Tools: I Let AI Write 100% of My Code for a Week — Here's What Actually Happened

The Rules ## Day 1-2: "This Is Amazing" ## Day 3-4: "Wait, Something's Off" ## 🔴 Problem 1: The Code Worked But I Didn't Understand It ## 🔴 Problem 2: Subtle Bugs That Pass Tests ## 🔴 Problem 3: Generic Solutions for Specific Problems ## Day 5-6: "I Need to Change My Approach" ## ✅ New Rule: AI Proposes, I Decide ## ✅ New Rule: Understand Before Merging ## ✅ New Rule: Context Is King ## Day 7: The Verdict ## What AI Is GREAT At ## What AI Is BAD At ## The Framework I Use Now ## The Real Lesson Last week, I ran an experiment. For 7 days straight, I didn't write a single line of code manually. Every function, every config file, every bug fix — I let AI handle it. Not as a flex. As an honest test. Here's what happened — the good, the bad, and the parts nobody talks about. Before we start, here's what I committed to: Tools I used: terminal-based AI agents, Cursor, and ChatGPT as backup. The first two days were incredible. I felt like a 10x developer. All in about 4 hours of "work" — which was mostly me describing features and reviewing code. I literally made coffee while my API was being built. The future felt real. Then things got... interesting. I started noticing patterns: The AI generated a middleware for rate limiting. It worked perfectly. But when I tried to explain it to a colleague, I couldn't. I had shipped code I didn't understand. That's not engineering. That's gambling. The AI wrote a function to calculate subscription billing. All tests passed. But it was rounding currency to 3 decimal places instead of 2. That's a bug that costs real money — and I almost missed it. I asked for a caching layer. The AI gave me a perfectly fine Redis implementation. But my app only has 50 users. I needed an in-memory cache with a 5-minute TTL. The AI over-engineered it because it didn't understand my scale. By day 5, I realized the experiment wasn't failing — my approach was wrong. I switched strategies: Instead of blindly accepting code, I started treating AI like a junior developer: The code quality jumped immediately. If I couldn't explain every line to a rubber duck, I asked the AI to simplify or explain. No more mystery code in my codebase. The more context I gave the AI, the better the output. Instead of: Night and day difference. Here's my honest scorecard: After this experiment, here's my personal rule: AI is a power tool, not autopilot. You still need to know how to drive. The developers who will thrive aren't the ones who write the most code OR the ones who blindly delegate to AI. It's the ones who know when to write and when to delegate. That judgment — that human layer — is what makes a senior developer senior. And no AI can replace it. Yet. Have you tried going full AI for a period? What was your experience? I'd love to hear if you hit the same walls I did. 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 CODE_BLOCK: "Build a login page" Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: "Build a login page" CODE_BLOCK: "Build a login page" CODE_BLOCK: "Build a login page for a B2B SaaS app. Users are enterprise customers who expect SSO. We use Next.js 15, Tailwind, and our auth provider is Auth0. Keep it minimal — no fancy animations. Mobile-responsive." Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: "Build a login page for a B2B SaaS app. Users are enterprise customers who expect SSO. We use Next.js 15, Tailwind, and our auth provider is Auth0. Keep it minimal — no fancy animations. Mobile-responsive." CODE_BLOCK: "Build a login page for a B2B SaaS app. Users are enterprise customers who expect SSO. We use Next.js 15, Tailwind, and our auth provider is Auth0. Keep it minimal — no fancy animations. Mobile-responsive." CODE_BLOCK: 🟢 AI WRITES → Boilerplate, tests, configs, CRUD, docs 🟡 AI ASSISTS → Debugging, refactoring, learning, prototyping 🔴 I WRITE → Architecture, business logic, security, data models Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: 🟢 AI WRITES → Boilerplate, tests, configs, CRUD, docs 🟡 AI ASSISTS → Debugging, refactoring, learning, prototyping 🔴 I WRITE → Architecture, business logic, security, data models CODE_BLOCK: 🟢 AI WRITES → Boilerplate, tests, configs, CRUD, docs 🟡 AI ASSISTS → Debugging, refactoring, learning, prototyping 🔴 I WRITE → Architecture, business logic, security, data models - ✅ AI writes ALL code (no manual typing) - ✅ I can describe what I want in plain English - ✅ I can review, accept, or reject AI output - ❌ No manually editing AI-generated code - ❌ No copy-pasting from Stack Overflow or docs - REST API with 6 endpoints (Express + MongoDB) - JWT authentication - Input validation with Zod - Docker setup with docker-compose - "Write this function" → "Write this function and explain your approach" - "Fix this bug" → "What are three possible causes for this bug?" - "Add caching" → "Given 50 concurrent users, what's the simplest caching approach?" - Boilerplate — CRUD endpoints, config files, Docker setups. Let AI do this 100% of the time. - Debugging — Paste an error, get a fix. This is where copy-paste AI actually shines. - Learning new frameworks — "Show me how routing works in Hono" is faster than reading docs. - Tests — AI writes solid unit tests, especially when given the implementation first. - Refactoring — "Make this function more readable" works surprisingly well. - Architecture — It doesn't know your team, your scale, or your constraints. - Business logic — It doesn't understand that rounding to 3 decimals loses you money. - Security — It generates "working" auth code that might have subtle vulnerabilities. - Knowing when to say no — Ask AI to add a feature, and it will. Even if that feature is a terrible idea. - Context across files — Unless you're using an agent that can read your codebase (not just a chat window). - Days 1-2: AI is magic, I'm a 10x developer - Days 3-4: Wait, I don't understand my own code and there are subtle bugs - Days 5-6: Changed approach — AI proposes, I decide - Day 7: AI is a power tool, not autopilot - The rule: Green (AI writes), Yellow (AI assists), Red (I write)