AI Coding Is Like Having a Genius Intern Who Can't Follow Instructions
The hidden chaos of AI coding that nobody talks about – and how we solved it
Imagine hiring the most brilliant developer you've ever met. They can architect complex systems in minutes, write flawless code, and solve problems you didn't even know you had. There's just one tiny problem: they completely ignore your actual requests. They "move fast and break things" – your things. And security? That's tomorrow's problem, for another agent.
Ask them to add a button? They'll refactor your entire authentication system and hardcode the admin password. Need a quick bug fix? They'll reorganize your database schema and expose your API keys. This is AI coding in 2025, and it's driving everyone quietly insane.
Last month, I experienced this firsthand when I asked Cursor for a simple export button. Two hours later: 847 lines of changes, my intentionally-disabled authentication system restored with JWT secrets exposed in the client code, and the button? Still doesn't work.
The Flashlight That Started Everything
It all began with a morse code flashlight called Morselight Pro. Living in California during wildfire season, I couldn't stop thinking about emergency scenarios – what if you're stuck somewhere and need to signal for help? The app started simple but grew into something magical: morse code through sound, vibration, haptics, the works.
The real revelation wasn't the app itself. With vibe coding, I was building features I never even planned. It felt like trading a film camera for digital – suddenly you could shoot freely, learn through iteration, and the cost of experimentation dropped to zero.
For the first time, my tools moved at the speed of my ideas.
When AI Coding Hit Reality
Then came the real test: building an AI platform to match patients with clinical trials. This wasn't a side project anymore – this was work that could genuinely save lives by connecting people to treatments faster.
That's when AI coding's dark side emerged. Three specific problems kept sabotaging our progress, each requiring its own solution. Together, they exposed the fundamental flaws in how we collaborate on AI-generated code.
Problem 1: The Prompt Drift Disaster
I needed to add a simple button to our dashboard. Just a button. I told Cursor: "Add an 'Export Data' button to the user dashboard."
Two hours later, I'm staring at an 847-line diff. Cursor had added my button... but also "helpfully" restored our entire authentication system. I had intentionally disabled auth weeks earlier for testing. Cursor noticed this "security flaw" and decided to fix it.
The damage:
- ✅ Export button: Still broken
- ❌ Authentication: Completely refactored without permission
- 😵 My sanity: Gone
This happens constantly. AI agents see "opportunities" everywhere. Ask for a UI tweak? Get a database refactor. Request a bug fix? Receive a complete architecture overhaul.
How we solved it:
Our Bear-Check feature compares your original prompt with what the AI actually delivered, flagging every unauthorized change:
- 🎯 Prompt Compliance Score: "75% - AI mostly followed your prompt but added OAuth and missed the 'Forgot password' link"
- ✅ What the AI Got Right: Created login form with email and password fields, implemented form validation, styled using Tailwind CSS
- ⚠️ Unexpected Additions: Added Google OAuth login option (not requested), implemented dark mode toggle (not in prompt)
- ❌ What's Missing: No "Forgot password" link was implemented, error handling is incomplete
- 🔒 Critical Risks: Security concern - OAuth implementation stores tokens insecurely in localStorage
Plus a one-click Quick Fix prompt you can drop straight into your coding agent to patch every issue in one shot.
Problem 2: When Code Review Became Our Biggest Bottleneck
We had a problem nobody wanted to admit: our smartest engineers were terrified of AI-generated code.
Not because it was bad – because it was too good. Too complex. Too clever. Too far outside their expertise to review confidently.
The incident that broke us:
Jake's AI agent decided to "optimize" our payment processing while fixing a typo in an error message. The resulting PR touched 47 files across 6 different services. Stripe integration? Refactored. Database queries? Optimized. Authentication flow? "Improved."
The code was technically magnificent. It probably was better. But who could tell? Our frontend lead couldn't verify the payment logic. Our backend expert wasn't sure about the React changes. Our DevOps engineer saw infrastructure modifications they didn't request.
We sat in that PR for three days. Three. Days. Afraid to approve something we didn't understand. Unable to reject code that looked perfect.
The hidden cost of AI coding:
It's not the code generation – it's the human verification. When every PR becomes a research project, your entire pipeline grinds to a halt.
Bear-Improve: Our Universal Translator
Instead of forcing everyone to become full-stack experts overnight, we built a bridge between AI brilliance and human understanding:
- ✨ Plain English Summaries: "This changes how we charge credit cards – now we check if the card is valid before attempting payment"
- 🎯 Business Impact: "Reduces failed payment notifications by 90%"
- 🚦 Safety Check: "Safe to deploy – all changes are backwards compatible"
- 🧪 Verification Steps: "Try checkout with test card 4000-0000-0000-0002 (should fail gracefully)"
- 🚀 Fix Anything: One-click prompts to adjust the code without starting over
Reviews dropped from hours to minutes. More importantly, everyone felt confident in their approvals again.
Problem 3: The "Works on My Machine" Nightmare
The Crime Scene
Monday morning. Sarah's excited about her new caching feature. The PR looks clean, tests are passing, Bear-Check and Bear-Improve gave it the green light. You pull the branch, run npm start
, and...
Welcome to environment hell. Population: you.
The Investigation Begins
What follows is a painful ritual every developer knows:
- Slack: "Hey Sarah, getting some errors on your branch..."
- 20 minutes later: "Oh yeah, you need Redis running"
- "Cool, installed Redis. Still broken."
- "Did you set the env vars?"
- "What env vars?"
- "Check my .env.local... actually, let me just send you mine"
- "Still not working"
- "Weird, works on my machine™"
Three hours later, you discover the AI agent made assumptions about your local setup that only existed in Sarah's development environment. The feature requires Redis 7.2 specifically, three new environment variables, a database migration, and a background worker that nobody mentioned.
Bear-Launch: Your Setup Sherlock
We built Bear-Launch to solve this detective work once and for all. It scans every PR and generates two things:
1. Human Instructions (Copy-Paste Ready):
echo "REDIS_URL=redis://localhost:6379" >> .env
echo "CACHE_TTL=3600" >> .env
echo "CACHE_ENABLED=true" >> .env
2. AI Agent Setup Prompt:
1. Start Redis locally (requires v7.2+) docker run --name redis-cache -p 6379:6379 -d redis:7.2-alpine
2. Install new dependencies npm install redis bull
3. Run database migrations npm run migrate:latest
4. Start background worker npm run worker:start
5. Verify setup by checking /health endpoint shows "redis: connected"
No more detective work. No more "works on my machine." Just paste and go.
The Magic of Having All Three
Something beautiful happens when these three features work together. It's like switching from dial-up to fiber – suddenly, everything just flows.
Before Commit Bear (Our Daily Hell):
- Monday: AI adds 500 lines of "improvements" → Production breaks
- Tuesday: PR stuck in review purgatory → Sprint goals slip
- Wednesday: New dev can't run anything → Onboarding takes a week
- Thursday: Another scope creep incident → More firefighting
- Friday: Team morale in the basement → "Maybe we should go back to manual coding"
After Commit Bear (The New Normal):
- Bear-Check catches the scope creep → "Hey, those 500 lines weren't requested"
- Bear-Improve translates complexity → "This changes X, affects Y, test with Z"
- Bear-Launch automates setup → "Run this command. You're ready in 2 minutes."
The transformation is immediate. That authentication disaster I mentioned? We ran it through Bear-Check as a test. The verdict was instant: "⚠️ WARNING: 847 lines of authentication changes detected. Your prompt only mentioned adding an export button."
Would've saved us two days and one very awkward production rollback.
Why This Really Matters
Here's the thing nobody's saying out loud: We're in a collaboration crisis.
AI gave us superpowers. One developer can now build what used to take a team. But we're trying to manage Formula 1 speeds with horse-and-buggy processes. The tools that got us here can't get us where we're going.
Think about it:
- Your AI writes code at 1000 lines/minute
- Your review process moves at 100 lines/hour
- Your setup documentation updates... never
The math doesn't work. Something has to give.
The Three Pillars of Modern Development
Each problem we solved represents a fundamental shift in how we need to think about AI collaboration:
- Trust but Verify: AI agents are brilliant but need boundaries
- Translate, Don't Gate: Complex code needs human interpretation
- Automate the Obvious: Environment setup should be instant
These aren't just features. They're the foundation of a new development paradigm where AI and humans actually work together, not despite each other.
Building the Future We Want
I started this journey during California wildfire season, building an emergency app that ended up teaching me about the future of coding. Now, every time I'm hiking with my dog or working on an oil painting, I think about creative flow – that magical state where tools disappear and ideas just happen.
That's what development should feel like. That's what we're building.
Join the Revolution
Look, I'll be honest: Commit Bear is still in beta. We're still learning, still building, still discovering new ways AI breaks our old processes. But that's exactly why we need you.
If you're:
- Fighting AI scope creep daily
- Drowning in complex PRs
- Tired of "works on my machine"
- Ready for tools that match your speed
Then join our waitlist. Let's fix this together.
Because you shouldn't have to choose between AI's speed and your team's sanity. You can have both.
Ready to make GitHub as fast as your imagination? 🚀
Keira created Commit Bear after one too many AI-induced production incidents. When she's not revolutionizing developer workflows, you'll find her hiking California trails, experimenting with oil paints, or playing with her dog. She still uses Morselight Pro on every hike, just in case. Connect on LinkedIn.
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