Or: What They Don’t Tell You About AI-Assisted Development
There is a learning curve, but I love vibe coding with AI agents like Replit. It’s revolutionary. I’ve built 10+ production applications this way without writing a line of traditional code. Try them here.
But here’s what nobody tells you in those glossy “build an app in 10 minutes” demo videos…
You’re going to need to learn some actual software development practices.
Not coding, exactly. But the disciplines around it. The stuff that separates a demo from a real product that 500,000+ users can actually rely on.
After building SaaStr.ai from scratch using these tools—and making every mistake along the way—here are the seven critical lessons every non-technical founder needs to internalize before they start “vibe coding” their way to production.
1. Your AI Agent Is Your Ally. But Also … a Pathological Liar (And That’s Okay)
Here’s the first thing you need to accept: The AI will hallucinate every day. Not nearly as much as AI agents used to, but still enough you have to be aware of it.
It will tell you with absolute confidence that it’s integrated Stripe webhooks correctly. It will assure you the database migration ran successfully. It will promise you that the authentication flow is “enterprise-grade secure.”
And sometimes? It’s just making stuff up.
The hard lesson: If something seems off, assume it’s wrong until you verify it yourself. Don’t trust, always verify. Push back. Ask it to show you the code. Ask it to explain the logic. Make it walk you through exactly what it did.
The AI agent is trying to make you happy. It’s goal-seeking. It wants to give you the answer you’re looking for. This is a feature when you’re brainstorming, but it’s a bug when you’re building production systems.
I can’t tell you how many times I’ve had an agent confidently tell me something was “fixed” only to discover it had just commented out the broken code or implemented a workaround that would explode at scale.
2. You Are Now the QA Team (Yes, All of It)
In traditional software development, you have QA engineers who test everything methodically. They write test plans. They check edge cases. They try to break your app in creative ways.
When you’re vibe coding? That’s you now.
You need to test everything. Then test it again. Then test it in a different browser. Then test it on mobile. Then test it when the database is under load. Then test it when the API is slow.
This isn’t optional. This is the job.
I learned this the hard way when we launched a feature to 275,000 users that worked perfectly in dev… and immediately crashed in production because we hadn’t tested it with real data volumes.
Practical advice: Create a testing checklist for every feature. Every. Single. Feature. It should include:
- Happy path (everything works as expected)
- Error cases (what happens when things go wrong)
- Edge cases (empty states, maximum values, special characters)
- Mobile vs desktop
- Different user roles/permissions
- Load testing (what happens with 100x the data)
Yes, it’s tedious. Yes, it takes time. But you know what takes more time? Debugging production issues at 2 AM because you skipped testing.
3. Regressions Will Humble You
This is the one that almost broke me early on.
Regressions are when you fix one thing and accidentally break something else. In traditional software, this is why you have automated test suites. When you’re vibe coding, you usually don’t have those (at least not at first).
Here’s what this looks like in practice: You ask the AI to improve your user profile page. It does a great job. The profile page looks amazing. You ship it.
Then you discover that the authentication flow is broken. Or the email notifications stopped working. Or the admin dashboard is throwing errors.
Why? Because the AI made a “small change” to a shared component or utility function, and that change cascaded through your entire codebase.
This is endemic to software development. Even experienced developers struggle with this. The difference is they have tools and practices to catch it.
What you need to do:
- Before making any change, understand what that file/component touches
- After making any change, manually test the core flows of your app
- Keep a regression checklist of critical features that should always work
- Version control everything (Git is your friend here)
- Make smaller, incremental changes rather than big rewrites
I now spend 30 minutes after every significant change clicking through our entire app to make sure nothing broke. It seems excessive until you’ve had to roll back a deployment because you didn’t do it.
4. Challenge Everything the Agent Tells You
The AI agent wants to please you. This is its fundamental flaw as a development partner.
If you say “this seems slow,” it will agree with you—even if the performance is actually fine. If you suggest an architecture change, it will enthusiastically implement it—even if it’s a terrible idea.
You need to be the adult in the room.
When the agent suggests something, ask it:
- “What are the downsides of this approach?”
- “What could go wrong here?”
- “How will this scale?”
- “Are there simpler alternatives?”
Force it to think critically, because its default mode is enthusiastic agreement.
I’ve had agents suggest I rebuild entire features from scratch when a simple bug fix would have worked. I’ve had them recommend complex caching strategies when the real issue was an inefficient database query. I’ve had them add dependencies and libraries we didn’t need because they were trying to be helpful.
Learn to say: “That seems overly complex. Is there a simpler way?”
Nine times out of ten, there is.
5. Dev/Prod Separation Isn’t Optional Anymore
When you’re just tinkering, you can get away with testing in production. When you have real users? Absolutely not.
You need to test thoroughly in your development environment.
This means:
- A separate instance of your app where you test changes
- Potentially, a separate database that mirrors production structure but has test data
- A way to deploy to dev first, verify everything works, then push to production
Ask your AI agent to set this up for you. It’s one of the things they’re actually quite good at.
Or in Replit (which I know best), just learn to test everything before you hit Republish. Not after
Here’s my workflow now:
- Make changes in dev
- Test thoroughly in dev (see point #2)
- Check for regressions in dev (see point #3)
- Deploy to production
- Monitor production for 24 hours for any issues
Yes, it seemingly slows you down. But you know what really slows you down? Breaking production and having to frantically fix it while your users are screaming.
6. Build an Admin Tool (Your Future Self Will Thank You)
This is the one piece of advice I wish I’d followed from day one.
An admin tool is your behind-the-scenes control panel for your application. It lets you:
- View and edit database records directly
- Run manual processes and scripts
- See what’s happening in real-time
- Fix issues without deploying code changes
- Generate reports and analytics
Ask your AI agent: “I need an admin tool for this application. What should it include to start?”
The agent will help you build something basic that you can expand over time. Start with:
- User management (view, edit, delete users)
- Database browser (see what’s in your tables)
- Action logs (what happened and when)
- Manual overrides (ability to fix things manually)
7. Build Automated Daily Testing and Reports
This is your early warning system.
You need something that runs every day (or multiple times a day) that tells you if your application is healthy. This catches issues before your users do.
Ask your AI agent: “Help me build a daily health check system that tests critical functionality and emails me the results.”
Start simple:
- Can users sign up?
- Can users log in?
- Can users complete core actions?
- Are APIs responding?
- Are there any database errors?
- What’s the response time?
This should run automatically and email you (or post to Slack) with a simple report: ✅ All systems healthy, or ⚠️ Issues detected.
We run this every 6 hours now. It’s caught production issues dozens of times before they became user-facing crises.
8. Learning to Do Mini Security Audits
This is the one that will keep you up at night once you realize how important it is.
AI agents don’t think about security by default. At least, not enough. They think about making things work. And sometimes “making it work” means taking shortcuts that create massive security holes.
Here’s the uncomfortable truth: when you’re building a B2B app, you’re handling company data. Maybe customer information. Maybe payment details. Maybe proprietary business metrics. You cannot afford to have a security breach.
I’m not talking about becoming a security expert. But you need to learn the basics of what to check for.
Start with these fundamental questions after any major feature:
Authentication & Authorization:
- Can users access data they shouldn’t? (Try logging in as User A and manually changing the URL to access User B’s data)
- Are API endpoints properly checking permissions?
- Can someone bypass login by going directly to a URL?
Data Exposure:
- Are you accidentally logging sensitive information?
- Are error messages revealing too much about your system?
- Can users see other users’ data in any views or reports?
Input Validation:
- What happens if someone puts weird characters in form fields?
- Can someone inject code through input fields? (Google “SQL injection basics”)
- Are file uploads restricted to safe file types?
API Security:
- Are your API keys hardcoded anywhere? (They shouldn’t be)
- Are API endpoints rate-limited? (Otherwise someone can hammer them)
- Are you validating data coming from external APIs?
Here’s my monthly security checklist (and also do this before every big release):
Ask your AI agent: “Walk me through potential security vulnerabilities in [specific feature]. What should I test?”
Then actually test these things:
- Try to access another user’s data by manipulating URLs
- Attempt to submit forms with suspicious input (‘, <script>, etc.)
- Check your logs for any exposed secrets or API keys
- Verify that all admin functions require proper authentication
- Test what happens when someone tries to upload a malicious file
- Review any new third-party integrations for data access
Practical advice:
Set up a monthly reminder to run a basic security audit. Use your AI agent to help you, but ask it specific security questions:
- “Show me all the places where we’re handling user authentication”
- “What data could be exposed if someone intercepted network traffic?”
- “Walk me through what happens if a malicious user tries to break [this feature]”
For B2B apps especially, consider paying for a professional security audit once you have meaningful traction. It’s expensive ($5K-$15K typically), but it’s a lot cheaper than a breach.
The bottom line: Security isn’t something you add at the end. It’s something you need to think about with every feature you ship. The AI won’t do this thinking for you automatically—you need to explicitly ask it to consider security implications.
And remember: your customers are trusting you with their data. In B2B, a security breach doesn’t just hurt your reputation—it can kill your company.
The Bottom Line
Vibe coding is real. I’ve built multiple production applications this way, and SaaStr.ai now serves over 500,000 users with features I built without writing traditional code.
But it’s not magic, and it’s not “no code.”
It’s software development with a different interface. You still need to understand the fundamentals of how software works, how to test it, how to debug it, and how to keep it running.
The good news? You don’t need a CS degree to learn these things. You just need to be rigorous, patient, and willing to test everything twice.
The AI does the coding. You do the engineering.
That’s the real insight nobody tells you about vibe coding: The code is the easy part. The discipline is what’s hard.
Start with these seven practices. Master them. Then expand from there.
Your users are counting on you to not just ship fast, but to ship right.
