<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Generative-Ai on Ephemeral Dance Of Electrons</title><link>https://tmzh.github.io/tags/generative-ai/</link><description>Recent content in Generative-Ai on Ephemeral Dance Of Electrons</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 20 Jun 2024 12:00:00 +0000</lastBuildDate><atom:link href="https://tmzh.github.io/tags/generative-ai/index.xml" rel="self" type="application/rss+xml"/><item><title>Building a Codenames AI Assistant with Multi-Modal LLMs</title><link>https://tmzh.github.io/post/2024-06-20-llm-based-codenames-game-playing-assistant/</link><pubDate>Thu, 20 Jun 2024 12:00:00 +0000</pubDate><guid>https://tmzh.github.io/post/2024-06-20-llm-based-codenames-game-playing-assistant/</guid><description>&lt;p&gt;Codenames is a word association game where two teams guess secret words based on one-word clues. The game involves a 25-word grid, with each team identifying their words while avoiding the opposing team&amp;rsquo;s words and the &amp;ldquo;assassin&amp;rdquo; word.&lt;/p&gt;
&lt;p&gt;I knew that word embeddings could be used to group words based on their semantic similarity. This seemed like a good way to cluster words on the board and generate clues. I was largely successful in getting this to work along with few surprises and learnings along the way.&lt;/p&gt;</description></item><item><title>Generating Visual Illusions Using Diffusion Models On Low VRAM</title><link>https://tmzh.github.io/post/2024-01-22-running-deep-floyd-with-limited-memory/</link><pubDate>Mon, 22 Jan 2024 12:00:00 +0000</pubDate><guid>https://tmzh.github.io/post/2024-01-22-running-deep-floyd-with-limited-memory/</guid><description>&lt;p&gt;By now, many of us may be familiar with text-to-image models like Midjourney, DALL·E 3, StableDiffusion etc., Recently, I came across an interesting project called Visual Anagrams that utilizes text-to-image model to generate picture illusions. This project enables us to input two different text prompts, and the model generates pictures that match the prompts under various transformations, such as flips, rotations, or pixel permutations. Growing up, I had a nerdy fascination with illusions and ambigrams, so I was thrilled to give this a try.&lt;/p&gt;</description></item></channel></rss>