The Illusion of Competence in the Age of AI
http.HandleFunc or ServeMux. It was a terrifying moment of realization: I could review code perfectly, spot architectural flaws, and debate edge cases with an LLM, but I couldn't actually produce the logic myself.This is the "Recognition vs. Production" trap. AI has accelerated a problem that Stack Overflow started years ago. Because my brain is wired for pattern matching, seeing a snippet like this:
func (s Server) handleJobNext(w http.ResponseWriter, r http.Request) {
// ...
}...makes me feel like I understand it. I can critique the design and catch mistakes instantly, which gave me this false sense of mastery. But reviewing is a completely different neural pathway than retrieval and construction. I had become a high-level architect who couldn't actually lay a single brick.
I've spent the last few months trying to fix this "gap" in my engineering intuition. I started a personal project where I implement distributed systems algorithms in Go from scratch—zero AI assistance during the actual writing phase. I only bring the AI in at the very end to act as a reviewer. It’s been a brutal way to learn, but it's the only way to build that "scar tissue" that real experience requires.
If you're a dev relying heavily on Copilot or ChatGPT, you might be building a library of recognition without building actual production skills. You can't download experience, and you certainly can't outsource the struggle of debugging a failed design at 2 AM.
I’ve started documenting these implementations—the wins, the edge cases, and the absolute disasters—to keep my Go skills sharp and to help others avoid the data corruption issues that happen when you don't fully grasp the underlying mechanics of your distributed systems. I'm focusing on keeping the logic as simple as possible while refusing to ignore the messy failure modes that actually happen in production.