<story><title>DRµGS: Deep Random Micro-Glitch Sampling</title><url>https://github.com/EGjoni/DRUGS</url></story><parent_chain></parent_chain><comment><author>madamelic</author><text>I appreciate the README etc, it&amp;#x27;s very funny and cute.&lt;p&gt;As a bit of a layperson &amp;#x2F; just an AI integrator, I want to get some clarification of how this interfaces with a generative AI. I haven&amp;#x27;t dug too deep in and I have a mostly elementary understanding of neural nets. No need to _entirely_ dumb it down though, just needing a smarter person to confirm or correct my understanding. :)&lt;p&gt;Is this influencing the probabilities of token output? My understanding is that currently it is a static value that is essentially &amp;quot;how wild do you want it to be&amp;quot; where each generation has a different consistent static value of &amp;quot;wildness&amp;quot;.&lt;p&gt;So rather than using a static value for an entire generation, this dynamically injects randomness in so rather than the output being &amp;#x27;monotone&amp;#x27;, it is more dynamic rather than by-the-book that AI tends to be?</text></comment>
<story><title>DRµGS: Deep Random Micro-Glitch Sampling</title><url>https://github.com/EGjoni/DRUGS</url></story><parent_chain></parent_chain><comment><author>kstrauser</author><text>I appreciate the commitment to the metaphor. Bravo.</text></comment>