Six months ago, I decided to go all-in on AI tools. Every part of my workflow — coding, debugging, testing, documentation, deployment — I tried to replace with an AI alternative.
Some tools changed my life. Others were expensive toys. A few actively made me worse at my job.
Here's the honest breakdown of what survived the experiment.
The Tools That Stayed
1. AI Code Completion (Claude / Copilot)
Verdict: Essential. Can't go back.
This is the one tool that genuinely makes me faster. Not 10x faster — probably 30-40% faster. But consistently, every single day.
What it's great at:
- Boilerplate and repetitive patterns
- Writing tests (seriously, it's incredible at generating test cases)
- Auto-completing functions when you write a good function name
- Translating pseudocode comments into actual code
What it's NOT great at:
- Architecture decisions
- Complex business logic
- Anything that requires understanding your entire codebase
My rule: Accept completions for code you could write yourself. Never accept code you don't fully understand.
2. AI for Debugging
Verdict: Massive time saver.
Pasting an error message + relevant code into Claude and asking "what's wrong?" saves me 20-30 minutes of Googling per bug.
But the real power move: I paste my code and ask "what edge cases am I missing?" This catches bugs BEFORE they happen.
Prompt I use daily:
"Here's my function. What inputs would break it?
List edge cases I haven't handled."
This single prompt has prevented more production bugs than any linting tool I've ever used.
3. AI for Documentation
Verdict: Good enough. Not perfect.
I use AI to generate first drafts of:
- README files
- API documentation
- Code comments for complex logic
- Changelogs
The drafts are about 70% correct. I still need to review and edit, but starting from 70% is way better than starting from 0%.
Warning: AI-generated docs have a specific "voice" that screams "a robot wrote this." Always edit for your own tone.
The Tools I Dropped
1. AI Code Review Bots
Verdict: More noise than signal.
I tried three different AI code review tools. All of them:
- Flagged "issues" that were intentional design choices
- Suggested "improvements" that made code worse
- Generated so many comments that real issues got buried
The final straw: one bot suggested refactoring a 15-line function into 4 separate files "for better separation of concerns." No thanks.
Human code review is irreplaceable. The context, the tribal knowledge, the understanding of WHY code exists — AI doesn't have that.
2. AI-Generated Commit Messages
Verdict: Sounds good, works terribly.
Every commit message was technically accurate but completely useless:
// AI-generated
"Update user.js to modify the handleSubmit function
by adding a try-catch block and changing the
API endpoint from /v1/users to /v2/users"
// What I actually needed
"Fix: user creation fails silently on API timeout"
The AI describes WHAT changed. Good commit messages explain WHY it changed. These are fundamentally different skills.
3. AI Test Generation (Fully Automated)
Verdict: Dangerous false confidence.
AI-generated tests pass. That's the problem. They pass because they test the implementation, not the behavior.
// AI-generated test
test('processOrder returns correct value', () => {
const result = processOrder(mockOrder);
expect(result).toEqual({
total: 99.99,
tax: 8.99,
discount: 0
});
});
// What this actually tests: nothing useful
// It just verifies the current output matches expectations
// If processOrder has a bug, this test has the same bug
AI is great at HELPING you write tests (suggesting edge cases, generating boilerplate). But fully automated test generation creates a false sense of security.
4. "AI Project Managers"
Verdict: Expensive todo list.
Every AI project management tool I tried was basically: takes your Jira tickets, summarizes them slightly differently, and calls it "AI-powered insights."
Saving: $0. Time saved: 0 hours. Meetings avoided: 0.
The Surprising Lessons
AI Made Me a Better Programmer (Not a Lazier One)
Counterintuitively, using AI for code completion made me a BETTER programmer because:
- I read more code (reviewing AI suggestions forces you to think critically)
- I learned new patterns (AI sometimes suggests approaches I hadn't considered)
- I focus on architecture (less time on syntax = more time on design)
The 80/20 Rule Applies Hard
80% of the value comes from 2 tools: code completion and debugging assistance. The other 15 tools I tried provided maybe 20% of value combined.
Stop chasing every new AI dev tool. Master two and ignore the rest.
AI Can't Replace Thinking
The hardest parts of programming — understanding requirements, designing systems, making tradeoffs — AI can't do. And those are the parts that actually matter.
If AI can do your entire job, your job was already automatable. The valuable work is everything AI can't touch.
My Current Stack
| Task | Tool | Time Saved |
|---|---|---|
| Code completion | Claude Code | 30-40% |
| Debugging | Claude | 20-30 min/bug |
| Documentation | Claude | First drafts only |
| Everything else | My brain | N/A |
Three tools. That's it. Everything else was noise.
I document my experiments with AI tools, development workflows, and building products at boosty.to/swiftuidev. No hype, just what actually works.
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