On social media, we're constantly hearing about individuals who never knew how to code and were able to vibecode a product within a short period of time.

Whenever I come across these posts, I always wonder if their product is truly marketable and profitable. Or, they are just selling the dream.

I've been thinking about this a lot lately.

As an SAP ABAP Developer, I had app ideas sitting in my head for years. Before AI, the learning curve for mobile development felt impossibly steep. Now? I shipped my iOS app in weeks.

But here's my honest question:

How many of us vibe coders are actually building sustainable products?

Running into a wall I haven't seen written about clearly.

Single agent in production is fine. Logs, traces, normal observability.

I spent a long time thinking at the code level.

Take an issue. Write code. Open a PR. Smoke test it. Fix CI. Debug. Review. Merge.

When I started using agents, I carried that same workflow across:

Take issue X. Refine it first. Follow the acceptance criteria. Implement it. Review the output. Smoke test it. Rebase it for human review.

Tried to settle this for myself. Same task on both: refactor a 14-file React component tree to migrate from class components to hooks.

Cursor Composer:

- Faster start, immediately surfaced the right files

I review a lot of apps built with Cursor / Lovable / v0 / Bolt, and the same

handful of security holes show up almost every time. None of it is the

builder's fault it's just what the tools leave behind. Sharing the pattern in

case it saves someone a bad week.

Last week we timed one small decision in our app:
picking which part of the system should handle a message. 2.4 seconds, on a model that can write essays, to answer a multiple-choice question.

It made me look at every model call differently. Some need to write. Most just need to choose, score, or check.

Where are you using a big model for something a small, fast one could decide? And has swapping one ever bitten you?

Let me know if this sounds familiar:

Me: Implement issue ABC and satisfy every acceptance criterion in the spec.

Hey everyone! I had a massive reality check this month and wanted to see if other founders have experienced this.

We use Stripe for billing and HubSpot as our CRM. Our dashboard looked incredibly healthy, MRR was growing, and deals were marked as "Active." But out of curiosity, we cross-referenced our active paying users with our actual product analytics (Mixpanel/PostHog).

Turns out, almost 12% of our paying customers hadn't logged into the app in over 20 days. They were "ghosts." Because their Stripe subscription hadn't failed yet, our CRM was completely blind to the drop-off, and our CS team was doing nothing to save them.

We ended up having to build a native bridge to pipe usage data directly into HubSpot to fix this.

I was blown away by how fast I built my initial dashboard using AI assistants.

But after asking for a tiny logic tweak a week later the model secretly changed date handling across multiple files.

Nothing crashed which made it even worse the bug was completely silent until numbers stopped matching up. Now I inspect every single git diff before moving to the next prompt.

What habits or checks do you use to keep AI coders from introducing silent bugs into existing features?