There's a difference between working with AI and working practically with AI.
The former is using ChatGPT from a browser. The latter is building something where AI is one step among many—where data needs to be fetched, validated, sent to a model, checked, transformed, and stored elsewhere. Almost all commercial AI work is of the latter kind.
And if you're doing the latter, Nushell should live in your terminal.
I recently wrote about how I automate my blog releases in one command — text in, article with image out. That flow runs in a shell most people have never heard of: Nushell. And it's not a random choice.
When I build what I call compound AI systems — meaning systems where eg. language models and image models are combined with other components to solve a real task — the most important question isn't "which model are we using?". It's "what do we surround the model with?".
And that's where Nushell comes in.
I talk a lot about AI solving real business problems, not just impressing in a demo. So let me show you an example from my own daily life.
Every time I write an article for docujai.com, it has to go through the same chain of small tasks: post the text to Drupal via JSON:API, generate an illustrative image, give the image the right visual expression with my logo in the corner, upload it to the article, set alt-text, patch the relationship between node and file. It doesn't take long to do manually. It takes a long time to do it every time.
There's a conversation I hear repeatedly at companies that want AI to "understand" their knowledge.
It typically starts like this: "We have our procedures, our documents, our emails — but the AI can't find the right information. We need better search."
The solution vendors offer is always technical and always expensive: embeddings, vector databases, RAG pipelines. Words that sound impressive and cost accordingly.
The problem is, the solution is rarely what's missing.
I recently gave a talk to twelve people, the vast majority of whom had no prior knowledge of AI. Several were openly negative and made no secret of it. I agreed with many of their objections — they were based more on intuition than experience, but the intuition wasn't wrong.
When I realized what kind of audience I was facing, I ditched the slides and preparation and improvised for the rest of the time. It turned out to be one of my best talks.
That's not what I want to write about.
Mise, IWE, and Nushell together give me a workflow that works every day.
The biggest productivity problem I know is not a lack of tools. It's the transitions between them.
You know what you need to do. You open the terminal. And then you spend five minutes trying to remember which folder you were in, what happened last, which note was relevant, and what you actually decided on Friday. Before you've even started, you're already a bit tired.
With one command in Nushell, you can view your knowledge base from the outside — and discover things you didn't know you didn't know.
It's all about overview.
I discovered something new today. Not in a note, not in a meeting — but in a terminal window.
Your AI assistant doesn't remember yesterday.
IWE fixes that.
There's a specific frustration that anyone who works regularly with AI assistants knows well:
You've had a great session — you explained your project, your goals, your constraints, the language you use, the things that matter. The AI was genuinely helpful. And then you close the window, open a new one, and start from scratch. The context is gone. You're strangers again.
My knowledge base isn't an archive — it's an ASSET!
I use it all the time. And keep it updated all the time. And then I share it with artificial intelligence.
It wasn't always like that.
Before most notes ended up dying quietly deep in a folder structure.
That's not a problem with the notes. It's a structural problem. And that's the problem I've spent the last month solving.
You probably know the situation:
In the world of functional programming, there are two titans of iteration: Unfold (generating a stream from a seed) and Fold (collapsing a stream into a single value).
In Nushell, these are represented by generate and reduce. Having built agents with both, it's clear that while they can often achieve the same goals, their "philosophy" is fundamentally different.