XKCD comics have become a cornerstone of internet culture, particularly in technical circles, with their witty takes on science, programming, and mathematics. However, finding the perfect XKCD for a particular topic or reference can be challenging - there are now over 3,000 comics in the archive, and traditional search methods rely heavily on exact keyword matches or remembering specific comic numbers.

This project explores how modern Natural Language Processing (NLP) techniques can be used to search XKCD comics semantically, understanding the underlying meaning rather than just matching keywords. By applying vector embeddings and Retrieval Augmented Generation (RAG) to comic descriptions, we can now perform a search based on concepts, themes, and abstract ideas.

Large Language Model (LLM) tools, such as ChatGPT and DeepSeek, have become a key part of people’s workflow, in professional and everyday usage. However, there are dozens of different providers now offering a myriad of options all at different price points; even a single provider has a multitude of models to choose from.

So where do you begin? The Parallellm Pump offers developers a power tool for making response comparisons, asynchronously, to let you be the judge of which provider returns the best result. Still not sure? You can even ask the LLMs themselves to make the decision for you!

In this post, I will share my experiences of running one of the DeepSeek open-weights models (DeepSeek-R1-Distill-Qwen-32B) directly on AWS hardware in the cloud - no need for API tokens.

The good news is that it’s easier than you think - modern libraries, such as PyTorch and the Hugging Face (🤗) transformers package, facilitate much of the heavy lifting. I found some extra tips and tricks along the way to speed things up and I will share these with you in this post.