🔍 When Technology Becomes a Mirror (And We Don’t Always Like the Reflection)
Sometimes technology reveals more about us than we’re ready to admit.🙂
Years ago, a friend of mine - someone completely outside the tech world - asked me to “fix his internet.” He was genuinely worried that every website kept showing him very specific ads. In his mind, it had to be a virus, a hack, or at least some kind of digital conspiracy.
So we opened his search history.
And suddenly it became clear that the only “conspiracy” was between his own interests and the algorithms faithfully reflecting them back at him 🙈
He turned bright red - the kind of red that has nothing to do with browser settings and everything to do with being caught by your own data. We laughed, I explained how targeted advertising works, and the issue was resolved. We all have our weaknesses - not everyone’s get indexed so precisely.
I think about this every time I see a developer complaining that an LLM “writes weird code,” “produces bugs,” or “doesn’t understand the task” 🤷♂️
Because the principle is exactly the same:
what you get is directly connected to what you put in.
LLMs aren’t magic. They reflect your context, your instructions, your clarity, and your understanding of the problem.
And in real engineering work, that context isn’t abstract - it’s built from:
• the actual codebase of the project,
• the patterns and decisions your team has accumulated over time,
• the engineering history of how you structure tasks, name things, and solve problems,
• and the quality of communication inside the team.
If the model outputs chaos - maybe the chaos was already in the codebase.
If the model writes strange code - maybe the surrounding architecture is sending mixed signals.
If the model “doesn’t get it” - maybe the team itself hasn’t aligned on what “it” actually is.
From my own experience, modern models consistently produce sensible, clean, and professional code 💡
They save time, reduce cognitive load, and adapt remarkably well to the style and expectations of a senior engineer - as long as those expectations are communicated clearly and the underlying project context is healthy.
Tools don’t judge.
They mirror.
And sometimes that mirror is more honest than we’d like 🪞