Most people’s journey with AI coding starts the same: you give it a simple task. You’re impressed. So you give it a large task. You’re even more impressed.
You open X and draft up a rant on job displacement.
If you’ve persisted past this point: congratulations, you understand AI coding better than 99% of people.
Serious engineers using AI to do real work and not just weekend projects largely also follow a predictable development arc.
Still amazed at the big task you gave it, you wonder if you can keep giving it bigger and bigger tasks. Maybe even that haunting refactor no one wants to take on?
But here’s where the curtain starts to crinkle.
On the one hand, you’re amazed at how well it seems to understand you. On the other hand, it makes frustrating errors and decisions that clearly go against the shared understanding you’ve developed.
You quickly learn that being angry at the model serves no purpose, so you begin to internalize any unsatisfactory output.
“It’s me. My prompt sucked. It was under-specified.”
“If I can specify it, it can build it. The sky’s the limit,” you think.
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