Kai Long

For the first few months of vibe-coding, I did what everyone does: describe the feature in chat, let the agent write it, fix what broke, repeat. Worked great for week one. By week three, every "small change" was touching six files I didn't expect, and the agent kept re-deciding architecture choices we'd already settled two days earlier - because the only record of those decisions was buried in scrollback.

What actually fixed it for me wasn't a better prompt or a bigger context window. It was writing a spec before touching the agent at all - a short doc covering what the feature needs to do, the inputs/outputs, the constraints, and what "done" looks like. Then I have the agent work strictly against that doc instead of my memory of a conversation from three days ago.

A few things I noticed once I switched:

• Way fewer "wait, why did you change that file too" moments, because the spec draws the boundary of what's in scope.

• Reviewing AI output got faster - I'm checking against written acceptance criteria instead of re-explaining intent in my head.

• Onboarding a second AI session (or a human collaborator) mid-project stopped being painful, since the spec carries context the chat history used to.

Everyone talks about which tool they're using right now. Fewer people talk about the tool they left and why.

I switched from Bolt to Cursor halfway through a project a few months ago, not because of a big feature gap, but because of something small: Bolt kept losing context after a few file edits and I'd have to re-explain the whole architecture every session. It wasn't a benchmark loss, it was a patience loss.

That's the kind of thing that never shows up in comparison posts. Nobody writes "Tool A has 12% worse long-context memory," they just quietly open a new tab and start using Tool B.

So, curious about real switches, not hypotheticals:

I've noticed a shift in how I work over the past year, and I'm not 100% sure it's a good one.

A year ago, I'd use AI to speed up execution like writing boilerplate code, drafting an email or summarizing a doc. The thinking was still mine. Now, more and more often, I catch myself asking AI to decide things too: which architecture to pick, how to frame a pricing page, even how to respond to a tricky customer email. The line between "helping me think" and "thinking for me" has gotten blurry.

Here's what worries me a bit: the moments where I learn the most are usually the moments where I struggle first. If AI removes the struggle, does it also remove the learning? Or is that just an old-fashioned way of looking at it - the same way people worried calculators would ruin math skills, and mostly they didn't?

Do you think AI has made you better at thinking, or just faster at producing? And any moment where relying on AI backfired because you skipped the "struggle" step?