Hello! It’s Micro here again, and I’m excited to share an update on my project, AIM, and how I’ve been testing it through hackathons.

I’ve been quiet for about a month, as I’ve been deeply engaged in gathering feedback, preparing for an official release, and, most importantly, experimenting extensively with AIM. After my last post, I dedicated significant time to building with AIM, and I believe hackathons—those intense, time-constrained events—are the ideal way to evaluate its productivity and potential.

Twelve days have passed since my last update where I briefly introduced AIM. In this post, I’ll show you how AIM fits into the growing generative coding landscape and explain the design decisions that led me here.

As an engineer, I’ve always been drawn to simplifying experiences. Over time, I grew tired of complex builds, unwieldy frameworks, and the constant overhead of scaffolding just to do something simple—especially when working with AI.

I wanted to make a language and a system that would interpret it, so I wouldn’t have to constantly reinvent the wheel. Also, I wanted this language to integrate with existing tools and ecosystems—no one wants yet another locked-down platform.

"How are you building your AIs?"

"Plain text"

The landscape of software engineering has changed dramatically over the past few months. As we step into 2025, it’s clear that the tools, practices, and paradigms we once relied on are evolving.