techniques are augmenting the world of software development and security research. One such example is the LangChain framework, which provides a powerful toolkit for developing applications powered by language models. This framework can enable developers to gain deep insights into their codebase, identify potential bugs or vulnerabilities (this is the part we&amp;rsquo;re interested in 😉), and improve the overall quality and efficiency of their software.</description></item></channel></rss>