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.

In this project a stunning timelapse video was created from an image stock of over 3,000 photos of a construction site, tracking the progress of a new residential building from breaking ground to completion, a process lasting more than three years. Those images were taken without a tripod, so the variability in camera positions and angles was of course high. To correct for this, Computer Vision techniques were used to predict key points in the images, that could then be used to straighten them and produce the final, steady timelapse.

I’m very excited to launch my new website! Going forward, I will be available for contract work, and will use this space, zl-labs.tech, to showcase my work and connect.

If you have a spare moment, please take a look around and check out my articles. I value your feedback! If you have any improvement suggestions (or proposed projects), please reach out via the contact page or via traditional channels.

Keep an eye for exciting new content coming your way in the coming weeks and months 🚀

This is an article I wrote for Verv during their Seedrs fundraising campaign in March 2024. Link to article.

This January, we finished the installation of the first Verv Smart Isolator with our partner Nido, a Spanish heat pump provider. A Verv Smart Isolator was attached to an air-to-water heat pump and in less than a week, we provided actionable insights for the customer.

One of the tools that helps us understand the energy usage patterns is the energy-utilization heatmap. The heatmap visualizes the hourly energy consumption over different days (vertical axis) and times of day (horizontal axis). The intensity of colour - marked in the legend - represents the energy expenditure during each hour, with darker colours indicating higher usage.