Separation of Concerns Is Not Optional


Separation of concerns feels like an old idea. Yet many new engineers ignore it. I recently explained in a whiteboarding as to why a monolith is dangerous. Not just in code. In data models. Even in large prompts. When everything lives in one place, everything depends on everything else. It works at first. It feels fast. I built monoliths in college projects. We all did. It was the easiest way to get something running.

The problem is not that monoliths are evil. The problem is that they hide boundaries. They mix logic with storage. They mix reasoning with data. The same mistake now shows up in AI prompts. One giant instruction. No modules. No composition. No clean interfaces. It becomes fragile. Hard to test. Hard to scale. Hard to reason about. Easy to hallucinate.

As engineers grow, they return to first principles. Modularize. Compose. Define clear ownership. Explicit boundaries create flexibility. Hidden coupling creates pain. Tenet #4 — Make Assumptions Explicit. Separation of concerns is not an advanced technique. It is a reminder that clarity beats convenience.

Comments

Post a Comment

Popular posts from this blog

Breaking Systems, One Mistake at a Time

Image
I’ve broken more things than I’ve built — and that’s exactly why this blog exists. Over the years, I’ve learned that the most valuable lessons rarely come from perfectly executed plans. They come from the quiet moments after things break — when you realize what you missed, what you assumed, or what you could have communicated better. Most of those insights seem obvious in hindsight. This space, Obvious in Hindsight , is my running log of those realizations — from building reliable systems at scale to learning how people, teams, and code all fail (and recover) in similar ways. I’m writing to understand — out loud. Why I’m Doing This Every engineer I respect shares one thing in common: they remember what they got wrong. Not because they’re proud of the mistake, but because they learned something from it that documentation never could. This blog is where I collect my own version of those reminders — along with some movie quotes. Each post will tie back to one of my core tenets ...
Read more

Why Now Matters More Than the Idea

Image
Years ago, I proposed building a large data warehouse that captured how our creators structured their content. Everything. The semantics. The syntax. The patterns. Which HTML tags appeared together. Which musical octaves worked in combination. Which ad-tags influenced consumption. It was a massive idea. It was also far ahead of its time. The cost was high. The ROI was unclear. We didn’t know what we would do with the data. The idea made sense, but the timing didn’t. Five years later, things changed. GenAI arrived. Personalization matured. Analytics became deeper and cheaper. Suddenly, the same idea became useful. Now we had models that could learn from the patterns. Now we had creators who wanted insights. Now we had consumers who expected smarter experiences. The idea didn’t change. The world around it did. And with that, the timing flipped from “not now” to “why aren’t we doing this already?” It reminded me that ideas don’t win on its merit alone. They win when why , why now , and ...
Read more

Optimize for Being Less Wrong

Image
The brain learns in uncomfortable ways. When you repeat something you are already good at, very little changes inside. The wiring stays the same. But the moment you make a mistake, something shifts. The brain is agitated. You feel annoyed. Embarrassed. Restless. That discomfort is not failure. That is learning starting. No errors. No rewiring. The fastest learners are not the ones who avoid mistakes. They are the ones who make many small, high-quality mistakes and correct them quickly. LLM tools understand this instinctively. Tools like Claude, Cursor, or Cline don’t try to look good. They don’t protect their ego. They optimize for one thing only — being less wrong next time. They reduce their loss function again and again, billions of times. They don’t feel shame when they fail. They don’t slow down to preserve confidence. Humans do the opposite. We avoid mistakes. We repeat what feels safe. We do what makes us look competent. And that is how talented people plateau after yea...
Read more