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Three months since the last update, give or take a day.

As I was writing this post I could not crystallize who the target audience for it is, other than for myself to put into writing how I view and think about LLMs.

It’s been three months almost exactly since I last documented my workflows and setup.

Last time I wrote about my AI assisted workflows was 3 months ago.

Alright, we covered the conceptual stuff in Part 1a.

Before we get into the coding part of the series I think it would be beneficial to give some background to what the ReAct loop is that we are going to make use of to build this agent and also describing a bit how the code is going to look like.

Ever since I switched to Linux as my main OS and started building my own tools, I’ve had this nagging feeling about the coding agents I use daily.

Current limitation as I see it So I have been experimenting quite a bit with LLMs recently (see my other posts, especially /state-of-ai-assisted-workflows-october-2025/) and dedicated some serious amount of effort to play around with the tech to both learn

This space is moving at a dizzying pace currently and as such there are many new “frameworks”, methods and approaches popping up and promising the world.

I wrote a post a while ago about my current workflow when working with AI, my initial skepticism and some brief mentions of the concept of recreational programming.

Lately I have been thinking quite a bit about the question “What is a data platform in its most basic form”.

This is just like a note or dump of everything I research on AI agents, MCP, and other generative AI stuff.

Let me preface this post with the fact that I was a huge macOS fanboy for a decade or so and only started feeling that the OS was getting in my way a couple of years ago.

About Me Hi, I’m Ramtin Javanmardi!