I love how fast AI lets me move now. I can ask for a page, a form, or a test, and get a working start in minutes. But speed is not the goal. If I do not understand the why behind the code, I am just pasting blocks and hoping they hold.
A few years ago this was slow. I spent weekends wiring auth and basic CRUD. Even a sign in flow could take a week. Today I can set that up in hours, as long as I know what I am asking for and how it should work.
Before, the painful parts
Back then I did a lot by hand. I wrote forms from scratch, wired inputs, and fixed tiny bugs that only showed up in the browser. I built auth, login, signup, reset, email, and roles. None of it was fun. It was a lot of time for very little visible progress.
Docs were scattered. I jumped between tabs to find one missing line. Versions did not match. A simple change in a package broke something else. I kept notes just to remember what worked last time.
Testing was slow to start. I set up the runner, picked a library, and wrote helpers before I could test one rule. I knew I should do it, but it ate a full evening.
Most weekends went to glue. Not new features. Not the real problem. Just the pieces that every app needs.
For the bigger industry shift behind this, here is more of the market view: AI is changing SaaS for everyone.
Where AI helps today
It cuts the busy work. I still decide what the feature should do, but I do not start from a blank file.
I describe the screen and the rules it must follow. I get a first pass with inputs, validation, and a clean layout. I outline the data and the flow. I get handlers, routes, and basic tests. I paste an error with the stack trace. I get a fix plus a short reason.
Docs are quicker too. Instead of ten tabs, I ask for the exact call and a small example. I still check the official docs, but I land on the right line faster.
Refactors are lighter. I can take a long function and ask for smaller parts with better names. I keep what fits my style and drop the rest.
The key is simple. I bring the goal, the inputs, and the checks. AI drafts the middle. I read it, learn from it, and make it match how the app should work.
Where AI can hurt if you are not careful
It can look correct and still be wrong. Code that runs is not always code you should ship. Silent bugs slip in when you copy without checking the logic.
Security can suffer. Quick snippets skip auth checks, rate limits, and input rules. If you do not review those parts, you invite trouble.
Docs drift. Models mix versions and old APIs. You get answers that look current but are not. Always check the real docs before you trust a call.
Costs creep. More API calls, bigger prompts, extra tokens during experiments. It feels small until the bill shows up.
Style gets messy. If you accept every suggestion, your code reads like five people wrote it. Pick a pattern and stick to it.
The fix is simple. Keep ownership. Ask for reasons, not just code. Check auth paths. Run tests. Compare with live docs. If you cannot explain a line, pause and learn it before you merge.
The real key, learn while you use AI
I do not ask for code first. I write down the goal, the inputs, and how I will check it works. Then I ask.
I read the output like feedback from a mentor. What files changed, what each function does, where data comes from and where it goes. If I cannot explain it in plain words, I slow down and learn that part.
Simple loop I follow:
- Write the goal in one line.
- List inputs and rules.
- Ask for a small piece, not the whole app.
- Run it and read it.
- Explain it back in my own words.
- Add tests for the rules.
- Move to the next small piece.
This way I build and learn at the same time. AI gives me speed. My understanding keeps the result correct and easy to change later.
What matters
Knowledge is the edge. Tools can draft a page, but knowing the goal, the inputs, the rules, and the checks turns drafts into real progress. Build small, read what you get, test the paths that matter, and keep learning as you go.
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