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%{ title: "Will It Blend?", author: "Willem van den Ende", tags: ~w(AI pi.dev ComprehensionMaxxing), description: "Will it Blend was a series of videos promoting a blender brand, in which various unlikely objects were put in a blender to see if they would blend.", image: "/images/blog/2026/will-it-blend.svg", published: true }
Problem You find something interesting, but don't completely understand it, or are not sure where to start.
Solution Point your coding agent at the thing you read (a blog post, research paper, product announcement) and build something so that you can play with it.
Will it Blend was a series of infomercials promoting a blender brand, in which various unlikely objects, such as tablets, were put in a blender to see if they would blend.
I like it as a metaphor for trying to get something to work that you have just come across. I often use it when reading about the new hot AI thing of the week (say decision models a couple of weeks ago). I’ll go and look for an open source implementation and have a play with it, asking my coding agent for help installing it and coming up with a micro project I can do with it.
Worst case I lost half an hour to an hour, and put the idea back on the backburner. But more often than not, I get something out of it that I can take forward: working software and better, more grounded, understanding of a topic.
Examples of when I used this
I use this all the time now. And so do other people, it is not that original. I want to encourage you to see if things blend too, only takes half an hour or so.
Decision models
For instance, a day after Jevcame out, Laya popped up on Reddit. I cloned the repository, opened Pi and asked some questions about what you could do with it. Then I had a look at the example code on the Laya page, used my coding agent to run it, had a chat about how I could use it in a coding agent, and had the agent make some sample questions with sample data. I also wondered why it was taking so long to start, got the coding agent to reduce startup by 40 times (28 seconds to about 0.6 seconds or so), and I learnt in the process that this was a BERT model with some pre-training. I had seen BERT’s but not played with one in the wild before. This enabled me to write some comments on LinkedIn, and learn, indirectly, about my mistakes :-). Not bad for an hour or two on a sunday afternoon.
Getting started with a local model in an open coding agent
Another example would be John Nolan , who put my post on a local coding agent on Mac OS through his coding agent to get Pi + local llms set up on his machine. I liked this, because this is also how I got started with making my own coding agent, and later installing Pi. With a coding agent in hand, and some vigilance or sandboxing, this is not much time well spent.
Background
I learnt this in the spring of 2025 through Julia Turc, probably from her video on Mixture of Experts: How LLMs Are Getting Smarter Without Getting Slower. In this video, Julia takes a 1991 research paper on mixture of experts, pulls it through ChatGPT (this is several hundred AI summer years ago) and creates an executable Collab Notebook out of it.
I wrote a comment at the time:
+1. I was wondering if llms would make it easier to implement papers (something I haven’t done in a while), and later that day, youtube surfaced this video with a live demo. I came for the mixture of experts explanation and stayed for the implementation and paper reading. It is insightful to see what you look at in a paper.
I was primed for this by Katherine Breslin’s talk at Agile Manchester 2025 (spring, this is important in AI summer years), in which she mentioned making a voice assistant in an afternoon on her own, something which had taken the Alexa team (on which she worked) 150+ people and multiple years.
Your turn
Do you have research papers, blog posts or videos that leave you wondering? Wonder no more. These days I often start with putting my question in a (gemini) notebook, getting a summary, finding more sources, and asking questions. Then I take a summary document to my coding agent, refer to that in an initial prompt and then it usually works. I create a new git repository for every project, making small commits as I go. Often with tests: they make it easy to get started, express expectations, and keep going if the experiment is a success.
If you want specifics on what I use now: I use Pi as coding agent. Models that I can run on a 64GB mac (e.g. Qwen 3.8 27B or 3.6 35B) are usually plenty. When I want it faster, or with less fan noise, I use a hosted GLM 5.3 Flash.
I hope you find this useful and will see if something you are curious about will Blend. Happy blending!