Tools: I Let AI Write Most of My Code for a Month. Here’s What Happened.

The Good Stuff (Why It's Tempting) ## The Problem With Code You Don't Understand ## The Debugging Hell ## What You Actually Lose ## The Middle Ground (What I Do Now) ## AI Handles The Boring Stuff ## I Handle The Important Stuff ## The Golden Rule ## What This Actually Looks Like In Practice ## The Real Question ## Where I Landed ## The Bottom Line It started innocently enough. I was staring at a blank editor, trying to build a simple CLI tool. Something that should take a few days max. But you know how it goes — stack overflow rabbit holes, documentation that reads like legalese, the constant context switching between "what am I building?" and "how do I even do this?" So I did what many of us do now. I fired up an AI assistant. "Write a CLI tool in Golang that accepts a port number and kills any process running on that port." A couple of minutes later, I had working code. It wasn't just working — it was polished. Error handling, helpful messages, cross-platform compatibility. The stuff that would have taken me hours to get right. Here's the thing — over the last month, I kept going. I let AI write more and more of my code. Everything from boilerplate to business logic. And I learned something important about this approach. Something I think every developer should hear. Let's start with the obvious: AI makes you feel unstoppable. Projects that used to take a week suddenly take two days. You're shipping faster than ever. The friction just... disappears. Stuck on a gnarly regex? AI handles it. Can't remember that one library API? AI shows you. That weird bug that's been driving you crazy? AI suggests three possible fixes. And the momentum — that's the real drug. You're actually finishing things. You're not abandoning projects halfway through because you hit a wall. You ship something, feel good about it, and move on to the next thing. For someone who struggles with overwhelm and perfectionism (guilty), this feels revolutionary. But here's what I noticed happening. About two weeks in, I shipped a side project. A simple tool that pulled data from an API and displayed it nicely. AI had written most of it — the API calls, the data parsing, the error handling, the whole deal. It worked. I was proud. I moved on. A week later, someone reported a bug. The data wasn't displaying correctly in a specific case. I opened the code and stared at it. And I realized... I had no idea how it worked. Like, I could tell you what it did. But I couldn't tell you why it worked that way. I couldn't explain the data flow. I couldn't debug it because I didn't understand the decisions AI had made. So I did what felt natural — I asked AI to fix the bug. It did. The bug disappeared. But I felt... weird about it. I'd shipped a fix I didn't understand, for a project I didn't understand, to solve a problem I didn't fully grasp. I was essentially a copy-paster, but with better branding. Here's where it gets worse. A month into this experiment, I was working on something more complex. Multiple files, some tricky state management, the kind of thing where small changes have big ripple effects. Something broke. Not a clear error — just... wrong behavior. I spent three hours trying to fix it. Three hours of pasting code into AI, getting suggestions that almost worked but not quite, watching the bug mutate into different bugs, feeling my confidence erode. Here's the thing about AI: it's great at writing code that works. It's okay at fixing code. But when you're deep in the weeds of a complex system you don't fully understand? AI can't save you. Because it doesn't understand the context either. It doesn't know the history of decisions you made. It can't see the system as a whole. It just sees tokens and patterns. That night, at 2 AM, staring at code I'd technically "written" but didn't understand, I realized something: I'd outsourced the thinking, not just the typing. It's not just about understanding your code, though that's huge. I looked back at that month of AI-assisted development and asked myself: "What did I learn?" The honest answer? Not much. I'd shipped more projects than usual, sure. But I hadn't become a better developer. I hadn't leveled up my problem-solving. I hadn't even really engaged with the interesting parts of the problems I was solving. I'd become really good at... prompting. At assembling pieces. At shipping fast. But the deep satisfaction that comes from genuinely solving a hard problem? That was gone. And here's the scary part: I could feel myself getting dependent. I'd start a project and immediately think "I'll just have AI handle that part." I was avoiding the hard stuff — the stuff that actually makes you grow. So I stopped. Not completely — that would be throwing the baby out with the bathwater. But I changed my approach. Here's what works for me: ✅ Boilerplate code — project setup, config files, repetitive patterns ✅ Research — "What's the best way to parse JSON in Go?" or "How does this library work?" ✅ Unblocking — when I'm genuinely stuck, AI helps me see options I hadn't considered ✅ Code review — "Find security issues in this" or "What would you improve?" ✅ Tests — generating unit tests, especially for edge cases I hadn't thought of ✅ Documentation — writing READMEs, adding comments (but only after I understand what's happening) ❌ Complex business logic — if I don't understand the core problem deeply, no amount of AI will save me ❌ Security-sensitive code — AI misses things, and I need to be able to defend every security decision ❌ Performance-critical paths — AI writes "okay" code, not fast code ❌ Architecture decisions — AI doesn't know my context, my constraints, my tradeoffs ❌ Production debugging — if I don't understand the system, I'm helpless when things break Here's the rule I live by now: Never ship code you couldn't debug yourself. Before committing AI-generated code, I ask myself: If the answer to any of these is "no" — I don't ship it. Let me give you a concrete example. Recently, I built a CLI tool that kills processes running on specific ports. Simple stuff. Here's how AI helped: Here's what I did myself: The result? I built it in two days instead of five. But I understood what I built. And when someone reported a bug on Windows (naturally), I knew exactly where to look. I wasn't guessing. I wasn't pasting errors into AI hoping for the best. I was a developer who'd used tools to work faster — not an assembler of other people's code. So should you use AI when you code? I think the better question is: What's your goal? If you're trying to ship a side project fast and you don't care about growing as a developer? Go for it. Let AI write everything. Ship fast, learn what happens. If you're building something serious — something that needs to last, something that might break in production at 3 AM? Then you need to understand what you're building. Use AI as a tool, not a replacement for thinking. If you're early in your career and trying to grow? Be careful. There's no substitute for struggling through hard problems. That struggle is where the learning happens. If you're experienced and just want to move faster? Use AI intelligently. Let it handle the stuff that's below your skill level. Do the stuff that's at or above it. A month later, here's what I've settled into: AI writes about 50-60% of my code. But I read 100% of it. I understand 100% of it. And I could debug 100% of it if I had to. The speed gains are still there. The momentum is still there. But the growth is back too. The satisfaction of solving hard problems. The confidence that comes from truly understanding what I've built. I'm not just shipping faster. I'm becoming a better developer while I do it. AI is an incredible tool. Maybe the best thing to happen to developer productivity in my lifetime. But it's just that — a tool. Like any tool, it can amplify your abilities or it can become a crutch. The difference isn't in the tool itself. It's in how you use it. So use AI. Embrace it. Let it make you faster, more productive, less frustrated. But never, ever ship code you don't understand. Your future self — the one debugging production issues at 2 AM — will thank you. What's your experience with AI-assisted coding? Have you found a balance that works for you? I'd love to hear your story. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - Can I explain what this code does, line by line? - Do I understand why it's written this way and not some other way? - If this breaks at 3 AM on a Sunday, could I fix it without AI's help? - It generated the CLI argument parsing boilerplate - It showed me how to find processes by port (I hadn't done this before) - It suggested cross-platform considerations I hadn't thought of - It helped me write helpful error messages - I decided on the overall structure and flow - I wrote the core logic (finding the process, killing it safely) - I tested edge cases manually to understand failure modes - I read every line of code before committing it - I could explain the entire program to you if you asked