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.

I am delighted to announce that ZL Labs is now providing expert training services to Skiller Whale, supporting their mission to deliver high-impact, interactive micro-workshops to tech teams across Europe.

In this role, I’ll leverage my expertise in AI, Python and SQL to deliver Skiller Whale’s personalised learning plans, providing focused, interactive coaching and hands-on exercises designed to rapidly upskill technical teams.

Stay tuna-ed for further updates! 🐟 🗞️

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.

With the release of the Segment Anything Model¹ (SAM) released by Meta AI Research last year, the lie of the land changed quite substantially in Computer Vision, as now images could be segmented easily, with great results even zero-shot. With the release of SAM2² earlier this year, I wanted to get hands on and experiment with these models myself.

This post walks you through how SAM2 could be used in practice, provides a mini analysis of segmentation results and will be released with code so that you can explore further if you want to. This could be expanded to interesting use cases, such as facilitating object grasping in robotic systems, branded product addition or removal in marketing images, or mapping changes in forested areas from satellite imagery over time for environmental monitoring.